摘要
针对退役动力电池梯次用于电力系统等领域存在初始参数不一致、筛选重组复杂等问题,提出一种基于退役动力电池模组静动态特性的阶梯式筛选方法。首先,构建退役动力电池模组端电压、荷电状态(state of charge,SOC)、健康状态(state of health,SOH)及循环次数等参数间的关联特性,以电池模组内阻、剩余容量作为表征参数,采用密度权重Canopy改进的K-medoids聚类方法对外部特性参数相近的电池模组进行初次筛选;其次,将电池模组SOH动态一致性特性曲线作为表征对象,对其进行再次筛选;最后,采用非参数Bootstrap概率方法解析阶梯式静动态筛选下退役动力SOH估计的置信区间,评估动力电池模组筛选精度。结果表明,该文所提方法可将电池模组的筛选精度至少提高6.2%,为退役动力电池大规模筛选及梯次利用奠定理论基础。
Aiming at the problems of inconsistent initial capacity and complex selection and reorganization of retired power batteries’echelon utilization in power system and other fields,a stepped screening method considering the static and dynamic characteristics of retired power battery modules(BMs)is proposed.First,the correlation characteristics among parameters such as terminal voltage,state of charge(SOC),state of health(SOH)and cycle numbers of retired power BMs is constructed.Taking battery module’s internal resistance and residual capacity as characterization parameters,K-medoids clustering method based on density weighted canopy is adopted to screen BMs with similar external characteristics.Secondly,taking the SOH dynamic consistency characteristic curve as the characterization object,the BMs are further screened.Finally,the non-parametric Bootstrap probability method is used to analyze the confidence interval of SOH estimation of retired power BMs under static-dynamic screening.The screening accuracy of power battery modules is evaluated.The results show that the method proposed can improve the screening accuracy of battery modules by at least 6.2%.It lays a theoretical foundation for large-scale screening and echelon utilization of retired power batteries.
作者
颜宁
李相俊
钟瑶
马少华
YAN Ning;LI Xiangjun;ZHONG Yao;MA Shaohua(School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,Liaoning Province,China;State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems(China Electric Power Research Institute),Haidian District,Beijing 100192,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2023年第5期2060-2069,共10页
Proceedings of the CSEE
基金
内蒙古自治区科技重大专项(2020ZD0018)。
关键词
退役动力电池
电池模组
阶梯式筛选
动态一致性
置信区间
retired power battery
battery module
stepwise screening
dynamic consistency
confidence interval
