融合聚类与分类的退役锂离子电池快速分选

doi:10.19562/j.chinasae.qcgc.2025.06.004

Abstract

Abstract:

In order to improve the sorting efficiency of retired batteries and reduce data collection cost， to achieve a sorting effect close to complete data by using incomplete data， in this paper， a fast sorting method for retired batteries that integrates clustering and classification is proposed. Firstly， cluster analysis is conducted on complete data to preliminarily group the batteries， and the grouping results are used as battery labels. Secondly， the most valuable segments are selected from incomplete datasets， and a two-layer classification model is trained for classification fitting， thereby achieving rapid sorting of retired lithium-ion batteries. Finally， a validation experiment is conducted on 235 lithium-ion batteries， and the results show that the proposed method has a classification detection accuracy of 95.1% and significantly reduces the time required for sorting.

Key words: lithium-ion battery, retired batteries, battery sorting, clustering, electrochemical impedance spectroscopy

Hanqing Tang,Li Wang,Mingqiang Lin,Ji Wu. Rapid Sorting of Retired Lithium-Ion Batteries by Integrating Clustering and Classification[J].Automotive Engineering, 2025, 47(6): 1048-1059.

Figures/Tables 16

References 28

1	LU L， HAN X， LI J， et al. A review on the key issues for lithium-ion battery management in electric vehicles［J/OL］. Journal of Power Sources， 2013， 226： 272-288.
2	JIANG Y， JIANG J， ZHANG C， et al. State of health estimation of second-life LiFePO₄ batteries for energy storage applications［J/OL］. Journal of Cleaner Production， 2018， 205： 754-762.
3	CUSENZA M A， GUARINO F， LONGO S， et al. Reuse of electric vehicle batteries in buildings： an integrated load match analysis and life cycle assessment approach［J/OL］. Energy and Buildings， 2019， 186： 339-354.
4	HUANG Z， XIE Z， ZHANG C， et al. Modeling and multi-objective optimization of a stand-alone PV-hydrogen-retired EV battery hybrid energy system［J/OL］. Energy Conversion and Management， 2019， 181： 80-92.
5	HUA Y， ZHOU S， HUANG Y， et al. Sustainable value chain of retired lithium-ion batteries for electric vehicles［J/OL］. Journal of Power Sources， 2020， 478： 228753.
6	NEUBAUER J， PESARAN A. The ability of battery second use strategies to impact plug-in electric vehicle prices and serve utility energy storage applications［J/OL］. Journal of Power Sources， 2011， 196（23）： 10351-10358.
7	LAI X， HUANG Y， GU H， et al. Turning waste into wealth： a systematic review on echelon utilization and material recycling of retired lithium-ion batteries［J/OL］. Energy Storage Materials， 2021， 40： 96-123.
8	XU Z， WANG J， LUND P D， et al. A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries［J/OL］. Journal of Energy Storage， 2020， 29： 101303.
9	PENG S， SUN Y， LIU D， et al. State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network ［J/OL］. Energy， 2023， 282： 128956.
10	LIN M， WU J， ZOU J， et al. Retired battery screening based on Markov transition field and swin transformer［J/OL］. IEEE Transactions on Transportation Electrification， 2024， 10（2）： 4217-4227.
11	陈琳，何熳平，吴淑孝，等. 基于自适应模糊C-均值算法的退役锂离子电池快速聚类［J］. 汽车工程， 2024， 46（4）： 643-651.
	CHEN L， HE M P， WU S X， et al. Fast clustering of retired lithium-ion batteries based on adaptive fuzzy C-means algorithm［J］. Automotive Engineering， 2024， 46（4）： 643-651.
12	LIU X， TANG Q， FENG Y， et al. Fast sorting method of retired batteries based on multi-feature extraction from partial charging segment［J/OL］. Applied Energy， 2023， 351： 121930.
13	来鑫，陈权威，邓聪，等. 一种基于电化学阻抗谱的大规模退役锂离子电池的软聚类方法［J］. 电工技术学报， 2022， 37（23）： 6054-6064.
