Nonlinear aging knee-point prediction for lithium-ion batteries faced with different application scenarios

Abstract

The capacity degradation of lithium-ion batteries (LIBs) will accelerate after long-term cycling, showing nonlinear aging features, which not only shortens the long-term life of LIBs, but also seriously endangers their safety. In this paper, by introducing the concept of nonlinear aging degree, a knee-point identification method based on the maximum distance method is established, and the nonlinear aging behavior of LIBs is identified and marked, so as to know whether the nonlinear aging phenomenon has occurred. Furthermore, two knee-point prediction methods have been proposed and compared. The direct knee-point prediction method based on stacked long short-term memory (S-LSTM) neural network and sliding window method is proposed for the scenarios of battery development, early performance evaluation and online application. For scenarios such as echelon utilization and post-safety evaluation, an indirect knee-point prediction method combining capacity prediction and knee-point identification algorithm is proposed. Through multi-dimensional comparison of the two methods, the strengths and weaknesses of their applicable scenarios are analyzed. Our work has guiding significance for finding the ideal replacement opportunity of LIBs in different scenarios, so that the user can be reminded whether to maintain or replace the battery, which greatly reduces the risk of battery safety problems.