基于自适应模糊C-均值算法的退役锂离子电池快速聚类

doi:10.19562/j.chinasae.qcgc.2024.04.010

Abstract

Abstract:

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.

Key words: decommissioned lithium batteries, echelon utilization, sorting and restructuring, machine learning

Lin Chen,Manping He,Shuxiao Wu,Deqian Chen,Mingsi Zhao,Haihong Pan. Fast Clustering of Retired Lithium-ion Batteries Based on Adaptive Fuzzy C-means Algorithm[J].Automotive Engineering, 2024, 46(4): 643-651.

Figures/Tables 13

References 21

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簇号	数量	聚类中心	电池编号
1	14	（2299.90，71.02，6.36）	R2/R21/R33/R57/R58/R65/R79/R80/R81/R83/R84/R89/R94/R96
2	7	（1933.87，75.18，2.92）	R4/R9/R10/R34/R51/R98/R107
3	8	（1683.67，83.05，1.78）	R16/R30/R52/R104/R113/R116/R117/R135
4	10	（2241.24，74.35，5.04）	R3/R23/R24/R26/R45/R48/R49/R60/R61/R62
5	7	（1819.09，90.18，2.62）	R8/R13/R14/R35/R43/R56/R68
6	6	（2066.44，79.18，3.72）	R25/R28/R39/R53/R54/R110
7	3	（2532.60，66.18，6.11）	R17/R90/R121
8	5	（1747.09，82.44，2.17）	R37/R38/R41/R67/R87
9	6	（1872.04，74.71，2.70）	R12/R15/R36/R40/R64/R93
10	3	（2161.55，82.09，3.4）	R20/R27/R91
11	10	（2207.02，75.74，4.88）	R5/R18/R22/R44/R46/R47/R76/R77/R78/R106
12	10	（2576.13，65.94，6.31）	R42/R59/R119/R125/R126/R129/R130/R131/R133/R134
13	11	（2411.62，73.18，6.15）	R86/R103/R111/R112/R118/R120/R122/R123/R124/R128/R132
14	3	（2126.14，80.91，3.50）	R31/R32/R70
15	4	（1603.96，86.67，1.52）	R88/R97/R99/R114
16	10	（2021.94，78.60，3.43）	R1/R6/R7/R11/R19/R29/R50/R55/R85/R136

算法	c	最大误差			平均误差
算法	c	容量/ （mA·h）	内阻/ mΩ	FD	容量/ （mA·h）	内阻/ mΩ	FD
AFCM	16	79.00	44.62	5.35	41.06	15.46	1.73
K-mean	10	106.00	49.00	5.42	70.50	20.23	2.13
	12	110.00	49.00	5.99	57.75	18.27	1.77
	16	86.00	45.69	5.99	42.63	15.77	1.71

分类因素	名称	电池编号
AFCM三因素	Pack1	R46/R47/R76/R77/R78/R106
	Pack2	R42/R59/R126/R129/R130/R131
	Pack3	R57/R65/R79/R80/R81/R96
AFCM容量单因素	Pack4	R16/R30/R37/R38/R41/R52
AFCM内阻单因素	Pack5	R9/R15/R34/R45/R111/R118
AFCM双因素	Pack6	R8/R35/R43/R56/R68/R87

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

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