WO2020232315A1 - Planification de trajet économe en énergie d'un véhicule électrique autonome - Google Patents

Planification de trajet économe en énergie d'un véhicule électrique autonome Download PDF

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Publication number
WO2020232315A1
WO2020232315A1 PCT/US2020/033005 US2020033005W WO2020232315A1 WO 2020232315 A1 WO2020232315 A1 WO 2020232315A1 US 2020033005 W US2020033005 W US 2020033005W WO 2020232315 A1 WO2020232315 A1 WO 2020232315A1
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WIPO (PCT)
Prior art keywords
route
energy
path
costs
cost
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Application number
PCT/US2020/033005
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English (en)
Inventor
Rui Guo
Yifan Tang
Yiqian Li
Fan Wang
Original Assignee
Sf Motors, Inc.
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Publication date
Application filed by Sf Motors, Inc. filed Critical Sf Motors, Inc.
Publication of WO2020232315A1 publication Critical patent/WO2020232315A1/fr

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0217—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with energy consumption, time reduction or distance reduction criteria
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/12—Controlling the power contribution of each of the prime movers to meet required power demand using control strategies taking into account route information
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/13—Controlling the power contribution of each of the prime movers to meet required power demand in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion
    • B60W20/14—Controlling the power contribution of each of the prime movers to meet required power demand in order to stay within battery power input or output limits; in order to prevent overcharging or battery depletion in conjunction with braking regeneration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18109—Braking
    • B60W30/18127—Regenerative braking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097—Predicting future conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0023—Planning or execution of driving tasks in response to energy consumption
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469—Fuel consumption; Energy use; Emission aspects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0026—Lookup tables or parameter maps
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/24—Energy storage means
    • B60W2510/242—Energy storage means for electrical energy
    • B60W2510/244—Charge state
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/20—Road profile, i.e. the change in elevation or curvature of a plurality of continuous road segments
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data

Definitions

  • AV Autonomous vehicles
  • the navigation between points involves following map data while on a road while detecting other vehicles and objects in the road to avoid collisions.
  • a typical AV will proceed along a selected route that keeps it within a particular lane.
  • the present technology provides an AV which can automatically determine a route that is optimized based on the route energy costs and an optimized path within the route based on energy efficiency associated with the path.
  • a driving range of an AV may be extended, and battery usage can be reduced.
  • the energy consumption optimization may be performed at the routing level when selecting a route and at a path level when determining how to proceed or navigate along a particular path.
  • the energy costs determination may be performed for an autonomous vehicle implemented as an electronic vehicle (EV) as well as an autonomous vehicle implemented as an electronic vehicle with a range extender system.
  • a route of a plurality of routes for an electric vehicle may be chosen based on an energy consumption cost, a charging cost, and an elevation cost.
  • a route may also be selected based on costs associated with a combustion engine within the range extender system, such as emission costs. Other costs and/or objectives may also be considered in selecting a route.
  • a path along a selected route may be chosen based on the efficiency or power loss of different parts of the EV, such as a battery, inverter, engine, gears and/or transmission, and wheels, Other path costs may also be considered when selecting a path within a particular route.
  • the present technology may proceed along a route with a lowest energy cost and along paths subsections that optimize energy efficiency and power loss, thereby extending the use of the battery during use and over it the battery lifetime.
  • the high-level route energy costs and low level path energy efficiency optimizations can be applied to an electric vehicle, electric vehicle with a range extender system (range extended electric vehicle, or E-REV), or a hybrid vehicle.
  • a system for automatically navigating a vehicle based on energy costs of a route and a path along the route incudes a data processing system comprising one or more processors, memory, a planning module, and a control module.
  • the planning module can include a routing module and a plurality of data sets.
  • the routing module selects a route from a plurality of routes for the vehicle based at least in part on the energy costs of each of the plurality of routes, the route having an origin and a destination.
  • the path module determines a path from a plurality of paths for a subsection of the route based at least on energy costs of each of the plurality of paths.
  • a plurality of data sets have energy cost and efficiency data, such that the routing module accessing a portion of the plurality of data sets to determine energy cost for at least one of the routes and the path module accessing a portion of the plurality of data sets to determine energy costs of for at least one of the paths.
  • the control module generating commands to navigate to the vehicle along the selected path within the selected route
  • a system for automatically navigating a vehicle based on energy costs of a route and a path along the route includes a data processing system comprising one or more processors, memory, a planning module, and a control module.
  • the routing module selects a route from a plurality of routes for the electric vehicle based at least in part on the energy costs of each of the plurality of routes, at least a portion of the energy costs for each route associated with the range extender system, the route having an origin and a destination, the range extender having a combustion engine,.
  • the path module determines a path from a plurality of paths for a subsection of the route based at least on energy costs of each of the plurality of paths.
  • the control module generating commands to navigate to the vehicle along the selected path within the selected route.
  • a non-transitory computer readable storage medium includes a program, the program being executable by a processor to perform a method for automatically navigating an electric vehicle based on energy costs of a route and a path along the route.
  • the energy cost for an electric vehicle is determined to navigate each of a plurality of routes between an origin and a destination.
  • a route is selected based at least in part on the energy cost for each route.
  • An energy cost for the electric vehicle is determined for each of a plurality of paths, each of the plurality of paths within the route.
  • a path is selected based at least in part on the energy loss for each path.
  • a command is generated for the electric vehicle to navigate along the selected path within the selected route.
  • the electric vehicle is navigated based on the selected path and route.
  • FIGURE 1 is a block diagram of an EV powertrain.
