Machine Learning Approaches in Battery Management Systems: State of the Art: Remaining useful life and fault detection

Abstract

Lithium-ion battery packs have been widely applied in many high-power applications which need battery management system (BMS), such as electric vehicles (EVs) and smart grids. Implementations of the BMS needs a combination between software and hardware, which includes battery state estimation, fault detection, monitoring and control tasks. This paper provides a comprehensive study on the state-of-the-art of machine learning approaches on BMS. It differentiates between these methods on the basis of principle, type, structure, and performance evaluation.