Battery Remaining Useful Life Prediction Based on a Combination of ARMA and Degradation Model

The remaining useful life (RUL) of batteries is an important and helpful reference for battery management system. Since autoregressive moving average (ARMA) model is a relatively mature time series analysis method for prognostics, the long-term prediction results are not reliable due to dynamic noise and constantly cumulative system errors. In order to improve the accuracy of long-term prediction for battery RUL, a method combining ARMA and exponential degradation model is proposed in this paper. A case study using battery dataset from CALCE is performed to demonstrate the effectiveness of the proposed method, and the results show that the proposed method gives better prediction accuracy.