Prediction of remaining useful life for lithium-ion battery based on particle filter with residual resampling

Accurate prediction of the remaining useful life for lithium-ion battery is beneficial to prolong the life of the battery and increase safety. With the capacity degradation curve obtained from the data of the battery charge and discharge experiment, the remaining useful life of the battery was predicted by using particle filter. In order to improve the prediction accuracy, the particle filter with residual resampling method is used to overcome the lack of particle diversity which has an important effect on the accuracy of state estimation. Compared with the prediction result of the extended Kalman filter, it was found that the precision and stability of particle filter are better than those of extended Kalman filter. The research results presented in this paper provide some suggestions for the health monitoring of power battery for electric vehicles.

Automated summary

This study accurately predicts the remaining useful life of lithium-ion batteries using a particle filter with residual resampling method, which improves prediction accuracy by overcoming the lack of particle diversity. Comparing to the extended Kalman filter, the particle filter shows better precision and stability, providing valuable suggestions for health monitoring of power batteries in electric vehicles.