CN118366299A

CN118366299A - System and method for validating the rationality of an optimized vehicle speed profile for a route

Info

Publication number: CN118366299A
Application number: CN202410082313.XA
Authority: CN
Inventors: P·M·奥林; 唐立; D·S·塞麦诺夫
Original assignee: Delphi Technologies IP Ltd
Current assignee: Borgwarner US Technologies LLC
Priority date: 2023-01-19
Filing date: 2024-01-19
Publication date: 2024-07-19

Abstract

A method for providing a vehicle energy optimization rationality check, the method comprising: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and responsive to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold, performing a default action.

Description

System and method for verifying the rationality of an optimized vehicle speed profile for a route

Cross Reference to Related Applications

The priority of U.S. provisional patent application serial No. 63/439,928, filed on 1 month 19 2023, is hereby incorporated by reference in its entirety.

Government licensing rights

The present invention was made with government support under contract DE-AR0000794 awarded by the advanced research program agency of the United states department of energy (ARPA-E). The government has certain rights in this invention.

Technical Field

The present disclosure relates to vehicle propulsion control, and in particular to a system and method for verifying the rationality of an optimized vehicle speed profile for a route.

Background

Vehicles such as automobiles, trucks, sport utility vehicles, cross-cars (cross-over), minivans, or other suitable vehicles may include various systems for controlling the propulsion of the vehicle. Such systems include cruise control systems, auto-cruise control systems, autonomous vehicle control systems, and the like. Typically, vehicle propulsion control systems control vehicle propulsion based on a desired speed or motor torque. For example, the cruise control system may receive input from an operator, such as setting a speed. The cruise control system can adjust various aspects of the vehicle to maintain a set speed.

Disclosure of Invention

The present disclosure relates generally to systems and methods for verifying the rationality of an optimized vehicle speed profile.

An aspect of the disclosed embodiments includes a method for providing visual driving speed recommendations to an operator of a vehicle. The method includes determining a current speed. The method further includes determining a corresponding energy efficiency rate. The method further includes determining a higher corresponding energy inefficiency speed. The method further includes determining a first transition range between the corresponding energy efficient speed and the higher corresponding energy inefficient speed. The method further includes displaying the display area on a display. The display area includes a first indicator corresponding to the current speed. The display area further includes a first mode corresponding to the corresponding energy efficiency rate. The display area further includes a second mode different from the first mode and corresponding to a higher corresponding energy-inefficient speed. The display area further includes a first mode range corresponding to the first transition range.

Another aspect of the disclosed embodiments includes an apparatus for providing visual driving speed recommendations to an operator of a vehicle. The apparatus includes a processor and a memory including instructions that when executed by the processor cause the processor to: the current speed is determined. The instructions further cause the processor to determine a corresponding energy efficiency rate. The instructions further cause the processor to determine a higher corresponding energy inefficiency speed. The instructions further cause the processor to determine a first transition range between the corresponding energy efficient speed and the higher corresponding energy inefficient speed. The instructions further cause the processor to display a display area on the display. The display area includes a first indicator corresponding to the current speed. The display area further includes a first mode corresponding to the corresponding energy efficiency rate. The display area further includes a second mode different from the first mode and corresponding to a higher corresponding energy-inefficient speed. The display area further includes a first mode range corresponding to the first transition range.

Another aspect of the disclosed embodiments includes a non-transitory computer-readable storage medium. The non-transitory computer-readable medium includes executable instructions that, when executed by a processor, facilitate performance of operations including determining a current speed. The operations further include determining a corresponding energy efficiency rate. The operations further include determining a higher corresponding energy inefficiency speed. The operations further include determining a first transition range between the corresponding energy efficient speed and the higher corresponding energy inefficient speed. The operations further include displaying the display area on a display. The display area includes a first indicator corresponding to the current speed. The display area further includes a first mode corresponding to the corresponding energy efficiency rate. The display area further includes. The display area further includes a second mode different from the first mode and corresponding to a higher corresponding energy-inefficient speed. The display area further includes a first mode range corresponding to the first transition range. These and other aspects of the disclosure are provided in the following detailed description of the embodiments, the appended claims, and the accompanying drawings.

Another aspect of the disclosed embodiments includes a method for providing a vehicle energy optimization plausibility check. The method includes identifying at least one route characteristic of a portion of a route being traversed by the vehicle, and determining a vehicle energy consumption profile of the vehicle based on at least one of: historical data indicating energy consumption of a vehicle previously traversed by the vehicle for at least a portion of a route having at least one route characteristic corresponding to at least one route characteristic of a portion of the route being traversed by the vehicle; a plurality of vehicle parameters of the vehicle, wherein the plurality of vehicle parameters include a weight of the vehicle, a rolling friction of the vehicle, and a drag coefficient of the vehicle; and historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of the route having at least one route characteristic corresponding to at least one route characteristic of the route being traversed by the vehicle. The method further comprises the steps of: determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based on the at least one route characteristic; and responsive to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold, performing a default action.

Another aspect of the disclosed embodiments includes a system for providing a vehicle energy optimization plausibility check. The system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle based on at least one of: historical data indicating energy consumption of a vehicle previously traversed by the vehicle for at least a portion of a route having at least one route characteristic corresponding to at least one route characteristic of a portion of the route being traversed by the vehicle; a plurality of vehicle parameters of the vehicle, wherein the plurality of vehicle parameters include a weight of the vehicle, a rolling friction of the vehicle, and a coefficient of resistance of the vehicle; and historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of the route having at least one route characteristic corresponding to at least one route characteristic of the route being traversed by the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and responsive to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold, performing a default action.

Another aspect of the disclosed embodiments includes a method for providing a vehicle energy optimization plausibility check. The method includes identifying at least one route characteristic of a portion of a route that the vehicle is traversing, and determining a vehicle energy consumption profile of the vehicle. The method further comprises the steps of: determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based on the at least one route characteristic; and responsive to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold, performing a default action.

Another aspect of the disclosed embodiments includes a system for providing a vehicle energy optimization plausibility check. The system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and, in response to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold, performing a default action.

Another aspect of the disclosed embodiments includes an apparatus for providing a vehicle energy optimization plausibility check. The apparatus includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle based on at least one of: a plurality of vehicle parameters of the vehicle; and historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of the route having at least one route characteristic corresponding to at least one route characteristic of the route being traversed by the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and in response to the difference between the target vehicle speed and the alternative target vehicle speed being greater than a threshold, performing a default action.

Drawings

The disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawing figures. It is emphasized that, according to common practice, the various features of the drawing are not to scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity.

Fig. 1 illustrates generally a vehicle in accordance with the principles of the present disclosure.

Fig. 2 generally illustrates a block diagram of a vehicle propulsion control system in accordance with the principles of the present disclosure.

Fig. 3 illustrates generally a block diagram of a computing device in accordance with the principles of the present disclosure.

Fig. 4A-4B generally illustrate velocity envelopes in accordance with the principles of the present disclosure.

Fig. 5A-5B generally illustrate velocity envelopes in accordance with the principles of the present disclosure.

Fig. 6 illustrates generally a speed envelope in accordance with the principles of the present disclosure.

Fig. 7A-7C generally illustrate a method for providing taxi recommendations to an operator of a vehicle.

Fig. 8 illustrates generally a gradient-speed look-up chart in accordance with the principles of the present disclosure.

Fig. 9A-9B generally illustrate routes segmented according to the principles of the present disclosure.

10A-10B generally illustrate various vehicle routes according to the principles of the present disclosure.

11A-11B generally illustrate routes segmented according to the principles of the present disclosure.

FIG. 12 illustrates a comparison of fuel economy in accordance with the principles of the present disclosure.

13A-13C generally illustrate a method for providing cruise speed recommendations according to the principles of the present disclosure.

Fig. 14 generally illustrates a method for providing recommendations to an operator in accordance with the principles of the present disclosure.

Fig. 15 generally illustrates a method for providing recommendations to an operator in accordance with the principles of the present disclosure.

Fig. 16 generally illustrates a method for providing recommendations to an operator in accordance with the principles of the present disclosure.

17A-17H generally illustrate various configurations of display areas in accordance with the principles of the present disclosure.

18A-18C generally illustrate methods for providing driving speed recommendations in accordance with the principles of the present disclosure.

Fig. 19 is a flowchart generally illustrating an energy consumption estimation method in accordance with the principles of the present disclosure.

Fig. 20 is a flow chart generally illustrating an alternative energy consumption estimation method in accordance with the principles of the present disclosure.

Fig. 21 is a flow chart generally illustrating an alternative energy consumption estimation method in accordance with the principles of the present disclosure.

Fig. 22 is a flow chart generally illustrating an alternative energy consumption estimation method in accordance with the principles of the present disclosure.

Fig. 23 is a flow chart generally illustrating a vehicle speed profile rationality verification method in accordance with the principles of the present disclosure.

Fig. 24 is a flow chart generally illustrating an alternative vehicle speed profile plausibility verification method in accordance with the principles of the present disclosure.

Detailed Description

The following discussion is directed to various embodiments of the invention. While one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, those skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment.

As described, a vehicle such as an automobile, truck, sport utility vehicle, cross-car (cross-over), minivan, or other suitable vehicle may include various systems for controlling the propulsion of the vehicle. Such systems include cruise control systems, auto-cruise control systems, autonomous vehicle control systems, and the like. Typically, vehicle propulsion control systems control vehicle propulsion based on a desired speed or motor torque. For example, the cruise control system may receive input from an operator, such as setting a speed. The cruise control system can adjust various aspects of the vehicle to maintain a set speed.

Typically, an operator of the vehicle may set a desired speed to facilitate operation of the vehicle as it traverses the route. However, such propulsion control systems are not capable of controlling vehicle propulsion to achieve a desired energy consumption (e.g., fuel, battery, etc.) efficiency. Accordingly, systems and methods that provide vehicle propulsion control to achieve optimal energy consumption, such as those disclosed herein, may be desirable.

Logic for determining an optimized target vehicle speed profile based on characteristics of a route traversed by a self (e.g., connected) vehicle may suffer from errors in determining an optimized target value that result in incorrect and undesirable operation of the self vehicle. Such errors may be due to faults or problems in the computing routines or implementations in the software, faults in the computing hardware executing the optimization logic, or errors due to random or intentional corruption of memory associated with the optimization logic. Thus, in some embodiments, the systems and methods described herein may be configured to address this problem by using route characteristics to calculate alternative profiles of target propulsion and vehicle control inputs in parallel and independently. These alternative sets may be suboptimal but, because they are independent, may be used as a rationality check for the optimized value.

The systems and methods described herein may be configured to generate an alternative target vehicle speed profile in parallel with the calculation of the optimized target vehicle speed profile. The systems and methods described herein may be configured to provide a rationality comparison with an optimized target vehicle speed profile in order to determine whether to take a default action. For systems that use route characteristics (e.g., static and dynamic information about the road infrastructure and about surrounding vehicles, as widely understood) to generate more efficient vehicle operation, the system may produce erroneous optimal target values for propulsion and vehicle control due to various error conditions. The systems and methods described herein may be configured to determine a set of alternative target values for propulsion and vehicle control, which may be used to provide a rationality check for optimizing logic output.

In some embodiments, the systems and methods described herein may be configured to determine alternative values based on the same route characteristics, driver customizable settings, vehicle physical characteristics and limitations, and other inputs for determining an optimized target vehicle speed profile. All input data is passed through a reconstructor (Reconstructor) function that can convert the information into a format suitable for both optimization and surrogate value computation. The calculation of the surrogate value is independent of and in parallel with the optimization calculation and may provide a sub-optimal target speed profile.

The systems and methods described herein may be configured to compare the surrogate target speed profile value to an optimized target speed profile value. The systems and methods described herein may be configured to perform a default action (e.g., such as notifying an operator of the condition and disabling the optimization logic and/or not using the optimization target vehicle speed profile) in response to the difference between the alternate target speed profile value and the optimization target speed profile value being greater than a defined threshold.

The systems and methods described herein may be configured to generate surrogate target vehicle speed profile values using various information including, but not limited to, information of operational constraints, such as driver customization settings, vehicle physical limitations, and constraints imposed by the environment, such as weather conditions. The systems and methods described herein may be configured to prevent an optimization target vehicle speed profile value from violating a maximum allowable vehicle speed along a route traversed by the vehicle. Additionally or alternatively, the systems and methods described herein may be configured to verify an optimization target vehicle speed profile against a reasonable minimum speed boundary to check if the optimization solution is reasonable.

In some embodiments, the systems and methods described herein may be configured to provide a plausibility check to any method or application that determines an optimal vehicle speed target based on route characteristics, driver settings, and/or vehicle characteristics. The systems and methods described herein may be configured for calculating the surrogate target vehicle speed profile and optimizing the target vehicle speed profile in a single processor or using two or more processors. Additionally or alternatively, the systems and methods described herein may be configured to calculate an alternative target vehicle speed profile and/or optimize the target vehicle speed profile on one or more processors of a vehicle such as those described herein and/or one or more processors associated with a remotely located computing device.

In some embodiments, the systems and methods described herein may be configured to provide an additional level of robustness by extracting calculated route characteristics for the optimized target vehicle speed profile and the surrogate target vehicle speed profile from different sources (e.g., effectively verifying one route characteristic source against another route characteristic source). The systems and methods described herein may be configured to: in comparing the optimized target vehicle speed profile value with the alternative target vehicle speed profile value, various thresholds of positive and negative differences between the values are used, and/or different thresholds are used as a function of different vehicle parameters (e.g., such as vehicle speed; after a defined number of differences are detected (e.g., 3 or other suitable values), each time resulting in the optimized logic operation being disabled, etc.). The systems and methods described herein may be configured to prevent the driver from re-enabling the optimization logic until another condition is met, such as a calibratable number of critical cycles and/or other suitable conditions have occurred.

The systems and methods described herein may be configured to use alternative reconstructor functions to add additional robustness to the overall system by detecting faults in the reconstructor functions. The reconstructor function may first output route characteristics associated with the initial portion of the route to allow optimization logic to be enabled and the driver to begin advancing toward the destination while processing route characteristics of the remaining routes.

In some embodiments, the systems and methods described herein may be configured to: at least one route characteristic of a portion of a route being traversed by the vehicle is identified. The systems and methods described herein may be configured to determine a vehicle energy consumption profile of a vehicle based on at least: historical data indicating vehicle energy consumption of at least a portion of a route previously traversed by the vehicle having at least one route characteristic corresponding to at least one route characteristic of a portion of the route being traversed by the vehicle; a plurality of vehicle parameters of the vehicle (e.g., wherein the plurality of vehicle parameters includes a weight of the vehicle, a rolling friction of the vehicle, and a coefficient of resistance of the vehicle); and historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of the route having at least one route characteristic corresponding to at least one route characteristic of the route being traversed by the vehicle. The systems and methods described herein may be configured to determine a profile of a target vehicle speed based on at least one route characteristic and a vehicle energy consumption profile. The systems and methods described herein may be configured to determine an alternative target vehicle speed based at least on at least one route characteristic.

The systems and methods described herein may be configured to perform a default action in response to a difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold. The default actions may include selectively adjusting the vehicle speed control input based on the alternative target vehicle speed, determining a default target vehicle speed, and selectively adjusting the vehicle speed control input based on the default target vehicle speed, and/or any other suitable default action. In some embodiments, the systems and methods described herein may be configured to selectively adjust the vehicle speed control input based on the target vehicle speed in response to a difference between the target vehicle speed and the alternative vehicle speed being less than a threshold.

Fig. 1 generally illustrates a vehicle 10 in accordance with the principles of the present disclosure. The vehicle 10 may include any suitable vehicle, such as an automobile, truck, sport utility vehicle, minivan, cross-car, any other passenger vehicle, any suitable commercial vehicle, or any other suitable vehicle. Although the vehicle 10 is shown as a passenger vehicle having wheels and for use on a roadway, the principles of the present disclosure may be applied to other vehicles, such as aircraft, ships, trains, drones, or other suitable vehicles. The vehicle 10 includes a vehicle body 12 and a hood 14. A portion of the vehicle body 12 defines a passenger compartment 18. Another portion of the vehicle body 12 defines an engine compartment 20. The hood 14 may be movably attached to a portion of the vehicle body 12 such that the hood 14 provides access to the engine compartment 20 when the hood 14 is in a first or open position and the hood 14 covers the engine compartment 20 when the hood 14 is in a second or closed position.

The passenger compartment 18 may be disposed rearward of the engine compartment 20. The vehicle 10 may include any suitable propulsion system including an internal combustion engine, one or more electric motors (e.g., an electric vehicle), one or more fuel cells, a hybrid (e.g., a hybrid vehicle) propulsion system including a combination of an internal combustion engine, one or more electric motors, and/or any other suitable propulsion system. In some embodiments, the vehicle 10 may include a petroleum (petrol) or gasoline (gasline) fuel engine, such as a spark ignition engine. In some embodiments, the vehicle 10 may include a diesel fuel engine, such as a compression ignition engine. In some embodiments, the vehicle 10 may include a Battery Electric Vehicle (BEV) that includes one or more on-board batteries or battery packs configured to provide energy to one or more electric motors of the propulsion system.

The engine compartment 20 houses and/or encloses at least some components of the propulsion system of the vehicle 10. Additionally or alternatively, propulsion controls (controls) such as accelerator actuators (e.g., accelerator pedals), brake actuators (e.g., brake pedals), steering wheels, and other such components are provided in the passenger compartment 18 of the vehicle 10. The propulsion controls may be actuated or controlled by an operator of the vehicle 10 and may be directly connected to corresponding components of the propulsion system, such as a throttle, brake, vehicle axle, vehicle transmission (transmission), and the like, respectively. In some embodiments, the propulsion control may transmit a signal to a vehicle computer (e.g., driven by an electrical wire), which in turn may control a corresponding propulsion component of the propulsion system.

In some embodiments, the vehicle 10 includes a transmission that communicates with the crankshaft via a flywheel or clutch or fluid coupling. In some embodiments, the transmission comprises a manual transmission. In some embodiments, the transmission comprises an automatic transmission. In the case of an internal combustion engine or hybrid vehicle, the vehicle 10 may include one or more pistons that operate in cooperation with a crankshaft to generate a force that is translated (translated) through a transmission to one or more axles that rotate the wheels 22. When the vehicle 10 includes one or more electric motors, the vehicle battery and/or fuel cell provides energy to the electric motors to rotate the wheels 22. Where the vehicle 10 includes a vehicle battery for powering one or more electric motors, the battery may be connected to the power grid (e.g., using a wall outlet) to recharge the battery cells when the battery is depleted. Additionally or alternatively, the vehicle 10 may employ regenerative braking that uses one or more electric motors of the vehicle 10 as a generator for converting kinetic energy lost due to deceleration back into energy stored in the battery.

The vehicle 10 may include an automatic vehicle propulsion system, such as cruise control, adaptive cruise control, automatic brake control, other automatic vehicle propulsion systems, or a combination thereof. The vehicle 10 may be an autonomous or semi-autonomous vehicle, or other suitable type of vehicle. The vehicle 10 may include more or fewer features than are generally illustrated and/or disclosed herein.

Fig. 2 generally illustrates a block diagram of a vehicle propulsion control system 100 in accordance with the principles of the present disclosure. The system 100 may be disposed within a vehicle, such as the vehicle 10. The system 100 may be configured to selectively control propulsion of the vehicle 10, and in some embodiments, the system 100 is configured to: a profile of the target vehicle speed and/or target vehicle torque distribution is determined based on various input information (e.g., route information, vehicle characteristic information, traffic information, other suitable information, or a combination thereof). The profile of the target vehicle speed and/or target vehicle torque distribution corresponds to such vehicle speed: at this vehicle speed, the vehicle 10 achieves optimal energy consumption efficiency with respect to a portion of the route traversed by the vehicle 10.

In some embodiments, the system 100 may include a Vehicle Propulsion Controller (VPC) 102, a human-machine interface (HMI) control 104, vehicle sensors 108, a torque controller 110, a brake controller 112, a torque distribution controller 116, a brake system 118, a propulsion system 120, and a display 122. In some embodiments, the display 122 may include a portion of a dashboard or console of the vehicle 10, a navigation display of the vehicle 10, or other suitable display of the vehicle 10. In some embodiments, the display 122 may be provided on a computing device, such as a mobile computing device used by an operator. In some embodiments, the system 100 may include a Propulsion Adjustment Controller (PAC) 124, a Global Positioning System (GPS) antenna 126 (hereinafter GPS 126) in communication with a mapping characteristics module (not shown), an advanced driver (operator) assistance system (ADAS) module 128, and a vehicle-to-other system (V2X) communication module 130. The V2X communication module 130 may be configured to communicate with: other vehicles, other infrastructure (such as, for example, a traffic infrastructure, a mobile computing device, and/or other suitable infrastructure), remote computing devices (e.g., remote computing device 132), other suitable systems, or combinations thereof. As will be described, the system 100 may communicate with one or more remote computing devices 132. In some embodiments, at least some of the components of the system 100 may be provided in a Propulsion Control Module (PCM) or other on-board vehicle computing device. For example, at least PAC 124 and VPC 102 may be disposed within a PCM. In some embodiments, the system 100 may be disposed at least partially within a PCM, while other components of the system 100 may be disposed on a standalone computing device having a memory storing instructions that, when executed by a processor, cause the processor to perform the operations of the components. For example, PAC 124 may be disposed on a memory and executed by a processor. It should be appreciated that the system 100 may include any combination of computing devices that are locally disposed in the vehicle 10 and/or computing devices that are remotely disposed, as will be described.

