Machine Learning-Based Estimation of State of Charge and State of Health in Lithium-Ion Batteries for Electric Vehicles

Battery management systems (BMSs) play a crucial role in electric vehicles (EVs), relying significantly on two essential parameters: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains challenging. Many researchers have turned to machine learning (ML) techniques to tackle this issue. In this review, we provide a comprehensive study of popular ML methods for estimating the SOC and SOH. Beyond traditional models such as equivalent circuit models (ECMs) and electrochemical battery models, the review emphasizes the importance of supervised and unsupervised learning models, deep learning architectures, hybrid systems, and their respective performance metrics, in addition to data collection and feature engineering. Additionally, the review discusses current challenges and future opportunities for research.