CN113022558A

CN113022558A - Method for controlling hybrid power system

Info

Publication number: CN113022558A
Application number: CN201911357824.3A
Authority: CN
Inventors: 黄琳
Original assignee: Cummins Inc
Current assignee: Cummins Inc
Priority date: 2019-12-25
Filing date: 2019-12-25
Publication date: 2021-06-25

Abstract

A method of hybrid powertrain control. Methods for optimizing hybrid powertrain system efficiency using predetermined or real-time transmitted route data information are provided herein. For example, a method for energy recovery within one or more battery packs of a vehicle is provided. In other embodiments, a method of automatically implementing a neutral mode of a vehicle is provided. In a further embodiment, a method of preloading the state of charge of a battery pack when necessary is provided.

Description

Method for controlling hybrid power system

Technical Field

The present disclosure relates to optimization of hybrid internal combustion engine power (power) units and electrified power units. In particular, the present disclosure relates to the use of predictive road mapping and energy recovery in hybrid power units.

Background

Electrification of vehicles is a common way to save fuel costs and provide "cleaner" travel and other benefits. Many electrified vehicles are not purely electric, but include hybrid systems that utilize an electrical system or battery pack and internal combustion engine power. The application of electrification to commercial vehicles has proven to be more difficult due to the heavy load of commercial vehicles and the long distances traveled by most commercial vehicles. For example, battery power, the weight of the vehicle, and the electric motors that provide power for such vehicles present greater challenges compared to the electrification of cars. Thus, hybrid systems continue to play a vital role in the electrification of commercial vehicles.

Optimization of hybrid systems for large commercial vehicles is desired. For example, recovering energy within one or more battery packs in an efficient manner may save fuel consumption, save battery costs, and reduce brake hardware costs. In addition, preloading the state of charge of the battery pack when necessary may improve transportation efficiency and reduce travel time. Such applications may also be provided for cars and other vehicles.

Disclosure of Invention

Methods for optimizing hybrid powertrain system efficiency using predetermined or real-time transmitted route data information are provided herein. For example, a method for energy recovery within one or more battery packs of a vehicle is provided. In other embodiments, a method of automatically implementing a neutral mode of a vehicle is provided. In a further embodiment, a method of preloading the state of charge of a battery pack when necessary is provided.

In accordance with an exemplary embodiment of the present disclosure, a method of estimating a braking event is disclosed. The method comprises the following steps: providing information relating to the route to a route data processor of the vehicle; identifying an upcoming uphill event or an upcoming downhill event based on the information relating to the route; estimating a speed of the vehicle at any given time during the upcoming uphill event or the upcoming downhill event as a function of at least one of: a current speed of the vehicle, a grade of the upcoming uphill event or the upcoming downhill event, a length of the upcoming uphill event or the upcoming downhill event, and a machine mass estimate of the vehicle; comparing the estimated vehicle speed to a predetermined speed threshold; and estimating the occurrence of a braking event.

The method may be performed using a smart taxi management processor. The predetermined speed threshold may be a forcibly imposed route speed limit. The predetermined speed threshold may be a speed set on a cruise control of the vehicle. When the estimated vehicle speed is below a predetermined speed threshold, a braking event may not occur. The method may further comprise: a command is issued to a transmission control unit of the vehicle to shift the vehicle to neutral mode. The braking event may be estimated to be performed by an engine brake of the vehicle when the estimated vehicle speed is above a predetermined speed threshold. When the estimated vehicle speed is above a predetermined speed threshold, a braking event may be estimated to be performed by wheel brakes of the vehicle. The method may further comprise: energy recovery of at least one battery pack of the vehicle is performed in order to increase the state of charge of the at least one battery pack before a braking event actually occurs. The method may further comprise: engaging an engine brake of the vehicle prior to the estimated occurrence of the braking event. The step of charging the at least one battery pack may be initiated before the occurrence of the terrain event. Information relating to the route may be provided to the route data processor by a global positioning system.

In accordance with another exemplary embodiment of the present disclosure, a method of managing vehicle speed is disclosed. The method comprises the following steps: providing information relating to the route to a route data processor of the vehicle; identifying a predetermined speed threshold; identifying an upcoming topographical event based on the information relating to the route; estimating the speed of the vehicle at any given time during the upcoming terrain event based on at least one of: a current speed of the vehicle, a grade of the upcoming terrain event, a length of the upcoming terrain event, and a machine mass estimate of the vehicle; comparing the estimated vehicle speed to a predetermined speed threshold; and charging at least one battery pack of the vehicle by recovering energy, thereby reducing the speed of the vehicle.

The step of charging the at least one battery pack may be initiated before the occurrence of the terrain event. Information relating to the route may be provided to the route data processor by a global positioning system. Information relating to the route may be provided by the controller to the route data processor. The terrain event may be an uphill event. The predetermined speed threshold may be determined by setting a desired speed value using a vehicle cruise controller. The method may further comprise: a driving status of an uphill event is identified. The method may further comprise: the state of charge of at least one battery pack is compared to a predetermined threshold value of state of charge value. The estimated speed may be less than a predetermined speed threshold. The method may further comprise: it is determined whether the vehicle is in an energy saving mode, a power mode, or a balancing mode. When the vehicle is in the balancing mode, the estimated speed may be less than a difference between the predetermined speed threshold and a calibrated value for the at least one battery pack. The terrain event may be a downhill event. The method may further comprise: when the estimated vehicle speed is greater than the predetermined speed threshold, the speed of the vehicle is reduced to a second speed by a predetermined amount using the predictive cruise control processor. The method may further comprise: the second speed is compared to a predetermined speed threshold, wherein charging at least one battery pack of the vehicle reduces the second speed of the vehicle to a third speed of the vehicle. The method may further comprise: an engine brake of the vehicle is engaged to reduce the speed of the vehicle. The step of charging the at least one battery pack may occur during a terrain event.