In some embodiments, system 100 further includes additional output device 134. Additional output devices 134 may include non-display output, such as audio devices. Such audio devices may include speakers, ring tones, or other audio devices. The additional output device 134 may include a vibration motor, such as one mounted in a vehicle seat.

In some embodiments, VPC 102 may include an automatic vehicle propulsion system. For example, the VPC 102 may include a cruise control mechanism, an adaptive cruise control mechanism, an automatic braking system, other suitable automatic vehicle propulsion systems, or a combination thereof. Additionally or alternatively, the VPC 102 may include or may be part of an autonomous vehicle system as follows: the portion controls all or a portion of vehicle propulsion, steering, braking, safety, route management, other autonomous features, or combinations thereof. It should be appreciated that while only limited components of system 100 are shown, system 100 may include additional autonomous components or other suitable components.

The VPC 102 communicates with one or more human-machine interfaces (HMI) 104. HMI control 104 may include any suitable HMI. For example, the HMI control 104 may include a plurality of switches disposed on the steering wheel of the vehicle 10, on a dashboard or console of the vehicle 10, or any other suitable location on the vehicle 10. In some embodiments, the HMI control 104 may be provided on a mobile computing device, such as a smart phone, tablet, laptop, or other suitable mobile computing device. In some embodiments, an operator of the vehicle 10 may interact with the HMI control 104 to control vehicle propulsion and/or other features of the VPC 102 using the VPC 102. For example, the operator may actuate an HMI switch of an HMI control 104 disposed on a steering wheel of the vehicle 10. HMI control 104 may communicate a signal to VPC 102. The signal may indicate a desired vehicle speed selected by the operator. The VPC 102 generates a torque demand corresponding to the desired vehicle speed and communicates the torque demand to the torque controller 110. The torque controller 110 may be in communication with the propulsion system 120 and/or other vehicle propulsion systems of the vehicle 10. Torque controller 110 uses the torque demand to selectively control propulsion system 120 and/or other vehicle propulsion systems to achieve a desired vehicle speed. The operator may increase or decrease the desired vehicle speed by actuating additional switches of the HMI control 104. The VPC 102 may adjust the torque demand to achieve a desired increase or decrease in vehicle speed.

The VPC 102 may continuously adjust the torque demand in order to maintain the desired vehicle speed. For example, the VPC 102 may communicate with vehicle sensors 108. The vehicle sensors 108 may include cameras, speed sensors, proximity sensors, other suitable sensors as will be described, or combinations thereof. The VPC 102 may receive a signal from the vehicle sensor 108 indicating the current vehicle speed. When the signal indicates that the current vehicle speed is different from the desired vehicle speed, VPC 102 may adjust the torque demand to adjust the vehicle speed. For example, the vehicle 10 may traverse a ramp that causes the vehicle 10 to decrease the current vehicle speed (e.g., because the torque demand applied by the torque controller 110 may be insufficient to maintain the vehicle speed while on the ramp). The VPC 102 may increase the torque demand in order to adjust the current vehicle speed to achieve the desired vehicle speed.

In some embodiments, such as when the VPC 102 includes an adaptive cruise control mechanism, the VPC 102 may adjust the torque demand based on the proximity of the vehicle ahead (e.g., the vehicle immediately in front of the vehicle 10). For example, the VPC 102 may receive information from the vehicle sensor 108 indicating the presence of a vehicle in front. This information may be captured by the vehicle sensor 108 using a camera, a proximity sensor, a radar, a V2X communication module 130, other suitable sensors or input devices, or a combination thereof. The VPC 102 may determine whether to maintain a desired vehicle speed or to increase or decrease the torque demand to increase or decrease the current vehicle speed. For example, the operator may use the HMI control 104 to instruct maintaining the cadence of the front vehicle while maintaining a safe stopping distance between the vehicle 10 and the front vehicle. The VPC 102 may selectively increase the torque demand if the front vehicle is traveling faster than the vehicle 10, and the VPC 102 may selectively decrease the torque demand if the front vehicle is traveling slower than the vehicle 10.

When the front vehicle becomes completely stopped, the VPC 102 may bring the vehicle 10 to a complete stop. For example, VPC 102 may communicate with brake controller 112 to send a plurality of signals over a period of time that instruct brake controller 112 to control vehicle braking (e.g., VPC 102 may stop the vehicle over a period of time to avoid a sudden stop of the vehicle, however, in the event of a sudden stop of the front vehicle, VPC 102 suddenly stops vehicle 10 to avoid a collision with the front vehicle). The brake controller 112 may be in communication with a brake system 118. The braking system 118 may include a plurality of braking components that are actuated in response to the braking controller 112 implementing a braking program based on a plurality of signals from the VPC 102. In some embodiments, VPC 102 may achieve engine braking by a regenerative braking system by adjusting torque demand to allow vehicle 10 to come to a stop without using braking system 118, or VPC 102 may use a combination of regenerative braking and braking system 118 to bring vehicle 10 to a full stop. To resume vehicle propulsion control, the operator instructs to resume vehicle propulsion control using HMI control 104 (e.g., VPC 102 may not be configured to resume vehicle propulsion control without interaction from the operator). In some embodiments, the vehicle 10 may include a higher level of automation including a higher level of propulsion control, as described, and the vehicle 10 may include suitable controls for completely stopping the vehicle 10 without interacting with an operator of the vehicle 10.

In some embodiments, the VPC 102 may determine the torque distribution to utilize the internal combustion engine and the electric motor of the vehicle 10 (e.g., where the vehicle 10 is a hybrid vehicle). It should be appreciated that although only an internal combustion engine and an electric motor are described, the vehicle 10 may include any suitable hybrid combination of vehicle engines and electric motors. The torque distribution indicates a portion of the torque demand to be applied to the internal combustion engine and a portion of the torque demand to be applied to the electric motor. For example, when the torque demand is below a threshold, the motor may be used for vehicle propulsion. However, when the torque demand is above a threshold (e.g., such as when the vehicle 10 is on a steep incline), the internal combustion engine may provide at least a portion of the vehicle propulsion to assist the electric motor. The VPC 102 communicates the torque distribution to the torque distribution controller 116. The torque distribution controller 116 may be in communication with the propulsion system 120 to apply torque distribution.

In some embodiments, VPC 102 includes a plurality of security controls. For example, the VPC 102 may determine whether to increase or decrease the torque demand based on input from the safety controls, thereby increasing or decreasing the desired vehicle speed or the current vehicle speed. The safety controls may receive input from the vehicle sensors 108. For example, the security control may receive proximity sensor information, camera information, other information, or a combination thereof, and may generate a security signal that indicates to VPC 102 to perform one or more security operations. For example, in the event that the front vehicle becomes suddenly stopped, the safety control may generate a safety signal based on the proximity information from the vehicle sensor 108 that indicates to the VPC 102 that the vehicle 10 is immediately brought to a full stop. In some embodiments, the VPC 102 may determine whether to apply the desired vehicle speed set by the operator using the HMI control 104 based on the signal from the safety control. For example, the operator may increase the desired vehicle speed, which may cause the vehicle 10 to be closer to the front vehicle (e.g., if the desired vehicle speed is achieved, the vehicle 10 will travel faster than the front vehicle). VPC 102 may determine that the desired vehicle speed is not to be applied and, alternatively, may provide an indication to display 122 that increasing the desired vehicle speed may be unsafe to the operator, or VPC 102 may ignore the increase in the desired vehicle speed. In some embodiments, the VPC 102 may communicate with a Transmission Controller Module (TCM). The VPC 102 may receive information from the TCM (e.g., automatically selected gears) and may determine and/or adjust the total torque demand based on the information received from the TCM.

As depicted, system 100 includes PAC 124.PAC 124 may be configured to determine a profile for the target vehicle speed based at least on: route information for a route being traversed by the vehicle 10, vehicle parameters of the vehicle 10, information related to other vehicles in proximity to the vehicle 10, traffic information, weather information, current vehicle speed, desired vehicle speed, other information, or combinations thereof. As will be described, PAC 124 may determine a profile of the target vehicle speed based on the energy consumption profile of vehicle 10. The energy consumption profile may be generated using the information described above and may indicate optimal energy consumption of the vehicle 10 for various route characteristics, such as road grade, curvature, traffic, speed limits, stop signs, traffic signals, other route characteristics, or combinations thereof.

PAC 124 receives route characteristics (e.g., road grade characteristics, route distance, and route direction), vehicle parameters, traffic characteristics, weather characteristics, vehicle-to-vehicle parameters, other information or characteristics, or combinations thereof. In some embodiments, PAC 124 receives at least some of the route characteristics from mapping characteristics module based on location information from GPS 126. The mapping characteristics module is disposed within the vehicle 10 (e.g., within the system 100) or may be disposed on a remote computing device such as the remote computing device 132. When the mapping characteristics module is disposed on the remote computing device 132, the GPS antenna 126 may acquire various global positioning signals from various global positioning satellites or other institutions. GPS126 may communicate the captured signals to a mapping characteristics module. The mapping characteristics module may generate route characteristics based on signals received from the GPS126 and transmit the route characteristics to the PAC 124. For example, PAC 124 may receive route distance, route direction, road grade information for a route, other route characteristics, or a combination thereof. In some embodiments, PAC 124 may receive traffic signal location information, traffic stop sign location information, issued speed limit information, lane change information, other route characteristics or information, or a combination thereof, from a mapping characteristics module based on location information from GPS 126.

PAC 124 may receive further vehicle parameters from vehicle sensor 108. For example, the vehicle sensors 108 may include energy level sensors (e.g., fuel level sensors or battery charge sensors), oil sensors, speed sensors, weight sensors, other suitable sensors, or combinations thereof. PAC 124 may receive from vehicle sensor 108 an energy level of vehicle 10, a current weight of vehicle 10, an oil condition of vehicle 10, tire inflation information of vehicle 10, a current vehicle speed, engine temperature information, other suitable vehicle parameters of vehicle 10, or a combination thereof. In some embodiments, the vehicle sensor 108 may include a weather sensor, such as a precipitation sensor or a humidity sensor, an air pressure sensor, an ambient temperature sensor, other suitable sensors, or a combination thereof. PAC 124 may receive current weather information, such as precipitation information, barometric pressure information, ambient temperature information, other suitable weather information, or a combination thereof, from vehicle sensor 108.

PAC 124 can receive at least some of the route characteristics from ADAS module 128. The ADAS module 128 may help operators of the vehicle 10 to improve vehicle safety and road safety. The ADAS module 128 may be configured to automate and/or adapt and augment a vehicle system for safety and better driving. The ADAS module 128 may be configured to alert an operator of the vehicle 10 of an upcoming traffic condition or a disabled vehicle, and/or to alert the vehicle 10 of a vehicle approaching the vehicle 10 in order to avoid collisions and accidents. Further, the ADAS module 128 may autonomously avoid collisions by implementing safety measures and taking over control of the vehicle 10, such as by automatic lighting, initiating adaptive cruise control (e.g., via the VPC 102), and collision avoidance (e.g., by controlling the trajectory of the vehicle 10 using the VPC 102 or directly using the brake controller 112 or causing the vehicle 10 to come to a complete stop). PAC 124 may receive information from ADAS module 128, such as traffic characteristics, vehicle proximity information, disabling vehicle information, other suitable information, or a combination thereof.

PAC 124 may receive at least some of the route characteristics from V2X communication module 130. The V2X communication module 130 is configured to communicate with other systems near the vehicle 10 or remote from the vehicle 10, as described above, to obtain and share information, such as traffic information, vehicle speed information, construction information, other information, or a combination thereof. PAC 124 may receive other vehicle speed information, other vehicle location information, other traffic information, construction information, other suitable information, or a combination thereof from V2X communication module 130.

PAC 124 may receive at least some of the route characteristics from remote computing device 132. For example, PAC 124 may receive further information from remote computing device 132 regarding: route distance, route direction, road grade information for a route, traffic information, construction information, other vehicle location information, other vehicle speed information, vehicle maintenance information for the vehicle 10, other route characteristics, or combinations thereof. Additionally or alternatively, the PAC 124 may receive vehicle parameters from the remote computing device 132, such as a production (make) and model of the vehicle 10, a manufacturer-provided energy consumption efficiency of the vehicle 10, a weight of the vehicle 10, other vehicle parameters, or a combination thereof. In some embodiments, the PAC 124 may receive traffic signal location information, traffic stop sign location information, issued speed limit information, lane change information, other route characteristics or information, or a combination thereof, from the remote computing device 132. The remote computing device 132 may include any one or more suitable computing devices, such as a cloud computing device or system, one or more remotely located servers, a remotely or nearby located mobile computing device or application server that provides information to the mobile computing device, other suitable remote computing devices, or a combination thereof. The remote computing device 132 may be located remotely from the vehicle 10, such as in a data center or other suitable location. In some embodiments, the remote computing device 132 may be located within the vehicle 10 (e.g., a mobile computing device used by an operator of the vehicle 10).

In some embodiments, the PAC 124 may receive traffic signal information, such as traffic signal phase and timing from an intelligent algorithm used by the traffic data provider (SPaT). SPaT information may indicate when the traffic signal is changing and/or indicate timing of the traffic signal.

PAC 124 may receive route characteristics and/or vehicle parameters from an operator of vehicle 10. For example, an operator may interact with the interface of PAC 124, such as using display 122 or using a mobile computing device, to provide vehicle parameters of vehicle 10, such as vehicle weight, vehicle production and model, vehicle age, vehicle maintenance information, vehicle identification number, number of passengers, load information (e.g., luggage or other load information), other vehicle parameters, or a combination thereof. Additionally or alternatively, the operator may provide route characteristics such as route patterns, route distances, other route characteristics, or a combination thereof to the PAC 124. In some embodiments, PAC 124 learns the behavior of an operator of vehicle 10. For example, PAC 124 monitors the vehicle speed of the operator relative to the issued speed limit or whether the operator implements a vehicle speed recommendation provided by PAC 124 as will be described.

In some embodiments, PAC 124 may learn a pattern of traffic (pattern) of known routes traversed by vehicle 10. For example, PAC 124 may track traffic conditions as vehicle 10 routinely or periodically traverses one or more routes. PAC 124 may determine traffic patterns for these routes based on the monitored traffic conditions. In some embodiments, PAC 124 receives traffic patterns of routes being traversed by vehicle 10 from remote computing device 132 or from a mapping characteristics module based on signals from GPS126, as described.

It should be understood that PAC 124 may receive any characteristics or information associated with routes, traffic, signs and signals, other vehicles, vehicle parameters of vehicle 10, any other suitable characteristics or information, including those characteristics or information described or not described herein, from any of the components described or not described herein. Additionally or alternatively, PAC 124 may be configured to learn any suitable characteristics or information described or not described herein.

In some embodiments, PAC 124 may be configured to control propulsion of vehicle 10. PAC 124 may be an integrated component of VPC 102, or may be an overlay (overlay) component that communicates or interacts with VPC 102 and/or other components of vehicle 10. Additionally or alternatively, PAC 124 may be provided on a mobile computing device (such as a smart phone using at least some of the information described above) to present recommended vehicle speeds to an operator of vehicle 10. In some embodiments, the VPC 102 may include an adaptive cruise control mechanism. As described, the adaptive cruise control mechanism may be configured to maintain a desired vehicle speed provided by an operator of the vehicle 10 using the HMI control 104, and the adaptive cruise control mechanism may be configured to maintain a safe distance between the vehicle 10 and a preceding vehicle. Further, the adaptive cruise control mechanism may be configured to bring the vehicle 10 to a full stop in response to the front vehicle coming to a full stop. As described, the adaptive cruise control mechanism may not be able to restart vehicle propulsion without interaction from the operator of the vehicle 10. Additionally, the adaptive cruise control mechanism may not be able to bring the vehicle 10 to a complete stop without the front vehicle. Thus, the VPC 102 (e.g., an adaptive cruise control mechanism) may not be able to utilize energy efficient vehicle propulsion control (e.g., such as coasting to a stop in response to determining that the vehicle 10 is approaching a stop sign). PAC 124 may be configured to determine a target vehicle propulsion profile based on the energy consumption profile of vehicle 10, which may include one or more target vehicle speeds and one or more target torque allocations. PAC 124 may determine the target torque demand based on a profile of the target vehicle speed and/or the target torque allocation.

In some embodiments, PAC 124 uses the information described above to determine the vehicle energy consumption profile. For example, PAC 124 may determine the vehicle consumption profile using: vehicle weight, manufacturer provided vehicle energy efficiency, historical data corresponding to the vehicle 10 or similar vehicle indicating energy consumption of the vehicle 10 or similar vehicle when traversing portions of a particular route or a particular road grade, or other suitable route or road information, other suitable vehicle parameters, or combinations thereof. The vehicle energy consumption profile may indicate: the vehicle 10 consumes a specified amount of energy (e.g., a specified amount of energy within a marginal mileage) when operating at a particular vehicle speed (a particular vehicle speed within a margin) while traversing a route having a particular road, traffic, and other conditions. For example, the energy consumption of the vehicle 10 may be greater when the vehicle 10 is on a grade, and the energy consumption of the vehicle 10 may be less when the vehicle 10 is coasting to a stop. In some embodiments, PAC 124 receives or retrieves (such as by remote computing device 132) a vehicle energy profile of vehicle 10 that is determined remotely from vehicle 10.

PAC 124 may be configured to: the vehicle energy consumption profile and various route characteristics are used to determine a profile of target vehicle speed and/or torque target torque distribution for a portion of the route being traversed by the vehicle 10. For example, PAC 124 may determine that vehicle 10 is approaching a particular grade change over a portion of the route being traversed by vehicle 10. PAC 124 uses the vehicle energy consumption profile to identify vehicle speed (within a threshold mileage of a desired vehicle speed provided by the operator to VPC 102) and/or torque distribution with optimal energy consumption for the change in slope of the portion of the route being traversed by the vehicle. In some embodiments, PAC 124 may use historical energy consumption for known routes, such as routes previously traversed by vehicle 10 or similar vehicles, to determine vehicle speed and torque distribution. PAC 124 determines a target torque demand from the identified vehicle speed and a target torque split from the identified torque split. It should be appreciated that as described, PAC 124 continuously monitors the various characteristics received and continues to generate a profile of vehicle speed and/or target torque distribution such that vehicle 10 maintains optimal or improved energy consumption while maintaining operator and/or passenger comfort (e.g., by avoiding abrupt, unnecessary changes in vehicle speed).

In some embodiments, PAC 124 may be configured to determine when vehicle 10 should coast to achieve optimal or improved energy consumption of vehicle 10. For example, PAC 124 may use known traffic conditions, as described, to determine when vehicle 10 should skid. Additionally or alternatively, PAC 124 may learn about known traffic conditions, as described, and may determine whether vehicle 10 should skid in an area along a route known to generally have traffic, e.g., based on time of day. In some embodiments, PAC 124 may use SPaT information to determine when vehicle 10 should coast in response to changing the traffic signal. Additionally or alternatively, PAC 124 may determine to increase a target vehicle speed associated with a profile of target vehicle speeds (e.g., within a published speed limit) in order to increase the likelihood that vehicle 10 will arrive at the traffic signal when the traffic signal indicates forward progress based on a single timing of the traffic, which may allow vehicle 10 to avoid having to stop at the traffic signal.

In some embodiments, PAC 124 may be configured to: a coasting function and/or a road load function (see equation (1)) is calculated to identify specific vehicle parameters using the speed-dependent resistance. Parameters of the road load function include vehicle parameters such as vehicle mass or weight, vehicle rolling friction, vehicle resistance (drag) coefficients, other vehicle parameters, or combinations thereof, as described that may be received by PAC 124. These parameters may then be updated using the taxi self-learning function such that the PAC 124 identifies or requests a taxi sequence (e.g., from historical information and/or from the remote computing device 132) and calculates a taxi function result. The PAC 124 may calculate the coasting function when requested by an operator of the vehicle 10, who may be prompted by the PAC 124 to perform a particular learning maneuver, or may learn in the background.

Equation (1) resistance related to speed: f = wind, tire forces, bearing forces and other forces plus inertial forces related to acceleration plus gravity related to gradient:

F= (a+ (B x v) + (C x v ²)) + ((1+drive shaft% + non-drive shaft%) (test mass

* Acceleration)) + (test mass × g × (arc tan (slope%))))

Where a represents a constant and possibly non-speed-varying resistance (e.g., bearing, seal, tire, etc.), B represents a speed-linearly varying resistance (e.g., drive train, differential, etc.), and C represents a speed-squared varying resistance (e.g., wind, tire deformation, etc.).

