Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

Scientific estimation and prediction of the state of health (SOH) of lithium-ion battery, especially the remaining useful life (RUL), has important significance to guarantee the battery safety and reliability in the full life cycle to avoid catastrophic accidents as much as possible. In order to accurately predict the RUL of the lithium-ion battery, this paper firstly analyzes the problems of the standard particle filter (PF). Then, a novel extended Kalman particle filter (EKPF) is proposed, in which the extended Kalman filter (EKF) is used as the sampling density function to optimize PF algorithm. The life cycle tests are designed and carried out to get accurate and reliable data for the RUL prediction. And, the aging properties of lithium-ion battery are analyzed in detail. The RUL prediction is done based on the established capacity degradation model and the proposed EKPF method. Results show that the RUL prediction error of the proposed method is less than 5%, which has higher precision compared with the standard PF method and can be used both offline and online.