Granted Patent B2

US 12,623,570 · App. 18/657,032 · Granted May 12, 2026

Inventors: Muhammad Khalid (Dhahran, SA); Huzaifa Rauf (Dhahran, SA); Naveed Arshad (Dhahran, SA)

Assignee: KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS

B60L58/16G06N20/00B60L2240/545H01M10/0525

Abstract

Claims (39)

1 . A computer-implemented method of predicting a battery capacity degradation for an electric vehicle having a battery, comprising:

extracting and pre-processing a raw dataset comprising a plurality of battery loss indicators and a plurality of battery loss values each corresponding to a plurality of time steps to obtain a pre-processed dataset;

selecting a loss indicator subset from the pre-processed dataset at a first time step and a second time step of the plurality of time steps based on a smart feature selection (SFS) algorithm, wherein the first time step is immediately prior to the second time step;

training a machine learning model with each battery loss indicator of the plurality of battery loss indicators in the loss indicator subset at the first and second time steps of the plurality of time steps and the battery loss value at the first time step; and

determining the battery loss value at the second time step with the machine learning model to predict the battery capacity degradation;

wherein the plurality of battery loss values includes a battery cyclic loss value and a battery calendar loss value.

2 . The method of claim 1 , wherein the SFS algorithm comprises:

extrapolating the loss indicator subset to fill missing values in the pre-processed dataset;

extracting a mapping relationship between the plurality of battery loss indicators and the plurality of battery loss values from the pre-processed dataset at each time steps of the plurality of time steps based on a quantitative correlation analysis; and

selecting one or more battery loss indicators from the plurality of battery loss indicators in the pre-processed dataset based on the mapping relationship to obtain the loss indicator subset.

3 . The method of claim 1 , wherein the plurality of battery loss indicators includes a distance travelled by an electronic vehicle having the lithium-ion battery, a charging efficiency of the lithium-ion battery, a discharging efficiency of the lithium-ion battery, an energy consumption at the first and second time steps, an internal resistance of the lithium-ion battery, and a temperature.

4 . The method of claim 1 , wherein the training further comprises:

splitting the loss indicator subset into a training data and a testing data;

training the machine learning model with the training data; and

validating the machine learning model with the testing data.

5 . The method of claim 4 , wherein the machine learning model is selected from Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, Gaussian Process Regression, Random Forest, ElasticNet, and XGBoost.

6 . The method of claim 1 , wherein the battery is a lithium-ion battery.

7 . The method of claim 1 , wherein the raw dataset includes a real-time data including a plurality of operating conditions obtained from the electric vehicle while operating.

8 . A battery health management system to predict a battery capacity degradation for an electric vehicle having a battery, comprising:

a system processor communicatively connected to a vehicle control unit of the electric vehicle and configured to execute a program instruction; and

a memory connected to the system processor and configured to store a raw data;

wherein the program instruction comprises:

extracting and pre-processing the raw dataset comprising a plurality of battery loss indicators and a plurality of battery loss values each corresponding to a plurality of time steps to obtain a pre-processed dataset;

selecting a loss indicator subset from the pre-processed dataset at a first time step and a second time step of the plurality of time steps based on a smart feature selection (SFS) algorithm, wherein the first time step is immediately prior to the second time step;

training a machine learning model with each battery loss indicator of the plurality of battery loss indicators in the loss indicator subset at the first and second time steps of the plurality of time steps and the battery loss value at the first time step; and

determining the battery loss value at the second time step with the machine learning model to predict the battery capacity degradation;

wherein the plurality of battery loss values includes a battery cyclic loss value and a battery calendar loss value.

9 . The system of claim 8 , wherein the SFS algorithm comprises:

extrapolating the loss indicator subset to fill missing values in the pre-processed dataset;

extracting a mapping relationship between the plurality of battery loss indicators and the plurality of battery loss values from the pre-processed dataset at each time steps of the plurality of time steps based on a quantitative correlation analysis; and

selecting one or more battery loss indicators from the plurality of battery loss indicators in the pre-processed dataset based on the mapping relationship to obtain the loss indicator subset.

10 . The system of claim 8 , wherein the plurality of battery loss indicators includes a distance travelled by an electronic vehicle having the lithium-ion battery, a charging efficiency of the lithium-ion battery, a discharging efficiency of the lithium-ion battery, an energy consumption at the first and second time steps, an internal resistance of the lithium-ion battery, and a temperature.

11 . The system of claim 8 , wherein the training further comprises:

splitting the loss indicator subset into a training data and a testing data;

training the machine learning model with the training data; and

validating the machine learning model with the testing data.

12 . The system of claim 11 , wherein the machine learning model is selected from Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, Gaussian Process Regression, Random Forest, ElasticNet, and XGBoost.

13 . The system of claim 8 , wherein the battery is a lithium-ion battery.

14 . The system of claim 8 , wherein the raw dataset includes a real-time data including a plurality of operating conditions obtained from the electric vehicle while operating.

Assignments (1)

Continuity (1)

References Cited (10)