In accordance with yet another embodiment of the present disclosure, a method of managing vehicle speed is disclosed. The method comprises the following steps: providing information relating to the route to a route data processor of the vehicle; identifying a predetermined speed threshold; identifying an upcoming downhill event based on information relating to the route; estimating the speed of the vehicle at any given time during an upcoming downhill event based on at least one of: a current speed of the vehicle, a grade of an upcoming downhill event, a length of the upcoming downhill event, and a machine quality estimate of the vehicle; comparing the estimated vehicle speed to a predetermined speed threshold; and selectively issuing a neutral command to a transmission control unit of the vehicle to cause the vehicle to enter a neutral mode, wherein the estimated vehicle speed is less than a predetermined speed threshold.

Information relating to the route may be provided to the route data processor by a global positioning system. Information relating to the route may be provided by the controller to the route data processor. A neutral command may be issued during a downhill event.

Other features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the following detailed description, which illustrates presently known exemplary embodiments of the disclosure.

Drawings

The above-mentioned and other features and advantages of this disclosure, and the manner of attaining them, will become more apparent and the invention will be better understood by reference to the following description of exemplary embodiments taken in conjunction with the accompanying drawings, wherein:

FIG. 1 is a flow chart illustrating a first exemplary method of the present disclosure for controlling vehicle speed and energy recovery within a hybrid powertrain system during a downhill event utilizing an intelligent coasting management processor of the present disclosure, and a second exemplary method of the present disclosure for controlling vehicle speed during a downhill event utilizing a predictive cruise control processor of the present disclosure;

FIG. 2 is a graphical representation of the first exemplary method of FIG. 1, wherein a neutral mode is used to control vehicle speed during a downhill event;

FIG. 3 is a graphical representation of the first exemplary method of FIG. 1 or the second exemplary method of FIG. 1, wherein the vehicle is decelerated by engaging an engine brake of the vehicle, by energy recovery, or by utilizing a predictive cruise control processor of the present disclosure;

FIG. 4 is a graphical representation of the first exemplary method of FIG. 1 or the second exemplary method of FIG. 1, wherein the vehicle is initially decelerated by engaging an engine brake of the vehicle, by energy recovery, or by utilizing the predictive cruise control processor of the present disclosure before a downhill event begins;

FIG. 5 is a flow chart illustrating an exemplary method of the present disclosure for preloading one or more battery packs of a vehicle having a hybrid powertrain system prior to a hill ascent event;

FIG. 6 is a graphical illustration of the exemplary method of FIG. 5, wherein one or more battery packs of the vehicle are preloaded prior to the beginning of an uphill event;

FIG. 7 is a comparative illustration of differences in several aspects of vehicle operation between a vehicle utilizing predictive energy recovery and a vehicle utilizing braking energy recovery;

FIG. 8A is a graphical representation of a vehicle utilizing braking energy recovery, and in particular a graphical representation of the relative amount of time the vehicle takes to charge, brake, and engine drive (motoring) during a downhill event;

FIG. 8B is a graphical representation of a vehicle utilizing predictive energy recovery, and in particular a graphical representation of the relative amount of time the vehicle takes to charge, brake, and engine drive during a downhill event;

FIG. 9 is a graphical representation of a comparison of a state of charge of a vehicle using predictive energy recovery compared to a state of charge of a vehicle using braking energy recovery; and

FIG. 10 is a graphical representation of a comparison of fuel consumption improvement between a vehicle using predictive energy recovery and a vehicle using braking energy recovery.

Corresponding reference characters indicate corresponding parts throughout the several views. Although the drawings illustrate embodiments of various features and components in accordance with the present disclosure, the drawings are not necessarily to scale and certain features may be exaggerated in order to better illustrate and explain the present disclosure. The exemplifications set out herein illustrate embodiments of the invention, and such exemplifications are not to be construed as limiting the scope of the invention in any manner.

Detailed Description

Reference will now be made to the embodiments illustrated in the drawings described below to facilitate an understanding of the principles of the disclosure. The exemplary embodiments disclosed herein are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed in the following detailed description. Rather, the exemplary embodiments are chosen and described so that others skilled in the art may utilize the teachings of the exemplary embodiments.

Global positioning systems ("GPS") are commonly used during operation of vehicles for short and long distance driving. In many vehicles, the GPS processor has been integrated into the vehicle operating system. The GPS may help predict upcoming road conditions based on the route input by the user or driver. For example, the user or driver may enter a start location and an end location and allow the GPS to automatically populate the route that best suits the user's needs and/or provide the user or driver with a plurality of alternative routes. For example, the user may select between a fastest route, a shortest route, a route consisting of major highways, a route without major highways, a route including toll roads, a route not including toll roads, and the like. In some embodiments, the GPS may detect the location of the vehicle without manual input from the user or driver. The user or driver may use GPS assistance with or without a predetermined route to predict upcoming road conditions. Road conditions may include, but are not limited to, uphill events, downhill events, curve events, turning events, traffic events (including accidents, stalled vehicles, emergency vehicles, etc.), and the presence of congested or uncongested traffic. Such operations may include predictive road mapping. Vehicle systems may utilize predictive road mapping to optimize the operating efficiency of the vehicle. Such methods are further disclosed herein. In other embodiments, a route parameter manager block may be utilized that integrates map data with a GPS sensor.

Moreover, many commercial vehicles are often repeatedly driven cyclically, sometimes several times a day, by running the same route or loop. Such commercial vehicles may include transportation vehicles and distribution vehicles. For example, a transit bus may have a fixed driving cycle (which includes an exact route or an exact loop that repeats to form a route) and a stopping time set by a schedule issued by the responsible transportation department. Thus, route characteristics or statistics for countless driving cycles may be defined by associating these route characteristics with known route identification references (e.g., particular route numbers). The route identification reference may be used to reference information such as the distance of a single loop of the route, the number of loops of the route per day or other unit of time, the number of battery boost charging opportunities in each loop, the number of planned stops in each loop, the distance to and between stops, the nominal total energy required by the vehicle to complete a single loop, altitude (elevation) range, route surface grade, route surface type, maximum speed limit, minimum speed limit, maximum route travel time, traffic conditions, and other statistics.