  • FIGURE 2 is a block diagram of an electric vehicle with range extension (E-REV)) powertrain.
  • E-REV range extension
  • FIGURE 3 is a block diagram of an autonomous vehicle.
  • FIGURE 4 is a block diagram of a data processing system within an AV.
  • FIGURE 5 is a block diagram of a planning module within a data processing system.
  • FIGURE 6 is a method for navigating an AV based on energy costs and efficiency.
  • FIGURE 7 is a method for determining a route for an AV based at least in part on energy costs.
  • FIGURE 8 is method for determining energy related costs for a plurality of routes for an EV.
  • FIGURE 9 is a method for determining an energy consumption cost for an EV.
  • FIGURE 10 is a method for determining a charging cost for an EV.
  • FIGURE 11 is a method for determining an elevation cost for an EV.
  • FIGURE 12 is a method for determining energy related costs for a plurality of routes for an E-REV .
  • FIGURE 13 is a method for determining a path for an EV based on energy efficiency.
  • FIGURE 14 is a method for determining the energy cost of a path.
  • FIGURE 15 is a block diagram of logic for optimizing the selection of a path for an EV.
  • FIGURE 16 is a block diagram of logic for optimizing the selection of a path for an E-REV.
  • FIGURE 17 is an illustration of three alternate routes between two points.
  • FIGURE 18 is a graph of average speed for the routes of FIGURE 17.
  • FIGURE 19 is a graph of the power loss associated with each route of FIGURE 17.
  • FIGURE 20 is an illustration of two routes with different elevation changes.
  • FIGURE 21 is a method for automatically setting a route and path based on user input or intention.
  • FIGURE 22 is a block diagram of a computing environment for implementing a data processing system.
  • the present technology provides an AV which can automatically determine a route that is optimized based on the route energy costs and an optimized path within the route based on energy efficiency associated with the path.
  • a driving range of an AV may be extended, and battery usage can be reduced.
  • the energy consumption optimization may be performed at the routing level when selecting a route and at a path level when determining how to proceed or navigate along a particular path.
  • the energy costs determination may be performed for an autonomous vehicle implemented as an electronic vehicle (EV) as well as an autonomous vehicle implemented as an electronic vehicle with a range extender system.
  • a route may be chosen based at least in part on energy costs associated with the route.
  • a particular route of a plurality of routes may be chosen based on an energy consumption cost, the charging cost, and elevation cost.
  • a route may be selected based on energy consumption costs, charging or fueling costs, elevation cost, and emission costs.
  • other costs and/or objectives may also be considered in selecting a route, including but not limited to the total distance, time of arrival, traffic along a route, and other costs.
  • a path along a selected route may be chosen at least in part on energy efficiency or energy lost along a particular path.
  • Energy loss for navigating a path, or in motion energy costs can be based on the efficiency of different parts of the EV or E-REV.
  • components of an EV such as a battery, inverter, engine, gears and/or transmission, and wheels, can have a power loss model that can be used to determine the energy efficiency of the current path.
  • Other path considerations may include a speed limit, acceleration, jerk, collision avoidance, centripetal acceleration, and other costs associated with selecting a path within a particular route.
  • the present technology may proceed along a route with a lowest energy cost and along path subsections that optimize energy efficiency and power loss, thereby extending the use of the battery during use and over it the battery lifetime.
  • the route level and path level energy costs may be specific to an EV and an E-REV.
  • routes and path may be selected in order to reduce the use of the range extension system which utilizes a combustion engine.
  • use of a combustion engine may be inevitable, for example in order to reduce the risk of collision, assist with going up a hill, or in other situations.
  • the technical problem addressed by the present technology involves navigating from one point to another by an autonomous EV or E-REV in in an efficient manner that conserves energy.
  • an autonomous EV selects a route between two points based on the time it takes to travel between the routes, distance, or some other static basis.
  • the AV navigation ignores the energy consumed between the different route options and energy can be wasted by the EV while in-route.
  • Energy efficiency is vital in EVs that have only limited stores of power and, more often than not, few if any recharging options along a route to be travelled.
  • the present technology provides a technical solution to the technical problem of navigating from one point to another by an autonomous EV or E-REV in in an efficient manner that conserves energy.
  • the solution selects a route from a plurality of routes between an origin and a destination, at least in part based on the energy costs associated with each route.
  • An energy cost score is assigned to each route, with the lowest scored route being selected as the route to travel between the origin and the destination.
  • path planning - determining how to navigate the path for certain time period in the future, such as 10 seconds - can be optimized based on energy efficiency (i.e., energy loss) such that the particular path or motion along the route is selected based on a low energy loss.
  • the solution provided by the present system reduces energy consumption at the route level and the path level, thereby reducing computing resources used by the system to navigate the autonomous EV to a destination.
  • FIGURE 1 is a block diagram of an electric vehicle powertrain.
  • Electric vehicle 100 of FIGURE 1 includes battery 110, power inverter 120, electric motor 130, gears and/or transmission 140, and wheels 152, 154, 156, and 158.
  • Battery 110 may include a rechargeable battery that ultimately provides power to propel EV 100 and power electronic systems of EV 100.
  • Power inverter 120 converts charge from battery 110 to drive elective motor 130.
  • Electric motor 130 can in turn drive o gears and/or transmission 140 of electric vehicle 100.
  • the gears/transmission 140 converts the power from motor 130 to drive wheels 152 and 154. In some instances, transmission and/or gears may be placed elsewhere in EV 100 to drive wheels 152-158.
