Artificial Intelligence for Battery Management Systems: Challenges and Future Directions

Abstract

Battery Management Systems (BMS) are indispensable for ensuring the operational safety, reliability, and performance optimization of rechargeable energy storage systems, particularly lithium-ion batteries employed in electric vehicles, grid-scale energy storage, and portable electronic devices. Conventional BMS methodologies predominantly rely on electrochemical models, equivalent circuit models, and rule-based control strategies, which often exhibit limited adaptability under dynamic operating and environmental conditions. Recent advancements in Artificial Intelligence (AI), including machine learning, deep learning, and reinforcement learning techniques, have demonstrated significant potential in enhancing battery state estimation, fault diagnosis, degradation prediction, and energy management. AI-driven approaches facilitate the extraction of complex nonlinear relationships from battery data, thereby improving estimation accuracy and operational efficiency. This review comprehensively examines the latest developments in AI-based battery management systems, highlighting various intelligent algorithms, their applications, associated challenges, and prospective research directions for next-generation energy storage technologies. Keywords: Artificial Intelligence, Battery Management System, Lithium-Ion Batteries, Machine Learning, Deep Learning, State of Charge Estimation, State of Health, Electric Vehicles, Energy Storage Systems.