For a hybrid vehicle system, many of these route characteristics may be provided to and stored in the hybrid controller. Route characteristic statistics such as these may be preprogrammed into the hybrid controller with relatively minimal computer memory burden, such that all operations required to adjust the decisions identified and discussed further herein are input, for example, through an operator interface or via the controller, current route identification references. More information regarding the storage of ROUTE characteristics and ROUTE IDENTIFICATION may be found in U.S. patent application publication No.2018/0134275a1 entitled "HYBRID vehicle drive cycle OPTIMIZATION BASED ON ROUTE IDENTIFICATION," filed ON Books et al, 11/15/2017, the disclosure of which is incorporated herein by reference in its entirety.

As shown in FIG. 1, a method 100 for determining vehicle operation for an upcoming downhill event using intelligent taxi management is shown. For an upcoming downhill event, the GPS processor 102 and/or the controller 104 transmits information related to the vehicle route to the vehicle's route data processor 106, which route data processor 106 identifies the upcoming downhill event 114 based on the information received from the GPS processor 102 and/or the controller 104. The route data processor 106 further identifies a speed limit 126 for the route via information received from the GPS processor 102 and/or the controller 104 to ensure the ride comfort (compliance) of the vehicle as further described herein.

Additionally, during operation of the vehicle, the machine mass estimate 108 or vehicle mass is calculated to determine a gross vehicle weight 110 of the operating vehicle. The gross vehicle weight may include the weight of the vehicle chassis, the vehicle body, the vehicle engine, vehicle engine antifreeze, vehicle fuel, vehicle accessories, the vehicle operator, other parts of the vehicle, and any cargo carried by the vehicle, including but not limited to items, equipment, passengers, or anything attached to or supported by the vehicle. An internal or engine brake 112 is also included in the vehicle.

The route data processor 106 communicates information about the upcoming downhill event 114 and the speed limit 126 of the route to the intelligent taxi management processor 116. In one embodiment, speed limit 126 may be a speed limit imposed by local, state, regional, or federal law. In another embodiment, speed limit 126 may also be a predetermined limit set for driving safety or any other goal. Other information transmitted by route data processor 106 regarding upcoming downhill events 114 may include the route grade, the length of the hill event, or any other information related to downhill events 114. The intelligent coasting management processor 116 also receives the gross vehicle weight 110 of the vehicle calculated from the machine mass estimate 108, data relating to the efficiency of the engine brake 112, and/or any potential rate of change of vehicle speed due to the use of the engine brake 112. In performing intelligent taxi management, processor 116 is configured to perform processing based on information received from each of route data processor 106, machine mass estimate 108, and engine brake 112 to determine a need for the vehicle to enter a neutral taxi mode, an engine braking mode, or a wheel braking mode to achieve a desired speed during a downhill event 114.

Still referring to the method 100 of fig. 1, the intelligent coasting management processor 116 may implement a vehicle speed model to estimate vehicle speed throughout the downhill event 114. For example, the intelligent coasting management processor 116 may use calculations consisting of the current speed of the vehicle, the route grade, gravity, vehicle mass, and engine drive (i.e., friction generated by the engine while downhill motion is controlled by gravity but the engine is still operating) to calculate an estimated speed of the vehicle at any given time during the upcoming downhill event 114. This estimated speed is communicated to block 138 for use with the intelligent taxi management predictive controller, as discussed further herein.

The user or driver of the vehicle may choose whether to implement intelligent taxi management. For example, as shown in fig. 1, the smart coasting management processor 116 is in communication with the transmission control unit 128 such that the transmission control unit 128 may transition the vehicle to neutral mode upon receiving a neutral command 130 from the smart coasting management processor 116. Before implementing the neutral command 130, the intelligent taxi management processor 116 determines whether a neutral mode is required. If the neutral mode is zero, as shown in block 132, the user or driver of the vehicle has disabled neutral mode and the method 100 ends at block 122. If the neutral mode is not disabled at block 132, i.e., if the neutral mode is not equal to zero, the smart taxi management processor 116 determines a mode of the smart taxi management predictive controller at block 136. For example, if an overspeed event is not estimated, the method 100 ends at block 122.

If an over-speed event is estimated, the vehicle speed model is used at block 138 to compare the estimated speed of the vehicle during the downhill event 114 to the speed limit 126 communicated by the route data processor 106 at block 134 to determine if the estimated speed of the vehicle during the downhill event 114 will exceed the speed limit 126. If the estimated speed is less than the speed limit, the smart coast management processor 116 issues a neutral command 130 to the transmission control unit 128. The transmission control unit 128 shifts the vehicle to neutral mode and the method 100 ends at block 124. If the estimated speed is greater than or equal to the speed limit 126, energy recovery is enabled at block 144 to charge one or more battery packs within the vehicle, as indicated at block 146, which may also begin to slow the vehicle. After power recovery begins, the intelligent coasting management processor may perform additional vehicle speed estimation and comparison based on the speed of the vehicle. If a new estimation is performed and the new estimated speed is greater than or equal to the speed limit 126, the intelligent taxi management processor 116 also engages the engine brake 112 to reduce the vehicle speed by a predetermined amount (e.g., about 1km/h, about 3km/h, about 5km/h, or other amount) at block 140. If the new estimate is not being performed, the smart taxi management processor 116 also engages the engine brake 112 to reduce the vehicle speed by a predetermined amount at block 140, correspondingly enabling energy recovery at block 144.

