Joint Prediction of Li-Ion Battery Cycle Life and Knee Point Based on Early Charging Performance

Abstract

With the rapid development of lithium-ion batteries, predicting battery life is critical to the safe operation of devices such as electric ships, electric vehicles, and energy storage systems. Given the complexity of the internal aging mechanism of batteries, their aging process exhibits prominent nonlinear characteristics. Knee point, as a distinctive sign of this nonlinear aging process, plays a crucial role in predicting the battery’s lifetime. In this paper, the cycle life and cycle to the knee point of the battery are firstly predicted using the time dimension and space dimension features of the early external characteristics of the battery, respectively. Then, to capture the aging characteristics of batteries more comprehensively, we innovatively propose a joint prediction method of battery cycle life and knee point. Knee point features are incorporated into the battery cycle life prediction model in this method to fully account for the nonlinear aging characteristics of batteries. The experimental validation results show that the TECAN model, which combines time series features and knee point information, performs well, with a root mean square error (RMSE) of 106 cycles and a mean absolute percentage error (MAPE) of only 12%.