Route Planner Optimization for Hybrid-Electric Vehicles

Patent metadata

Field	Value
Publication number	US-2022205796-A1
Application number	US-202017130392-A
Country	US
Kind code	A1
Filing date	Dec 22, 2020
Priority date	Dec 22, 2020
Publication date	Jun 30, 2022
Grant date	—

Abstract

Route planning for a hybrid electric vehicle (HEV) includes obtaining a route between an origin and a destination, where the route is optimized for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV, and where the route comprises respective engine activation actions for at least some segments of the route; and controlling the HEV to follow the segments of the route and to activate the engine according to the respective engine activation actions.

First claim

What is claimed is:

1 . A method of route planning for a hybrid electric vehicle (HEV), comprising:
obtaining a route between an origin and a destination,
wherein the route is optimized for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV, and
wherein the route comprises respective engine activation actions for at least some segments of the route; and
controlling the HEV to follow the segments of the route and to activate the engine according to the respective engine activation actions.

2 . The method of claim 1 , wherein the route is obtained using engine activation rules comprising maintaining homeostasis within a range.

3 . The method of claim 1 , wherein the route is obtained using a multi-objective stochastic shortest path (MOSSP) comprising selecting a next road segment and an engine activation action for the next road segment given a current state.

4 . The method of claim 3 , wherein the MOSSP comprises a state space that includes a navigation map including a current road segment of the HEV, a current charge level of the battery, and whether the engine is currently on or off.

5 . The method of claim 1 , wherein the destination is determined stochastically.

6 . The method of claim 1 , further comprising:
receiving, from a driver of the HEV, an optimization criterion for the route, wherein the optimization criterion relates to one the noise level or the energy consumption.

7 . The method of claim 1 , wherein the route is optimized using a scalarization function of time, noise level, and battery level.

8 . The method of claim 1 , wherein the route is optimized by minimizing an objective related to time constrained by at least one of noise level or battery level.

9 . The method of claim 1 , wherein the route is optimized by minimizing an objective related to battery level constrained by at least one of time or noise level.

10 . The method of claim 1 , wherein the route is optimized by minimizing an objective related to noise level constrained by at least one of time or battery level.

11 . The method of claim 1 , wherein obtaining the route between the origin and the destination comprises:
in anticipation of upcoming road segments resulting in regenerative energy that charges the battery, not turning on the engine.

12 . An apparatus for driver assistance in a hybrid electric vehicle (HEV), comprising:
a battery; an engine; and a processor configured to:
when a driver assistance feature of the HEV is enabled and a destination of the HEV is not known, determine, using a policy of a Markov decision process a next road segment for the HEV and an engine activation action; and
in response to the next road segment being followed by the HEV, activate the engine according to the engine activation action, wherein the engine activation action is selected from a set comprising a first activation action of turning the engine on and a second activation action of turning the engine off, and the first activation action causes the engine to turn on to charge the battery of the HEV.

13 . The apparatus of claim 12 , wherein the Markov decision process comprises:
a state space that includes a navigation map including a current road segment of the HEV, a current charge level of the battery, and whether the engine is currently on or off; and an action space wherein an action of the action space comprises the next road segment and the engine activation action.

14 . A non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations for route planning in a hybrid electric vehicle (HEV), comprising:
obtaining a route comprising respective engine activation actions for at least some segments of the route, wherein the route is optimized for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV; and controlling the HEV to follow the segments of the route and to activate the engine according to the respective engine activation actions.

15 . The non-transitory computer-readable storage medium of claim 14 , wherein the route is obtained using a multi-objective stochastic shortest path (MOSSP) comprising selecting a next road segment and an engine activation action for the next road segment given a current state.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the MOSSP comprises a state space that includes a navigation map including a current road segment of the HEV, a current charge level of the battery, and whether the engine is currently on or off.

17 . The non-transitory computer-readable storage medium of claim 14 , wherein the route is optimized using a scalarization function of time, noise level, and battery level.

18 . The non-transitory computer-readable storage medium of claim 14 , wherein the route is optimized by minimizing an objective related to time constrained by at least one of noise level or battery level.

19 . The non-transitory computer-readable storage medium of claim 14 , wherein the route is optimized by minimizing an objective related to battery level constrained by at least one of time or noise level.

20 . The non-transitory computer-readable storage medium of claim 14 , wherein the route is optimized by minimizing an objective related to noise level constrained by at least one of time or battery level.

Assignees

• Nissan North America Inc

Inventors

• Wray Kyle Hollins
• Pedersen Liam
• Lui Richard
• Ostafew Christopher

Classifications

• G01C21/3446

  Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags or using precalculated routes · CPC title

• G01C21/3469 Primary

  Fuel consumption; Energy use; Emission aspects · CPC title

• G01C21/3453

  Special cost functions, i.e. other than distance or default speed limit of road segments · CPC title

• G01C21/3407

  specially adapted for specific applications · CPC title

• G01C21/3685

  the POI's being parking facilities · CPC title