The Lithium-ion Battery Nonlinear Aging Knee-Point Prediction Based on Sliding Window with Stacked Long Short-Term Memory Neural Network

Abstract

Lithium-ion batteries (LIBs) will accelerate the degradation of capacity after long-term cycling, showing nonlinear aging features. The onset of the nonlinear aging feature is called the “knee-point”. The appearance of nonlinear aging will not only lead to a rapid drop in the overall performance of LIBs, but also the serious collapse of battery safety, which makes the identification and early prediction of nonlinear aging knee-point particularly important. In this paper, we propose a nonlinear aging knee-point prediction method of LIBs based on sliding window with stacked long short-term memory (LSTM) neural network. We compared our method with other common machine learning algorithms, and found that the prediction results of knee-point under this method are significantly better than other algorithms. When the sliding window size is 20, the root mean square error (RMSE) of prediction result is 72.3 and the mean absolute error (MAE) is 51.6. In addition, we further study the effect of different sliding window sizes on the prediction results. By predicting the knee-point, it can be recognized when the nonlinear aging begins so that the user can be reminded whether to replace the battery, which greatly reduces the risk of battery safety problems.