Fast Clustering of Retired Lithium-ion Batteries Based on Adaptive Fuzzy C-means Algorithm

被引：0
|
作者：
Chen L. [1 ,2 ]
He M. [1 ]
Wu S. [1 ]
Chen D. [1 ]
Zhao M. [1 ]
Pan H. [1 ]
机构：
[1] School of Mechanical Engineering, Guangxi University, Nanning
[2] Guangxi Key Lab of Manufacturing System and Advanced Manufacturing Technology, Nanning
来源：
Qiche Gongcheng/Automotive Engineering | 2024年 / 46卷 / 04期
关键词：
decommissioned lithium batteries; echelon utilization; machine learning; sorting and restructuring;
D O I：
10.19562/j.chinasae.qcgc.2024.04.010
中图分类号：
学科分类号：
摘要：
The treatment of retired lithium-ion batteries(LiBs)by echelon utilization has great economic and environmental values, and how to sort and reconstitute decommissioned batteries retired LiBs efficiently and accurately is a prominent technical challenge in stepwise utilization. Firstly, to accurately reflect the consistency of retired batteries LiBs, the three factors of maximum available capacity(MAC), discharge ohmic internal resistance (DOIR)and Frechet distance(FD)of incremental capacity curve, are extracted together as clustering factors. Then the three clustering factors are combined with the adaptive fuzzy C-mean(AFCM)algorithm to construct a clustering method for retired batteries LiBs. The results show that the maximum error of MAC within the clustered clusters of the AFCM algorithm is 79 mAh with the DOIR less than 45 mΩ. The clustering method of the three factors into groups of batteries has better consistency;and the AFCM algorithm clustering takes the shortest time when 117 batteries are clustered. © 2024 SAE-China. All rights reserved.
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页码：643 / 651
页数：8
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