As described, PAC 124 may control VPC 102 or interact with VPC 102, and/or interact with an operator of vehicle 10, to achieve a target vehicle speed and/or a target torque distribution profile, which may result in optimal or improved energy consumption efficiency of vehicle 10. Additionally or alternatively, PAC 124 may control VPC 102 or interact with VPC 102 to bring vehicle 10 to a full stop in response to vehicle 10 approaching a stop sign, a traffic signal, traffic, an disabling vehicle, or other suitable condition. PAC 124 may also control VPC 102 or interact with VPC 102 to resume vehicle propulsion after vehicle 10 has become completely stopped.

In some embodiments, PAC 124 may use virtual inputs to control VPC 102 or interact with VPC 102 in order to achieve a target vehicle speed and/or a target torque distribution profile. As described, the VPC 102 may receive a desired vehicle speed from the vehicle 10 operator using the HMI control 104. The virtual inputs as described herein may include inputs generated by PAC 124 or other suitable components disposed within vehicle 10 or located remotely from vehicle 10, such inputs being such as to allow PAC 124 or other suitable components to control various aspects of vehicle 10 in accordance with one or more control targets or other targets, such as the targets described herein. Additionally or alternatively, the VPC 102 (e.g., when the VPC 102 includes an adaptive cruise control mechanism) may adjust a desired vehicle speed in response to a speed of the front vehicle.

In some embodiments, PAC 124 initializes VPC 102 with a desired speed provided by an operator of vehicle 10 when the operator of vehicle 10 first occupies (engage) VPC 102 during a critical period. PAC 124 may then provide virtual inputs to VPC 102 in order to control vehicle speed to achieve optimal or improved energy consumption efficiency of vehicle 10. In some embodiments, PAC 124 may generate a virtual input comprising a virtual HMI signal that, when received by VPC 102, may cause VPC 102 to be enabled, disabled, and/or set or adjust a current vehicle speed. PAC 124 generates a virtual HMI signal based on the target vehicle speed profile. PAC 124 may communicate and/or interact with HMI control 104. The PAC 124 replaces the HMI signal provided by the operator of the vehicle 10 with the virtual HMI signal generated by the PAC 124. As depicted, VPC 102 includes a plurality of security controls. As described, VPC 102 then applies the target vehicle speed indicated by the virtual HMI signal associated with the target vehicle speed profile in the same manner that VPC 102 applies the desired vehicle speed provided by the operator using HMI control 104. The VPC 102 may determine whether to apply the target vehicle speed and/or the target torque allocation indicated by the virtual HMI signal based on the safety control.

In some embodiments, PAC 124 generates virtual inputs including virtual front cars to control VPC 102 to bring vehicle 10 to a full stop without an actual front car. For example, as described, PAC 124 may cause vehicle 10 to stop when vehicle 10 approaches a stop sign, a traffic signal, traffic, an disabled vehicle, or other suitable stop condition that vehicle 10 may encounter. PAC 124 replaces information received by VPC 102 from vehicle sensors 108 (e.g., information that VPC 102 uses to detect an actual front car) with virtual inputs, signals, and/or inputs corresponding to virtual front cars.

The VPC 102 detects the presence of a virtual front car and performs operations associated with following the front car (e.g., maintaining a safe distance between the vehicle 10 and the front car, keeping the front car in step, and stopping the vehicle in response to the front car being within a target range of the vehicle 10 and coming to a complete stop). PAC 124 may then control the virtual speed of the virtual front car based on the target vehicle speed profile. The VPC 102 may then adjust the current vehicle speed of the vehicle 10 to follow the virtual front car. In this manner, PAC 124 may implement a target vehicle speed profile of vehicle 10 to provide an optimal or improved energy consumption efficiency of vehicle 10. While PAC 124 is controlling VPC 102 using the described virtual inputs, vehicle sensors 108 (such as cameras, radars, proximity sensors, etc.) continue to provide information to VPC 102 so that VPC 102 may continue to detect actual vehicles or objects in front of vehicle 10 while VPC 102 may apply or follow the virtual inputs provided by PAC 124. The safety controls of the VPC 102 may be configured to override the VPC 102 (including the virtual input provided by the PAC 124) to safely bring the vehicle 10 to a full stop or increase or decrease the vehicle speed in response to information from the vehicle sensor 108.

In some embodiments, PAC 124 may be in direct communication with VPC 102 and torque distribution controller 116 to provide recommended target torque demand and target torque distribution to VPC 102 and torque distribution controller 116, respectively, to achieve optimal or improved energy consumption efficiency of vehicle 10. For example, the VPC 102 may be configured to receive HMI signals (e.g., as described), follow a preceding vehicle (e.g., as described) based on information from the vehicle sensors 108, and receive recommended target vehicle speed signals from the PAC 124. The VPC 102 may determine whether to apply the target vehicle speed indicated by the recommended target vehicle speed signal, e.g., based on operator input, detection of a forward vehicle, and/or safety control of the VPC 102.

The torque distribution controller 116 may be configured to receive a recommended torque distribution signal from the VPC 102 based on operator input as described, and may be configured to receive a recommended target torque distribution signal from the PAC 124. It should be appreciated that PAC 124 may communicate the recommended target torque split signal to VPC 102, which VPC 102 may then communicate the recommended target torque split signal and/or a recommended torque demand signal (e.g., generated by VPC 102) to torque split controller 116. The torque distribution controller 116 determines whether to apply the target torque distribution indicated by the recommended target torque distribution signal based on a comparison with the torque distribution indicated by the recommended torque distribution signal provided by the VPC 102 and/or based on an existing propulsion state of the vehicle 10 (e.g., including a diagnostic condition).

In some embodiments, PAC 124 may communicate with display 122 to provide an operator with an indicator that vehicle speed may be changing in order to improve the energy consumption efficiency of vehicle 10. For example, PAC 124 may use display 122 to illustrate an energy efficiency symbol that indicates to an operator of vehicle 10 that the vehicle speed may be changing in order to improve the energy consumption efficiency of vehicle 10.

In some embodiments, as described above, the VPC 102 may not include an adaptive cruise control system and may include a base cruise control system. Additionally or alternatively, the operator of the vehicle 10 may not occupy the VPC 102 in order to control propulsion of the vehicle 10 (e.g., the operator of the vehicle 10 may manually control propulsion). Accordingly, PAC 124 may be configured to provide recommendations to an operator indicating a target vehicle speed of the target vehicle speed profile. Recommendations may be provided to an operator of the vehicle 10 using one or more integrated displays of the vehicle 10, such as the display 122, which may include a portion of a dashboard or console of the vehicle 10, a navigation display of the vehicle 10, or other suitable integrated displays of the vehicle 10. In some embodiments, recommendations may be provided to an operator of the vehicle 10 using a mobile computing device within the vehicle 10. The recommendation may include a symbol or text message indicating to the operator of the vehicle 10 to increase or decrease the speed of the vehicle. Additionally or alternatively, the recommendation may include a taxi recommendation that may be displayed for a calibratable amount of time and then may be withdrawn in response to the operator of the vehicle 10 ignoring the recommendation. The recommendation may include information indicating that the recommendation may be a response to a change in speed limit, a stop sign that the vehicle 10 is approaching, traffic signal timing, and status or other information. This information may be visually displayed and may decay as the vehicle 10 recommendation becomes outdated.

The operator of the vehicle 10 may determine to follow (honor) the recommendation and change the vehicle speed accordingly, or the operator may choose to ignore the recommendation. PAC 124 may be configured to monitor driving actions in response to the recommendation to determine whether an operator of vehicle 10 is adhering to the recommendation or ignoring the recommendation. PAC 124 may determine whether to adjust the recommendation based on the monitored operator actions. For example, PAC 124 may determine not to recommend taxiing in response to the operator ignoring a threshold number of taxi recommendations. Additionally or alternatively, PAC 124 may use the monitored operator actions and the route traversed by vehicle 10to determine whether the operator of vehicle 10 is following the recommendation at some portions of the route and ignoring the recommendation at other portions of the route. PAC 124 may selectively provide recommendations to an operator of vehicle 10 based on the monitored operator actions and vehicle routes. Additionally or alternatively, PAC 124 may monitor operator actions in response to recommendations based on traffic patterns, stop signs, traffic signals, and the like. PAC 124 may selectively determine whether to provide recommendations to an operator of vehicle 10 based on operator actions monitored in response to traffic patterns, stop signs, traffic signals, and the like.

In some embodiments, PAC 124 and/or VPC 102 may perform the methods described herein. However, the methods described herein as being performed by PAC 124 and/or VPC 102 may not be meant to be limiting, and any type of software executing on the controller may perform the methods described herein without departing from the scope of the present disclosure. For example, a controller, such as a processor executing software within a computing device on the vehicle 10, may perform the methods described herein.

In some embodiments, system 100 may include additional, fewer, or other components than those shown in FIG. 2. In some embodiments, system 100 may perform more or less than the above-described functions.

As described, vehicles such as automobiles, trucks, sport utility vehicles, fork lifts, minivans, or other suitable vehicles may include various automatic vehicle propulsion control systems that may provide a particular level of automation to the vehicle. For example, the vehicle may include cruise control, adaptive cruise control, automatic braking, a fully autonomous vehicle control system, or any suitable vehicle propulsion control system, or a combination thereof. Typically, systems such as cruise control and adaptive cruise control receive input from an operator indicative of a desired vehicle speed. For purposes of this disclosure, an operator may include a human driver (either near or remote from the vehicle), an automated system (either near or remote from the vehicle), or a combination thereof. In the case of fully autonomous vehicles, the autonomous vehicle control system may determine vehicle speed based on issued (supported) speed limits and various safety systems and protocols. An automatic vehicle propulsion control system typically interacts with various vehicle components, such as throttle, brake systems, and the like, to achieve a desired speed.

While autonomous vehicles may be able to bring the vehicle to a full stop, cruise control systems are typically able to maintain a desired vehicle speed by adjusting the torque demand provided to various vehicle components, rather than being able to bring the vehicle to a full stop without operator interaction. Additionally or alternatively, the adaptive cruise control system is generally capable of maintaining a desired vehicle speed and adjusting the vehicle speed to maintain a safe distance from a vehicle in front (e.g., a vehicle immediately in front of the vehicle operating the automatic vehicle propulsion control system). However, cruise control and adaptive cruise control systems may not be able to bring the vehicle to a complete stop, as in the case of cruise control, or the system may not be able to bring the vehicle to a complete stop without a preceding vehicle, as in the case of adaptive cruise control. Furthermore, such systems typically do not continue vehicle propulsion after the vehicle has completely stopped without further input from the operator (such as the operator actuating a resume switch). In addition to the above, the described automatic vehicle propulsion control system may not be able to control vehicle propulsion to achieve a desired energy consumption (e.g., fuel, battery, etc.) efficiency. Accordingly, systems and methods that provide vehicle propulsion control to achieve optimal energy consumption, such as those disclosed herein, may be desirable.

Additionally or alternatively to the above, while there may be a system that provides a taxi speed recommendation to an operator, such a system may not be able to selectively determine a taxi speed based on various factors. Examples of such factors include vehicle location data, vehicle geography, vehicle characteristics, and subsequent speed change events. For example, the projected route information may be valuable in determining the taxi speed, particularly with respect to the elevation of the route being traversed by the vehicle 10. A stop event may occur when passing over a mountain top and coasting without regard to the minimum acceptable speed of the mountain top may cause driver discomfort (e.g., on a 45 mile/hour road, but with so much loss of speed when coasting uphill that the vehicle is traveling at only 10 miles/hour at the mountain top). A stop event may occur shortly after a speed reduction event (e.g., a stop flag occurs 100 feet after a speed limit is reduced to 25 miles per hour). Calculating when coasting for speed limit changes may result in inefficiency without regard to the next stop flag. Conversely, it may be more efficient to coast to a stop sign ignoring speed limit changes because the 25 miles per hour speed 100 feet before the stop sign may be much higher than the desired remaining speed of the stop sign. For example, a reduction in taxi speed to 15 miles per hour 50 feet before the stop sign may be more effective than a reduction in taxi speed to 20 miles per hour 50 feet before the stop sign. However, in this example, a reduction in the taxi speed of 100 feet before the stop sign to 25 miles per hour may result in a reduction in the taxi speed of only 20 miles per hour at 50 feet before the stop sign, requiring more fuel usage and more braking than a taxi target of 15 miles per hour at 50 feet before the stop sign. Accordingly, systems and methods of providing taxi recommendations to achieve optimal energy consumption, such as those disclosed herein, may be desirable.

Additionally or alternatively to the above, while there may be travel speed recommendation systems provided, such systems may not provide segmented, efficient cruise recommendations that avoid operator discomfort and distraction. For example, some systems may determine a single effective cruising speed for a trip, but the single effective cruising speed may be relatively inefficient over a large number of cruising speeds associated with uphill and downhill segments. As another example, some systems may provide constant speed updates, periodically changing speeds may distract the driver and reduce energy efficiency by repeated accelerations and decelerations. Accordingly, there may be a need for systems and methods such as those disclosed herein that provide cruise speed recommendations to achieve optimal energy consumption while minimizing operator distraction and discomfort.

Additionally or alternatively to the above, there may be some systems for providing recommended energy efficient operation to an operator of the vehicle. However, such systems may distract or present recommendations in a non-intuitive manner. Thus, systems and methods such as those disclosed herein provide energy efficiency recommendations in an intuitive, low-interference manner to achieve optimal energy consumption.

As noted above, typical vehicle propulsion control and recommendation systems may only allow limited taxiing capability. However, as the vehicle traverses the route, there may be an opportunity to allow the vehicle to coast, for example, to improve energy efficiency. However, as the vehicle traverses the route, there may be an opportunity to allow the vehicle to coast, for example, to improve energy efficiency. Various challenges exist in providing glide characteristics. For example, in the case of a known desired speed at the target (e.g., a stop or a reduction in speed limit), it may be difficult to determine the starting point of the taxi distance and the corresponding pre-taxi vehicle speed. Furthermore, rolling (e.g., coasting) the vehicle 10 until stopped (e.g., such as at a stop sign with a speed target of zero) may cause discomfort to the operator or to other vehicles following the operator.

To address this issue, the target speed of the stop sign (i.e., the remaining speed, the speed to be reached before the speed change event, such as the speed to be slid to before the stop sign brakes) may reach approximately 15mph at the target position 30 feet before the stop sign (the remaining position, the position where the operator will begin to slide). The target speed and/or target position may be selectable choices for the operator. The target speed and/or target position may be determined based on past driver behavior and responses to taxi recommendations.

Coasting is the movement of a vehicle due to its embedded kinetic energy and lack of propulsion provided by the powertrain. The movement of the vehicle during taxiing is further affected by various forces acting on the vehicle (e.g., aerodynamic drag) and is based on the speed and aerodynamic characteristics of the particular vehicle, gravity (based on road elevation profile (road grade [% ]), mass associated with the vehicle (e.g., vehicle dry mass, gasoline mass, mass associated with vehicle occupants, vehicle loads associated with cargo, etc.), gear/engine friction, wind force, road curvature friction, rolling friction, and/or other forces.

For any known Vehicle Identification Number (VIN), information regarding air resistance coefficients (represented by A, B and C), vehicle mass, and engine characteristics may be obtained via various remote computing devices, such as "cloud" computing devices, environmental Protection Agency (EPA) databases, and the like. Additionally or alternatively, the road grade and/or road characteristics (stop sign/stop light, speed limit, traffic, etc.) between any starting point and target point may be accessed via Global Positioning System (GPS) detailed map metadata, other suitable sources, etc. Vehicle characteristics and other factors, such as driver behavior, may be determined by a dedicated learning algorithm based on the difference between the predicted target speed and the actual speed of the target location found in the previous taxi recommendation.

Since the change in vehicle kinetic energy is proportional to the change in vehicle speed caused by all forces Σf (i), resulting in deceleration (negative acceleration) of the vehicle while coasting, the equation for the conversion of the fundamental kinetic energy over the incremental distance Δs of point i on the road to work can be used to formulate the equation:

equation (2) Δek=1/2·mass·Δv2= Σf (i) xΔs

An estimate of the total force to slow the vehicle can be used to calculate the instantaneous negative acceleration:

Equation (3) acceleration (i) = Σf (i)/mass

The vehicle speed at the earlier point i-1, i.e., vx (i-1) located at a distance Δs from point i, if the speed Vx (i) at point i can be found:

equation (4) Vx (i-1) =sqrt { [ Vx (i) ] 2+2. Acceleration (i) ·Δs) }

Where Δs represents the change in distance between two consecutive road grade readings.

The grade reading at each point i may be determined by interpolating grade data received from the GPS to fit a constant or position-changing distance step deltas.

Using iterative techniques and starting from the desired target speed at the desired target location (e.g., at or before the stop flag or speed limit), the processor may calculate a "speed envelope" back along the travel path to a maximum desired speed (typically the speed limit on the road segment under consideration).

For a flat or substantially flat surface (e.g., surface or road grade equal to 0% or near 0%), the speed envelope may look like that shown in fig. 4A, which shows the calculated speed envelope for a particular vehicle (particular mass, aerodynamic definition, and engine/gear friction). The speed envelope may be calculated assuming a target vehicle speed of 15.26mph (start of iteration). Arrow 402 points to an example of a vehicle pre-taxiing speed of about 50mph, such that the processor may identify that the start of the taxiing is at a distance from the target that is approximately equal to 2000m-780 m=1220 m (or 780m along the road if a speed envelope can be calculated at a distance of 2000m from the target).

Typically, the surface traversed by the vehicle 10 may deviate from a substantially planar surface. The speed envelope (as shown in fig. 4A) is modulated by uneven road slope due to the gravity component variation of the total force Σf (i). An example of an uneven grade road profile and the resulting "speed envelope" is shown in FIG. 4B using a target speed equal to 14.78mph (the start of the iteration).

Fig. 4B shows the calculated speed envelope (specific mass, aerodynamic definition and engine/gear friction) for a specific vehicle. This example shows the uneven road grade and the result of the "envelope" calculation. The envelope may be calculated assuming a target vehicle speed of 14.78mhp and may be limited to a maximum of 55mhp, as it may be performed in urban traffic where the speed limit is 55mhp or less. Arrow 402 shows an example of a vehicle pre-taxi speed equal to 40mph, which translates into a recommendation to start taxi at a distance (target distance) from the stop event approximately equal to 1325 m-600m=725 m (or 600m to travel along the road if a calculation of the "speed envelope" is available at 1325m from the target).

The dash-dot line 404 in fig. 4B, where it may be assumed that the vehicle is cruising at a speed of 35mph, indicates that in some cases the coasting speed may occasionally exceed the initial cruising speed (steep downhill) or be below the target speed (steep uphill). This may lead to unacceptably low speeds for the operator and should be handled by an additional pre-generated speed envelope in order to provide reasonable taxi recommendations on the human-machine interface.

Fig. 5A shows an example where without a minimum (i.e., lower) speed envelope, the corresponding coast recommendation places the coast point before the downhill segment, which would result in a very low speed at the mountain top.

Fig. 5B shows that introducing a minimum speed envelope based on the current speed limit or percentage of typical average driving speed contained in the map metadata together with a minimum deceleration limit before reaching, for example, a stop sign, will delay the glide indication, maintain a realistic speed profile and have limited impact on energy economy.

The minimum speed envelope parameter may depend on the selected driving profile. For example: the normal driving profile may have an economic goal of 5% (e.g., energy economy goal or mileage extension), a minimum speed of 80% of the speed limit, and a minimum deceleration of 1m/s 2; the economic objective of the Eco driving profile is 10%, the minimum speed is 70% of the speed limit, and the minimum deceleration is 0.5m/s2; and the economic objective of the eco+ driving profile is 15%, the minimum speed is 60% of the speed limit, and the minimum deceleration is 0.25m/s2.

Fig. 6 shows an example of event masking. In fig. 6, there are two speed reduction events, a speed limit reduction and a subsequent stop flag. In the illustrated case, the vehicle is traveling at 48mph, the system determines a speed limit of 25mph as the next event, the target speed is 25mph, and a coast indication is given at the blue arrow. The stop event may be masked by a speed limit event. If it further considers the stop flag, it may give a coast indication faster to provide additional fuel savings.

In some embodiments of the present disclosure, significant fuel savings may be achieved by optimizing the duration of the coasting phase with zero fuel combustion. The system may have advantages over a human operator because it knows the exact road topography, the distance from deceleration events outside the visual range (stops, traffic lights, speed limit decreases), and can accurately predict deceleration and stopping distance based on knowledge of vehicle road load parameters. The system may prioritize subsequent events to maximize fuel savings and to account for actual minimum speeds. The algorithm may rely on existing vehicle sensors enhanced by a location-based navigation database.

In some embodiments, the system 100 may be configured to receive vehicle position data. For example, PAC 124 may receive vehicle location data from GPS 126. For example, the vehicle location data may be received by the PAC from the remote computing device 132.

The system 100 may be configured to receive vehicle characteristic data. For example, PAC 124 may retrieve vehicle characteristic data from remote computing device 132. In some embodiments, the vehicle characteristic data may include vehicle aerodynamics. In some embodiments, the vehicle characteristic data may include vehicle quality. In some embodiments, the vehicle characteristic data may include a vehicle energy consumption profile. In some embodiments, the vehicle energy consumption profile may be based on one or more Environmental Protection Agency (EPA) energy economy tests. In some embodiments, the vehicle energy consumption profile may be based on historical analysis of past vehicle energy consumption.

