Improving Li-ion battery health: Predicting remaining useful life using IWBOA-ELM algorithm

Abstract

The prediction of Li-ion battery remaining useful life (RUL) is primarily used to prevent battery health damage caused by overcharging and discharging Li-ion batteries, which is critical for secondary battery life applications. To accurately predict the RUL of Li-ion batteries, a prediction method based on the improved black widow algorithm and extreme learning machine (IWBOA-ELM) is proposed in this paper. Because of the single method of black widow spider seeking, this paper employs the Archimedean spiral (AR) method to improve the search method of the BWOA algorithm and the global search capability of the BWOA algorithm; this is done to prevent the BWOA algorithm from being prone to failure. To avoid the problems of local optimal solutions and premature convergence that plague the BWOA algorithm, this paper employs the Golden-Sine strategy (Golden-SA) to update the positions of individual black widow spiders. Furthermore, for the cyclic trend of Li-ion batteries, this paper proposes an adaptive feedback factor to increase the convergence coefficient and control the early convergence rate while adapting to different battery capacity data. By comparing the outcomes of some CEC2017 test functions, the upgraded IBWOA performs well in determining the optimal. Finally, the IWBOA-ELM model is validated using NASA data, and the RUL prediction is performed under three scenarios: constant current discharge at different temperatures, constant current discharge at room temperature, and random current discharge at room temperature, with the results compared to the conventional method. The results reveal that the IWBOA-ELM method's RMSE error of RUL prediction is lower than that of other algorithms, indicating that IWBOA-ELM has strong generalization and robustness.