  • FIGURE 2 is a block diagram of an E-REV powertrain.
  • E-REV 260 includes battery 210, power inverter 220, electric motor 230 and gears/transmission 240. Similar to the EV of FIGURE 1, a battery provides power to inverter 220, which then drives electric motor 230. Electric motor drives the gears/transmission 240, which then drives the wheels 252 and 254. In some instances, using transmission 240 may also drive wheels 256 and 258, in addition to or in place of wheels 252 and 254.
  • the E-REV 200 may also include range extender 260.
  • Range extender 260 includes combustion engine 270 and generator 280. Combustion engine 270 can be controlled to drive generator 280. Generator 280 may in turn provide power to power inverter 220, which may ultimately drive the wheels of the EV.
  • use of range extender 260 within the E- REV can be avoided by selecting routes and paths which do not require the extra power or range. In some instances, however, use of range extender 260 is required, such as for example when more powers and to avoid a collision, when the SOC of battery 210 is reduced to an unusable level, when passing power is needed is above the ability of battery 210, and so on. Use of the range extender for these reasons will factor into the route and path selection based on energy cost and efficiency of an E-REV.
  • FIGURE 3 is a block diagram of an autonomous vehicle.
  • the autonomous vehicle 300 of FIGURE 3 includes a data processing system 360 in communication with an inertia measurement unit (IMU) 305, cameras 310, radar 315, lidar 320, microphones 325, and output 330.
  • Data processing system 360 may also communicate with acceleration 335, steering 340, braking system 345, battery system 350, and propulsion system 355.
  • the data processing system and the components it communicates with are intended to be exemplary for purposes of discussion. It is not intended to be limiting, and additional elements of an autonomous vehicle may be implemented in a system of the present technology, as will be understood by those of ordinary skill in the art.
  • IMU 305 may track and measure the autonomous vehicle acceleration, yaw rate, and other measurements and provide that data to data processing system 325.
  • Cameras 310, radar 315, lidar 320, microphones, and optionally other sensors such as ultrasound and infrared sensors may form part of a perception component of AV 310.
  • the autonomous vehicle may include one or more cameras 310 to capture visual data inside and outside of the autonomous vehicle. On the outside of the autonomous vehicle, multiple cameras may be implemented. For example, cameras on the outside of the vehicle may capture a forward facing view, a rear facing view, and optionally other views.
  • the cameras may include HD cameras to collect detailed image data of the environment. Images from the cameras may be processed to detect objects such as streetlights, stop signs, lines or borders of one or more lanes of a road, and other aspects of the environment for which an image may be used to better ascertain the nature of an object than radar. To detect the objects, pixels of images are processed to recognize objects, and singular images and series of images. The processing may be performed by image and video detection algorithms, machine learning models which are trained to detect particular objects of interest, and other techniques.
  • Radar 315 may include multiple radar sensing systems and devices to detect objects around the autonomous vehicle.
  • a radar system may be implemented at one or more of each of the four corners of the vehicle, a front of the vehicle, a rear of the vehicle, and on the left side and right side of the vehicle.
  • the radar elements may be used to detect stationary and moving objects in adjacent lanes as well as in the current lane in front of and behind the autonomous vehicle.
  • Lidar may also be used to detect objects in adjacent lanes, as well as in front of and behind the current vehicle.
  • Other sensors, such as ultrasound, can also be used as part of the AV 300.
  • Microphones 325 may include one or more microphones receive audio content from the external environment and the internal environment. Regarding the external, microphones may pick up sirens, questions, and other cues related to elements of the external environment. The internal environment microphones may be positioned to recognize the user and a driver seat of a vehicle, or in some other position of the vehicle.
  • Output 330 may include one or more output mechanisms to communicate to people and objects outside and inside the AV.
  • the output mechanisms may include, for example, LEDs, graphic indicators, turn signals, and other components that visually communicate externally and internally to the AV.
  • Data processing system 360 may include one or more processors, memory, and instructions stored in memory and executable by the one or more processors to perform the functionality described herein.
  • the data processing system may include a planning module, a control module, and a drive-by wire module.
  • the modules communicate with each other to receive data from a perception component, plan actions, and generate commands to execute lane changes.
  • the data processing system 360 is discussed in more detail below with respect to the system of FIGURE 4.
  • Acceleration 335 may receive commands from the data processing system to accelerate the AV. Acceleration 335 may be implemented as one or more mechanisms to apply acceleration to the propulsion system 355.
  • Steering module 340 controls the steering of the vehicle, and may receive commands to steer the AV from data processing system 360.
  • Brake system 345 may handle braking applied to the wheels of autonomous vehicle 300, and may receive commands from data processing system 360.
  • Battery system 350 may include a battery, charging control, a battery management system, and other modules and components related to a battery system on an AV.
  • Propulsion system 355 may manage and control propulsion of the vehicle, and may include components of a combustion engine, electric motor, drivetrain, and other components of a propulsion system utilizing an electric motor with or without a combustion engine.
  • FIGURE 4 is a block diagram of a data processing system within an AV.
  • Data processing system 360 of FIGURE 4 provides more detail for data processing system of the FIGURE 3.
  • Data processing system 360 may receive data and information from perception component 410.
  • Perception component 410 may include radar, lidar, ultrasound, and camera elements, as well as logic for processing the radar and camera output to identify objects of interest, lane lines, and other elements.
  • Perception 410 may provide a list of objects, object status, and lane detection data to planning module 420.