The system 100 may be configured to receive planned route data. For example, PAC 124 may receive planned route data from remote computing device 132, on which remote computing device 132 the planned route has been generated. The planned route data may be received through the network interface 312. In some embodiments, the planned route data may be received from navigation software operating on one or more components of the system.

The system 100 may be configured to determine an intended route based at least in part on the vehicle location data. In some embodiments, determining the projected route may be based at least in part on the planned route data. For example, PAC 124 may receive the planned route data and determine that the planned route is a planned route based on the planned route data. In some embodiments, determining the projected route may be based at least in part on the vehicle location data. In some embodiments, determining the projected route may be based at least in part on historical analysis of the operator's travel. For example, if the operator has driven the same route multiple times at the same time of day, the predicted route may be determined based on an assumption that the user may be walking the same route.

The system 100 may be configured to receive route characteristic data. For example, PAC 124 may receive route characteristic data from a remote computing device. In some embodiments, the route characteristic data may include elevation data. In some embodiments, the route characteristic data includes flag information (e.g., a position of a stop flag, timing of a stop light, etc.). In some embodiments, the route elevation data may include elevations at a plurality of points along the predicted route. In some embodiments, the route characteristic data may further include road curvature data. Examples of road curvature data may include one or more of road curvature along the length of the road and road curvature across the road (i.e., the extent to which the road may be crowned or inclined). In some embodiments, the route characteristic data may further include road surface condition data. Examples of road surface condition data may include coefficient of friction, road rolling resistance contribution, presence of known potholes, and recent icing conditions. In some embodiments, the route characteristic data further includes weather data. Examples of weather data may include whether it was recently rained, whether it was recently snowed, and whether fog may interfere with visual identification of other vehicles. In some embodiments, the route characteristic data further includes speed limit data. In some embodiments, the route characteristic data further includes traffic data.

The system 100 may be configured to determine a first speed change location and a first speed change target speed based at least in part on the predicted route. For example, the PAC 124 may determine the first speed change location and the first speed change target speed. In some embodiments, determining the first speed change target speed may be based at least in part on the route characteristic data. For example, the flag information may be used to identify a stop flag or stop light in front. As another example, speed limit information may be used to identify a speed decrease ahead. In some embodiments, determining the first speed change target speed may be based at least in part on traffic data. For example, traffic data may be used to identify a backup in front. In some embodiments, determining the first speed change location may be based at least in part on the route characteristic data.

The system 100 may be configured to determine a second speed change location and a second speed change target speed based at least in part on the projected route. For example, PAC 124 may determine the second speed change location and the second speed change target speed. In some embodiments, determining the second speed change target speed may be based at least in part on the route characteristic data. For example, the flag information may be used to identify a stop flag or stop light in front. As another example, speed limit information may be used to identify a speed decrease ahead. In some embodiments, determining the first speed change target speed may be based at least in part on traffic data. For example, traffic data may be used to identify a backup in front. In some embodiments, determining the first speed change location may be based at least in part on the route characteristic data.

The system 100 may be configured to determine a first remaining speed and a first remaining speed position based at least in part on the first speed change position and the first speed change target speed. For example, the first remaining speed and the first remaining speed position may be determined by the PAC 124 by calculating a maximum desired braking deceleration based on a percentage from the first speed change position and the first speed change target speed to the selected speed limit. In some embodiments, determining the target speed profile may be based at least in part on traffic data. For example, traffic may move too fast to allow a low first residual speed.

The system 100 may be configured to determine a second remaining speed and a second remaining speed position based at least in part on the second speed change position and the second speed change target speed. For example, the second remaining speed and the second remaining speed position may be determined by PAC 124 by calculating a maximum desired braking deceleration based on a percentage of the speed limit selected from the second speed change position and the second speed change target speed. In some embodiments, determining the target speed profile may be based at least in part on traffic data. For example, traffic may move too fast to allow a low first residual speed.

The system 100 may be configured to determine a first speed lower margin (i.e., the vehicle should not slide to a speed below that speed; the speed may be adjusted lower as the first remaining speed position gets closer). In some embodiments, the first speed lower margin may be retrieved by PAC 124 from a storage device or received from remote computing device 132. In some embodiments, the first speed lower margin may be determined based on speed limit information.

The system 100 may be configured to determine a second speed lower margin (i.e., the vehicle should not slide to a speed below that speed; the speed may be adjusted lower as the first remaining speed position gets closer). In some embodiments, the second speed lower margin may be retrieved from the storage device by PAC 124. In some embodiments, the second speed lower margin may be determined based on the speed limit information.

The system 100 may be configured to determine a first speed lower envelope based at least in part on the first remaining speed. For example, PAC 124 may determine a first speed envelope. For example, the envelope may not pass below the first remaining speed at the first speed. In some embodiments, determining the first speed lower envelope may be further based on a first speed lower margin. For example, the first speed envelope may not drop below the first speed envelope.

The system 100 may be configured to determine a second speed lower envelope based at least in part on the second remaining speed and the second speed lower margin. PAC 124 may determine the under-speed envelope. For example, the envelope may not pass below the second remaining speed. In some embodiments, determining the second speed lower envelope may be further based on a second speed lower tolerance. For example, the second speed lower envelope may not be reduced below the second speed lower tolerance.

The system 100 may be configured to determine an overall speed envelope based at least in part on the first remaining speed. For example, PAC 124 may determine an overall speed envelope. In some embodiments, the overall speed envelope may be determined further based on the second speed envelope such that the overall speed envelope is the smaller of the second speed envelope and the second speed envelope.

The system 100 may be configured to determine an up-speed tolerance based at least in part on the speed limit information. For example, PAC 124 may determine an upper speed margin based on the speed limit information. For example, in some embodiments, the margin in speed may not exceed the speed limit, or may not exceed the speed limit plus a fixed value or percentage value that exceeds the speed limit.

The system 100 may be configured to determine an upper speed envelope. For example, PAC 124 may determine an upper speed envelope. In some embodiments, determining the upper speed envelope may be based at least in part on the speed limit information. In some embodiments, the speed upper envelope may be based at least in part on the speed upper tolerance.

The system 100 may be configured to determine the target speed profile based at least in part on the first remaining speed, the first remaining speed position, the overall speed lower envelope, and the speed upper envelope. For example, PAC 124 may determine the target speed profile. In some embodiments, the target speed profile is located above the overall speed envelope and below the speed envelope. In some embodiments, determining the target speed profile may be further based on vehicle characteristic data. For example, the target speed profile may vary depending on vehicle weight, aerodynamics, and the like.

The system 100 may be configured to determine a taxiing origin based at least in part on the target speed profile. For example, PAC 124 may calculate the start of the coast by selecting a point on the target speed profile where the current speed intersects the target speed profile, and the target speed profile may not exceed the speed upper envelope or be below the overall speed lower envelope.

The system 100 may be configured for communicating a taxi start point to an operator of the vehicle. For example, PAC 124 may send a signal to cause an audio change of additional output device 134 to produce an audible signal (e.g., an audible "coast now" or ringing tone) indicating that coasting is now beginning. In some embodiments, PAC 124 may send a signal to cause display 122 to present a visual indicator (e.g., a written message such as "coast now" or an image that the foot was lifted off the accelerator).

In addition, there is tremendous cost and environmental pressure to develop more energy efficient vehicle solutions. This is accomplished in part by continued improvements to the power transmission system, but may also be accomplished by "conscious" driving (i.e., enhanced, more energy efficient driving). By providing the operator with information about the energy efficiency of the intended vehicle along the planned driving path, and with recommendations about cruising speed, enhanced, more energy efficient driving may be achieved.

By using a tablet or smart phone equipped with a GPS receiver and containing detailed road metadata, more energy efficient driving can be facilitated. Energy economy may be based on individual vehicle characteristics (e.g., aerodynamics, mass, energy consumption characterized by specific standardized EPA tests, which may be accessed (via the "cloud") if the vehicle VIN number is known). The tablet or smartphone may then be able to provide recommendations to the driver, providing opportunities for improved energy economy for vehicles already on the road. Similarly, vehicles may provide such recommendations based on similar energy economy information.

During highway cruising, significant energy economy improvements can be achieved by operating the vehicle at speeds approaching optimum powertrain efficiency. This information may be calculated by algorithms on the smart device using map metadata (i.e., route characteristics) such as road grade, curvature, and speed limits, as well as knowledge about powertrain characteristics obtained from the vehicle database such as specific energy, gear ratio, mass, and air resistance.

When cruising at constant speed, the kinetic energy of the vehicle can be maintained by providing propulsion to balance the kinetic energy loss caused by: aerodynamic drag (based on speed and aerodynamic characteristics of a particular vehicle), gravity based on road profile (road grade [% ]) and vehicle mass, combined gear/engine friction, and other forces (e.g., wind, friction from road curvature, rolling friction, etc.).

According to the disclosed embodiments, the energy used by the propulsion system, adjusted by the powertrain's energy conversion factor and efficiency, provides the force required to maintain a constant vehicle speed, regardless of speed-dependent frictional losses and the weight defined by the road profile. Thus, one challenge is to provide acceptable approximations of vehicle energy consumption at various vehicle speeds and engine loads.

With constantly changing road grades, estimation of the energy consumption and speed of a vehicle traveling along a road is valuable information, allowing fuel savings at any speed to be calculated with reference to the vehicle's energy consumption while traveling at a speed limit. Energy efficiency data for any combination of vehicle speed and road grade may be obtained using a look-up table such as that shown in fig. 8.

FIG. 8 illustrates examples of energy efficiency characteristics of a vehicle depicted for various road grades. As indicated in fig. 8, when driving a given vehicle on a 1% incline, 35.86mpg may be provided when cruising at 70mph and 40.39mpg may be provided when cruising at 60 mph.

As shown in fig. 8, driving this particular vehicle along a road of constant inclination of 1% at a cruising speed of 60mph instead of 70mph saves fuel: 40.39-36.66 = 3.73mpg, or about 10%.

Notably, some steep downhill slopes are not represented in fig. 8, as gravity alone provides the force required to propel the vehicle forward. Of course, such a negative road grade may save fuel; the algorithm will provide recommendations to the taxi instead of calculating fuel savings.

The vehicle-specific family of characteristics may be provided by the OEM or, in the case of an after-market application, the estimates may be generated using the techniques described herein.

Finally, the algorithm may allow the driver to sacrifice a portion of the total travel time to save a certain amount (or percentage) of fuel.

In the case where the road profile is frequently changed, frequent changes in recommended speed may not be practical and may distract the driver. Even if the commanded change in speed is performed automatically, this scenario may be undesirable because frequent acceleration and deceleration may not only create discomfort, but may also result in additional energy consumption being ignored by the theoretical model assuming a quasi-constant (cruising) speed. In short, some type of road profile pattern recognition technique is highly desirable to establish a tradeoff between instantaneous change in energy consumption in response to instantaneous change in road slope and a large average method in which the instantaneous slope is replaced by the average road slope, which is calculated as the ratio of the difference in elevation between the end point and the start point divided by the distance between the start point and the target.

A technical example that can be used to divide the travel distance into road segments with constant cruising speed is given below. It may be arbitrarily assumed that changing the speed over a distance shorter than 1000m (to maintain a constant desired fuel saving) may be impractical due to distraction and too frequent acceleration, which may be counterproductive from a fuel saving perspective.

The common sense indicates that in general, long uphill segments should be separated from long downhill segments (different segments), and flat or nearly flat long load segments should be separated from long downhill and long uphill segments. Furthermore, since gravity compensates for the extra propulsion force generated by the engine, one can predict, without any calculation, that it is always recommended to roll down a hill at a higher speed (within the speed limit), and therefore acceleration should start on the hill slope rather than at the peak.

The observations listed above may result in an initial "pattern recognition algorithm" (i.e., "driver's common sense selection"), which is shown in fig. 9A and 9B, and which may be disputed-and possibly improved-by reference to a "more intelligent" selection. The marked road section of constant cruising speed is arbitrarily selected using the common sense method of fuzzy definition. Fig. 9A and 9B show a highway section covering approximately 18km twice in opposite directions. Thus, the distance and the elevation difference of the two distances are almost the same, but the boundaries of the respective road segments may not coincide on the way to and from.

The logic of "machine-based" pattern recognition techniques seeks to efficiently find the top of the main road slope and sets criteria that allow some local "road bumps" to be ignored. The combination of the initial strong filtering of the road elevation signal provided by the GPS with resampling (to illustrate the method, the elevation signal may be resampled at a distance of 100 m) still provides too many peaks for the "smoothing" operation of the cruise algorithm. Thus, subsequently, peaks and valleys are identified independently by calculating the elevation differences. Fig. 10A and 10B depict the results, with local maxima and minima of the elevation track marked.

The start of all downhill slopes is deliberately pushed down a arbitrarily chosen distance equal to 100 m. For individual types of vehicles using engine/vehicle models (e.g., GT Power), the best choice of such a "downhill start" distance delay can be verified theoretically. Excessive reaction to road grade changes can be detrimental to the algorithm, as too frequent changes in vehicle speed can result in undesirable energy consumption. The theoretical model of this effect can provide a "context best" distance for the minimum distance at which the savings from moderate speed outweigh the losses from the necessity of bond acceleration.

To illustrate the idea of this tradeoff and to prove that this can be achieved with even very coarse logic, it can be assumed that the travel distance between commands requesting a change in the current cruise speed can be no shorter than 1000 meters. This, together with the removal of clusters representing short distance interval transitions from valley to peak (or peak to valley), results in the results shown in fig. 11A and 11B, which progressively illustrate pattern recognition filtering techniques.

Fig. 11A shows a link splitting technique shown on a sample of a road profile covering a distance of approximately 18 km. The left graph is the same as fig. 9A and the right upper graph is the same as fig. 10A, both of which are combined for convenience in comparison with the results of the "automatic pattern recognition technique", the results of which are shown in the lower right corner.

Fig. 11B illustrates a link splitting technique shown on a sample of a road profile covering a distance of approximately 18 km. The left graph is the same as fig. 9B and the right upper graph is the same as fig. 10B, both of which are combined for convenience in comparison with the results of the "automatic pattern recognition technique", the results of which are shown in the lower right corner.

Although there are some differences in the choice of the constant cruise portion, both algorithms yield nearly identical results when validated in practice. Fig. 12 shows the accumulated energy consumption on the road section B represented in fig. 11B, and the results of reference driving representing a speed limit of 70mph are given below, wherein the reference is the upper line, the script-generated portion is the middle line, and the manually-selected portion is the lower line.

In some embodiments, the system 100 may be configured to receive vehicle position data. For example, PAC 124 may receive vehicle location data from GPS 126. In some embodiments, PAC 124 may receive vehicle location data from remote computing device 132.

The system 100 may be configured to receive planned route data. In some embodiments, the planned route data may be received from navigation software. For example, PAC 124 may receive planned route data from remote computing device 132, on which remote computing device 132 the planned route has been generated.

The system 100 may be configured to determine an intended route. In some embodiments, determining the projected route may be based at least in part on the planned route data. For example, determining the predicted route may be as simple as receiving the planned route. In some embodiments, determining the projected route may be based at least in part on the vehicle location data. For example, if the vehicle 10 is traveling at least 30 kilometers on a highway of little interest, the PAC may determine that the predicted route continues on the highway for 30 kilometers. In some embodiments, determining the projected route may be based at least in part on a historical analysis of the user's travel. For example, if the user has driven the same route multiple times at the same time of day, the predicted route may be determined based on the assumption that the user is walking the same route.

The system 100 may be configured to receive route characteristic data including at least route elevation data. For example, PAC 124 may receive route characteristic data from a storage device. In some embodiments, the route elevation data may include elevations at a plurality of points along the predicted route. In some embodiments, the route characteristic data may further include road curvature data. In some embodiments, the route characteristic data may further include flag data. Examples of road curvature data may include one or more of road curvature along the length of the road and road curvature across the road (i.e., the degree to which the road is crowned or inclined). In some embodiments, the route characteristic data may further include road surface condition data. Examples of road surface condition data may include coefficient of friction, road rolling resistance contribution, presence of known potholes, and recent icing conditions. In some embodiments, the route characteristic data further includes weather data. Examples of weather data may include whether it was recently rained, whether it was recently snowed, and whether fog prevented visual identification of other vehicles. In some embodiments, the route characteristic data further includes speed limit data. In some embodiments, the route characteristic data further includes traffic data.

The system 100 may be configured to receive vehicle characteristic data. For example, PAC 124 may receive vehicle characteristic data from remote computing device 132 or a storage device. In some embodiments, the vehicle characteristic data may include vehicle aerodynamics. In some embodiments, the vehicle characteristic data may include vehicle quality. In some embodiments, the vehicle characteristic data may include a vehicle energy consumption profile. In some embodiments, the vehicle energy consumption profile may be based on Environmental Protection Agency (EPA) energy economy testing. In some embodiments, the vehicle energy consumption profile may be based on historical analysis of past vehicle energy consumption.

The system 100 may be configured to determine a sampling resolution. For example, PAC 124 may determine the sampling resolution based on a value received from the storage device. In some embodiments, determining the sampling resolution may be based at least in part on the vehicle characteristic data.

The system 100 may be configured to sample the route elevation data at a sampling resolution to generate sampled route elevation data. For example, PAC 124 may sample the route elevation data every 100m to generate sampled route elevation data.

The system 100 may be configured to receive a fuel economy target. For example, the operator of the vehicle may input a desired fuel savings target (e.g., 5%, 10%, etc.) via the HMI control 104, which PAC 124 then receives … …, in some embodiments, determining the fuel savings target may include retrieving a value (e.g., 100 m) from a storage device.

The system 100 may be configured to determine a starting point for an uphill delay. In some embodiments, determining the start of the uphill delay may be based at least in part on the vehicle characteristic data. In some embodiments, determining the start of the uphill delay may include retrieving a value (e.g., 100 m) from a storage device.

The system 100 may be configured to determine a starting point of the downhill delay. In some embodiments, determining the start of the uphill delay may be based at least in part on the vehicle characteristic data. In some embodiments, PAC 124 may determine the start of the downhill delay by retrieving a value (e.g., 100 m) from a storage device.

The system 100 may be configured to determine at least one starting point for an uphill location and at least one starting point for a downhill location based at least in part on the sampled route elevation data. In some embodiments, the starting point of the at least one uphill position may be a starting point of two uphill positions, a starting point of three uphill positions, a starting point of four uphill positions, a starting point of five uphill positions, or a starting point of more uphill positions. In some embodiments, the at least one starting point of the downhill position may be two starting points of the downhill position, three starting points of the downhill position, four starting points of the downhill position, five starting points of the downhill position, or more. For example, PAC 124 may determine at least one starting point for an uphill location and at least one starting point for a downhill location by analyzing the sampled route elevation data and determining local peaks and valleys by comparing the elevations of the different points. In some embodiments, the at least one starting point of the uphill location and the at least one starting point of the downhill location are determined by automatic pattern recognition. In some embodiments, at least one starting point of the uphill starting point position may be adjusted forward along the predicted route a distance equal to the uphill delay starting point (e.g., 100 m). In some embodiments, at least one of the plurality of uphill start points may be adjusted forward along the predicted route a distance (e.g., 100 m) equal to the uphill delay start point.

The system 100 may be configured to determine a minimum speed change distance. In some embodiments, determining the start of the uphill delay may be based at least in part on the vehicle characteristic data. In some embodiments, determining the start of the uphill delay may include PAC 124 receiving a value (e.g., 1,000 m) from a storage device. In some embodiments, the minimum speed change distance may be based on vehicle characteristic data.

The system 100 may be configured to determine at least one cruise speed route segment based at least in part on at least one starting point of an uphill location and at least one starting point of a downhill location. In some embodiments, the at least one cruise speed route segment may be two cruise speed route segments, three cruise speed route segments, four cruise speed route segments, five cruise speed route segments, or more cruise speed route segments. For example, PAC 124 may determine at least one cruise speed segment by starting at one start point of a downhill position and ending at one start point of an uphill position. In some embodiments, determining at least one cruise speed route segment may further change distance based on the minimum speed. For example, at least one cruise speed route segment may be limited to at least 1000m. Requiring at least one cruise speed route segment to reach a minimum speed change distance may increase operator compliance and reduce fuel costs by avoiding periodic acceleration.

