Remaining useful life prediction for lithium-ion batteries with an improved grey particle filter model

Accurate prediction of remaining useful life is of great value for the maintenance and replacement of electric vehicles lithium-ion batteries. This paper aims to present a grey particle filter model for improving remaining useful life forecast accuracy. Firstly, a grey particle filter model with recursive least square parameter estimation is built, and the proposed model's parameters are trained. Secondly, RUL is predicted by using the parameters and proposed model. Finally, NASA lithium-ion battery open data set was used for verification. The model was evaluated from two perspectives of RUL accuracy and mean absolute percentage error. Predictions are also made for lithium-ion batteries under conditions of elevated temperature. The findings demonstrate that the proposed model outperforms the other models.