Remaining useful life prediction of lithium-ion battery based on an improved particle filter algorithm

Because lithium-ion batteries are the main power source of industrial electronic equipment, their degradation process modelling and remaining useful life (RUL) prediction problems have attracted wide attention. The particle filter (PF) method has been successfully applied to suppress the model uncertainty and predict the RUL of the lithium-ion battery. In order to further enhance the stability of the PF method and realize a more satisfactory prediction result, a RUL prediction method based on the hybrid algorithm, which combines the PF algorithm and extended unbiased finite impulse response (EFIR) filter, is proposed. Firstly, the state space model of capacity degradation for the lithium-ion battery is established, and the model parameters are estimated by the extended Kalman filter (EKF) algorithm. Secondly, a preliminary battery capacity is predicted by using a regularized particle filter. The preliminary predictions with large deviations are diagnosed and repaired by combining the EFIR filter and diagnostic strategy. Finally, the optimized RUL prediction results of the lithium-ion battery are extrapolated based on the failure threshold. The experiment results demonstrate that the proposed method has good stability and accuracy in predicting the RUL of a lithium-ion battery.