The smart taxi management processor 116 may then calculate another estimated speed of the vehicle from the reduced vehicle speed. The new estimated vehicle speed is then compared to the speed limit 126 at block 142. If the new estimated vehicle speed is still above the speed limit 126, energy recovery continues at block 144 and a user or driver initiated wheel braking event is anticipated to slow the vehicle down during the downhill event 114. If the new estimated vehicle speed is less than the speed limit 126, the method 100 ends at block 150. Energy recovery may or may not continue.

Still referring to fig. 1, a method 200 of determining vehicle operation for an upcoming downhill event using a predictive cruise controller is also disclosed. Similar to the method 100, the GPS processor 102 and/or the controller 104 transmits information related to the vehicle route to a route data processor 106 of the vehicle, the route data processor 106 identifying a topographical event of the route based on the information received from the GPS processor 102 and/or the controller 104. The route data processor 106 also identifies a speed limit 126 for the route via information received from the GPS processor 102 and/or the controller 104 to ensure the ride comfort of the vehicle. Additionally, as described above, during operation of the vehicle, the machine mass estimate 108 is calculated to determine a gross vehicle weight 110 of the operating vehicle.

The route data processor 106 communicates information to the predictive cruise control processor 118 regarding upcoming terrain events for the route and the speed limit 126 for the route, similar to the communication to the intelligent taxi management processor 116 in the method 100 described above. The information transmitted by the route data processor 106 regarding upcoming terrain events may include identification of substantially flat terrain, pre-hill events, uphill events, pre-downhill events, or downhill events. The further information transferred may include the grade of the route, the length of the ramp event, or any other information. The predictive cruise control processor 118 also receives a gross vehicle weight 110 for the vehicle calculated based on the machine mass estimate 108. The predictive cruise control processor 118 also receives a current set speed of the vehicle from the cruise controller 148 of the vehicle.

The predictive cruise control processor 118 is configured to operate according to five different modes of predictive cruise control. When the vehicle is operating along substantially flat terrain, the predictive cruise control processor 118 is in the mode A configuration. The predictive cruise control processor 118 is in the mode B configuration when the vehicle is in a pre-uphill position, for example, when the route data processor 106 identifies an approaching uphill event on the vehicle route. When the vehicle is in an uphill event position, the predictive cruise control processor 118 is in a mode C configuration. The predictive cruise control processor 118 is in the mode D configuration when the vehicle is in a pre-downhill position, for example, when the route data processor 106 identifies an approaching downhill event on the vehicle route. When the vehicle is in the downhill event position, the predictive cruise control processor 118 is in the mode E configuration.

Referring to the method 200 of FIG. 1, as described above, when the route data processor 106 identifies an upcoming downhill event, the predictive cruise control processor 118 enters a mode D configuration, as shown at block 127. In the mode D configuration, the predictive cruise control processor 118 may implement a vehicle speed model to estimate vehicle speed throughout the downhill event 114, as indicated at block 129. For example, the predictive cruise control processor 118 may use calculations made up of the speed set by the cruise controller 148, the route grade, gravity, vehicle mass, and engine drive (i.e., friction generated by the engine while downhill motion is controlled by gravity but the engine is still running) to calculate an estimated speed of the vehicle at any given time during the upcoming downhill event 114.

Then, at block 131, the predictive cruise control processor 118 compares the estimated speed of the vehicle during the downhill event 114 to the speed limit 126 communicated by the route data processor 106 to determine whether the estimated speed of the vehicle during the downhill event 114 will exceed the speed limit 126. If the estimated speed of the vehicle during the downhill event 114 will exceed the speed limit 126, the predictive cruise control processor 118 changes the set speed of the cruise controller 148 by a predetermined amount (e.g., about 1km/h, about 3km/h, about 5km/h, or other amount) at block 140. The predictive cruise control processor 118 may then calculate a new estimated speed of the vehicle based on the reduced vehicle speed. The new estimated vehicle speed is then compared again to the speed limit 126 at block 142. If the new estimated vehicle speed is still above the speed limit 126, energy recovery is enabled at block 144 to charge one or more battery packs within the vehicle, as shown at block 146, and also to decelerate the vehicle in anticipation of a user or driver initiated engine braking event or wheel braking event, thereby decelerating the vehicle during the downhill event 114. If the estimated vehicle speed is less than the speed limit 126, the method 200 ends at block 150.

Referring now to fig. 2-4, predictive control of energy recovery described in method 100 and method 200 is shown. With particular reference to fig. 2, an exemplary downhill event is illustrated by a terrain profile 225. The satellite system or GPS 227 communicates with the controller 229 of the vehicle 231 describing the upcoming downhill event. In one embodiment, the controller 229 may receive GPS information via a GPS processor similar to the GPS processor 102 (fig. 1). In another embodiment, the controller 229 may include a GPS processor. In yet another embodiment, the controller 229 may identify an upcoming downhill event from memory without using the GPS 227. Line 233 shows the predetermined speed of the engine brake engaging vehicle 231 and/or the speed limit 126 of vehicle 231 (fig. 1). Line 235 shows the speed of the vehicle 231 as the vehicle 231 is coasting down the terrain profile 225. Line 237 shows the speed of the vehicle 231 as the vehicle 231 coasts along the terrain profile 225 in neutral mode.

For example, still referring to fig. 2, according to the method 100 (fig. 1), the route data processor 106 (fig. 1) identifies an upcoming downhill event, as indicated by the terrain profile 225, and will transmit the downhill event to the intelligent taxi management processor 116 (fig. 1). If the smart coasting management processor 116 (FIG. 1) is active and the neutral mode is enabled, the smart coasting management processor 116 (FIG. 1) may determine that the vehicle 231 will not be traveling above the speed limit 126 (FIG. 1) indicated by 233 and cause the vehicle 231 to enter the neutral mode by sending the neutral command 130 (FIG. 1) to the transmission control unit 128 (FIG. 1). As can be seen by comparing line 233 to line 235, vehicle 231 completes terrain profile 225 without reaching or exceeding speed limit 233 while in neutral coasting mode.

