SOH prediction for lithium-ion battery based on improved support vector regression
Tiezhou Wu, Yiheng Huang, Yuhong Xu, Jiuchun Jiang, Sizhe Liu, Zihao Li
Abstract
As a good energy storage element, lithium-ion batteries are used widely in electric vehicles and electric energy storage systems now, but its own State of Health (SOH) is difficult to measure directly. The Support Vector Regression (SVR) method can use the cell characteristics extracted from the experiment to predict the SOH of the cell.However,the method itself is difficult to determine the hyperparameters and has a large impact on the prediction accuracy.For this reason, this paper proposes the joint algorithm of Bat Algorithms (BA) and Support Vector Regression to predict the SOH of lithium-ion batteries, uses the BA algorithm to find the optimal SVR hyperparameters and uses Grey Relationship Analysis (GRA) to verify their high correlation with the battery SOH correlation.The proposed method was validated using a publicly available experimental dataset, and the results showed that the method has higher prediction accuracy compared with the conventional GA-SVR and PSO-SVR.