Experimental Study on Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Three Regression Models for Electric Vehicle Application

Abstract

This paper presents three regression models that predict the lithium-ion battery life for electric cars based on a supervised machine learning regression algorithm. The linear regression, bagging regressor, and random forest regressor models will be compared for the capacity prediction of lithium-ion batteries based on voltage-dependent per-cell modeling. When sufficient test data are available, three linear regression learning algorithms will train this model to give a promising battery capacity prediction result. The effectiveness of the three linear regression models will be demonstrated experimentally. The experiment table system is built with an NVIDIA Jetson Nano 4 GB Developer Kit B01, a battery, an Arduino, and a voltage sensor. The random forest regressor model has evaluated the model’s accuracy based on the average of the square of the difference between the initial value and the predicted value in the data set (MSE (mean square error)) and RMSE (root mean squared error), which is smaller than the linear regression model and bagging regressor model (MSE is 516.332762; RMSE is 22.722957). The linear regression model with MSE and RMSE is the biggest (MSE is 22060.500669; RMSE is 148.527777). This result allows the random forest regressor model to remain helpful in predicting the life of lithium-ion batteries. Moreover, this result allows rapid identification of battery manufacturing processes and will enable users to decide to replace defective batteries when deterioration in battery performance and lifespan is identified.

Keywords

• linear regression
• bagging regressor
• random forest regressor
• machine learning
• lithium-ion battery