基于XGBoost算法的锂离子电池健康状态估算

SOH Estimation of Li-ion Battery Based on XGBoost Algorithm

由于锂离子电池内部电化学反应的复杂性以及外部工作环境的不确定性,难以对电池SOH(健康状态)进行准确估算。为了提高SOH的估算精度,提出一种基于XGBoost算法的锂离子电池健康状态估算方法。首先,利用Matplotlib对锂离子电池数据进行分析,并提取平均电压、电压差、电流差和温度差等特征量来描述电池的老化过程。然后,利用XGBoost算法建立估算模型,对电池SOH进行智能估算。实验结果表明:与线性回归、随机森林、支持向量机、K近邻算法这4种回归算法相比,所提方法的估算精度和泛化能力更优,并且可将估算误差控制在±0.4%左右。

Due to the extremely complex electrochemical reactions inside the lithium-ion battery and the uncertainty of the external working environment,it is difficult to achieve an accurate estimation of SOH(state of health).To improve the accuracy of SOH estimation,an estimation method of lithium-ion battery health state based on XGBoost algorithm is proposed.First,Matplotlib is used to analyze the lithium-ion battery data and to extract the characteristic quantities such as average voltage,voltage difference,current difference and temperature difference to describe the aging process of the battery.Then,the XGBoost algorithm is used to build an estimation model for the intelligent SOH estimation of battery.The experimental results show that the proposed method has higher estimation accuracy and superior generalization ability compared with four regression algorithms,viz.,linear regression,random forest,support vector machine(SVM),and K-nearest neighbors algorithm,and the estimation error can be controlled within±0.4%.