Referring now to fig. 3, another exemplary terrain profile 250 associated with a downhill event is illustrated. As described above with respect to fig. 2, the satellite system or GPS 227 communicates with the controller 229 of the vehicle 231 describing the upcoming downhill event. In one embodiment, the controller 229 may receive GPS information via a GPS processor similar to the GPS processor 102 (fig. 1). In another embodiment, the controller 229 may include a GPS processor. In yet another embodiment, the controller 229 may identify an upcoming downhill event from memory without using the GPS 227. Line 252 shows the predetermined speed at which the engine brake of vehicle 231 is engaged and/or speed limit 126 (FIG. 1) of vehicle 231. Line 254 shows the speed of the vehicle 231 as the vehicle 231 is coasting down the terrain profile 250 without predictive energy recovery. Line 256 shows the speed of the vehicle 231 as the vehicle 231 is coasting down the terrain profile 250 and predictive energy recovery is performed.

For example, still referring to fig. 3, according to the method 100 (fig. 1), the route data processor 106 (fig. 1) identifies an upcoming downhill event, illustrated by a terrain profile 250, and communicates the downhill event to the intelligent taxi management processor 116 (fig. 1). If the smart coasting management processor 116 (fig. 1) is active and the smart coasting management prediction controller 134 (fig. 1) is engaged, the smart coasting management processor 116 (fig. 1) may determine that the vehicle 231 will reach a speed higher than the engine brake limit speed shown by line 252. The intelligent coasting management processor 116 (fig. 1) may also determine that the rate of engine braking combined with the grade of the terrain requires only that the engine brake be activated when the vehicle reaches such a speed. In this case, the smart coast management processor 116 (fig. 1) enables energy recovery at the engine brake limit line 252 to slow the vehicle 231 and correspondingly engages the engine brake 112 (fig. 1) to further slow the vehicle. As can be seen by comparing line 256 to line 252, vehicle 231 completes terrain profile 250 when in energy recovery mode without reaching or exceeding speed limit 252.

According to method 200 (fig. 1), route data processor 106 (fig. 1) may communicate an upcoming downhill event, as indicated by terrain profile 250, to predictive cruise control processor 118 (fig. 1). If the cruise controller 148 (FIG. 1) maintains the current setting, the predictive cruise control processor 118 (FIG. 1) may determine that the vehicle 231 will reach a speed greater than the engine brake limit speed shown by line 252. In this case, the predictive cruise control processor 118 (fig. 1) may change the cruise controller settings of the vehicle 231 to slow the vehicle during a downhill event to prevent overspeeding while also enabling the energy recovery mode.

Referring now to fig. 4, another exemplary terrain profile 275 associated with a downhill event is shown. As described above with respect to fig. 2-3, the satellite system or GPS 227 communicates with the controller 229 of the vehicle 231 describing the upcoming downhill event. In one embodiment, the controller 229 may receive GPS information via a GPS processor similar to the GPS processor 102 (fig. 1). In another embodiment, the controller 229 may include a GPS processor. In yet another embodiment, the controller 229 may identify an upcoming downhill event from memory without using the GPS 227. Line 277 shows the predetermined speed at which the engine brake of vehicle 231 is engaged and/or speed limit 126 (FIG. 1) of vehicle 231. Line 279 shows the wheel brake limits of the vehicle 231, or the speed at which the driver is predicted to engage the wheel brakes to successfully complete the downhill event. Line 281 shows the speed of the vehicle 231 when the vehicle 231 is coasting along the terrain profile 275 without predictive energy recovery. Line 283 shows the speed of the vehicle 231 as the vehicle 231 is coasting down the terrain profile 275 and performing predictive energy recovery.

For example, still referring to fig. 4, according to the method 100 (fig. 1), the route data processor 106 (fig. 1) identifies an upcoming downhill event, as indicated by the terrain profile 275, and communicates the downhill event to the intelligent taxi management processor 116 (fig. 1). If the smart coasting management processor 116 (FIG. 1) is active and the smart coasting management prediction controller 134 (FIG. 1) is engaged, the smart coasting management processor may determine that the vehicle 231 will reach a higher speed than the engine brake limiting speed shown by line 277. The intelligent coasting management processor 116 (fig. 1) may also determine that the rate of engine braking combined with the grade of the terrain requires an early engine braking event and enable energy recovery at engine brake limit line 252 to slow the vehicle 231 before the start of a downhill event and correspondingly engage the engine brake 112 (fig. 1) to further slow the vehicle. As can be seen by comparing line 281 to line 283, vehicle 231 completes terrain profile 275 without reaching or exceeding speed limit 277 when in energy recovery mode; without predictive control or early recovery mode, the driver or user would have to engage the wheel brakes to successfully complete the downhill event (as shown by line 281) while still ignoring the (bypass) speed limit 277.

According to method 200 (fig. 1), route data processor 106 (fig. 1) may also communicate an upcoming downhill event, as indicated by terrain profile 275, to predictive cruise control processor 118 (fig. 1). If the cruise controller 148 (FIG. 1) maintains the current setting, the predictive cruise control processor 118 (FIG. 1) may determine that the vehicle 231 will reach a higher speed than the engine brake limit speed indicated by line 277. In this case, the predictive cruise control processor 118 (fig. 1) may change the cruise control settings of the vehicle 231 to slow the vehicle before a downhill event occurs to prevent excessive speed while also enabling the energy recovery mode.