  • a perception component that captures data, processes data, and provides processed data to a data processing system is described in more detail in U.S. patent application 16/237,576, filed on December 31, 2019, titled "Automatic Lane Change with Minimum Gap Distance," the disclosure of which is incorporated herein by reference.
  • Planning module 420 may receive and process data and information received from the perception component to plan actions for the autonomous vehicle.
  • the actions may include determining a route from a plurality of routes based at least in part on energy costs, determining a path from a plurality of paths based at least in part on energy efficiency, navigating from a current lane to adjacent lane, stopping, accelerating, turning, and performing other actions.
  • Planning module 420 is discussed in more detail with respect to the system of FIGURE 5.
  • Control module may receive information from the planning module, such as a selected route or path.
  • Control module 214 may generate commands to be executed in order to navigate the AV along the selected route or path.
  • the commands may include instructions for accelerating, breaking, and steering to effectuate navigation.
  • Drive-by wire module 216 may receive the commands from control 214 and actuate the AV navigation components based on the commands.
  • drive-by wire 216 may control the accelerator, steering wheel, brakes, turn signals, and other aspects of the AV.
  • FIGURE 5 is a block diagram of a planning module within a data processing system.
  • Planning module 420 of FIGURE 5 provides more detail for the planning module of FIGURE 4.
  • Planning module 420 includes routing module 510, energy cost model 520, path module 530, energy efficiency module 540, lookup tables 560, and constraints module 560.
  • Each of these modules may be implemented as one or more software programs, objects, or other programming elements, which may be complete or distributed over one or more physical or logical machines.
  • Routing module 510 may determine a route for the AV to take based on energy costs. The routing module may consider energy costs and other costs to arrive at a score for each route, and then select the best route based on the route scores. The energy costs for each route may be received by energy cost module 520, while the other costs can be received from constraints module 560. [0055] Energy cost module can determine the total energy-related cost for each of a plurality of routes, and provide those costs to routing module 510. In some instances, for an EV, the energy related costs can include an energy consumption cost, a charging cost, and an elevation cost. For an E-REV, that energy-related cost may also include emission costs and other energy costs related to a combustion engine.
  • Path module 510 may determine one or more paths for the AV to take a long a selected portion of the selected route.
  • the path may be a specific length or period of time, such as for example the next 10 seconds.
  • the path may include a speed, trajectory, lane changes, passing of other cars and objects, and other in-motion maneuvers along the selected route.
  • Path module 530 may optimize the current route based on objectives, such as for example a speed limit, acceleration, jerk, collisions, and centripetal acceleration, as well as with energy efficiency characteristics for the different paths.
  • Energy efficiency module 540 may determine energy efficiency characteristics for a particular EV and provide that data to path module 530.
  • the energy efficiency characteristics may be associated with one or more components of an EV powertrain system. Examples of energy efficiency characteristics may include a power loss associated with the battery model, inverter model, engine model, and gear and/or transmission model. For an E-REV, the energy efficiency characteristics can also be determined for a combustion engine for the planned path of the vehicle.
  • Lookup tables 560 may be used when determining an energy efficiency for the components of an EV or E-REV powertrain and/or drivetrain.
  • a lookup table may be associated with the power loss associated with each component of a powertrain, including a battery model, inverter model, engine model, and transmission model.
  • Each component lookup table may provide a corresponding power loss associated with a particular speed of the electric vehicle.
  • Information from the lookup tables 560 is accessed by energy efficiency module 540, and then provided to path module 530.
  • Constraints module 560 may determine constraint values and scores for one or more constraints for determining a path.
  • the path constraints may include but are not limited to speed limit, acceleration, jerk, distance to leading obstacles, collision avoidance, and centripetal acceleration.
  • the constraints may be determined and provided to path module 530, allowing the path module to select the highest-rated path for the AV to navigate.
  • FIGURE 6 is a method for navigating an AV based on energy costs and efficiency.
  • the autonomous vehicle is initialized at step 610.
  • Initializing the autonomous vehicle may include starting the autonomous vehicle, performing an initial system check, calibrating the vehicle to the current ambient temperature and weather, and calibrating any systems as needed at startup.
  • Destination information is received at step 620.
  • the destination information may be received from a user or other source, within the AV or outside of the AV.
  • the destination information may be received in the form of a verbal command from a user in the car such as "take me to work," or from a verbal command received from a remote user through a mobile application on a mobile device, instructing the car to "bring my son home from school.”
  • the destination information may be determined from a user intention, for example an expression from a user nodding yes to a question, pointing in a direction, or saying, "I just need to go to the store before we watch TV at home.”
  • the destination may be identified using a coordinate system or geographical mapping system, such as for example a GPS coordinate system.
  • a vehicle state of charge may be accessed at step 630.
  • the vehicle state of charge may include the current state of charge (SOC) as retrieved by a battery management system (BMS) within battery system 350.
  • SOC current state of charge
  • BMS battery management system
  • a route based at least in part on energy costs is determined at step 640.
  • the route may extend from the AV's current location to the received destination.
  • the route may be determined based at least in part on the energy costs associated with each of a plurality of routes.
  • the plurality of routes may be evaluated to identify the best scored route.
  • the best scored route may be one that has the lowest energy costs.
  • other constraints and costs will be used to consider the best route as well, including but not limited to the distance, total travel time, and traffic associated with a route.
  • Determining a route may be performed differently based on the type of AV. Determining a route for an EV vehicle is discussed in more detail below with respect to the method of FIGURES 7-11. Determining a route for an E-REV is discussed in more detail with respect to the method of FIGURE 12.
  • a path is determined based on energy efficiency of the route at step 650.