The system 100 may be configured for determining a corresponding cruise speed of the at least one cruise speed route segment based at least in part on one or more of the route elevation data and the sampled route elevation data. In embodiments where there is more than one cruise speed route segment, more corresponding cruise speeds may be determined. For example, PAC 124 may determine that for a typical downhill cruise speed route segment, the corresponding cruise speed is 65 miles per hour, and for a typical uphill cruise speed route segment, the corresponding cruise speed is 60 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the vehicle characteristic data. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the fuel economy target. For example, the corresponding cruise speed for an exemplary cruise speed route segment may be 65 miles per hour with a fuel economy target of 5%, and 60 miles per hour with a fuel economy target of 10%. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the road curvature data. For example, if there is a curvature in the cruise speed route segment that is not safe to navigate at 70 miles per hour but safe to navigate at 65 miles per hour, and the corresponding cruise speed is 70 miles per hour, the corresponding cruise speed may be set to 65 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the road surface condition data. For example, if there is a set of pits in the cruise speed route segment that are unsafe to navigate at a speed of 70 miles per hour, but safe to navigate at a speed of 60 miles per hour, and the corresponding cruise speed is 70 miles per hour, the corresponding cruise speed may be set to 65 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on weather data. For example, if there is a visibility limiting fog in the cruise speed route segment that is not safe to navigate at 70 miles per hour but safe to navigate at 55 miles per hour, and the corresponding cruise speed is 70 miles per hour, the corresponding cruise speed may be set to 55 miles per hour. In some embodiments, the respective cruise speeds of the at least one cruise speed route segment may be determined further based on the speed limit data. For example, if the speed limit is 55 miles per hour and the corresponding cruise speed is 65 miles per hour, the corresponding cruise speed may be set to 55 miles per hour. In some embodiments, the respective cruise speeds of at least one cruise speed route segment may be determined further based on the traffic data. For example, if traffic moves at 65 miles per hour and the corresponding cruising speed is 70 miles per hour, the corresponding cruising speed may be set to 65 miles per hour.

The system 100 may be configured for communicating a corresponding cruise speed of the at least one cruise speed route segment. For example, in embodiments where the system 100 includes the audio type of the additional output device 134, the PAC 124 may send a signal to cause the audio type of the additional output device 134 to generate an audible signal that recommends the operator to change the cruise control to a corresponding cruise speed (e.g., audible "coasting at 67 miles per hour"). In embodiments where the system includes a display 122, PAC 124 may send a signal to cause the display to present a visual indicator (e.g., a text message such as "coast at 67 miles per hour").

One challenge in presenting advice to the operator is to do so in an intuitive and minimally intrusive manner. Thus, an exemplary smart driving application (application) may be designed to provide recommendations in an intuitive manner while minimizing interference and functionality on a vehicle or in a standalone environment (e.g., on a smart device, such as a smart phone or smart tablet). The application utilizes computing resources of the device, vehicle positioning, and wireless communication hardware to accomplish the energy economy optimization task.

A method 1400 for using the application is shown in fig. 14 (at 1402), where a user may introduce the vehicle characteristics (mass, drag coefficient, engine and gearbox parameters, etc.) of their vehicle manually (at 1404) or by entering the VIN number of the vehicle (at 1406) that the device may use to download detailed vehicle characteristics from a public or proprietary database using a cellular or WI-FI connection (at 1408). Vehicle characteristics may include (but are not limited to): empty mass, drag coefficient, EPA standardized energy consumption, and gearbox ratio.

As shown in fig. 15, another method 1500 for using the application is presented (at 1502) in which operators may or may not enter their destinations (at 1504). In the case of input destination, the application may determine (at 1506) a corresponding route from the available traffic and/or road profile information. Without the input destination, the application may calculate a recommendation (at 1508) regarding the most likely short-term path (fallback horizon) that the vehicle may take. The application may receive information about the characteristics of the surrounding roads, for example by a resident map and/or by downloading map and traffic information in real-time as needed. Road characteristics may include (but are not limited to): speed limit, signal (stop sign, traffic light, etc.), road slope (grade), radius of curvature, etc.

Real-time traffic light information, if available in the future through the cellular network, can be optimized to achieve a "green wave" effect by modulating the recommended speed (i.e., avoiding having to brake and accelerate traffic lights, but rather timing them so that the vehicle 10 can pass through the traffic lights without slowing down).

The application may sense the vehicle position, speed, and direction in real time through its onboard GNSS equipment (e.g., GPS; as used herein, GPS includes any GNSS equipment), which may be enhanced by sensor data from embedded accelerometers and/or gyroscopes and/or compasses. The position may also be corrected by using map information (i.e., map matching). In the event of loss or intermittent GNSS signals, the application may extrapolate the information until an absolute position is returned.

The application may also communicate with the vehicle via an optional OBD adapter to retrieve vehicle real-time information such as speed, load, temperature, battery state of charge, and injected fuel quantity to enhance the optimization parameters.

The application may also have a self-learning algorithm to adjust preload parameters such as actual current vehicle mass, actual road load factor, and actual energy consumption.

If the destination is known, the application may pre-calculate the corresponding speed along the route and retrieve and display recommendations based on the current location and speed of the vehicle on the route. These recommendations may be scaled and formatted according to the human interface principles described within the application.

Such techniques may allow improved optimization, particularly if the vehicle has a hybrid or electric powertrain, as the prior knowledge of the energy recovery potential may help optimize battery state of charge over the entire route.

If the destination is unknown, the application can calculate the best speed over some look-ahead distance by identifying the most likely path. The application may also calculate the optimal speed to the subsequent intersection.

The application may also collect a plurality of geographic location parameters and upload them into cloud storage via a cellular or WI-FI connection for future use and/or statistical purposes.

The collected parameters may be used by data analysis methods, for example, to construct operator profiles, energy consumption maps, speed profiles, etc.

The application may also access the collected and processed information to further optimize recommendations, e.g., based on consumed routing criteria, real world speed profile knowledge, etc.

Fig. 16 illustrates an example of a method 1600 for using the application.

The system may be expected to provide a visual driving speed that follows several principles. It may be desirable for the system to not require active user input during driving.

It may be desirable to complete all basic configurations before driving and to limit to a minimum (e.g., selection of vehicles in the case of several vehicles). If the application can learn in advance of the operator's intended route, it is recommended to enter the destination before starting the journey (i.e. as is done in common navigation applications).

The system may be expected to provide "safe" (i.e., safe-to-follow) driving recommendations and avoid giving instructions (e.g., "acceleration" or "braking") that the operator may subconsciously follow and may lead to dangerous situations. Other instructions, such as "coast immediately" or "slow down", recommend that the speed of the vehicle be reduced in a gentle manner, thus making unsafe conditions less likely.

It may be desirable that the driving recommendation is explicit. The instructions should explicitly indicate what the operator should do (e.g., coasting, holding speed, etc.) without explanation. This is especially true for speed recommendations, where the user may want to always know if they are within the recommended speed range.

The system may be expected to avoid presenting text information. For visual purposes, it may be desirable that the visual interface not include written text unless necessary: for example, a current speed limit.

The system may be expected to prioritize the relevant information such that the most relevant information is prioritized over a lower priority indication (e.g., "speed limit exceeded" is prioritized over a recommended cruise speed indication).

It may be desirable for the system to include tolerances and smoothness in its function. Desirably, in addition to the binary indication (e.g., coasting), the visual display should display a tolerance band that can be updated smoothly (without discontinuities) to allow the operator to follow the recommendation.

The system may communicate with a user (operator) through one or more channels, including visual display and acoustic output. The visual display may be based on a screen of the mobile device. The mobile device may be placed on a stand, which is preferably placed in the operator's view without obstructing the view of the traffic. The mobile device may be expected to be in the near peripheral field of view of the operator.

In some embodiments, the color coding may reflect a standard traffic light scheme (green-yellow-red) for explicit message interpretation. The additional color may indicate a special recommendation. For binary events and immediate warnings, the acoustic message may be more suitable for attracting the attention of the user and minimizing visual disturbances. In general, acoustic signals may also be combined with visual cues. The acoustic message may be a suitable and intuitive sound (e.g., a bell for speed warning) or a synthetic voice message of a user preferred language. It may be desirable for applications to relay messages through a vehicle hands-free system to improve readability. The acoustic message may be limited to a minimum to avoid distraction and annoyance, and may be configured to be disabled.

17A-17H illustrate various configurations of a display area 1700 presented on a display for providing visual driving speed recommendations to a vehicle operator. A first indicator 1702 indicates the current speed and is located in the center of the display area. An example of the first indicator 1702 is a vertical blue line. The first mode 1704 indicates the corresponding energy efficiency speed. In the context of the present disclosure, a speed may include a range of speeds. As discussed in this disclosure, the pattern may be solid. An example of the first mode 1704 is pure green. Other modes may be achromatic modes, which may provide utility to operators of achromatopsia. For example, the pattern may be stripe, X-shaped, O-shaped, + shaped, etc. The second mode 1706 indicates a higher corresponding energy inefficiency speed. An example of the second mode 1706 is pure yellow. The first mode range 1708 is located between the first mode 1704 and the second mode 1706 and indicates a transition range between a corresponding energy efficient speed and a higher corresponding energy inefficient speed. An example of the first mode range 1708 is a range that smoothly transitions from green to yellow without substantial discontinuity. For purposes of this disclosure, an indicator such as the first indicator is not considered a discontinuity in a smooth transition. One example of a pattern range involving symbols is a pattern in which the O-shape "fades in" an X-shape, or where the O-shape is small and the X-shape is large between two regions of pure O-shape and pure X-shape. The third mode 1709 indicates an unrealistically low speed. An example of the third pattern 1709 is pure black. The second mode range 1710 indicates a transition range between an unrealistically low speed and a corresponding energy efficient speed. An example of the second mode range 1710 may be a range that smoothly transitions from black to green without substantial discontinuity. The second indicator 1712 indicates an upper speed limit. An example of a second indicator 1712 is a vertical red line. A third indicator (not shown) may indicate a lower speed limit (i.e., on many interstate highways, 45 miles per hour is the lower speed limit). The lower speed limit may be a vertical line. Fourth mode 1714 indicates an overspeed-tolerance speed (i.e., a speed above a speed limit that is an upper speed limit). An example of the fourth mode 1714 is pure red. The third mode range 1716 indicates a transition range between the higher respective energy inefficient speed and the overspeed tolerance speed. An example of the third mode range 1716 is a range that smoothly transitions from yellow to red without substantial discontinuity. In some embodiments, the fifth mode 1718 indicates a taxi speed in which the vehicle will taxi at approximately the same speed without getting or losing speed. An example of the fifth mode 1718 may be solid white. In some embodiments, sixth mode 1720 indicates a sub-coasting speed (i.e., a speed at which the operator accelerates or actively brakes to maintain that speed due to gravity). An example of the sixth mode may be pure blue. Fourth mode range 1722 indicates a transition range between the sub-coast speed and the coast speed. An example of the third mode range 1716 is a range that smoothly transitions from blue to white without substantial discontinuity.

Fig. 17A shows a display area 1700 in a configuration in which a first indicator 1702 corresponding to a current speed is located within a first mode 1704 corresponding to a corresponding energy efficiency speed. The operator of the vehicle may observe that they are within the desired speed range.

Fig. 17B shows a display area 1700 in a configuration in which a first indicator 1702 corresponding to a current speed is located within a second mode 1706 corresponding to a higher corresponding energy inefficient speed. The operator of the vehicle may observe that their speed is higher than the desired speed range.

Fig. 17C shows the display area 1700 in a configuration in which the first indicator 1702 corresponding to the current speed is located within the third mode 1709 corresponding to an impractical low speed. The operator of the vehicle may observe that their speed is below the desired speed range.

Fig. 17D shows a display area 1700 in a configuration in which the vehicle is traveling on a flat ground. The first indicator 1702 corresponding to the current speed is located within the first mode 1704 corresponding to the corresponding energy efficiency speed and the operator of the vehicle can observe that they are within the desired speed range.

Fig. 17E shows a display area 1700 in a configuration in which the vehicle is traveling uphill. In response to the vehicle traveling uphill, the first mode 1704, the second mode 1706, and the third mode 1709 have moved to the left. The first indicator 1702 corresponding to the current speed is located within the second mode 1706 corresponding to the higher corresponding energy inefficient speed, and the operator of the vehicle can observe that their speed is above the desired speed range.

Fig. 17F shows a display area 1700 in a configuration in which the vehicle is traveling downhill. In response to the vehicle traveling downhill, the first mode 1704, the second mode 1706, and the third mode 1709 have moved to the right. The first indicator 1702 corresponding to the current speed is located within the third mode 1709 corresponding to an impractical low speed and the operator of the vehicle can observe that their speed is below the desired speed range.

Fig. 17G shows the display area 1700 in a configuration in which the vehicle is coasting downhill so that there is no corresponding energy efficient speed to adjust the vehicle, as any increase would require unnecessary fuel and potentially propel the vehicle beyond the speed limit.

Fig. 17H shows the display area 1700 in a configuration in which the current speed of the vehicle has reached or exceeded the overspeed tolerance speed, and the display area 1700 has changed to match the fourth mode 1714. Operators of vehicles can easily observe that they are traveling over speed.

The system 100 may be configured to determine a current speed. In some embodiments, PAC 124 may receive a current speed from vehicle sensor 108. In some embodiments, PAC 124 may calculate the current speed based on vehicle position data obtained by GPS 126.

The system 100 may be configured to determine a corresponding energy efficiency rate. In some embodiments, the corresponding energy efficiency speed may be based at least in part on vehicle characteristic data (e.g., vehicle mass, energy consumption profile, etc.). In some embodiments, the corresponding energy efficiency speed may be based at least in part on route characteristic data (elevation, road curvature, road surface information, weather, speed limits, etc.). For example, PAC 124 may receive vehicle characteristic data and route characteristic data and determine a corresponding energy efficiency speed.

The system 100 may be configured to determine a higher corresponding energy inefficiency speed. In some embodiments, a higher corresponding energy-inefficient speed is determined based on the speed limit. For example, PAC 124 may receive speed limit information from remote computing device 132 and determine that the higher corresponding energy inefficient speed is equal to the speed limit.

The system 100 may be configured to determine a first transition range between the corresponding energy efficient speed and the higher corresponding energy inefficient speed. PAC 124 may determine this by comparing the corresponding energy efficiency speed to the higher corresponding energy inefficiency speed.

The system 100 may be configured to display a display area on a display. For example, PAC 124 may transmit a signal to display 122 to cause display 122 to show the display area and its content. In some embodiments, the display area may include a first indicator corresponding to the current speed. In some embodiments, the first indicator is a vertical line. In some embodiments, the first indicator is located at a fixed position within the display area. In some embodiments, the fixed location is centered within the display area. In some embodiments, the first indicator may be an arrow. In some embodiments, the display area may include a first mode corresponding to a corresponding energy efficiency rate. In some embodiments, the first mode is green. In some embodiments, the display area may include a second mode that is different from the first mode and corresponds to a higher corresponding energy-inefficient speed. In some embodiments, the second mode is yellow. In some embodiments. In some embodiments, the display region may include a first mode range corresponding to the first transition range. In some embodiments, the mode of the first mode range is within a range between the first mode and the second mode, and the first mode range has no substantial discontinuity between the first mode and the second mode.

The system 100 may be configured to determine an impractical low speed range. In some embodiments, PAC 124 may determine an impractical low speed range based on speed limit information (e.g., a given percentage of the speed limit or a flat speed value below the speed limit).

The system 100 may be configured to determine a second transition range between the unrealistic low speed and the corresponding energy efficiency speed. PAC 124 may determine the second transition range by comparing the unrealistically low speed and the corresponding energy efficiency speed.

The system 100 may be configured to present a third mode in the display area that is different from the first mode and corresponds to an impractical low speed. In some embodiments, the third mode is black. For example, PAC 124 may send a signal to display 122 to display the third mode.

The system 100 may be configured for presenting in the display area a second mode range disposed between the third mode and the first mode and corresponding to the second transition range. For example, PAC 124 may send a signal to display 122 to cause display 122 to display the second mode range. In some embodiments, the mode of the second mode range is within a range between the first mode and the third mode, and the first mode range has no substantial discontinuity between the first mode and the third mode.

The system 100 may be configured to determine an upper speed limit. For example, PAC 124 may determine an upper speed limit. In some embodiments, the upper speed limit may be based on speed limit information (i.e., the speed limit for the portion of the route is the upper speed limit).

The system 100 may be configured for presenting a second indicator corresponding to an upper speed limit in the display area. For example, PAC 124 may send a signal to display 122 to display the second indicator. In some embodiments, the second indicator is a vertical line.

The system 100 may be configured to determine a lower speed limit. For example, PAC 124 may determine a lower speed limit. In some embodiments, a lower speed limit may be determined based on the speed limit information. For example, the minimum speed limit for many highways in the united states is 45 miles per hour.

The system 100 may be configured to present a third indicator corresponding to the lower speed limit in the display area. For example, PAC 124 may send a signal to display 122 to display the third indicator. In some embodiments, the third indicator is a vertical line.

System 100 may be configured to determine an overspeed tolerance speed. For example, PAC 124 may determine the overspeed-tolerance speed by multiplying the speed limit or an upper speed limit by a factor (e.g., multiplying the speed limit by 1.1) or adding a set point to the speed limit (e.g., 5 miles per hour).

System 100 may be configured to determine a third transition range between the overspeed tolerance speed and the higher corresponding energy inefficient speed. For example, PAC 124 may determine the third transition range by comparing the overspeed-tolerance speed to a higher corresponding energy inefficiency speed.

System 100 may be configured to present a fourth mode in the display area that is different from the second mode and corresponds to the overspeed tolerance speed. For example, PAC 124 may send a signal to display 122 to display the fourth mode. In some embodiments, the fourth mode is red.

The system 100 may be configured for presenting a third mode range disposed between the second mode and the fourth mode and corresponding to the third transition range in the display area. For example, PAC 124 may send a signal to display 122 to display the third mode range. In some embodiments, the mode of the third mode range is within a range between the second mode and the fourth mode, and the third mode range has no substantial discontinuity between the second mode and the fourth mode.

Fig. 3 generally illustrates a computing device 300 in accordance with the principles of the present disclosure. The computing device 300 may be configured to perform various operations and methods. The computing device 300 may include a processor 302 and a memory 314, the processor 302 being configured to control overall operation of the computing device 300, the memory 314 containing instructions that when executed by the processor 302 cause the processor to perform various operations. It should be appreciated that the processor 302 (e.g., and/or any of the processors described herein) may include any suitable processor, including those described herein. Memory 314 may include Random Access Memory (RAM), read Only Memory (ROM), or a combination thereof. In some embodiments, one or both of storage device 310 and memory 314 may comprise flash memory, semiconductor (solid state) memory, or the like. One or both of the storage device 310 and the memory 314 may include Random Access Memory (RAM), read Only Memory (ROM), or a combination thereof. Memory 314 may store programs, utilities or processes to be executed by processor 302. Memory 314 may provide volatile data storage and store instructions related to the operation of the computing device.

Computing device 300 may also include a user input device 304, which user input device 304 may be configured to receive input from a user of computing device 300 and communicate signals representative of the input received from the user to processor 302. For example, the user input device 304 may include buttons, a keypad, a dial, a touch screen, an audio input interface, a visual/image capture input interface, input in the form of sensor data, and the like.

Computing device 300 may include an output device 306 (e.g., a display screen, speakers, or any other suitable output device), which output device 306 may be controlled by processor 302 to present information to a user. The data bus 308 may be configured to facilitate data transfer between at least the storage device 310 and the processor 302. Computing device 300 may also include a network interface 312, which network interface 312 is configured to couple or connect computing device 300 to various other computing devices or network devices via a network connection, such as a wired or wireless connection. In some embodiments, the network interface 312 includes a wireless transceiver.

Storage device 310 may include a single disk or multiple disks (e.g., hard disk drives), one or more solid state drives, one or more hybrid hard disk drives, and the like. Storage device 310 may include a storage management module that manages one or more partitions within storage device 310.

In some embodiments, the computing device 300 may be in the vehicle 10. In some embodiments, the computing device 300 may be proximate to the vehicle 10. In some embodiments, the computing device 300 may be remote from the vehicle 10. In some embodiments, computing device 300 may be in a vehicle. In some embodiments, instructions are stored on memory 314 that, when executed by processor 302, cause the processor to perform the steps of the methods described herein.

In some embodiments, computing device 300 may include additional, fewer, or other components than those shown in FIG. 3. In some embodiments, computing device 300 may perform more or less functionality than that described above.

In some embodiments, the computing device 300 may be configured to receive vehicle location data. For example, the processor 302 may receive vehicle location data from the GPS 316. For example, the vehicle location data may be received by the PAC via the network interface 312.

The computing device 300 may be configured to receive vehicle characteristic data. For example, the processor 302 may retrieve vehicle characteristic data from the storage device 310. In some embodiments, the vehicle characteristic data may include vehicle aerodynamics. In some embodiments, the vehicle characteristic data may include vehicle quality. In some embodiments, the vehicle characteristic data may include a vehicle energy consumption profile. In some embodiments, the vehicle energy consumption profile may be based on one or more Environmental Protection Agency (EPA) energy economy tests. In some embodiments, the vehicle energy consumption profile may be based on historical analysis of past vehicle energy consumption.

The computing device 300 may be configured to receive planned route data. For example, the processor 302 may receive the planned route data from the network interface 312, on which network interface 312 the planned route has been generated. In some embodiments, the planned route data may be received from navigation software operating on the processor 302.

The computing device 300 may be configured to determine an intended route based at least in part on the vehicle location data. In some embodiments, determining the projected route may be based at least in part on the planned route data. For example, the processor 302 may receive the planned route data and determine that the planned route is a planned route based on the planned route data. In some embodiments, determining the projected route may be based at least in part on the vehicle location data. In some embodiments, determining the projected route may be based at least in part on a historical analysis of the operator's travel. For example, if the operator has driven the same route multiple times at the same time of day, the predicted route may be determined based on an assumption that the user may be walking the same route.

