Abstract

With the rapid development of new energy electric vehicles, the demand for batteries is increasing. The battery management system (BMS) plays a crucial role in the battery-powered energy storage system. Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their Remaining Useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. The development of a machine-learning method with high accuracy, high generalization, and strong robustness for evaluating battery health states is essential in the field of battery health management. Current research review on data-driven methodologies using Machine Learning Algorithm is summarized in this paper

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