基于DBSCAN聚类的退役动力电池深度配组方法  

A reutilization grouping method for retired power batteries based on DBSCAN clustering

作　　者：张彦龙 朱华炳[1] 刘征宇[1] 温剑 ZHANG Yanlong;ZHU Huabing;LIU Zhengyu;WEN Jian(School of Mechanical Engineering,Heifei University of Technology,Hefei Anhui 230009,China)

机构地区：[1]合肥工业大学机械工程学院,安徽合肥230009

出　　处：《电源技术》2023年第4期462-468,共7页Chinese Journal of Power Sources

摘　　要：退役动力电池在梯次利用时,因单体一致性差异较大,常难以满足使用要求。综合考量退役动力电池动态特性和静态特性,提出一种改进的多参数DBSCAN聚类算法对退役动力电池进行深度配组。对比实验表明,与Kmeans++聚类的结果相比,采用该方法聚类后电池最大容量差减少了86.04%;循环充放电实验表明,采用该方法得到的电池组充电性能提高约3%~5%,其放电量更大,其容量衰减速率降低了64.96%。该方法可有效提高电池组内单体的一致性并延长电池组的使用寿命。When retired power batteries are used in the process of reutilization,they are often laborious to meet the requirements because of the large difference in the consistency of the battery cells.This paper comprehensively took into account the dynamic and static characteristics of retired power batteries,and proposed an improved multiparameter DBSCAN clustering algorithm for deep grouping of retired power batteries.The comparative experiment indicates that the maximum difference of the battery capacity produced by our method is reduced by 86.04%compared with that of K-means++clustering algorithm;the cyclic charge and discharge experiment shows that the battery pack obtained by the proposed method has a better charging performance,enhanced by 3%to 5%,and a higher discharge capacity,and its capacity decay rate is reduced by 64.96%,compared with randomly grouped battery pack.The proposed method can effectively improve the consistency of the cells in the battery pack and prolong the service life of the battery pack.

关 键 词：退役动力电池梯次利用 电池单体一致性 电池动态特性 深度配组 DBSCAN聚类算法 

分 类 号：TM912[电气工程—电力电子与电力传动]