  • a path may be determined based on a speed profile for a set distance to be travelled by the AV or a set period of time in the future. In some instances, the path may be determined for the next 500 or 1000 feet, or some other distance. In some instances, the path may be set for the next 5, 7, 10, 12, or 15 seconds, or some other period of time. In some instances, determining a path may occur repeatedly during subsequent computing cycles, for example once every be 10 th of a second. In this instance, an optimized path based at least in part on energy efficiency is determined for the AV to proceed on, and that path is updated every be computing cycle (e.g., every the 10 th of a second).
  • Determining a path for an AV within a route based on energy efficiency may be performed differently for an EV and E-REV.
  • the path may be based on a speed profile, battery SOC, and drivetrain/powertrain characteristics for components of the EV powertrain, such as a battery, power inverter, electric motor, and gears and/or transmission.
  • the route may be determined based on speed profile, battery SOC, and drivetrain/powertrain characteristics for the vehicle battery, power inverter, electric motor, gears and/or transmission, and combustion engine. More detail for determining a path based on energy efficiency is discussed with respect to FIGURE 13.
  • the control module generates commands to navigate the AV at 660.
  • the commands can navigate the AV along a path within the route selected by the AV.
  • the commands may include how and when to accelerate the vehicle, apply braking by the vehicle, and the angle of steering to apply to the vehicle and at what times.
  • the commands are provided by the control module to the drive-by wire module.
  • the generated commands are executed by the drive-by wire module at step 670.
  • the drive-by wire module may control the autonomous vehicle brakes, acceleration, starting or stopping a combustion engine of an EV with range extension, and steering wheel, based on the commands received from the control module. By executing the commands, the drive-by wire module makes the autonomous vehicle proceed along the selected path as part of the navigation along the selected route.
  • FIGURE 7 is a method for determining a route for an AV based at least in part on energy costs.
  • the method of FIGURE 7 provides more detail for step 640.
  • a plurality of routes are generated at step 710.
  • the plurality of routes may extend from the origin, such as the current location of the AV or some other origin, and end at a destination.
  • the routes may be generated over several types of travel ways that each start and end at the two points, including highways, roads, and other travel ways accessible by the AV.
  • the energy related costs for each route of the plurality of routes is determined at step 720.
  • the energy related costs for each route is based on different types of energies spent and consumed during each route. For example, that related energy cost can be generated from an energy consumption cost, charging costs, elevation cost, and other costs.
  • the energy-related cost can depend on the type of vehicle that is traveling over the route.
  • a method for determining the energy-related costs for a route traveled by an EV is discussed in more detail with respect to the method of FIGURE 8.
  • the energy-related costs for a route being traveled by an E-REV is discussed in more detail with respect to the method of FIGURE 12.
  • a score may be determined for each characteristic, and the score is summed for the total cost, along with the energy-related cost, for each route.
  • each cost may be weighted with one or more parameters when calculating the final overall cost for the particular route. The weightings may give a higher emphasis on certain costs, such as for example the energy-related cost for the route.
  • a route of the plurality of routes is selected at step 740. Once the costs have been weighted and summed for each route, the route with the best score is selected. In some instances, the best score is the lowest energy cost score of all the route scores. In some instances, the best score is the lowest overall score of all the route scores. Once the route is selected, the selected route is provided by the planning module to a control module in order to generate commands to start navigating the AV along that particular route. [0073] FIGURE 8 is method for determining energy related costs for a plurality of routes. The method of FIGURE 8 provides more detail for step 720 of the method of FIGURE 7. The energy- related costs determined the method of FIGURE 8 are determined for an EV.
  • an energy consumption cost for an EV is determined at step 810.
  • Energy consumption may be calculated based on a speed profile, drivetrain and powertrain characteristics, from which power consumption and route energy loss may be determined. More details for determining an energy consumption cost for an EV are discussed with respect to the method of FIGURE 9.
  • a charging cost for an EV is determined at step 820.
  • a charging cost for an EV may be determined by the predicted energy loss of the route, the battery SOC at the start of the trip, and the availability of any charging stations, and the charging capacity of the battery. More detail for determining a charging cost for an EV is discussed with respect to the method of FIGURE 10.
  • An elevation cost for an EV is determined at step 830. Determining an elevation cost may include determining if a route includes a downhill portion, and whether any potential regeneration may successfully charge a battery during the downhill. More details for determining an elevation cost for an EV for a particular route is discussed with respect to the method of FIGURE 11.
  • FIGURE 9 is a method for determining an energy consumption cost for an EV.
  • the method of FIGURE 9 provides more detail for step 810 of the method of FIGURE 8.
  • an estimated speed profile is determined for a particular route at step 910.
  • the estimated speed profile may be determined based on the speed limits of the travel ways that comprise a route. For example, if a room route is entirely on a freeway, the estimated speed profile will be determined as this speed limit associated with the freeway.
  • Drivetrain and powertrain characteristic lookup tables are accessed at step 920.
  • the drivetrain and powertrain characteristic lookup tables provide power consumption data for the particular vehicle based on an estimated speed of the vehicle.
  • a power consumption is determined for the route at step 930.
  • the power consumption is determined for the route by identifying portions of the route associated with a particular speed estimation, determining the amount of time the vehicle will be traveling at that particular speed during the route, and retrieving power consumption data from the lookup tables for the estimated time and speed of the vehicle. For example, if a route includes a freeway portion on which the EV will be traveling for 20 minutes at 60 miles an hour, a query is sent to the lookup table to retrieve the corresponding power consumption for the particular speed.
