Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining

Abstract

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is a key, challenging research direction. In this study, a battery degradation model is built based on the LIB dataset of NASA. A data decomposition prediction method, which extracts the linear trend from the capacity degradation data and then predicts the residuals of time series with nonlinear relations, is proposed. Then, an autoregressive integrated moving average-long short-term memory (ARIMA-LSTM) combined model for predicting the RUL of LIBs is established. The linear trend of capacity degradation is predicted by the ARIMA model and the LSTM model is established to predict the nonlinear residuals separated from the capacity degradation. The summation of both obtains the final battery RUL prediction result. Compared with the LSTM model and prediction models of similar types in existing literature in recent two years, the experimental results show that the RUL prediction error of the proposed model in this article is no more than one cycle, which is smaller than that of all the models involved in the comparison. Thus, this method has higher prediction accuracy and model generalization ability in the prediction of the remaining useful life of LIBs.