The computing device 300 may be configured to receive route characteristic data. For example, the processor 302 may receive route characteristic data from a remote computing device. In some embodiments, the route characteristic data may include elevation data. In some embodiments, the route characteristic data includes sign information (e.g., location of stop signs, timing of stop lights, etc.). In some embodiments, the route elevation data may include elevations at a plurality of points along the predicted route. In some embodiments, the route characteristic data may further include road curvature data. Examples of road curvature data may include one or more of road curvature along the length of the road and road curvature across the road (i.e., the extent to which the road may be crowned or inclined). In some embodiments, the route characteristic data may further include road surface condition data. Examples of road surface condition data may include coefficient of friction, road rolling resistance contribution, presence of known potholes, and recent icing conditions. In some embodiments, the route characteristic data further includes weather data. Examples of weather data may include whether it was recently rained, whether it was recently snowed, and whether fog may interfere with visual identification of other vehicles. In some embodiments, the route characteristic data further includes speed limit data. In some embodiments, the route characteristic data further includes traffic data.

The computing device 300 may be configured to determine a first speed change location and a first speed change target speed based at least in part on the projected route. For example, the processor 302 may determine a first speed change location and a first speed change target speed. In some embodiments, determining the first speed change target speed may be based at least in part on the route characteristic data. For example, the flag information may be used to identify a stop flag or stop light in front. As another example, speed limit information may be used to identify a speed decrease ahead. In some embodiments, determining the first speed change target speed may be based at least in part on traffic data. For example, traffic data may be used to identify a backup in front. In some embodiments, determining the first speed change location may be based at least in part on the route characteristic data.

The computing device 300 may be configured to determine a second speed change location and a second speed change target speed based at least in part on the projected route. For example, the processor 302 may determine a second speed change location and a second speed change target speed. In some embodiments, determining the second speed change target speed may be based at least in part on the route characteristic data. For example, the flag information may be used to identify a stop flag or stop light in front. As another example, speed limit information may be used to identify a speed decrease ahead. In some embodiments, determining the first speed change target speed may be based at least in part on traffic data. For example, traffic data may be used to identify a backup in front. In some embodiments, determining the first speed change location may be based at least in part on the route characteristic data.

The computing device 300 may be configured to determine a first remaining speed and a first remaining speed position based at least in part on the first speed change position and the first speed change target speed. For example, the first remaining speed and the first remaining speed position may be determined by the processor 302 by calculating a maximum desired braking deceleration based on a percentage from the first speed change position and the first speed change target speed to the selected speed limit. In some embodiments, determining the target speed profile may be based at least in part on traffic data. For example, traffic may move too fast to allow a low first residual speed.

The computing device 300 may be configured to determine a second remaining speed and a second remaining speed position based at least in part on the second speed change location and the second speed change target speed. For example, the second remaining speed and the second remaining speed position may be determined by processor 302 by calculating a maximum desired braking deceleration based on a percentage of the selected speed limit from the second speed change position and the second speed change target speed. In some embodiments, determining the target speed profile may be based at least in part on traffic data. For example, traffic may move too fast to allow a low first residual speed.

The computing device 300 may be configured to determine a first speed lower margin (i.e., the vehicle should not slide to a speed below that speed; the speed may be adjusted lower as the first remaining speed position gets closer). In some embodiments, the first speed lower margin may be retrieved by the processor 302 from a storage device or received via the network interface 312. In some embodiments, the first speed lower margin may be determined based on speed limit information.

The computing device 300 may be configured to determine a two-speed margin (i.e., the vehicle should not slide to a speed below that speed; the speed may be adjusted lower as the first remaining speed position gets closer and closer). In some embodiments, the second speed lower margin may be retrieved from the storage device by the processor 302. In some embodiments, the second speed lower margin may be determined based on the speed limit information.

The computing device 300 may be configured to determine a first speed lower envelope based at least in part on the first remaining speed. For example, the processor 302 may determine the first speed envelope. For example, the envelope may not pass below the first remaining speed at the first speed. In some embodiments, determining the first speed lower envelope may be further based on a first speed lower margin. For example, the first speed envelope may not drop below the first speed envelope.

The computing device 300 may be configured to determine a second speed lower envelope based at least in part on the second remaining speed and the second speed lower tolerance. Processor 302 may determine the under-speed envelope. For example, the envelope may not pass below the second remaining speed. In some embodiments, determining the second speed lower envelope may be further based on a second speed lower tolerance. For example, the second speed lower envelope may not be reduced below the second speed lower tolerance.

The computing device 300 may be configured to determine an overall speed envelope based at least in part on the first remaining speed. For example, the processor 302 may determine the overall speed envelope. In some embodiments, the overall speed envelope may be determined further based on the second speed envelope such that the overall speed envelope is the smaller of the second speed envelope and the second speed envelope.

The computing device 300 may be configured to determine an up-speed tolerance based at least in part on the speed limit information. For example, the processor 302 may determine the margin in speed based on the speed limit information. For example, in some embodiments, the margin in speed may not exceed the speed limit, or may not exceed the speed limit plus a fixed value or percentage value that exceeds the speed limit.

The computing device 300 may be configured to determine an envelope over speed. For example, the processor 302 may determine an upper speed envelope. In some embodiments, determining the upper speed envelope may be based at least in part on the speed limit information. In some embodiments, determining the upper speed envelope may be based at least in part on the upper speed margin.

The computing device 300 may be configured to determine a target speed profile based at least in part on the first remaining speed, the first remaining speed position, the overall speed lower envelope, and the speed upper envelope. For example, the processor 302 may determine the target speed profile. In some embodiments, the target speed profile is located above the overall speed envelope and below the speed envelope. In some embodiments, determining the target speed profile may be further based on vehicle characteristic data. For example, the target speed profile may vary depending on vehicle weight, aerodynamics, and the like.

The computing device 300 may be configured to determine a taxiing origin based at least in part on the target speed profile. For example, the processor 302 may calculate the taxiing start point by selecting a point on the target speed profile where the current speed intersects the target speed profile, and the target speed profile may not exceed an upper speed envelope or a lower overall speed envelope.

The computing device 300 may be configured to communicate the starting point of the taxi to an operator of the vehicle. For example, in embodiments in which the output device 306 includes a speaker, the processor 302 may send a signal to cause the speaker to generate an audible signal (e.g., an audible "coast now" or bell sound) indicating that coasting is now being initiated. In some embodiments in which the output device 306 includes a display, the processor 302 may send a signal to cause the display to present a visual indicator (e.g., a written message such as "coast now" or an image that the foot was lifted off the accelerator).

In some embodiments, the computing device 300 may be configured to receive vehicle location data. For example, the processor 302 may receive vehicle location data from the GPS 316. In some embodiments, the processor 302 may receive vehicle location data through the network interface 312.

The computing device 300 may be configured to receive planned route data. In some embodiments, the planned route data may be received from navigation software. For example, the processor 302 may receive the planned route data from a remote computing device on which the planned route has been generated.

The computing device 300 may be configured to determine an intended route. In some embodiments, determining the projected route may be based at least in part on the planned route data. For example, determining the predicted route may be as simple as receiving the planned route. In some embodiments, determining the projected route may be based at least in part on the vehicle location data. For example, if the vehicle 10 is traveling at least 30 kilometers on a highway of little interest, the processor 302 may determine that the predicted route continues on the highway for 30 kilometers. In some embodiments, determining the projected route may be based at least in part on a historical analysis of the user's travel. For example, if the user has driven the same route multiple times at the same time of day, the predicted route may be determined based on the assumption that the user is walking the same route.

The computing device 300 may be configured to receive route characteristic data including at least route elevation data. For example, the processor 302 may receive route characteristic data from the storage device 310. In some embodiments, the route elevation data may include elevations at a plurality of points along the predicted route. In some embodiments, the route characteristic data may further include road curvature data. In some embodiments, the route characteristic data may further include flag data. Examples of road curvature data may include one or more of road curvature along the length of the road and road curvature across the road (i.e., the degree to which the road is crowned or inclined). In some embodiments, the route characteristic data may further include road surface condition data. Examples of road surface condition data may include coefficient of friction, road rolling resistance contribution, presence of known potholes, and recent icing conditions. In some embodiments, the route characteristic data further includes weather data. Examples of weather data may include whether it was recently rained, whether it was recently snowed, and whether fog is interfering with visual identification of other vehicles. In some embodiments, the route characteristic data further includes speed limit data. In some embodiments, the route characteristic data further includes traffic data.

The computing device 300 may be configured to receive vehicle characteristic data. For example, the processor 302 may receive vehicle characteristic data through the network interface 312 or from the storage device 310. In some embodiments, the vehicle characteristic data may include vehicle aerodynamics. In some embodiments, the vehicle characteristic data may include vehicle quality. In some embodiments, the vehicle characteristic data may include a vehicle energy consumption profile. In some embodiments, the vehicle energy consumption profile may be based on Environmental Protection Agency (EPA) energy economy testing. In some embodiments, the vehicle energy consumption profile may be based on historical analysis of past vehicle energy consumption.

The computing device 300 may be configured to determine a sampling resolution. For example, the processor 302 may determine the sampling resolution based on the values received from the storage device 310. In some embodiments, determining the sampling resolution may be based at least in part on the vehicle characteristic data.

The computing device 300 may be configured to sample the route elevation data at a sampling resolution to generate sampled route elevation data. For example, the processor 302 may sample the route elevation data every 100m to generate sampled route elevation data.

The computing device 300 may be configured to receive a fuel economy target. For example, an operator of the vehicle may input a desired fuel economy target (e.g., 5%, 10%, etc.) via the user input device 304, which the processor 302 then receives. In some embodiments, determining the fuel economy target may include retrieving a value (e.g., 100 m) from the storage device 310.

The computing device 300 may be configured to determine a starting point for the uphill delay. In some embodiments, determining the start of the uphill delay may be based at least in part on the vehicle characteristic data. In some embodiments, determining the start of the uphill delay may include processor 302 retrieving a value (e.g., 100 m) from storage device 310.

The computing device 300 may be configured to determine a starting point for the uphill delay. In some embodiments, determining the start of the uphill delay may be based at least in part on the vehicle characteristic data. In some embodiments, the processor 302 may determine the start of the downhill delay by retrieving a value (e.g., 100 m) from the storage device 310.

The computing device 300 may be configured to determine at least one starting point for an uphill location and at least one starting point for a downhill location based at least in part on the sampled route elevation data. In some embodiments, the starting point of the at least one uphill location may be a starting point of two uphill locations, a starting point of three uphill locations, a starting point of four uphill locations, a starting point of five uphill locations, or more. In some embodiments, the at least one starting point of the downhill position may be two starting points of the downhill position, three starting points of the downhill position, four starting points of the downhill position, five starting points of the downhill position, or more. For example, the processor 302 may determine at least one starting point for an uphill location and at least one starting point for a downhill location by analyzing the sampled route elevation data and determining local peaks and valleys by comparing the elevations of the different points. In some embodiments, the at least one starting point of the uphill location and the at least one starting point of the downhill location are determined by automatic pattern recognition. In some embodiments, at least one of the starting points of the uphill location may be adjusted forward along the predicted route a distance equal to the starting point of the uphill delay (e.g., 100 m). In some embodiments, at least one of the starting points of the uphill location may be adjusted forward along the predicted route a distance equal to the starting point of the uphill delay (e.g., 100 m).

The computing device 300 may be configured to determine a minimum speed change distance. In some embodiments, determining the start of the uphill delay may be based at least in part on the vehicle characteristic data. In some embodiments, determining the start of the uphill delay may include the processor 302 receiving a value (e.g., 1000 m) from the storage device 310. In some embodiments, the minimum speed change distance may be based on vehicle characteristic data.

The computing device 300 may be configured to determine at least one cruise speed route segment based at least in part on at least one starting point of an uphill location and at least one starting point of a downhill location. In some embodiments, the at least one cruise speed route segment may be two cruise speed route segments, three cruise speed route segments, four cruise speed route segments, five cruise speed route segments, or more. For example, the processor 302 may determine at least one cruise speed segment by starting at one start point of a downhill position and ending at one start point of an uphill position. In some embodiments, determining at least one cruise speed route segment may further change distance based on the minimum speed. For example, at least one cruise speed route segment may be limited to at least 1000m. Requiring at least one cruise speed route segment to reach a minimum speed change distance may increase operator compliance and reduce fuel costs by avoiding periodic acceleration.

The computing device 300 may be configured to determine a corresponding cruise speed for at least one cruise speed route segment based at least in part on one or more of the route elevation data and the sampled route elevation data. In embodiments where there is more than one cruise speed route segment, more corresponding cruise speeds may be determined. For example, the processor 302 may determine that for a typical downhill cruise speed route segment, the corresponding cruise speed is 65 miles per hour, and for a typical uphill cruise speed route segment, the corresponding cruise speed is 60 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the vehicle characteristic data. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the fuel economy target. For example, the corresponding cruise speed for an exemplary cruise speed route segment may be 65 miles per hour with a fuel economy target of 5%, and 60 miles per hour with a fuel economy target of 10%. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the road curvature data. For example, if there is a curvature in the cruise speed route segment that is not safe to navigate at 70 miles per hour but safe to navigate at 65 miles per hour, and the corresponding cruise speed will be 70 miles per hour, then the corresponding cruise speed may be set to 65 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the road surface condition data. For example, if there is a set of pits in the cruise speed route segment that are unsafe to navigate at a speed of 70 miles per hour, but safe to navigate at a speed of 60 miles per hour, and the corresponding cruise speed is 70 miles per hour, the corresponding cruise speed may be set to 65 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on weather data. For example, if there is a visibility limiting fog in the cruise speed route segment that is not safe to navigate at 70 miles per hour but safe to navigate at 55 miles per hour, and the corresponding cruise speed is 70 miles per hour, the corresponding cruise speed may be set to 55 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the speed limit data. For example, if the speed limit is 55 miles per hour and the corresponding cruise speed is 65 miles per hour, the corresponding cruise speed may be set to 55 miles per hour. In some embodiments, the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the traffic data. For example, if traffic is at 65 miles per hour and the corresponding cruising speed is 70 miles per hour, the corresponding cruising speed may be set to 65 miles per hour.

The computing device 300 may be configured to communicate a corresponding cruise speed of the at least one cruise speed route segment. For example, in embodiments in which the output device 306 includes a speaker, the processor 302 may send a signal to cause the speaker to generate an audible signal that advises the operator to change the cruise control to a corresponding cruise speed (e.g., audible "coast at 67 miles per hour"). In embodiments where computing device 300 includes a display, processor 302 may send a signal to cause the display to present a visual indicator (e.g., a written message such as "coast at 67 miles per hour").

The computing device 300 may be configured to determine a current speed. In some embodiments, the processor 302 may receive the current speed from the GPS 316 through the network interface 312. In some embodiments, the processor 302 may calculate the current speed based on vehicle position data obtained by the GPS 316.

Computing device 300 may be configured to determine a corresponding energy efficiency rate. In some embodiments, the corresponding energy efficiency speed may be based at least in part on vehicle characteristic data (e.g., vehicle mass, energy consumption profile, etc.). In some embodiments, the corresponding energy efficiency speed may be based at least in part on route characteristic data (elevation, road curvature, road surface information, weather, speed limits, etc.). For example, the processor 302 may receive vehicle characteristic data and route characteristic data and determine a corresponding energy efficiency rate.

Computing device 300 may be configured to determine a higher corresponding energy inefficiency speed. In some embodiments, a higher corresponding energy-inefficient speed is determined based on the speed limit. For example, the processor 302 may receive the speed limit information via the network interface 312 and determine that the higher corresponding energy inefficiency speed is equal to the speed limit.

The computing device 300 may be configured to determine a first transition range between a corresponding energy efficient speed and a higher corresponding energy inefficient speed. Processor 302 may determine this by comparing the corresponding energy efficiency rate to the higher corresponding energy efficiency rate.

The computing device 300 may be configured to display a display area on a display. For example, the processor 302 may send a signal to a display of an output device to cause the display to show the display area and its content. In some embodiments, the display area may include a first indicator corresponding to the current speed. In some embodiments, the first indicator is a vertical line. In some embodiments, the first indicator is located at a fixed position within the display area. In some embodiments, the fixed location is centered within the display area. In some embodiments, the first indicator may be an arrow. In some embodiments, the display area may include a first mode corresponding to a corresponding energy efficiency rate. In some embodiments, the first mode is green. In some embodiments, the display area may include a second mode that is different from the first mode and corresponds to a higher corresponding energy-inefficient speed. In some embodiments, the second mode is yellow. In some embodiments. In some embodiments, the display region may include a first mode range corresponding to the first transition range. In some embodiments, the mode of the first mode range is within a range between the first mode and the second mode, and the first mode range has no substantial discontinuity between the first mode and the second mode.

The computing device 300 may be configured to determine an impractical low speed range. In some embodiments, the processor 302 may determine an impractical low speed range based on speed limit information (e.g., a given percentage of the speed limit or a flat speed value below the speed limit).

Computing device 300 may be configured to determine a second transition range between the impractical low speed and the corresponding energy efficiency speed. Processor 302 may determine the second transition range by comparing the unrealistically low speed to the corresponding energy efficiency speed.

The computing device 300 may be configured to present a third mode in the display area that is different from the first mode and corresponds to an impractical low speed. In some embodiments, the third mode is black. For example, the processor 302 may send a signal to the display 122 to display the third mode.

The computing device 300 may be configured to present a second mode range in the display area that is disposed between the third mode and the first mode and corresponds to the second transition range. For example, the processor 302 may send a signal to the display to cause the display to display the second mode range. In some embodiments, the mode of the second mode range is within a range between the first mode and the third mode, and the first mode range has no substantial discontinuity between the first mode and the third mode.

The computing device 300 may be configured to determine an upper speed limit. For example, the processor 302 may determine an upper speed limit. In some embodiments, the upper speed limit may be based on speed limit information (i.e., the speed limit for the portion of the route is the upper speed limit).

The computing device 300 may be configured to present a second indicator corresponding to the upper speed limit in the display area. For example, the processor 302 may send a signal to the display to display the second indicator. In some embodiments, the second indicator is a vertical line.

The system 100 may be configured to determine a lower speed limit. For example, the processor 302 may determine a lower speed limit. In some embodiments, a lower speed limit may be determined based on the speed limit information. For example, the minimum speed limit for many highways in the united states is 45 miles per hour.

The computing device 300 may be configured to present a third indicator corresponding to the lower speed limit in the display area. For example, PAC 124 may send a signal to display 122 to display the third indicator. In some embodiments, the third indicator is a vertical line.

Computing device 300 may be configured to determine an overspeed tolerance speed. For example, processor 302 may determine the overspeed tolerance speed by multiplying the speed limit or upper speed limit by a factor (e.g., multiplying the speed limit by 1.1) or adding a set point to the speed limit (e.g., 5 miles per hour).

Computing device 300 may be configured to determine a third transition range between the overspeed tolerance speed and the higher corresponding energy inefficient speed. For example, processor 302 may determine the third transition range by comparing the overspeed tolerance speed to a higher corresponding energy inefficiency speed.

Computing device 300 may be configured to present a fourth mode in the display area that is different from the second mode and corresponds to the overspeed tolerance speed. For example, the processor 302 may send a signal to the display to display the fourth mode. In some embodiments, the fourth mode is red.

The computing device 300 may be configured to present a third mode range disposed between the second mode and the fourth mode and corresponding to the third transition range in the display area. For example, the processor may send a signal to the display to display the third mode. In some embodiments, the mode of the third mode range is within a range between the second mode and the fourth mode, and the third mode range has no substantial discontinuity between the second mode and the fourth mode.

Fig. 7A-7C generally illustrate a flow chart of a method 700 for providing taxi recommendations to an operator of a vehicle, such as vehicle 10, in accordance with the principles of the present disclosure. In some embodiments, instructions are stored on a memory storage device that, when executed by a processor, cause the processor to perform the steps of method 700.

At 702, method 700 can include receiving vehicle position data.

At 704, method 700 may include receiving vehicle characteristic data.

At 706, method 700 may include receiving planned route data.

At 708, method 700 may include determining an expected route based at least in part on the vehicle location data.

At 710, method 700 may include receiving route characteristic data.

At 712, method 700 may include determining a first speed change location and a first speed change target speed based at least in part on the predicted route.

At 714, method 700 may include determining a second speed change location and a second speed change target speed based at least in part on the projected route.

At 706, method 700 may include determining a first remaining speed and a first remaining speed position based at least in part on the first speed change position and the first speed change target speed.

At 718, method 700 may include determining a second remaining speed and a second remaining speed position based at least in part on the second speed change position and the second speed change target speed.

At 720, method 700 may include determining a first speed lower margin.

At 722, method 700 may include determining a second speed lower margin.

At 724, method 700 may include determining a first speed lower envelope based at least in part on the first remaining speed.

At 726, method 700 may include determining a second speed lower envelope based at least in part on the second remaining speed and the second speed lower margin.

At 728, method 700 may include determining an overall speed envelope based at least in part on the first remaining speed.

