基于改进GWO-SVR的锂电池SOH估计  

Lithium battery SOH estimation based on improved GWO-SVR

作　　者：梁杨 周永军 蒋淑霞[1] 袁晓文 张淞 Liang Yang;Zhou Yongjun;Jiang Shuxia;Yuan Xiaowen;Zhang Song(Collegee of Electromechanical Engineering,Central South University of Forestry and Technology,Changsha 410000,China;Guizhou MeiLing Power Sources Co.,Ltd.,Zunyi 563000,China)

机构地区：[1]中南林业科技大学机电工程学院,长沙410000 [2]贵州梅岭电源有限公司,遵义563000

出　　处：《电子测量技术》2023年第7期13-18,共6页Electronic Measurement Technology

基　　金：湖南省自然科学基金(2019JJ60076)项目资助。

摘　　要：为了提高锂电池健康状态的估计精度,提出了一种基于IGWO-SVR的锂电池SOH估计方法。针对支持向量回归(SVR)内核参数选择的问题,采用改进灰狼(IGWO)算法优化支持向量回归的内核参数;选取合适的健康特征作为输入,电池SOH作为输出,建立IGWO-SVR估计模型,实现锂电池SOH的估计。基于NASA电池数据集,对该模型进行训练及验证,并与SVR和GWO-SVR方法相比。结果表明,IGWO-SVR方法能有效提高SOH估计的精度和稳定性,最大估计误差不超过2%。In order to improve the estimation accuracy of lithium battery state of health,a lithium battery SOH estimation method based on IGWO-SVR is proposed.Firstly,aiming at the problem of kernel parameter selection of support vector regression(SVR),the improved gray wolf(IGWO) algorithm is used to optimize the kernel parameters of support vector regression(SVR).The SVR estimation model realizes the estimation of the SOH of lithium batteries.Based on the NASA battery dataset,the model is trained and validated and compared with the SVR and GWO-SVR methods.The results show that the IGWO-SVR method can effectively improve the accuracy and stability of SOH estimation,and the maximum estimation error does not exceed 2%.

关 键 词：锂离子电池 健康状态 支持向量回归 改进灰狼算法 

分 类 号：TM912[电气工程—电力电子与电力传动]