Referring now to fig. 5, a method 500 of determining vehicle operation for an upcoming uphill event using a predictive cruise controller is disclosed. Similar to the method 200 of fig. 1 discussed above, the GPS processor 502 and/or the controller 504 communicate information relating to the vehicle route to the route data processor 50 of the vehicle, and the route data processor 506 identifies a topographical event 558 of the route and communicates the route information to the predictive cruise control processor 518. Additionally, during operation of the vehicle, the machine mass estimate 508 is calculated to determine a gross vehicle weight 510 of the operating vehicle, and the machine mass estimate 508 is transmitted to the predictive cruise control processor 518. The predictive cruise control processor 518 also receives a current set speed of the vehicle from a cruise controller 548 of the vehicle.

More information regarding the vehicle's GPS processor 502, controller 504, predictive cruise controller 518, machine mass estimate 508, gross vehicle weight 510, and speed limit 526 may be found in the discussion above regarding method 100 and method 200 of fig. 1. That is, the GPS processor 102 (FIG. 1), the controller 104 (FIG. 1), the predictive cruise controller 118 (FIG. 1), the machine mass estimate 108 (FIG. 1), the gross vehicle weight 110 (FIG. 1), and the speed limit 126 (FIG. 1) include the same functions and features as the GPS processor 502, the controller 504, the predictive cruise controller 518, the machine mass estimate 508, the gross vehicle weight 510, and the speed limit 526 of the vehicle discussed herein.

When the route data processor 506 identifies an upcoming ramp event 558, the predictive cruise control processor 518 identifies its current mode configuration at block 552. If the predictive cruise control processor 518 is in mode a, i.e., if the vehicle is traveling along substantially flat terrain, the predictive cruise control processor moves to block 554. If the predictive cruise control processor 518 is not in mode A, the method 500 ends at block 556. At block 554, predictive cruise control processor 518 uses the information identified by route data processor 506 to determine whether predictive cruise control processor 518 is going into mode B, i.e., whether the vehicle is entering the pre-hill range. If the predictive cruise control processor 518 is not to enter mode B, the method 500 ends at block 556. If the predictive cruise control processor 518 is to enter mode B during a pre-hill event, drivability of the upcoming hill ascent event 558 is to be analyzed by the predictive cruise control processor 518.

The predictive cruise control processor 518 may use calculations made up of the speed set by the cruise controller 548, the route grade, gravity, vehicle mass, and other factors to calculate an estimated speed of the vehicle at any given time during the upcoming uphill event 558. The predictive cruise control processor 518 then determines whether the vehicle is able to maintain the speed set by the cruise controller 548 throughout the uphill event 558 without additional power. If the estimated speed of the vehicle during the uphill event 558 is low, i.e., if the estimated speed of the vehicle during the uphill event 558 is lower than the speed set by the cruise controller 548, the predictive cruise control processor 118 identifies at block 560: for an upcoming uphill event 558, the vehicle drivability status is low.

At block 562, the state of charge of the battery pack of the vehicle is compared to a threshold or predetermined value. If the state of charge of the battery pack is equal to or greater than the threshold, the method 500 ends at block 556. If the state of charge of the battery pack is less than the threshold, then at block 564, the predictive cruise control processor 518 determines the driver-selected vehicle state. For example, a user or driver of the vehicle may selectively select between the eco mode state 566, the balance mode state 568, and the power mode state 570. If the user or driver of the vehicle has placed the vehicle in the energy savings mode state 566, the method 500 ends at block 572. If the user or driver of the vehicle has placed the vehicle in the balance mode state 568 or the power mode state 570, the predictive cruise control processor 518 enables the estimated vehicle speed to determine whether a preload recovery event should occur at block 573.

For example, if the user or driver of the vehicle has placed the vehicle in the power mode state 570, the predictive cruise control processor 518 compares the estimated speed of the vehicle throughout the uphill event 558 to the speed set by the cruise controller 548 at block 582. If the estimated speed is less than the speed set by the cruise controller 548, energy recovery is enabled at block 576 to preload or precharge the battery packs of the vehicle at block 578. If the estimated speed is equal to or greater than the speed set by the cruise controller 548, the method 500 ends at block 577. If the user or driver of the vehicle has placed the vehicle in the balance mode state 568, the predictive cruise control processor 518 compares the estimated speed of the vehicle throughout the uphill event 558 to the speed set by the cruise controller 548, at block 580, while also taking into account the calibration values for the battery pack of the vehicle. If the estimated speed is less than the difference between the speed set by the cruise controller 548 and the calibrated value for the battery packs, energy recovery is enabled at block 576 to preload or precharge the battery packs of the vehicle at block 578. If the estimated speed is equal to or greater than the speed set by the cruise controller 548, the method 500 ends at block 575.

Referring now to FIG. 6, predictive control of a method 500 for an uphill event is shown. An exemplary uphill event is illustrated by terrain profile 600. The satellite system or GPS 602 communicates with the controller 604 of the vehicle 606 describing the upcoming downhill event. In one embodiment, the controller 604 may receive GPS information via a GPS processor similar to the GPS processor 502 (FIG. 5). In another embodiment, the controller 604 may include a GPS processor. In yet another embodiment, the controller 604 may identify an upcoming uphill event from memory without using the GPS 602. Line 608a shows the speed of the vehicle 606 during the uphill event 600 without the use of pre-charge control. Line 608b shows the speed during the uphill event 600 of the vehicle 606 with pre-charge control. Line 610a shows the state of charge of the battery pack of the vehicle 606 during an uphill event 606 without the use of pre-charge control. Line 610b shows the state of charge of the battery pack of the vehicle 606 in the case where pre-charge control is used. Line 612a shows the engine power of vehicle 606 without the use of pre-charge control. Line 612b shows the engine power of vehicle 606 with the use of pre-charge control.

For example, still referring to fig. 6, according to method 500 (fig. 5), route data processor 506 (fig. 5) identifies an upcoming uphill event as shown by terrain profile 600 and communicates the uphill event to predictive cruise control processor 518 (fig. 5). If the predictive cruise control processor 518 (fig. 5) determines that the vehicle driving status is low and the battery pack state of charge is below a threshold amount for an upcoming uphill event 600, the predictive cruise control processor 518 (fig. 5) determines which of the driver-selected vehicle 606 states is to be enabled.