At 700, method 700 may include determining an up-speed tolerance based at least in part on the speed limit information.

At 732, method 700 may include determining an upper speed envelope.

At 734, method 700 may include determining a target speed profile based at least in part on the first remaining speed, the first remaining speed position, the overall speed lower envelope, and the speed upper envelope.

At 736, method 700 may include determining a taxiing origin based at least in part on the target speed profile.

At 738, method 700 may include communicating the starting point of the taxi to an operator of the vehicle.

Fig. 13A-13C generally illustrate a flow chart of a method 1300 for providing taxi recommendations to an operator of a vehicle, such as vehicle 10, in accordance with the principles of the present disclosure. In some embodiments, instructions are stored on a memory storage device that, when executed by a processor, cause the processor to perform the steps of method 1300.

At 1302, method 1300 may include receiving vehicle position data.

At 1304, method 1300 may include receiving planned route data.

At 1306, method 1300 may include determining an expected route.

At 1308, method 1300 may include receiving route characteristic data including at least route elevation data.

At 1310, method 1300 may include receiving vehicle characteristic data.

At 1312, method 1300 may include determining a sampling resolution.

At 1314, method 1300 may include sampling the route elevation data at a sampling resolution to generate sampled route elevation data.

At 1316, method 1300 may include receiving a fuel savings target.

At 1318, method 1300 may include determining a start of an uphill delay.

At 1320, method 1300 may include determining a start of a downhill delay.

At 1322, method 1300 may include determining at least one starting point for an uphill location and at least one starting point for a downhill location based at least in part on the sampled route elevation data.

At 1324, method 1300 may include determining a minimum speed change distance.

At 1326, method 1300 may include determining at least one cruise speed route segment based at least in part on the at least one start point of the uphill location and the at least one start point of the downhill location.

At 1328, method 1300 may include determining a corresponding cruise speed for the at least one cruise speed route segment based at least in part on one or more of the route elevation data and the sampled route elevation data.

At 1330, method 1300 may include communicating a corresponding cruise speed of at least one cruise speed route segment. Method 1300 may include more or fewer steps than those described above, and the steps of method 1300 may be performed in any suitable order.

A method 1800 for providing visual driving speed recommendations to an operator of a vehicle, such as the vehicle 10, is disclosed and illustrated in fig. 18A-18C.

At 1802, method 1800 may include determining a current speed.

At 1804, method 1800 may include determining a corresponding energy efficiency rate. In some embodiments, the corresponding energy efficiency speed may be based at least in part on vehicle characteristic data (e.g., vehicle mass, energy consumption profile, etc.).

At 1806, method 1800 may include determining a higher corresponding energy inefficiency speed.

At 1808, method 1800 may include determining a first transition range between a corresponding energy efficient speed and a higher corresponding energy inefficient speed.

At 1810, method 1800 may include displaying a display area on a display.

At 1812, method 1800 may include determining an impractical low speed range. In some embodiments, the processor 302 may determine an impractical low speed range based on speed limit information (e.g., a given percentage of the speed limit or a flat speed value below the speed limit).

At 1814, method 1800 may include determining a second transition range between the unrealistic low speed and the corresponding energy efficiency speed.

At 1816, method 1800 may include presenting a third mode in the display area that is different from the first mode and corresponds to an impractical low speed.

At 1818, method 1800 may include presenting, in the display area, a second mode range disposed between the third mode and the first mode and corresponding to the second transition range.

At 1820, method 1800 may include determining an upper speed limit. For example, the processor 302 may determine an upper speed limit.

At 1822, method 1800 may include presenting a second indicator corresponding to an upper speed limit in the display area.

At 1824, method 1800 may include determining a lower speed limit. For example, the processor 302 may determine a lower speed limit.

At 1826, method 1800 may include presenting a third indicator in the display area corresponding to the lower speed limit.

At 1828, method 1800 may include determining an overspeed tolerance speed.

At 1830, method 1800 may include determining a third transition range between the overspeed tolerance speed and the higher corresponding energy inefficient speed.

At 1832, method 1800 may include presenting a fourth mode in the display area that is different from the second mode and corresponds to the overspeed tolerance speed.

At 1834, method 1800 may include presenting a third mode range disposed between the second mode and the fourth mode and corresponding to the third transition range in the display area.

Method 1800 may include more or fewer steps than those described above, and the steps of method 1800 may be performed in any suitable order.

In some embodiments, a method for providing taxi recommendations to an operator of a vehicle is disclosed. The method may include receiving vehicle location data. The method may further include determining an expected route based at least in part on the vehicle location data. The method may further include determining a first speed change location and a first speed change target speed based at least in part on the predicted route. The method may further include determining a first remaining speed and a first remaining speed position based at least in part on the first speed change position and the first speed change target speed. The method may further include determining a first speed lower envelope based at least in part on the first remaining speed. The method may further include determining an overall speed envelope based at least in part on the first speed envelope. The method may further include determining an upper speed envelope. The method may further include determining a target speed profile based at least in part on the first remaining speed, the first remaining speed position, the first speed lower envelope, and the speed upper envelope. The method may further include determining a taxiing origin based at least in part on the target speed profile. The method may further include communicating the taxi start point to an operator of the vehicle.

In some embodiments, the target speed profile may be located above the overall speed lower envelope and below the speed upper envelope. In some embodiments, the method may further include determining a first speed down tolerance, and the first speed down envelope may be determined further based on the first speed down tolerance. In some embodiments, the method may further include receiving vehicle characteristic data, and the target speed profile may be determined further based on the vehicle characteristic data. In some embodiments, the method may further include receiving planned route data, and the predicted route may be determined further based on the planned route data. In some embodiments, the method may further include receiving traffic data, and the first speed change location may be determined further based on the traffic data. In some embodiments, the method may further include receiving route characteristic data, and wherein the target speed profile is determined based at least in part on the route characteristic data. In some embodiments, the method may include receiving route characteristic data. In some embodiments, the route characteristic data may further include elevation data, and the target speed profile may be determined based at least in part on the elevation data. In some embodiments, the route characteristic data may further include road surface data, and the target speed profile may be determined based at least in part on the road surface data. In some embodiments, the route characteristic data may include weather data, and the target speed profile may be determined based at least in part on the weather data. In some embodiments, the route characteristic data may include traffic data, and the target speed profile may be determined based at least in part on the traffic data. In some embodiments, the route characteristic data may include speed limit information, and the speed envelope may be determined further based on the speed limit information. In some embodiments, the route characteristic data may include speed limit information, the method may further include determining an upper speed margin based at least in part on the speed limit information, and the upper speed envelope may be determined further based on the upper speed margin. In some embodiments, the method may further include determining a second speed change location and a second speed change target speed based at least in part on the predicted route. In some embodiments, the method may further include determining a second remaining speed and a second remaining speed position based at least in part on the second speed change position and the second speed change target speed. In some embodiments, the method may further include determining a second speed lower envelope based at least in part on the second remaining speed. In some embodiments, the method may further include determining the overall speed envelope further based on the second speed envelope such that the overall speed envelope is the smaller of the first speed envelope and the second speed envelope. In some embodiments, the method may further include determining a second speed down tolerance, and the second speed down envelope may be determined further based on the second speed down tolerance. In some embodiments, an apparatus may include a processor and a memory including instructions that, when executed by the processor, cause the processor to perform the steps of the method. In some embodiments, a non-transitory computer-readable storage medium may include executable instructions that, when executed by a processor, facilitate performance of steps of the method.

In some embodiments, a method for providing cruise speed recommendations for an operator of a vehicle is disclosed. The method may include determining an expected route. The method may further include receiving route characteristic data including route elevation data. The method may further include determining a sampling resolution. The method may further include sampling the route elevation data at a sampling resolution to generate sampled route elevation data. The method may still further include determining at least one starting point for an uphill location and at least one starting point for a downhill location based at least in part on the sampled route elevation data. The method may further comprise: at least one cruise speed route segment is determined based at least in part on the at least one starting point of the uphill location and the at least one starting point of the downhill location. The method may still further include determining a corresponding cruise speed for the at least one cruise speed route segment based at least in part on one or more of the route elevation data and the sampled route elevation data. The method may still further include communicating a corresponding cruise speed of the at least one cruise speed route segment.

In some embodiments, the method may include receiving vehicle location data and may determine an intended route based at least in part on the vehicle location data. In some embodiments, the method may include determining a minimum speed change distance, wherein the cruise speed route segment may be determined further based on the minimum speed change distance. In some embodiments, the method may include determining a start of an uphill delay, wherein at least one start of an uphill position may be adjusted forward along the predicted route by a distance equal to the start of the uphill delay. In some embodiments, the method may include receiving vehicle characteristic data, wherein a start of an uphill delay may be determined based at least in part on the vehicle characteristic data. In some embodiments, the method may include determining a starting point of the downhill delay, wherein at least one starting point of the downhill position may be adjusted forward along the predicted route by a distance equal to the starting point of the downhill delay. In some embodiments, the method may include receiving vehicle characteristic data and determining a starting point of a downhill delay may be based at least in part on the vehicle characteristic data. In some embodiments, the method may include receiving vehicle characteristic data, wherein a corresponding cruising speed of the at least one cruising route segment may be determined further based on the vehicle characteristic data. In some embodiments, the method may include receiving a fuel saving target, wherein the corresponding cruising speed of the at least one cruising route segment may be determined further based on the fuel saving target. In some embodiments, the method may include receiving planned route data, wherein the predicted route may be determined based at least in part on the planned route data. In some embodiments, the method may include receiving vehicle location data, wherein the predicted route may be determined based at least in part on the vehicle location data. In some embodiments, the route characteristic data may further include road curvature data, and the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the road curvature data. In some embodiments, the route characteristic data may further include road surface condition data, and the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the road surface condition data. In some embodiments, the route characteristic data may further include weather data, and the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the weather data. In some embodiments, the route characteristic data may further include speed limit data, and the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the speed limit data. In some embodiments, the route characteristic data may further include traffic data, and the corresponding cruise speed of the at least one cruise speed route segment may be determined further based on the traffic data. In some embodiments, an apparatus may include a processor and a memory including instructions that, when executed by the processor, cause the processor to perform the steps of the method. In some embodiments, a non-transitory computer-readable storage medium may include executable instructions that, when executed by a processor, facilitate performance of steps of the method.

In some embodiments, a method for providing visual driving speed recommendations to an operator of a vehicle is disclosed. The method may include determining a current speed. The method further includes determining a corresponding energy efficiency rate. The method further includes determining a higher corresponding energy inefficiency speed. The method may further include determining a first transition range between the corresponding energy efficient speed and the higher corresponding energy inefficient speed. The method may further include displaying the display area on a display. The display area may include a first indicator corresponding to the current speed. The display area may further include a first mode corresponding to the corresponding energy efficiency rate. The display area may further include a second mode different from the first mode and corresponding to a higher corresponding energy-inefficient speed. The display region may further include a first mode range corresponding to the first transition range.

In some embodiments, the first indicator may be a vertical line. In some embodiments, the first mode may be green. In some embodiments, the second mode may be yellow. In some embodiments, the mode of the first mode range may be in a range between the first mode and the second mode, and the first mode range has no substantial discontinuity between the first mode and the second mode. In some embodiments, the method may further include determining an impractical low speed range. In some embodiments, the method may further include determining a second transition range between the unrealistically low speed and the corresponding energy efficiency speed. In some embodiments, the method may further comprise presenting in the display area: a third mode different from the first mode and corresponding to an impractical low speed; and a second mode range disposed between the third mode and the first mode and corresponding to the second transition range. In some embodiments, the third mode may be black. In some embodiments, the mode of the second mode range may be within a range between the first mode and the third mode, and the first mode range has no substantial discontinuity between the first mode and the third mode. In some embodiments, the method may further include determining an upper speed limit. In some embodiments, the method may include presenting a second indicator corresponding to an upper speed limit in the display area. In some embodiments, the second indicator may be a vertical line. In some embodiments, the method may further include determining a lower speed limit. In some embodiments, the method may further include presenting a third indicator corresponding to the lower speed limit in the display area. In some embodiments, the third indicator may be a vertical line. In some embodiments, the method may further include determining an overspeed tolerance speed. In some embodiments, the method may further include determining a third transition range between the overspeed tolerance speed and a higher corresponding energy inefficient speed. In some embodiments, the method may further comprise presenting in the display area: a fourth mode different from the second mode and corresponding to an overspeed-tolerance speed; and a third mode range disposed between the second mode and the fourth mode and corresponding to the third transition range. In some embodiments, the fourth mode may be red. In some embodiments, the mode of the third mode range may be within a range between the second mode and the fourth mode, and the third mode range has no substantial discontinuity between the second mode and the fourth mode. In some embodiments, the first indicator may be located at a fixed position within the display area. In some embodiments, an apparatus may include a processor and a memory including instructions that, when executed by the processor, cause the processor to perform the steps of the method. In some embodiments, a non-transitory computer-readable storage medium may include executable instructions that, when executed by a processor, facilitate performance of steps of the method.

Fig. 19 generally illustrates a flow chart of an energy consumption estimation method 1900 in accordance with the principles of the present disclosure. At 1902, the method 1900 receives a vehicle parameter. As described, PAC 124 may receive various vehicle parameters of vehicle 10 from any of the components described herein. At 1904, method 1900 determines a vehicle profile for energy consumption efficiency. For example, PAC 124 uses vehicle parameters and/or other route characteristics to determine an energy consumption efficiency profile of vehicle 10, such as historical route characteristics associated with routes previously traversed by the vehicle, route characteristics associated with routes previously traversed by similar vehicles (e.g., from remote computing device 132 and/or V2X communication module 130), other suitable route characteristics, or a combination thereof. In some embodiments, the V2X communication module may receive normalized energy consumption data, authentication (homologation) data, a plurality of normalized energy consumption data reference points, parabolic approximations of energy consumption, energy conservation saturation points corresponding to speeds above a threshold (where energy efficiency deviates from parabolic approximations), coefficients corresponding to modified energy consumption based on at least one characteristic of a gradient across a route segment, or a combination thereof for at least one other vehicle. At 1906, method 1900 receives route characteristics. As described, PAC 124 receives various route characteristics (e.g., route characteristics of a route that vehicle 10 is currently traversing or will traverse) and other information from any other component described herein. For example, PAC 124 may receive information regarding varying gradients along the route segment. In some embodiments, the method continues at 1908. In some embodiments, the method continues at 310. At 1908, method 1900 determines a profile of target vehicle speed, target torque distribution, and route characteristics as a function of energy consumption efficiency. As described, PAC 124 determines a profile of a target vehicle speed and/or a target torque distribution based on vehicle parameters, route characteristics, energy consumption efficiency profile of vehicle 10, other received information received from various components described herein. The profile of the target vehicle speed and/or target vehicle torque distribution corresponds to a vehicle speed and/or torque distribution that, when implemented by the vehicle 10, provides an optimal or improved energy consumption efficiency of the vehicle 10.

At 1910, method 1900 generates at least one signal. As described, PAC 124 generates at least one signal. The signals may include HMI signals and/or recommendations for improving the energy consumption efficiency of the vehicle 10. This signal, when applied by the VPC 102, achieves a target vehicle speed, target torque distribution, and route characteristics. For example, PAC 124 may generate recommendations to bypass certain segments of the route. In some embodiments, the recommendation is provided to the operator. In some embodiments, the recommendation is an autonomously executed instruction received by VPC 102. At 1912, method 1900 provides a signal to a vehicle propulsion controller. As described, PAC 124 may replace HMI signals transmitted from HMI controls 104 based on input from the driver of vehicle 10 with virtual HMI signals. Additionally or alternatively, PAC 124 may replace vehicle sensor information provided by vehicle sensor 108 to indicate a virtual front vehicle to VPC 102. As described, the VPC 102 may apply the virtual HMI signal and/or may follow the virtual front vehicle in order to achieve the target vehicle speed and/or torque distribution. As described, PAC 124 may continuously update the target vehicle speed and/or the target torque allocation as vehicle 10 continues to traverse the route and based on updated traffic information, vehicle information, route information, other information, or a combination thereof.

Fig. 20 generally illustrates a flow chart of an alternative energy consumption estimation method 2000 in accordance with the principles of the present disclosure. At 2002, method 2000 receives a vehicle parameter. As described, PAC 124 may receive various vehicle parameters of vehicle 10 from any of the components described herein. At 2004, method 2000 determines a vehicle profile of energy consumption efficiency. For example, PAC 124 uses vehicle parameters and/or other route characteristics to determine an energy consumption efficiency profile of vehicle 10, such as historical route characteristics associated with routes previously traversed by the vehicle, route characteristics associated with routes previously traversed by similar vehicles (e.g., from remote computing device 132 and/or V2X communication module 130), other suitable route characteristics, or a combination thereof. In some embodiments, PAC 124 uses normalized energy consumption data, authentication data, a plurality of normalized energy consumption data reference points, parabolic approximations of energy consumption, energy conservation saturation points corresponding to speeds above a threshold (where energy efficiency deviates from parabolic approximations), and coefficients corresponding to modified energy consumption based on at least one characteristic of gradients across route segments, or a combination thereof, to determine a profile of energy consumption efficiency of vehicle 10.

At 2006, method 2000 receives route characteristics. As described, PAC 124 receives various route characteristics (e.g., route characteristics of a route that vehicle 10 is currently traversing or will traverse) and other information from any other component described herein. For example, PAC 124 may receive information regarding route segments having varying gradients. In some embodiments, the method continues at 2008. In some embodiments, the method continues at 2010. At 2008, method 2000 determines a profile of target vehicle speed, target torque distribution, and route characteristics as a function of energy consumption efficiency. As described, PAC 124 determines a profile for a target vehicle speed, a target torque allocation, and route characteristics based on vehicle parameters, route characteristics, energy consumption efficiency profile of vehicle 10, other received information received from various components described herein. The profile of the target vehicle speed and/or target vehicle torque distribution corresponds to a vehicle speed and/or torque distribution that, when implemented by the vehicle 10, provides an optimal or improved energy consumption efficiency of the vehicle 10.

At 2010, method 2000 generates a vehicle propulsion controller signal. As depicted, PAC 124 is in direct communication with VPC 102 and may provide a signal as an input to VPC 102. PAC 124 generates a vehicle propulsion controller signal based on the target vehicle speed. The vehicle propulsion controller signal may be referred to as a recommended target vehicle speed. At 2012, the method 2000 generates a torque distribution controller signal. As described, PAC 124 may be in direct communication with torque distribution controller 116 and may provide signals as inputs to torque distribution controller 116. PAC 124 generates a torque split controller signal based on the target torque split. The torque distribution controller signal may be referred to as a recommended target torque distribution. At 2014, the method 2000 provides a vehicle propulsion controller signal and a torque distribution controller signal. As described, PAC 124 may provide a vehicle propulsion controller signal to VPC 102. As described, VPC 102 may determine whether to apply the target vehicle speed indicated by the vehicle propulsion controller signal. PAC 124 may provide a torque split controller signal to torque split controller 116, or may provide a torque split controller signal to VPC 102, which VPC 102 may then provide a torque split signal to torque split controller 116. As described, the torque distribution controller 116 may then determine whether to apply the torque distribution indicated by the torque distribution controller signal. The vehicle propulsion controller signal and the torque distribution controller signal correspond to vehicle speed and/or torque distribution that, when implemented by the vehicle 10, provides optimal or improved energy consumption efficiency of the vehicle 10. As described, PAC 124 may continuously update the target vehicle speed, target torque distribution, and route characteristics as vehicle 10 continues to traverse the route and based on updated traffic information, vehicle information, route information, other information, or a combination thereof.

Fig. 21 generally illustrates a flow chart of an alternative energy consumption estimation method 2100 in accordance with the principles of the present disclosure. At 2102, method 2100 receives a vehicle parameter. As described, PAC 124 may receive various vehicle parameters of vehicle 10 from any of the components described herein. At 2104, method 2100 determines a vehicle profile of energy consumption efficiency. For example, PAC 124 uses vehicle parameters and/or other route characteristics to determine an energy consumption efficiency profile of vehicle 10, such as historical route characteristics associated with routes previously traversed by the vehicle, route characteristics associated with routes previously traversed by similar vehicles (e.g., from remote computing device 132 and/or V2X communication module 130), other suitable route characteristics, or a combination thereof. In some embodiments, PAC 124 uses normalized energy consumption data, authentication data, a plurality of normalized energy consumption data reference points, parabolic approximations of energy consumption, energy conservation saturation points corresponding to speeds above a threshold (where energy efficiency deviates from parabolic approximations), and coefficients corresponding to modified energy consumption based on at least one characteristic of gradients across route segments, or a combination thereof, to determine a profile of energy consumption efficiency of vehicle 10.

At 2106, method 2100 receives route characteristics. As described, PAC 124 receives various route characteristics (e.g., route characteristics of a route that vehicle 10 is currently traversing or will traverse) and other information from any other component described herein. In some embodiments, the route characteristics include segments having different gradients. In some embodiments, the method continues at 2108. In some embodiments, the method continues at 2110. At 2108, method 2100 determines a profile of a target vehicle speed. As described, PAC 124 determines a profile of a target vehicle speed based on vehicle parameters, route characteristics, energy consumption efficiency profile of vehicle 10, other received information received from various components described herein. The profile of the target vehicle speed corresponds to a vehicle speed that, when implemented by the vehicle 10, provides optimal or improved energy consumption efficiency of the vehicle 10.

