Physical model-guided machine learning framework for energy management of vehicles

Patent metadata

Field	Value
Publication number	US-2020108732-A1
Application number	US-201916597441-A
Country	US
Kind code	A1
Filing date	Oct 9, 2019
Priority date	Oct 9, 2018
Publication date	Apr 9, 2020
Grant date	—

Abstract

A method of determining when to increase an amount electrical energy available to a vehicle includes setting a parameter for a function describing a reference state of charge as a function of distance traveled, wherein the reference state of charge represents a state of charge of the vehicle at which the amount of electrical energy available to the vehicle should be increased. For each trip of the vehicle, the parameter for the function is modified so that different trips of a same vehicle use different functions for the reference state of charge.

First claim

What is claimed is:

1 . A method of determining when to increase an amount electrical energy available to a vehicle, the method comprising:
setting at least one parameter for a function describing a reference state of charge as a function of distance traveled, wherein the reference state of charge represents a state of charge of the vehicle at which the amount of electrical energy available to the vehicle should be increased; for each trip of the vehicle, modifying at least one parameter for the function so that different trips of a same vehicle use different functions for the reference state of charge.

2 . The method of claim 1 wherein modifying at least one parameter comprises modifying at least one parameter before a trip to form at least one modified parameter and using the at least one modified parameter for an entirety of the trip.

3 . The method of claim 2 wherein modifying at least one parameter before a trip comprises modifying at least one parameter based on a prior model created at least in part from past trips of the vehicle.

4 . The method of claim 3 wherein the prior model is created at least in part from a respective best value for each of the at least one parameters determined for a last trip of the vehicle, wherein the respective best value for each of the at least one parameters results in the least amount of electrical energy being made available to the vehicle while preventing the state of charge from crossing below a threshold during the last trip.

5 . The method of claim 4 wherein the best value is determined using a vehicle model to estimate changes in the state of charge for the last trip.

6 . The method of claim 1 wherein modifying at least one parameter comprises modifying at least one parameter during the trip.

7 . The method of claim 6 wherein modifying at least one parameter comprises using a neural network to select a change in at least one parameter based on a state of the vehicle.

8 . The method of claim 7 wherein the neural network is trained using reinforcement learning.

9 . The method of claim 1 wherein the vehicle is a range extended hybrid electric vehicle.

10 . The method of claim 9 wherein the vehicle is an all-electric vehicle.

11 . A computer system comprising:
a communication interface receiving trip information from a vehicle for a time period;
a processor, receiving the trip information from the communication interface and performing steps comprising:
using the trip information for the time period to change how a reference state of charge is determined, wherein the reference state of charge represents a state of charge of the vehicle at which an amount of electrical energy available to the vehicle should be increased.

12 . The computer system of claim 11 wherein the time period covers the entirety of a previous trip.

13 . The computer system of claim 12 changing how the reference state of charge is determined comprises changing how the reference state of charge is determined based on a prior model of a parameter used to determine the reference state of charge.

14 . The computer system of claim 13 the prior model is created at least in part from a best value for the parameter determined for a last trip of the vehicle, wherein the best value results in the least amount of electrical energy being made available to the vehicle while preventing the state of charge from crossing below a threshold during the last trip.

15 . The computer system of claim 14 wherein the best value is determined using a vehicle model to estimate changes in the state of charge for the last trip.

16 . The computer system of claim 11 wherein the time period covers less than all of a trip in progress.

17 . The computer system of claim 16 wherein using the trip information for the time period to change how the reference state of charge is determined comprises using the trip information to identify a state and applying the state to a neural network to obtain the change in how the reference state of charge is determined.

18 . A computing device comprising:
a memory storing trip information for a vehicle; a processor executing instructions to perform steps comprising:
using at least some of the trip information to alter a function used to determine a reference state of charge, wherein the reference state of charge represents a state of charge of the vehicle at which an amount of electrical energy available to the vehicle should be increased and wherein the reference state of charge changes during a vehicle trip; and
using the altered function to determine the reference state of charge.

19 . The computing device of claim 18 wherein the trip information used to alter the function comprises trip information for an entirety of a latest trip.

20 . The computing device of claim 19 wherein the trip information is used to alter a prior probability distribution and the prior probability distribution is used to alter the function.

21 . The computing device of claim 18 wherein the trip information comprises trip information for a current trip.

22 . The computing device of claim 21 wherein altering the function comprises determining a state from the trip information and applying the state to a neural network to determine how to alter the function.

Assignees

• Univ Minnesota

Inventors

• Northrop William
• Kotz Andrew
• Wang Pengyue

Classifications

• B60L58/12 Primary

  responding to state of charge [SoC] · CPC title

• B60L50/60 Primary

  using power supplied by batteries (in combination with fuel cells B60L50/75) · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/08

  Learning methods · CPC title

• B60L2240/60

  Navigation input · CPC title