In one embodiment shown in FIG. 6, the power saving mode state 566 (FIG. 5) has been selected, and thus no precharge control is utilized. In other embodiments, the vehicle 606 may not utilize this function because the driver or user has selected not to activate predictive cruise control, or because predictive cruise control has failed or is unavailable. In such embodiments, the vehicle 606 operates according to

lines

608a, 610a, and 612a throughout the uphill event 600.

In another embodiment shown in FIG. 6, either the balance mode state 568 (FIG. 5) or the power mode state 570 (FIG. 5) has been selected. In such embodiments, predictive cruise control processor 518 (fig. 5) enables energy recovery before an uphill event 600 occurs. As shown by line 610b, the state of charge of the battery pack is increased before the hill ascent event 600 occurs, thereby providing the vehicle 606 with potential power to successfully complete the hill ascent event 600 without multiple gear change events. Further, as shown by a comparison of line 608a with line 608b, the speed is made more consistent throughout the hill ascent event 600 using pre-charge control.

Example 1

Referring to FIG. 7, a comparison of predictive energy recovery and braking energy recovery is shown. Graph 300 shows the height of the vehicle during a downhill event (elevation), where the position of the vehicle is represented between position 4.4 and position 5.2 on the x-axis, and the height in meters is represented on the y-axis. Line 302 represents the vehicle height as a function of vehicle position. Graph 310 shows the speed of the vehicle during a downhill event, represented by the height in graph 300, where the position of the vehicle is represented between position 4.4 and position 5.2 on the x-axis, and the speed of the vehicle in kilometers per hour (km/h) on the y-axis. Line 312 represents the actual speed of the vehicle as a function of vehicle position. Line 314 represents the set or preferred speed of the vehicle as a function of vehicle position. Line 316 represents the speed of the vehicle using predictive energy recovery as further detailed herein. Graph 320 shows the intelligent taxi management mode of the vehicle during a downhill event, represented by the height in graph 300, where the position of the vehicle is represented between position 4.4 and position 5.2 on the x-axis and the four modes of the intelligent taxi management system are represented on the y-axis. The intelligent taxi management system is configured to be selectively in one of four modes: namely OFF ("OFF"), request ("REQ"), acknowledgement ("ACK"), and implementation ("ACT"). Line 322 represents the mode of the intelligent taxi management system according to the vehicle position.

Still referring to fig. 7, a graph 330 illustrates the motor-generator power of the vehicle during a downhill event, as indicated by the height in the graph 300, where the position of the vehicle is represented between position 4.4 and position 5.2 on the x-axis, and the motor-generator power in kilowatts ("kw") is represented on the y-axis. Line 332 represents the motor generator power as a function of vehicle position. Graph 340 illustrates the state of charge of the battery pack of the vehicle during a downhill event, represented by the height in graph 300, where the position of the vehicle is represented between position 4.4 and position 5.2 on the x-axis, and the percentage of battery charge is represented on the y-axis. Line 342 represents the state of charge of the battery pack according to the vehicle position. Graph 350 illustrates a braking event of the vehicle during a downhill event, represented by the height in graph 300, where the position of the vehicle is represented between position 4.4 and position 5.2 on the x-axis and the brake engagement event is represented on the y-axis. Line 352 represents the engagement of the wheel brakes as a function of vehicle position. Line 354 represents the engagement of the engine brake as a function of vehicle position.

Graphs

300, 310, 320, 330, 340, and 350 show altitude, vehicle speed, intelligent taxi management system mode, electric generator power, state of charge of the battery pack, and braking events, respectively, measured within the vehicle without the use of predictive energy recovery during a downhill event, illustrated by the altitude in graph 300. In such an example, energy recovery does not begin until a braking event occurs, as can be seen by comparing the state of charge indicator line 342 with the engine brake line 354 at vehicle positions between 4.6 and position 4.7 of

graphs

340 and 350. The same measurements are then made using predictive energy recovery, as discussed further herein.

When the vehicle utilizes predictive energy recovery, the braking event is estimated by the

methods

100 and 200 discussed above with respect to fig. 1. For example, the smart taxi management processor 116 (FIG. 1) and/or the predictive cruise control processor 118 (FIG. 1) estimate any required braking events during a downhill event and control vehicle operation accordingly, e.g., initiate energy recovery before the actual braking event occurs. The graph 335 is a copy of the graph 330 with the addition of a line 337, the line 337 representing the motor-generator power of the vehicle using predictive energy recovery during a downhill event represented by the height of the graph 300. The graph 345 is a copy of the graph 340 with the addition of a line 347, the line 347 representing the state of charge of the battery packs of the vehicle using predictive energy recovery during a downhill event represented by the height in the graph 300. The chart 355 is a copy of the chart 350 with the addition of a line 357 and a line 359, the line 357 representing engine braking events for the vehicle using predictive energy recovery during a downhill event represented by the height in the chart 300, and the line 359 representing wheel braking events or no braking events occurring in the vehicle using predictive energy recovery during a downhill event represented by the height in the chart 300.

As can be seen by comparing line 332 and line 337 in graph 335, the motor-generator of the vehicle using predictive energy recovery uses less power throughout almost the entire downhill event than the vehicle not using predictive energy recovery. Similarly, comparing line 342 and line 347 in graph 345, the battery pack in the vehicle using predictive energy recovery will begin recovering energy earlier than a vehicle not using predictive energy recovery. At the end of a downhill event, the state of charge of the battery pack in the vehicle using predictive energy recovery is much higher, allowing the vehicle to have a higher potential energy during use of the vehicle, as shown by line 347 in graph 345.

