重庆理工大学学报2024，Vol.38Issue(7)：79-86,8.DOI:10.3969/j.issn.1674-8425(z).2024.04.011
基于充电曲线特征的退役动力电池快速分选方法
A fast sorting method for retired power batteries based on charging curve characteristics
摘要
Abstract
Accurate and rapid sorting is crucialin the echelon utilization of retired power batteries.The charging curve and capacity of retired power batteries are obtained by charging and discharging test.The grey correlation analysis method is employed to determine the voltage interval with the best capacity correlation.Based on the battery aging mechanism, the charging capacity ΔQ, charging time T, main peak center voltage V1 and the ratio of charging capacity to interval voltage K corresponding to the optimal voltage interval are extracted as the characteristic parameters to characterize the inconsistency of the battery.The local outlier factor algorithm is employed to screen the abnormal aging batteries while the K-means clustering algorithm is adopted to complete the sorting of retired batteries.Meanwhile, a static and dynamic two-dimensional index system is proposed to evaluate the sorting consistency, and two sets of charge and discharge data of decommissioned batteries are used for verification.Our experimental results show the battery's static consistency is increased by 55％ and its dynamic consistency by 82％ after sorting, and the average test time of a single battery is reduced to 30 minutes.Compared with the K-means clustering algorithm, the static and dynamic consistency of sorting is increased by 50％ and 33％respectively after fusing the local outlier factor algorithm.Compared with the capacity increment method and the static parameter sorting method, the static consistency of our method is up by 28％ and 5％respectively, and the dynamic consistency jumps by 76％ and 61％ respectively.关键词
退役动力电池/一致性/快速分选/K-means/LOFKey words
retired power battery/consistency/fast sorting/K-means/LOF分类
信息技术与安全科学引用本文复制引用
聂金泉,高洋洋,黄燕琴,李银银..基于充电曲线特征的退役动力电池快速分选方法[J].重庆理工大学学报,2024,38(7):79-86,8.基金项目
中央引导地方科技发展专项(2020ZYYD001) （2020ZYYD001）