At 2110, method 2100 generates a vehicle speed recommendation. For example, PAC 124 generates a vehicle speed recommendation based on a profile of the target vehicle speed. At 2112, method 2100 provides a vehicle speed recommendation to the driver. As described, PAC 124 may provide vehicle speed recommendations to the driver of vehicle 10 using display 122, a mobile computing device, or other suitable device or display capable of providing vehicle speed recommendations to the driver of vehicle 10. As described, the driver of the vehicle 10 may follow the vehicle speed recommendation or ignore the vehicle speed recommendation. The vehicle speed recommendation corresponds to a vehicle speed that, when implemented by the vehicle 10, provides optimal or improved energy consumption efficiency of the vehicle 10. As described, PAC 124 may continuously update the profile of the target vehicle speed allocation as vehicle 10 continues to traverse the route and based on updated traffic information, vehicle information, route information, other information, or a combination thereof.

Fig. 22 generally illustrates a flow chart of an alternative energy consumption estimation method 2200 in accordance with the principles of the present disclosure. At 2202, method 2200 receives normalized energy consumption data. For example, PAC 124 may receive, from a remotely located computing device, normalized energy consumption data corresponding to energy consumption of at least one other vehicle as a function of speed, corresponding to the at least one other vehicle. The data may be configured for authentication data. At 2204, method 2200 generates a scaling factor that normalizes the energy consumption data. For example, PAC 124 may generate the scaling factor by comparing energy consumption data corresponding to vehicle energy consumption as a function of speed with normalized energy consumption data. At 2206, method 2200 includes scaling the normalized energy consumption data. For example, at 2207, method 2200 can generate a profile of energy consumption efficiency of the vehicle. . At 2208, method 2200 may include inserting an artificial zero-speed point to have three different normalized energy consumption data reference points. At 2210, method 2200 may include generating parabolic approximations of energy consumption using three different normalized energy consumption data reference points. After step 2206, method 2200 may continue at step 2212 or 2216.

At 2212, method 2200 can include utilizing energy consumption of the vehicle to identify a saturation point of energy conservation, the saturation point corresponding to a speed above a threshold, wherein the energy efficiency deviates from a parabolic approximation. At 2214, method 2200 may include identifying at least one or more varying grades along at least a segment of the route, and modifying the profile of energy consumption efficiency by coefficients of at least one other vehicle. At 2216, method 2200 can include generating a signal to selectively indicate at least one of adjusting a speed of the vehicle, at least one route characteristic of a portion of a route the vehicle is traversing, and a torque demand of the vehicle. The signal may be generated in the form of a recommendation to the operator and/or an instruction to the VPC 102. At 2218, method 2200 can include generating a signal corresponding to the recommended route on the mobile computing device. At 2220, method 2200 can include generating a signal corresponding to a recommended speed along at least a segment of the route. For example, at 2220, the recommended speed may be a torque split controller signal and/or a signal of a target speed profile. In some embodiments, at 2220, the method includes adjusting a vehicle speed control input based on at least a segment of the route having a varying grade, and communicating the vehicle speed control input to the vehicle propulsion controller. At 2222, the method 2200 can include generating a signal on at least one of the HMI 104 or the mobile device. At 2224, method 2200 may include generating a signal and communicating the signal directly to VPC 102.

Fig. 23 generally illustrates a flow chart of an alternative vehicle speed profile rationality verification method 2300 in accordance with the principles of the present disclosure. At 2302, method 2300 receives driver settings.

At 2304, method 2300 receives static and dynamic routes and vehicle characteristics. At 2306, method 2300 receives vehicle characteristics.

At 2308, method 2300 processes the received information into a format suitable for optimization logic (e.g., which may be referred to herein as a reconstructor).

At 2310, method 2300 determines an alternative target value. In parallel, at 2312, method 2300 determines an optimization target value.

At 2314, method 2300 checks the feasibility of optimizing the target value.

At 2316, method 2300 determines whether optimizing the target value is feasible. If the optimized target value is viable, method 2300 continues at 2316. If the optimized target value is not viable, method 2300 continues at 2322.

At 2318, method 2300 compares the optimization target value to the replacement target value.

At 2320, method 2300 determines whether the optimization target value is reasonable (e.g., based on the comparison). If the optimized target value is reasonable, method 2300 continues at 2322. If the optimized target value is not reasonable, method 2300 continues at 2324.

At 2322, method 2300 controls vehicle propulsion based on the optimized target value.

At 2324, method 2300 performs a default action.

Fig. 24 generally illustrates a flow chart of an alternative vehicle speed profile rationality verification method 2400 in accordance with the principles of the present disclosure. At 2402, method 2400 identifies at least one route characteristic of a portion of a route being traversed by a vehicle.

At 2404, method 2400 determines a vehicle energy consumption profile of the vehicle based at least on: historical data indicating energy consumption of a vehicle previously traversed by the vehicle for at least a portion of a route having at least one route characteristic corresponding to at least one route characteristic of a portion of the route being traversed by the vehicle; a plurality of vehicle parameters of the vehicle (e.g., wherein the plurality of vehicle parameters include a weight of the vehicle, a rolling friction of the vehicle, and/or a coefficient of resistance of the vehicle); historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of a route having at least one route characteristic corresponding to at least one route characteristic of a route being traversed by the vehicle.

At 2406, method 2400 determines a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile.

At 2408, method 2400 determines an alternate target vehicle speed based at least on the at least one route characteristic.

At 2410, method 2400 performs a default action in response to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold.

In some embodiments, a method for estimating energy consumption of a vehicle includes receiving, from a remote computing device, normalized energy consumption data corresponding to at least one other vehicle as a function of speed. The method further includes generating a scaling factor by comparing energy consumption data with vehicle energy consumption as a function of speed with normalized energy consumption data. The method further includes scaling the normalized energy consumption data to generate a profile of energy consumption efficiency of the vehicle. The method further includes generating a signal to selectively indicate at least one of adjusting a speed of the vehicle, at least one route characteristic of a portion of a route the vehicle is traversing, and a torque demand of the vehicle.

In some embodiments, scaling the normalized energy consumption data includes inserting artificial zero-speed points to have three different normalized energy consumption data reference points. In some embodiments, generating a profile of energy consumption efficiency of the vehicle includes generating a parabolic approximation of energy consumption using three different normalized energy consumption data reference points. In some embodiments, generating the profile of the energy consumption efficiency of the vehicle includes utilizing the energy consumption of the vehicle to identify a saturation point of energy conservation, the saturation point corresponding to a speed above a threshold, wherein the energy efficiency deviates from a parabolic approximation.

In some embodiments, generating the profile of energy consumption efficiency of the vehicle includes identifying at least one or more different grades along at least a segment of the route and modifying the profile of energy consumption efficiency by coefficients of at least one other vehicle. In some embodiments, generating a signal to selectively indicate adjustment of at least one route characteristic of a portion of a route that the vehicle is traversing includes generating a signal corresponding to a recommended route on the mobile computing device. In some embodiments, generating a signal to selectively indicate adjustment of at least one route characteristic of a portion of a route that the vehicle is traversing includes generating a signal corresponding to a recommended route on the mobile computing device. In some embodiments, generating a signal along at least a segment of the route corresponding to the recommended speed includes adjusting a vehicle speed control input based on at least a segment of the route having a varying grade, and communicating the vehicle speed control input to the vehicle propulsion controller. In some embodiments, generating a signal to selectively indicate an adjustment of the vehicle speed includes generating, on the mobile computing device, a signal of the vehicle speed corresponding to at least a segment of a route the vehicle is traversing.

In some embodiments, an apparatus for estimating vehicle energy consumption includes a memory and a processor. The memory includes instructions executable by the processor to: receiving, from a remotely located computing device, normalized energy consumption data corresponding to energy consumption of at least one other vehicle as a function of speed; generating a scaling factor by comparing energy consumption data with vehicle energy consumption as a function of speed with standardized energy consumption data; scaling the normalized energy consumption data to generate a profile of energy consumption efficiency of the vehicle; and generating a signal to selectively indicate at least one of adjusting a speed of the vehicle, at least one route characteristic of a portion of a route the vehicle is traversing, and a torque demand of the vehicle.

In some embodiments, a non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, facilitate performance of operations comprising: receiving, from a remotely located computing device, normalized energy consumption data corresponding to energy consumption of at least one other vehicle as a function of speed; generating a scaling factor by comparing energy consumption data with vehicle energy consumption as a function of speed with standardized energy consumption data; scaling the normalized energy consumption data to generate a profile of energy consumption efficiency of the vehicle; and generating a signal to selectively indicate at least one of adjusting a speed of the vehicle, at least one route characteristic of a portion of a route the vehicle is traversing, and a torque demand of the vehicle.

In some embodiments, the standardized energy consumption data corresponding to at least one other vehicle includes authentication data corresponding to a plurality of vehicles.

In some embodiments, a method for providing a vehicle energy optimization rationality check includes identifying at least one route characteristic of a portion of a route that a vehicle is traversing, and determining a vehicle energy consumption profile of the vehicle based on at least one of: historical data indicating energy consumption of a vehicle previously traversed by the vehicle for at least a portion of a route having at least one route characteristic corresponding to at least one route characteristic of a portion of the route being traversed by the vehicle; a plurality of vehicle parameters of the vehicle, wherein the plurality of vehicle parameters include a weight of the vehicle, a rolling friction of the vehicle, and a drag coefficient of the vehicle; and historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of the route having at least one route characteristic corresponding to at least one route characteristic of the route being traversed by the vehicle. The method further comprises the steps of: determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based on the at least one route characteristic; and responsive to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold, performing a default action.

In some embodiments, the default action includes selectively adjusting the vehicle speed control input based on the surrogate target vehicle speed. In some embodiments, the default action includes determining a default target vehicle speed and selectively adjusting the vehicle speed control input based on the default target vehicle speed. In some embodiments, the method further includes selectively adjusting the vehicle speed control input based on the target vehicle speed in response to a difference between the target vehicle speed and the alternate vehicle speed being less than a threshold.

In some embodiments, a system for providing a vehicle energy optimization rationality check includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle based on at least one of: historical data indicating that at least a portion of the vehicle energy consumption previously traversed by the vehicle has route characteristics corresponding to at least one route characteristic of a portion of a route being traversed by the vehicle; a plurality of vehicle parameters of the vehicle, wherein the plurality of vehicle parameters include a weight of the vehicle, a rolling friction of the vehicle, and a drag coefficient of the vehicle; and historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of the route having at least one route characteristic corresponding to at least one route characteristic of the route being traversed by the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and responsive to the difference between the target vehicle speed and the alternative vehicle speed being greater than a threshold, performing a default action.

In some embodiments, the default action includes selectively adjusting the vehicle speed control input based on the surrogate target vehicle speed. In some embodiments, the default action includes determining a default target vehicle speed and selectively adjusting the vehicle speed control input based on the default target vehicle speed. In some embodiments, the instructions further cause the processor to selectively adjust the vehicle speed control input based on the target vehicle speed in response to a difference between the target vehicle speed and the alternative vehicle speed being less than a threshold.

In some embodiments, a method for providing a vehicle energy optimization rationality check includes: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and, in response to the difference between the target vehicle speed and the alternative target vehicle speed being greater than a threshold, performing a default action.

In some embodiments, the default action includes selectively adjusting the vehicle speed control input based on the surrogate target vehicle speed. In some embodiments, the default action includes determining a default target vehicle speed and selectively adjusting the vehicle speed control input based on the default target vehicle speed. In some embodiments, the method further includes selectively adjusting the vehicle speed control input based on the target vehicle speed in response to a difference between the target vehicle speed and the alternative target vehicle speed being less than a threshold. In some embodiments, a vehicle energy consumption profile of the vehicle is determined based on at least a plurality of vehicle parameters of the vehicle. In some embodiments, the plurality of vehicle parameters includes a weight of the vehicle, a rolling friction of the vehicle, and a coefficient of resistance of the vehicle. In some embodiments, a vehicle energy consumption profile of the vehicle is determined based on historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of a route having at least one route characteristic corresponding to at least one route characteristic of a route being traversed by the vehicle. In some embodiments, the surrogate target vehicle speed is determined further based on at least one operational constraint. In some embodiments, wherein the at least one operating constraint comprises at least one driver customized setting. In some embodiments, determining the surrogate target vehicle speed is further based on at least one physical limitation of the vehicle. In some embodiments, the surrogate target vehicle speed is determined further based on at least one environmental condition associated with the vehicle.

In some embodiments, a system for providing a vehicle energy optimization rationality check includes a processor and a memory, the memory including instructions that when executed by the processor cause the processor to: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and in response to the difference between the target vehicle speed and the alternative target vehicle speed being greater than a threshold, performing a default action.

In some embodiments, the default action includes selectively adjusting the vehicle speed control input based on the surrogate target vehicle speed. In some embodiments, the default action includes determining a default target vehicle speed and selectively adjusting the vehicle speed control input based on the default target vehicle speed. In some embodiments, the instructions further cause the processor to selectively adjust the vehicle speed control input based on the target vehicle speed in response to a difference between the target vehicle speed and the alternative target vehicle speed being less than a threshold. In some embodiments, the instructions further cause the processor to determine a vehicle energy consumption profile of the vehicle based on at least a plurality of vehicle parameters of the vehicle. In some embodiments, the plurality of vehicle parameters includes a weight of the vehicle, a rolling friction of the vehicle, and a coefficient of resistance of the vehicle. In some embodiments, the instructions further cause the processor to determine the vehicle energy consumption profile of the vehicle based at least on historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of a route having at least one route characteristic corresponding to at least one route characteristic of a route being traversed by the vehicle. In some embodiments, the instructions further cause the processor to determine the surrogate target vehicle speed further based on at least one operational constraint, at least one physical restriction of the vehicle, and at least one environmental condition associated with the vehicle.

In some embodiments, the means for providing vehicle energy optimization rationality checks the processor and the memory. The memory includes instructions that, when executed by the processor, cause the processor to: at least one route characteristic identifying a portion of a route being traversed by the vehicle; determining a vehicle energy consumption profile of the vehicle based on at least one of: and historical data associated with at least one other vehicle previously traversed by the at least one other vehicle having at least a portion of the route having at least one route characteristic corresponding to at least one route characteristic of the route being traversed by the vehicle; determining a profile of the target vehicle speed based on the at least one route characteristic and the vehicle energy profile; determining an alternative target vehicle speed based at least on the at least one route characteristic; and, in response to the difference between the target vehicle speed and the alternative target vehicle speed being greater than a threshold, performing a default action.

The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

The word "example" is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "example" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word "example" is intended to present concepts in a concrete fashion. As used in this disclosure, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise or clear from context, "X includes a or B" is intended to mean any of the natural inclusive permutations. That is, if X includes A; x comprises B; or X includes both a and B, then "X includes a or B" is satisfied under any of the foregoing instances. In addition, the articles "a/an" as used in this disclosure and the appended claims should generally be construed to mean one or more unless specified otherwise or clear from context to be directed to a singular form. Moreover, the use of the term "implementation" or "an implementation" throughout is not intended to mean the same embodiment or implementation unless described as such.

The system, algorithm, method, instructions, etc. implementations described herein may be implemented in hardware, software, or any combination thereof. The hardware may include, for example, a computer, an Intellectual Property (IP) core, an Application Specific Integrated Circuit (ASIC), a programmable logic array, an optical processor, a programmable logic controller, microcode, a microcontroller, a server, a microprocessor, a digital signal processor, or any other suitable circuit. In the claims, the term "processor" should be understood to encompass any of the foregoing hardware, whether single or combined. The term "signal" and "data" are used interchangeably.

As used herein, the term module may include an encapsulated functional hardware unit designed for use with other components, a set of instructions executable by a controller (e.g., a processor executing software or firmware), processing circuitry configured to perform specific functions, and self-contained hardware or software components that interact with a larger system. For example, a module may include an Application Specific Integrated Circuit (ASIC); a Field Programmable Gate Array (FPGA); a circuit; a digital logic circuit; an analog circuit; discrete circuits, gates, and other types of hardware; or a combination thereof. In other embodiments, a module may include a memory storing instructions executable by a controller to implement features of the module.

Further, in one aspect, for example, the systems described herein may be implemented using a general-purpose computer or general-purpose processor having a computer program that when executed implements the corresponding methods, algorithms, and/or instructions described herein. Additionally or alternatively, for example, a special purpose computer/processor may be utilized that may contain other hardware for carrying out any of the methods, algorithms, or instructions described herein.

Further, all or part of the implementations of the present disclosure may take the form of a computer program product accessible from, for example, a computer-usable or computer-readable medium. A computer-usable or computer-readable medium may be any apparatus that can, for example, tangibly contain, store, communicate, or transport the program for use by or in connection with any processor. The medium may be, for example, an electronic device, a magnetic device, an optical device, an electromagnetic device, or a semiconductor device. Other suitable media are also available.

The above-described embodiments, implementations and aspects have been described in order to allow easy understanding of the invention and are not limiting of the invention. On the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, which scope is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures as is permitted under the law.

Claims

1. A method for providing a vehicle energy optimization rationality check, the method comprising:

identifying at least one route characteristic of a portion of a route being traversed by a vehicle;

determining a vehicle energy consumption profile for the vehicle;

determining a profile of a target vehicle speed based on the at least one route characteristic and the vehicle energy consumption profile;

determining an alternative target vehicle speed based at least on the at least one route characteristic; and

In response to a difference between the target vehicle speed and the substitute target vehicle speed being greater than a threshold, a default action is performed.

2 . The method of claim 1 , wherein the default action comprises selectively adjusting a vehicle speed control input based on the alternative target vehicle speed.

3 . The method of claim 1 , wherein the default action comprises determining a default target vehicle speed and selectively adjusting a vehicle speed control input based on the default target vehicle speed.

4 . The method of claim 1 , further comprising selectively adjusting a vehicle speed control input based on the target vehicle speed in response to a difference between the target vehicle speed and the alternate target vehicle speed being less than the threshold.

5. The method of claim 1, wherein the vehicle energy consumption profile of the vehicle is determined based at least on a plurality of vehicle parameters of the vehicle.

6. The method of claim 5, wherein the plurality of vehicle parameters include a weight of the vehicle, a rolling friction of the vehicle, and a drag coefficient of the vehicle.

7. The method as claimed in claim 1 is characterized in that the vehicle energy consumption profile of the vehicle is determined based at least on historical data associated with at least one other vehicle of at least a portion of a route previously traversed by at least one other vehicle having at least one route characteristic corresponding to the at least one route characteristic of the route being traversed by the vehicle.

8. The method of claim 1, wherein the alternative target vehicle speed is determined further based on at least one operating constraint.

9. The method of claim 8, wherein the at least one operating constraint comprises at least one driver customized setting.

10. The method of claim 1, wherein the alternative target vehicle speed is determined further based on at least one physical limitation of the vehicle.

11. The method of claim 1, wherein the alternative target vehicle speed is determined further based on at least one environmental condition associated with the vehicle.

12. A system for providing a vehicle energy optimization rationality check, the system comprising:

Processor; and

a memory comprising instructions that, when executed by the processor, cause the processor to:

determining a vehicle energy consumption profile for the vehicle;

13. The system of claim 12, wherein the default action comprises selectively adjusting a vehicle speed control input based on the alternate target vehicle speed.

14. The system of claim 12, wherein the default action comprises determining a default target vehicle speed and selectively adjusting a vehicle speed control input based on the default target vehicle speed.

15. The system of claim 12, wherein the instructions further cause the processor to selectively adjust a vehicle speed control input based on the target vehicle speed in response to a difference between the target vehicle speed and the alternate target vehicle speed being less than the threshold.

16. The system of claim 12, wherein the instructions further cause the processor to determine a vehicle energy consumption profile for the vehicle based at least on a plurality of vehicle parameters of the vehicle.

17. The system of claim 16, wherein the plurality of vehicle parameters include a weight of the vehicle, a rolling friction of the vehicle, and a drag coefficient of the vehicle.

18. A system as described in claim 12, characterized in that the instructions further cause the processor to determine the vehicle energy consumption profile of the vehicle based on at least historical data associated with at least one other vehicle that has at least a portion of a route previously traversed by at least one other vehicle and has at least one route characteristic corresponding to the at least one route characteristic of the route being traversed by the vehicle.

19. The system of claim 12, wherein the instructions further cause the processor to determine the alternative target vehicle speed further based on at least one operating constraint, at least one physical limitation of the vehicle, and at least one environmental condition associated with the vehicle.

20. An apparatus for providing a vehicle energy optimization rationality check, the apparatus comprising:

Processor; and

Determining a vehicle energy consumption profile of the vehicle is based on at least one of:

a plurality of vehicle parameters of the vehicle; and

historical data associated with at least one other vehicle of at least a portion of a route previously traversed by the at least one other vehicle having at least one route characteristic corresponding to the at least one route characteristic of the route being traversed by the vehicle;