Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves
Xiong Wei, Gang Xu, Yumei Li, Feng Zhang, Peng Ye, Ben Li
Abstract
Accurately predicting the lifetime of lithium-ion batteries is critical for accelerating technological advancements and applications. Nevertheless, the complex aging mechanisms and dynamic operating conditions of lithium-ion batteries have remained major challenges. This paper proposes a method for early predicting lithium-ion batteries cycle life based on weighted least squares support vector machine (WLS-SVM) with health indicators (HIs) as input. The HIs are extracted from lithium-ion batteries voltage-capacity discharge curves, since these curves are easy to measure and strongly correlate to battery cycle life. Taking into account the nonlinearity of batteries cycle life, a support vector machine (SVM) that is capable of strong generalization is used to predict cycle life. As a solution to the problem of misleading results from outlier data in SVM, error square term combined with weight functions is used in this study to improve robustness and prediction accuracy. The datasets of 41 cells are used to verify the proposed early prediction method. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of cycle life prediction results by the proposed method are much lower, and the cycle life early prediction errors of the test cells are all <9 %, which notes that the proposed method is accurate and valid for cycle life early prediction.