  • That power consumption value for the particular speed is then multiplied for the time at which the EV is predicted to travel at that particular speed.
  • the results of the power consumption rate and time at that particular speed is a power consumption for that portion of the route.
  • the power consumption for each portion of the route is then added together to determine the total power consumption for the route at step 930.
  • Energy loss for a route is determined from the route power consumption at step 940.
  • the route energy loss can be calculated by integrating the power loss over time, according to the formula:
  • FIGURE 10 is a method for determining a charging cost for an EV.
  • the method of FIGURE 10 provides more detail for step 820 of the method of FIGURE 8.
  • the determined energy loss is accessed at step 1010.
  • the determined energy loss can be determined according to the method of FIGURE 9.
  • a state of charge (SOC) indicating the current charge of an EV battery is accessed at step 1020.
  • the current SOC can be retrieved by a battery management system on the autonomous vehicle.
  • a charge would be needed for the route if the total energy loss is greater than the current SOC for the AV, as the battery would not be able to provide enough energy to complete the route. If a charge is needed for the route, a low-cost would be assigned to a route with a charging station at which the vehicle could recharge before the battery runs out of charge, and a high cost can be applied to a route without a charging station available before the battery runs out of charge. If a charge is not needed during the route, and the current battery SOC is high enough to cover the determined energy loss associated with the route, then the charging stations would not affect the charging cost.
  • FIGURE 11 is a method for determining an elevation cost for an EV.
  • the method of FIGURE 11 provides more detail for step 830 of the method of FIGURE 8.
  • a determination is made as to whether a route includes a downhill portion at step 1110.
  • a downhill portion enables an EV to regenerate charge in its battery system. If a route does not include a downhill portion, an elevation cost is set high for the particular route at step 1140. If a route does include downhill portions, the vehicle SOC is accessed at step 1120.
  • a battery regeneration is successful when the entire potential regeneration from the downhill can be used to charge a battery. Hence, if a battery SOC is half-full, and therefore only a portion of the downhill regeneration would be utilized by the battery, and that regeneration would not be completely successful, as part of the downhill generation of the battery would not actually be stored into the battery.
  • the elevation cost is determined to be low.
  • the regeneration is not entirely successful, the elevation cost is set to be high at step 1140. In some instances, an elevation cost is set to be higher if there is no downhill compared to if there is some downhill although not all of the downhill regeneration can be stored into the battery.
  • FIGURE 20 is an illustration of two routes with different elevation changes.
  • Route 1 includes an uphill portion followed by a downhill portion between origin A and destination B.
  • Route 2 includes a downhill portion followed by an uphill portion between origin A and destination B.
  • an EV traveling along route 1 with can regenerate its battery by traveling downhill during the regenerating portion of the route.
  • an EV traveling along route 2 would regenerate energy, increasing any available capacity of a battery during the first part of route 2, followed by discharging the battery during the elevation gain and the second part of route 2.
  • FIGURE 12 is a method for determining energy related costs for a plurality of routes for an E-REV. The method FIGURE 12 provides more detail for step 720 of the method of FIGURE 7. Energy consumption cost for an E-REV is determined at step 1210. Energy consumption costs for an EV with range extension is determined at step 1210.
  • Determining energy consumption for an EV with range extension is similar to determining energy consumption cost for an EV except that the power consumption may have an additional cost associated with a combustion engine.
  • a separate table for an EV with range extension may be used to determine the energy consumption cost of the combustion engine for the rejected use of a combustion engine.
  • the combustion engine use may be used to supplement the battery based on the state of charge in total energy required for the route, provide additional power to the vehicle during an uphill or other portion of the route, and other uses.
  • Charging and fueling costs for the E-REV are determined at step 1220.
  • the E-REV charging and feeling costs will be similar to that of the EV, but will include an additional cost of fuel corresponding to a use of the combustion engine.
  • a route that has a gas station may have a lower cost in a row without a gas station, especially if gasoline or other fuel is needed to complete the route.
  • An elevation cost for the E-REV may be determined at step 1230.
  • the elevation cost for an E-REV may be determined in the same way as an EV, as discussed with respect to step 830 of the method of FIGURE 8.
  • An emission cost for an E-REV may be determined at step 1240. Determining the emissions cost may include determining the quantity of engine exhaust emission values associated with use of a combustion engine while navigating the route.
  • the emission values may include values for carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM).
  • values may be expressed as parts per million, normalized to a scale from 0 to 100, added together, and the normalized to determine the omission cost.
  • FIGURE 13 is a method for determining a path for an electric AV based on energy efficiency.
  • the method of FIGURE 13 provides more detail for step 650 of the method of FIGURE 6.
  • a plurality of paths are generated at step 1310.
  • the paths may include a sampling of paths along the current route that extend for a set period of length of time. In some instances, each path may extend for 10 seconds.
  • the different paths may include paths at different speeds, different accelerations, within the same lane or different lanes, and other options while navigating the current path.
  • a current vehicle status and speed profile is accessed at step 1320.
  • the current vehicle status may include an SOC
  • the speed profile may include the predicted speed along each of the plurality of paths.
  • Energy costs for each path are determined at step 1330.
  • the energy cost may be based on an energy efficiency associated with the vehicle as the vehicle proceeds along each path based on the speed profile for that path.
  • an efficiency model for different parts of a vehicle powertrain may be used to generate the energy costs for the particular path. More details for determining an energy cost for each of a plurality of paths is discussed with respect to the method of FIGURE 14.
  • Other costs may be determined for each path at step 1340.
