Remaining Useful Life Prediction of Lithium Battery Based on Sequential CNN–LSTM Method

Abstract

Abstract Among various methods for remaining useful life (RUL) prediction of lithium batteries, the data-driven approach shows the most attractive character for non-linear relation learning and accurate prediction. However, the existing neural network models for RUL prediction not only lack accuracy but also are time-consuming in model training. In this paper, the sequential convolutional neural network–long short-term memory (CNN–LSTM) method is proposed for accurate RUL prediction of lithium batteries. First, degradation trajectories are analyzed, and six features are adopted for RUL prediction. Then, the CNN model is introduced for filtering the data features of degradation characters. And the orthogonal experiment is studied for optimizing the hyperparameters of the CNN model. Furthermore, by inputting the time-series features flattened by CNN and non-time series feature, the LSTM is reconstructed for memorizing the long-term degradation data of lithium battery. Finally, the proposed method is validated by four cells under different aging conditions. Comparing with the isolated models, the RUL prediction of sequential CNN–LSTM method has higher accuracy.