Machine Learning Approaches for Battery Health Management in Electric Vehicles

One of the sustainable ways to effectively reduce greenhouse gas emissions (GHGE) and end the world's reliance on fossil fuels is by the usage of Electric Vehicles (EVs). The high density (energy) and reliability of Lithium-ion (Li-ion) batteries makes them dominate the EV market. While these (Li-ion) batteries have a great performance, it is crucial to accurately predict their State of Health (SOH) in order to maximize their lifespan, optimize maintenance, and guarantee safety. In this scenario, the Machine learning techniques are a commendable option to predict the (SOH) based on the battery measurement data, which includes necessary features in predictive methodologies. This study surveys the noteworthy advancements in Machine Learning (ML) driven SOH prediction methodologies from recent pasts, reviewing different solutions including ensemble models, hybrid frameworks, and optimized regression-based approaches in Battery Health Management (BHM). This research survey, reports the improved prediction accuracy and adaptability under diverse conditions for different approaches in BHM. Furthermore, this paper also underscores the different open challenges and further research scope in employing ML for an effective, feasible and scalable BHM scheme for EVs. This study therefore provides a strong foundational understanding of ML techniques in electric vehicular battery health management.