基于TWP-SVR的锂离子电池健康状态估计

Health state estimation of lithium ion battery based on TWP-SVR

健康状态(state of health,SOH)是评估锂离子电池老化程度和剩余使用寿命的重要指标。然而,SOH无法通过直接测量获得,本工作提出了一种基于时间规整图(time warp profile,TWP)提取间接健康特征参数,使用支持向量机回归(vector machine regression,SVR)模型估计SOH的方法。首先,通过TWP将锂离子电池不同循环充放电压曲线转换为相位差异曲线。然后,从相位差异曲线中提取出4个间接健康特征。接着,采用线性核函数的SVR模型估计SOH。最后,以美国航空航天局(National Aeronautics and Space Administration,NASA)、美国保险商实验室公司和普渡大学(Underwriters Laboratories Inc.-Purdue University,UL-PUR)的开源数据集和储能电站实测数据进行验证。其中,储能电站数据实验结果表明,TWP-SVR模型的均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)的样本标准差(sample standard deviation,SSD)小于0.0015,四分位距(inter-quartile range,IQR)小于0.0022;平均绝对百分比误差(mean absolute percentage error,MAPE)的SSD和IQR分别为0.0152和0.0220,表明所提TWP-SVR方法保持较高准确性的同时具有良好的稳定性。

State of Health(SOH)is an important index for evaluating the aging degree and remaining service life of lithium-ion batteries.However,SOH cannot be obtained by direct measurement.This study proposes a method to extract indirect health feature parameters based on time warp profile(TWP)and estimate the SOH using a vector machine regression(SVR)model.First,TWP converts the different cycle charge-discharge voltage curves of a lithium-ion battery into a phase difference curve.Then,four indirect health features are extracted from the phase difference curve.Further,SOH is estimated using the SVR model of the linear kernel function.Finally,it is verified by measured data of energy storage power station and the open-source data sets of NASA,Underwriters Laboratories Inc.-Purdue University(UL-PUR).The experimental results of energy storage power station data show that the sample standard deviation (SSD) of the root mean square error and mean absolute errorof the TWP-SVR model is less than 0.0015, and the interquartile range (IQR) is less than0.0022;the SSD and IQR of mean absolute percentage error are 0.0152 and 0.0220,respectively, indicating that the proposed TWP-SVR method maintains high accuracy andgood stability.