Prediction of Lithium Battery Health State Based on Temperature Rate of Change and Incremental Capacity Change

Abstract

With the use of Li-ion batteries, Li-ion batteries will experience unavoidable aging, which can cause battery safety issues, performance degradation, and inaccurate SOC estimation, so it is necessary to predict the state of health (SOH) of Li-ion batteries. Existing methods for Li-ion battery state of health assessment mainly focus on parameters such as constant voltage charging time, constant current charging time, and discharging time, with little consideration of the impact of changes in Li-ion battery temperature on the state of health of Li-ion batteries. In this paper, a new prediction method for Li-ion battery health state based on the surface difference temperature (DT), incremental capacity analysis (ICA), and differential voltage analysis (DVA) is proposed. Five health factors are extracted from each of the three curves as input features to the model, respectively, and the weights, thresholds, and number of hidden layers of the Elman neural network are optimized using the Whale of a Whale Algorithm (WOA), which results in an average decrease of 43%, 49%, and 46% in MAE, RMSE, and MAPE compared to the Elman neural network. For the problem where the three predictions depend on different sources, the features of the three curves are fused using the weighted average method and predicted using the WOA–Elman neural network, whose MAE, RMSE, and MAPE are 0.00054, 0.0007897, and 0.06547% on average. The results show that the proposed method has an overall error of less than 2% in SOH prediction, improves the accuracy and robustness of the overall SOH estimation, and reduces the computational burden to some extent.