  • the other costs may include aspects of navigating the path that may affect the viability and comfort of navigation along the particular path.
  • other costs for a particular path may include acceleration, speed limit, jerk, a distance to obstacles and collision avoidance risk, centripetal acceleration, and other costs.
  • FIGURE 14 is a method for determining the energy cost of a path for an EV.
  • the method of FIGURE 14 provides more detail for step 1330 of the method of FIGURE 13.
  • a power train efficiency model is used to retrieve power loss for a component of an EV powertrain.
  • the power efficiency between the battery and the power inverter may have a first value corresponding to traveling 50 miles an hour for one second and a second value corresponding to traveling 40 miles an hour for one second.
  • the power loss due to an inverter is determined at step 1420.
  • the power loss between the power inverter and the electric motor for the an EV is determined by looking up the corresponding power efficiency based on the speed profile of the electric vehicle.
  • the power loss due to the electric motor is determined at step 1430.
  • the power loss between the electric motor and transmission system is determined by retrieving the corresponding power efficiency value for the electric vehicle speed profile for the particular path.
  • FIGURE 17 is an illustration of three alternate routes between two points.
  • point A is the origin and point B is the destination.
  • Route 1 is a freeway route which includes a charging station.
  • Route 2 avoids highways and includes sharper turns than either route 1 or route 3.
  • Route 3 is the shortest route but does not include a charging station.
  • Route 1 would include a low score for charging costs as it includes a charging station.
  • Routes 1 and 3 may include higher energy consumption costs as they are both on highways and involve higher speeds. Route 2 involves travel ways other than highways, and therefore may include a lower energy consumption cost.
  • FIGURE 18 is a graph of average speed for the routes of FIGURE 17. As shown in FIGURE 18, the average speed is highest in route 3, which is along a highway with minimal curves. The next highest average speed is associated with route 1, which is also a highway. Route 2, a route that does not include any highways, has a low speed.
  • FIGURE 19 is a graph of the power loss associated with each route of FIGURE 17.
  • Power loss can be determined by a looking up a power loss characteristic associated with a speed for a route.
  • the speed may be an average speed for a route, or broken up into portions of a route which are easy associated with their own average speed.
  • route 3 associated with a high-speed as shown in illustration of FIGURE 18, is the highest power loss indicated by the area of the larger circle.
  • Route 1 associated with the next high-speed, has the next highest power loss.
  • Route 2 associated with the slower speed, has the smallest power loss area in the illustration of FIGURE 19.
  • a power loss between a combustion engine and a generator are determined based on a lookup table and the speed profile, as well as between a generator and a power inverter using another corresponding lookup table.
  • the two additional power loss values associated with range extender components are then added to the EV power loss values to determine the total energy costs for the path for the E-REV.
  • FIGURE 15 is a block diagram of logic for optimizing the selection of a path for an electric vehicle.
  • the current legal status 1510 and speed profile 1520 are used to determine the energy cost of a path, other costs of a path, such as speed, comfort, collision and acceleration, as well as other constraints such as Parliament and SOC.
  • the energy cost of the speed profile is used to determine the power train power loss characteristics based on retrieving values for a lookup table that correspond to the speed profile for a particular path.
  • the constraints 1530, energy costs 1515, and other costs 1540 are then added to determine the total cost for each path.
  • the path with the lowest score is then selected at optimization 1580, resulting in an optimized speed profile for the selected path having the lowest score.
  • FIGURE 16 is a block diagram of logic for optimizing the selection of a path for an E-REV.
  • the logic of the diagram of FIGURE 16 is similar to the logic of the block diagram in FIGURE 15 except for the logic of FIGURE 16 includes additional costs associated with range extension components for an EV with a range extender.
  • the current legal status and speed profile are used to determine the range extension energy costs.
  • the range extension energy costs may include costs associated to a combustion engine as well as the efficiency between the range extension components. These costs are added with the power limit and SOC constraints 1630, energy costs determined from the speed profile and powertrain and power loss characteristics, and other costs 1640 at her 1670.
  • the path of the lowest score is then selected at step 1682 optimize the selected path and provide a corresponding optimized speed profile.
  • an AV may receive input from a user, directly or as a detected intention, to travel a particular route and path.
  • the route and path navigation input from the user may be determined at least in part based on energy costs as described and disclosed herein.
  • FIGURE 21 is a method for automatically setting a route and path based on user input or intention.
  • a user input system may be initialized at step 2110. Initialization of a user input system may include detecting a location of a user, detecting an identity of a user, loading any preferences associated with the identified user, and other operations.
  • a route preference may be detected from a user at step 2120.
  • the route preference may be received as an instruction from user to proceed to a particular destination, a scheduled navigation from a user, for example to go to an appointment, or receiving a route preference in some other manner. Detecting a route preference may also include receiving or determining a user preference for a route, such as selecting a fastest route, avoiding particular highway or throughway, selecting a route a charging station, or selecting an energy efficient route. Based on the input, a route from a plurality of routes is determined at step 2130. Selection of the route may consider different objectives and costs, with a white waiting assigned to each objective and cost, wherein user preferred objectives will have a higher weighting than others.
  • the determined routes are executed by navigating the AV along the determined route at step 2140.
  • Paths can be continually selected along the route in discrete portions based at least in part on user input.
  • a path preference may be detected for user at step 2150.
  • a user path preference may include a user's preference in traveling along the route.
  • the user preference along a travel path may include a sports mode, comfort mode, standard mode, energy efficient mode, or some other mode.
