Accurately estimating the state of health(SOH)of lithium-ion batteries is a crucial prerequisite for ensuring the safe and stable operation of energy storage systems.The key to improving the accuracy of SOH estimation lies in the rational selection of health characteristics that can effectively reflect the state of health of lithium-ion batteries.By analyzing the current characteristics of lithium-ion batteries during the constant voltage charging stage,a healthy combination of features containing the slope of the first and last points of the current curve,the standard deviation,and the mean value were extracted from the current curve data during the constant voltage charging stage.To validate the effectiveness of the proposed feature combination,SOH estimation model based on kernel ridge regression(KRR)and support vector regression(SVR)was designed,and model validation was successfully completed.The experimental results demonstrate that the proposed feature combination can achieve high-precision SOH estimation across different models,exhibiting excellent model adaptability.
关键词
锂离子电池/健康状态估计/恒压充电阶段/核岭回归/支持向量回归
Key words
lithium-ion battery/state of health(SOH)estimation/constant voltage charging stage/kernel ridge regression(KRR)/support vector regression(SVR)
维普
万方数据