Method and System for Predicting Battery Health with Machine Learning Model

Patent metadata

Field	Value
Publication number	US-2022153166-A1
Application number	US-202016952097-A
Country	US
Kind code	A1
Filing date	Nov 19, 2020
Priority date	Nov 19, 2020
Publication date	May 19, 2022
Grant date	—

Abstract

A method and a system for predicting battery health based on distance driven on a full battery load with machine learning model are provided. The method includes: obtaining historical vehicle telematics of vehicles, wherein the historical vehicle telematics comprise at least one of the following: odometer readings, battery SOC, vehicle speed, battery-module temperatures, and battery-cell voltages; creating a distance driven model according to a relationship between a distance driven on a full battery load of vehicles and the historical vehicle telematics; obtaining a distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input; predicting battery health of the vehicle by comparing the obtained distance with a reference distance value.

First claim

What is claimed is:

1 . A method for predicting battery health with machine learning model, comprising:
obtaining historical vehicle telematics of vehicles, wherein the historical vehicle telematics comprise at least one of the following: odometer readings, battery state-of-charge (SOC), vehicle speed, battery-module temperatures, and battery-cell voltages; creating a distance driven model according to a relationship between a distance driven on a full battery load of vehicles and the historical vehicle telematics; obtaining a distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input; predicting battery health of the vehicle by comparing the obtained distance with a reference distance value.

2 . The method as claimed in claim 1 , creating a distance driven model according to a relationship between a distance driven on a full battery load of vehicles and the historical vehicle telematics comprises:
identifying trips on battery from the historical vehicle telematics of vehicles; extracting at least one of the following model features from the historical vehicle telematics: positive/negative acceleration counts, mean vehicle speed, mean battery temperature, battery temperature imbalance, regenerated energy, mean cell-voltage difference, and cumulative distance; modeling distance driven according to the relationship between the distance driven on a full battery load and the model features.

3 . The method as claimed in claim 2 , after creating the distance driven model, the method further comprises:
training the distance driven model based on historical data of vehicles to provide a predictive general model; training individual-vehicle models for different vehicles by using the predictive general model coefficients as a starting point.

4 . The method as claimed in claim 2 , before modeling distance driven on a full battery load of vehicles, the method further comprises:
scaling distance driven, positive/negative acceleration counts and regenerated energy for each trip based on SOC change.

5 . The method as claimed in claim 1 , before obtaining a distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input, the method further comprises:
collecting the real-time vehicle telematics from the vehicle at a preset frequency.

6 . The method as claimed in claim 2 , wherein obtaining the distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input, comprises at least one of the following:
obtaining changes in distance driven due to trip specifics according to the following model features: positive/negative acceleration counts, mean vehicle speed, and regenerated energy; obtaining changes in distance driven due to seasonal temperature fluctuations as claimed in the following model features: mean battery temperature, and battery temperature imbalance; obtaining changes in distance driven due to long-term loss of battery health according to the following model features: mean cell-voltage difference, and cumulative distance.

7 . The method as claimed in claim 6 , wherein the loss of distance driven with time is proportional to cumulative usage and mean cell-voltage difference.

8 . The method as claimed in claim 2 , wherein the relationship between the distance driven on a full battery load and the model features is determined by the following linear regression formula:
y=θ t X where y is the distance driven on in a trip, X is at least one of the model features, θ is a corresponding model coefficient, and θ t is the matrix transpose of θ.

9 . The method as claimed in claim 1 , after predicting battery health of the vehicle by comparing the predictive distance with a reference distance value, the method further comprises:
sending an alarm when the battery health of the vehicle lies below a preset threshold.

10 . A system for predicting battery health with machine learning model, comprising:
first obtaining module, configured to obtain historical vehicle telematics of vehicles, wherein the historical vehicle telematics comprise at least one of the following: odometer readings, battery state-of-charge (SOC), vehicle speed, battery-module temperatures, and battery-cell voltages; creation module, configured to create a distance driven model according to a relationship between a distance driven on a full battery load of vehicles and the historical vehicle telematics; second obtaining module, configured to obtain a distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input; prediction module, configured to predict battery health of the vehicle by comparing the obtained distance with a reference distance value.

11 . The system as claimed in claim 10 , further comprising:
sending module, configured to send an alarm when the battery health of the vehicle lies below a preset threshold.

12 . A non-volatile computer readable storage medium, in which a program is stored, the program is configured to be executed by a computer to perform the method as claimed in claim 1 .

13 . A non-volatile computer readable storage medium, in which a program is stored, the program is configured to be executed by a computer to perform the method as claimed in claim 2 .

14 . A non-volatile computer readable storage medium, in which a program is stored, the program is configured to be executed by a computer to perform the method as claimed in claim 3 .

15 . A non-volatile computer readable storage medium, in which a program is stored, the program is configured to be executed by a computer to perform the method as claimed in claim 4 .

16 . A non-volatile computer readable storage medium, in which a program is stored, the program is configured to be executed by a computer to perform the method as claimed in claim 5 .

17 . A non-volatile computer readable storage medium, in which a program is stored, the program is configured to be executed by a computer to perform the method as claimed in claim 6 .

18 . A non-volatile computer readable storage medium, in which a program is stored, the program is configured to be executed by a computer to perform the method as claimed in claim 7 .

19 . An electric vehicle, comprising the system as claimed in claim 10 .

20 . An electric vehicle, comprising the system as claimed in claim 11 .

Assignees

• Guangzhou Automobile Group Co

Inventors

• Rangel Tonatiuh
• Zhang Yizhen
• Ho Dao Hoang
• Fu Ying
• Xu Yonggang

Classifications

• B60L2260/54

  Energy consumption estimation · CPC title

• B60L2260/52

  drive range estimation, e.g. of estimation of available travel distance · CPC title

• B60L2260/46

  by self learning · CPC title

• B60L3/12 Primary

  Recording operating variables {; Monitoring of operating variables} · CPC title

• B60L2240/545

  Temperature · CPC title