Still referring to fig. 7, and in particular to graph 355, a vehicle using predictive energy recovery does not require the use of wheel brakes to successfully complete a downhill event, as shown by comparing line 359 to line 352. For example, line 352 represents a wheel braking event that occurs when a vehicle that does not use predictive energy recovery is between position 4.6 and position 5; however, line 359 remains at y-axis 0 throughout the entire downhill event for a vehicle using predictive energy recovery. Further, as shown by comparing graph 345 to graph 355, a vehicle using predictive energy recovery need not wait for a braking event to occur before energy recovery begins. Instead, predictive energy recovery allows the vehicle to begin recovering energy (line 347) before vehicle position 4.6, although the engine braking event does not occur until approximately 4.65 (line 357). In contrast, in a vehicle that does not utilize predictive energy recovery, energy recovery does not begin until a braking event occurs, as seen by comparing line 342 and line 354. By comparing line 357 with line 354, the engine brake is further utilized in vehicles using predictive energy recovery to maintain the vehicle at the set speed 314 (as shown by line 316 of chart 310), rather than requiring the driver or user to engage the wheel brakes. By initiating energy recovery before any braking event occurs, the vehicle will decelerate further during a downhill event, such that relying on the engine brakes alone is sufficient to successfully complete the downhill event, and thus no wheel brakes are required.

Example 2

Referring now to fig. 8A to 10, further results of comparative use of the vehicle are shown. For each of fig. 8A-10, a vehicle completing a downhill event without predictive energy recovery is compared to a vehicle completing a downhill event with predictive energy recovery. For example, the graph 400 of FIG. 8A illustrates the relative amount of time the vehicle spends in the energy recovery mode 402, the braking mode 404, and the engine-driven mode 406. The vehicle of graph 400 does not utilize a predictive energy recovery system and spends approximately 79.13% of the time in energy recovery mode 402 during a downhill event, approximately 11.11% of the time in braking mode 404 during a downhill event, and approximately 9.76% of the time in engine-driven mode 406 during a downhill event.

In contrast, the graph 410 of FIG. 8B shows the relative amount of time the vehicle spends in the energy recovery mode 412, the braking mode 414, and the engine-driven mode 416. The vehicle of chart 410 utilizes a predictive energy recovery system and spends about 80.45% of the time in the energy recovery mode 412 during a downhill event, about 10.07% of the time in the braking mode during a downhill event, and about 9.48% of the time in the engine-driven mode 416 during a downhill event. The conclusion can be drawn by comparing graph 400 of fig. 8A with graph 410 of fig. 8B: during a downhill event, vehicles using predictive energy recovery take less time to brake and more time to charge the vehicle's battery pack.

The effect of this difference between the relative times spent in each mode can be seen in fig. 9. The chart 420 of fig. 9 comparatively illustrates the state of charge of the battery packs of the respective vehicles completing the downhill event. The y-axis represents the level of state of charge of the battery pack, while the x-axis represents the position of the vehicle during a downhill event. Line 422 represents the state of charge of the battery pack at any given location of the vehicle for a vehicle that does not utilize predictive energy recovery. Line 424 represents the state of charge of the battery pack of the vehicle at any given location of the vehicle utilizing predictive energy recovery. Comparing line 422 to line 424, it can be seen that a vehicle utilizing predictive energy recovery begins recovering energy within the vehicle's battery pack earlier than a vehicle not utilizing predictive energy recovery. As a result, vehicles utilizing predictive energy recovery remain in the energy recovery mode for longer periods of time, thereby allowing the battery pack to have a higher state of charge during a downhill event. This allows the vehicle to be powered by the battery pack for a longer period of time, thereby reducing fuel consumption.

FIG. 10 illustrates a comparison fuel consumption between a vehicle that utilizes predictive energy recovery and a vehicle that does not. For example, in a mild hybrid 48V system, the vehicle fuel consumption improvement rate is about 4.59% when predictive energy recovery is not used, and about 4.91% when predictive energy recovery is used. Thus, there is a 0.32% improvement in fuel consumption for the mild hybrid 48V system using predictive energy recovery compared to the mild hybrid 48V system not using predictive energy recovery. Stronger hybrid vehicles may have higher performance improvements.

While this invention has been described as having an exemplary design, the present invention may be further modified within the spirit and scope of this disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the invention using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains.

Claims

1. A method of estimating a braking event, the method comprising the steps of:

providing information relating to the route to a route data processor of the vehicle;

identifying an upcoming uphill event or an upcoming downhill event from information relating to the route;

estimating a speed of the vehicle at any given time during the upcoming uphill event or the upcoming downhill event as a function of at least one of: a current speed of the vehicle, a grade of the upcoming uphill event or the upcoming downhill event, a length of the upcoming uphill event or the upcoming downhill event, and a machine mass estimate of the vehicle;

comparing the estimated vehicle speed to a predetermined speed threshold; and

an occurrence of a braking event is estimated.

2. The method of claim 1, wherein the method is performed using a smart taxi management processor.

3. The method of claim 1, wherein the predetermined speed threshold is a imposed route speed limit.

4. The method of claim 1, wherein the predetermined speed threshold is a speed set on a cruise control of the vehicle.

5. The method of claim 1, wherein the braking event does not occur when the estimated vehicle speed is below the predetermined speed threshold.

6. The method of claim 5, further comprising the steps of: a command is issued to a transmission control unit of the vehicle to shift the vehicle to a neutral mode.

7. The method of claim 1, wherein the braking event is estimated to be performed by an engine brake of the vehicle when the estimated vehicle speed is above the predetermined speed threshold.

8. The method of claim 1, wherein the braking event is estimated to be performed by wheel brakes of the vehicle when the estimated vehicle speed is above the predetermined speed threshold.

9. The method of claim 1, further comprising the steps of: energy recovery of at least one battery pack of the vehicle is performed to increase a state of charge of the at least one battery pack prior to an actual occurrence of the braking event.

10. The method of claim 1, further comprising: engaging an engine brake of the vehicle prior to the estimated occurrence of the braking event.