Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other technologies, their lifetime is not unlimited has be predicted ensure the economic viability of battery application. Furthermore, optimal system operation, remaining useful (RUL) prediction become essential feature modern management systems (BMSs). Thus, RUL a hot topic for both industry academia. The purpose this work review, classify, compare different machine learning (ML)-based methods batteries. First, article summarizes classifies various estimation that have been proposed in recent years. Secondly, innovative method was selected evaluation compared terms accuracy complexity. DNN more suitable due its strong independent ability generalization ability. In addition, challenges prospects BMS research also put forward. Finally, development summarized.