采用Morlet小波的锂电池相对健康状态估计  

An Estimation Method of Relative State-of-Health for Lithium-Ion Batteries Using Morlet Wavelet

作　　者：赵云飞 徐俊[1,2,3] 王海涛[1,2,3] 梅雪松 ZHAO Yunfei;XU Jun;WANG Haitao;MEI Xuesong(Shaanxi Provincial Key Laboratory of Intelligent Robots,Xi’an Jiaotong University,Xi’an 710049,China;State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China;School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]西安交通大学陕西省智能机器人重点实验室,西安710049 [2]西安交通大学机械制造与系统工程国家重点实验室,西安710049 [3]西安交通大学机械工程学院,西安710049

出　　处：《西安交通大学学报》2019年第12期97-103,130,共8页Journal of Xi'an Jiaotong University

基　　金：国家自然科学基金资助项目（51405374）;中央高校基本科研业务费专项资金资助项目（xjj2018043）

摘　　要：针对现有电池健康状态(SOH)估计方法存在估计精度低、计算量大等问题,提出了一种采用Morlet小波的锂电池相对健康状态估计方法。首先探究了电池电化学阻抗谱(EIS)与相对健康状态对应关系,然后获取电池实际工况下的电压电流数据,采用Morlet小波对所获得的电池电压和电流数据分别进行Morlet小波变换,用变换后的电压小波系数除以电流小波系数在线计算电池EIS,最后利用在线计算的EIS估计电池的相对健康状态。该方法无需进行大量实验,准确度高,计算快,能更好地应用于电动汽车中。在城市道路循环(UDDS)工况下对所提出的方法进行了验证,实验结果表明,该方法可以准确估计出电池的相对健康状态,估计误差控制在3.3%以内。A novel estimation method based on Morlet wavelet for the relative state-of-health(SOH)of lithium-ion batteries is proposed to solve the problem that the existing methods of estimating battery SOH have low estimation accuracy and large computation.Firstly,the correspondence between electrochemical impedance spectroscopy(EIS)and SOH is explored.Then,the data of voltage and current of a battery are obtained under actual working condition,and the Morlet wavelet is used to perform wavelet transform on the data.The EIS of the battery is estimated online by calculating the wavelet coefficient ratio of voltage signal to current signal.Finally,the SOH of the battery is predicted by the online estimated EIS.The proposed method has the advantages of no need for a lot of experimental data,high accuracy and high computational efficiency.Urban dynamometer driving schedule(UDDS)tests are conducted,and results show that the proposed method accurately estimates the relative SOH of the battery,and the estimation error is less than 3.3%.

关 键 词：锂电池 电化学阻抗谱 MORLET小波 健康状态估计 

分 类 号：TM912.8[电气工程—电力电子与电力传动]