  • a sports mode may take an aggressive approach to turns and maximum speed for the current path.
  • a comfort mode may ensure that turns, lane changes, and other navigation aspects are made smoothly without much jerk or acceleration.
  • An energy efficient mode may optimize travel along the path to reduce consumption of energy and overall energy costs.
  • a path may be continually determined, for example every l/10 th of a second or other short time span, be of a certain length or certain duration, such as every ten seconds.
  • the determine path may be executed at step 2170.
  • FIGURE 22 is a block diagram of a computing environment for implementing a data processing system.
  • System 2200 of FIGURE 22 may be implemented in the contexts a machine that implements data processing system 360 on an autonomous vehicle.
  • the computing system 2200 of FIGURE 22 includes one or more processors 2210 and memory 2220.
  • Main memory 2220 stores, in part, instructions and data for execution by processor 2210.
  • Main memory 2220 can store the executable code when in operation.
  • the system 2200 of FIGURE 22 further includes a mass storage device 2230, portable storage medium drive(s) 2240, output devices 2250, user input devices 2260, a graphics display 2270, and peripheral devices 2280.
  • the components shown in FIGURE 22 are depicted as being connected via a single bus 2290.
  • processor unit 2210 and main memory 2220 may be connected via a local microprocessor bus, and the mass storage device 2230, peripheral device(s) 2280, portable storage device 2240, and display system 2270 may be connected via one or more input/output (I/O) buses.
  • I/O input/output
  • Mass storage device 2230 which may be implemented with a magnetic disk drive, an optical disk drive, a flash drive, or other device, is a non-volatile storage device for storing data and instructions for use by processor unit 2210. Mass storage device 2230 can store the system software for implementing embodiments of the present technology for purposes of loading that software into main memory 2220.
  • Portable storage device 2240 operates in conjunction with a portable non-volatile storage medium, such as a flash drive, USB drive, memory card or stick, or other portable or removable memory, to input and output data and code to and from the computer system 2200 of FIGURE 22.
  • a portable non-volatile storage medium such as a flash drive, USB drive, memory card or stick, or other portable or removable memory
  • the system software for implementing embodiments of the present technology may be stored on such a portable medium and input to the computer system 2200 via the portable storage device 2240.
  • Input devices 2260 provide a portion of a user interface.
  • Input devices 2260 may include an alpha-numeric keypad, such as a keyboard, for inputting alpha-numeric and other information, a pointing device such as a mouse, a trackball, stylus, cursor direction keys, microphone, touch-screen, accelerometer, wireless device connected via radio frequency, motion sensing device, and other input devices.
  • a pointing device such as a mouse, a trackball, stylus, cursor direction keys
  • microphone touch-screen
  • accelerometer wireless device connected via radio frequency, motion sensing device, and other input devices.
  • output devices 2250 Examples of suitable output devices include speakers, printers, network interfaces, speakers, and monitors.
  • Display system 2270 may include a liquid crystal display (LCD) or other suitable display device. Display system 2270 receives textual and graphical information and processes the information for output to the display device. Display system 2270 may also receive input as a touch-screen.
  • Peripherals 2280 may include any type of computer support device to add additional functionality to the computer system. For example, peripheral device(s) 2280 may include a modem or a router, printer, and other device.
  • the system of 2200 may also include, in some implementations, antennas, radio transmitters and radio receivers 2290.
  • the antennas and radios may be implemented in devices such as smart phones, tablets, and other devices that may communicate wirelessly.
  • the one or more antennas may operate at one or more radio frequencies suitable to send and receive data over cellular networks, Wi-Fi networks, commercial device networks such as a Bluetooth device, and other radio frequency networks.
  • the devices may include one or more radio transmitters and receivers for processing signals sent and received using the antennas.
  • the components contained in the computer system 2200 of FIGURE 22 are those typically found in computer systems that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art.
  • the computer system 2200 of FIGURE 22 can be a personal computer, hand held computing device, smart phone, mobile computing device, workstation, server, minicomputer, mainframe computer, or any other computing device.
  • the computer can also include different bus configurations, networked platforms, multi-processor platforms, etc.
  • Various operating systems can be used including Unix, Finux, Windows, Macintosh OS, Android, as well as languages including Java, .NET, C, C++, Node.JS, and other suitable languages.

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Abstract

L'invention concerne un AV qui peut déterminer automatiquement un itinéraire qui est optimisé sur la base des coûts d'énergie d'itinéraire et un trajet optimisé à l'intérieur de l'itinéraire sur la base de l'efficacité énergétique associée au trajet. Par optimisation de la sélection d'itinéraire et de trajet sur la base de coûts d'énergie et de manière efficace, une plage de conduite d'un AV peut être étendue et l'utilisation de la batterie peut être réduite. L'optimisation de la consommation d'énergie peut être effectuée au niveau du routage lors de la sélection d'un itinéraire et à un niveau du trajet lors de la détermination de la manière de procéder ou de naviguer le long d'un trajet particulier. La détermination des coûts énergétiques peut être effectuée pour un véhicule autonome mis en œuvre sous la forme d'un véhicule électronique (EV) ainsi qu'un véhicule autonome mis en œuvre sous la forme d'un véhicule électronique avec un système d'extension de portée.
PCT/US2020/033005 2019-05-15 2020-05-15 Planification de trajet économe en énergie d'un véhicule électrique autonome WO2020232315A1 (fr)

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CN112525210A (zh) * 2020-11-24 2021-03-19 同济大学 一种面向节能的电动汽车全局路径和速度联合优化方法
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