Comparative Analysis of Artificial Intelligence and Statistical Models for Li-Ion Battery Cells State Estimation in Electric Vehicles

This paper presents a comparative study of statistical modeling and Artificial Intelligence (AI) approaches for estimating essential parameters of lithium-ion (Li-ion) batteries in electric vehicles (EVs). The key parameters estimated in this study are voltage, current, and temperature of Li-ion battery cells in EVs. A precise state estimation is required to ensure safety operation, to optimize the Battery Management System (BMS), and to enhance the battery life. Variations in EVs batteries parameters may occur due to different reasons such as sensor faults or attacks. As a result, obtaining an accurate estimation is essential to maintain EV battery efficiency. Using real-word measurements data, a statistical model presented by Linear Kalman Filter (KF) is obtained for each parameter. Furthermore, by taking the advantages of Machine Learning (ML) and AI, models using linear regression and Support Vector Regression (SVR) are extracted for each parameter. The performance of the three different models is evaluated based on the Mean Squared Error (MSE). The results demonstrate the strengths and weaknesses of each model in estimating every parameter. This work offers contribution to the state estimation of EV battery, promoting safety, reliability, and enhanced operation of EV battery.