Method and system for predicting battery health with machine learning model

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; and

predicting battery health of the vehicle by comparing the obtained distance with a reference distance value;

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 a 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, a mean vehicle speed, a mean battery temperature, battery temperature imbalance, regenerated energy, a mean cell-voltage difference, and a cumulative distance; and

modeling the distance driven according to the relationship between the distance driven on a full battery load and the model features extracted from the historical vehicle telematics.

2. The method as claimed in claim 1 , 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; and

training individual-vehicle models for different vehicles by using predictive general model coefficients as a starting point.

3. The method as claimed in claim 1 , before modeling distance driven on a full battery load of vehicles, the method further comprises:

scaling the distance driven, the positive/negative acceleration counts and the regenerated energy for each of the trips based on SOC change.

4. 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.

5. The method as claimed in claim 1 , 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, a mean vehicle speed, and regenerated energy;

obtaining changes in distance driven due to seasonal temperature fluctuations as claimed in the following model features: the mean battery temperature, and the battery temperature imbalance; and

obtaining changes in distance driven due to long-term loss of battery health according to the following model features: the mean cell-voltage difference, and the cumulative distance.

6. The method as claimed in claim 5 , wherein a loss of distance driven with time is proportional to a cumulative usage and the mean cell-voltage difference.

7. The method as claimed in claim 1 , wherein the relationship between the distance driven on a full battery load and the historical vehicle telematics is determined by the following linear regression formula:

y=θ t X

where y is a distance driven on in a trip, X is at least one of the model features from the historical vehicle telematics, θ is a corresponding model coefficient, and θ t is the matrix transpose of θ.

8. 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 is below a preset threshold.

9. 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 .

10. 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 .

11. 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 .

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 4 .

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 5 .

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 6 .