	LAI X， CHEN Q W， DENG C， et al. A soft clustering method for the large-scale retired lithium-ion batteries based on electro‐ chemical impedance spectroscopy［J］. Transactions of China Electrotechnical Society， 2022， 37（23）：6054-6064.
14	范茂松，耿萌萌，赵光金，等. 基于多频点阻抗的梯次利用电池分选技术研究［J］. 储能科学与技术， 2023， 12（7）： 2202-2210.
	FAN M S， GENG M M， ZHAO G J， et al. Research on battery sorting technology for echelon utilization based onmultifrequency impedance［J］. Energy Storage Science and Technology，2023， 12 （7）： 2202-2210.
15	WANG Y， HUANG H， WANG H. Rapid-regroup strategy for retired batteries based on short-time dynamic voltage and electrochemical impedance spectroscopy［J/OL］. Journal of Energy Storage， 2023， 63： 107102.
16	CHOI W， SHIN H C， KIM J M， et al. Modeling and applications of electrochemical impedance spectroscopy （EIS） for lithium-ion batteries［J/OL］. Journal of Electrochemical Science and Technology， 2020， 11（1）： 1-13.
17	BARAI A， CHOUCHELAMANE G H， GUO Y， et al. A study on the impact of lithium-ion cell relaxation on electrochemical impedance spectroscopy ［J/OL］. Journal of Power Sources， 2015， 280： 74-80.
18	LOU F， CHEN D. Aligned carbon nanostructures based 3D electrodes for energy storage ［J/OL］. Journal of Energy Chemistry， 2015， 24（5）： 559-586.
19	ZHU J， DEWI DARMA M S， KNAPP M， et al. Investigation of lithium-ion battery degradation mechanisms by combining differential voltage analysis and alternating current impedance ［J/OL］. Journal of Power Sources， 2020， 448： 227575.
20	ECKER M， NIETO N， KÄBITZ S， et al. Calendar and cycle life study of Li（NiMnCo）O₂-based 18650 lithium-ion batteries Li（NiMnCo）O₂ ［J/OL］. Journal of Power Sources， 2014， 248： 839-851.
21	DUBARRY M， LIAW B Y. Identify capacity fading mechanism in a commercial LiFePO4 cell ［J/OL］. Journal of Power Sources， 2009， 194（1）： 541-549.
22	DOLLÉ M， GRUGEON S， BEAUDOIN B， et al. In situ TEM study of the interface carbon/electrolyte［J/OL］. Journal of Power Sources， 2001， 97-98： 104-106.
23	LEE S B， PYUN S I. The effect of electrolyte temperature on the passivity of solid electrolyte interphase formed on a graphite electrode［J/OL］. Carbon， 2002， 40（13）： 2333-2339.
24	刘万里，李子涵，梁宏毅，等.基于相似性优化模型样本的实车锂电池健康状态分析［J］. 汽车工程， 2024， 46（3）： 489-497.
	LIU W L， LI Z H， LIANG H Y， et al. Real vehicle battery health state estimation based on similarity optimization model samples［J］. Automotive Engineering， 2024， 46（3）： 489-497.
25	郑岳久，李家琦，朱志伟，等.基于快速充电曲线的退役锂电池模块快速分选技术［J］.电网技术，2020，44（5）：1664-1673.
	ZHENG Y J， LI J Q， ZHU Z W， et al. Rapid classification based on fast charging curves for reuse of retired lithium-ion battery modules［J］. Power System Technology，2020，44（5）：1664-1673.
26	LI Q， LU T， LAI C， et al. Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation ［J/OL］. Energy， 2024， 290： 130208.
27	ZHANG L， ZHANG J， GAO T， et al. Improved LSTM based state of health estimation using random segments of the charging curves for lithium-ion batteries［J/OL］. Journal of Energy Storage， 2023， 74： 109370.
28	TAN C W， DEMPSTER A， BERGMEIR C， et al. MultiRocket： multiple pooling operators and transformations for fast and effective time series classification［J/OL］. arXiv， 2022. http：//arxiv.org/abs/2102.00457.

标签	5簇
SC	0.545
CHI	379.45
DBI	0.593 3

模型	准确率	精确率	召回率	F1分数
CNN	0.875	0.607	0.7	0.632
GRU	0.906	0.679	0.833	0.724
SVM	0.937	0.469	0.5	0.483 8
Multirocket	0.984	0.8	1	0.889

模型	准确率	精确率	召回率	F1分数
Multirocket	0.95	0.970 1	0.854 2	0.898 9
CNN	0.867 2	0.726 7	0.724 7	0.736 3
GRU	0.867	0.924 6	0.659 7	0.694 2
SVM	0.6	0.15	0.25	0.187

模型	准确率	精确率	召回率	F1分数
双层分类	0.951 4	0.928	0.95	0.93
单次分类	0.843	0.833 33	0.698 3	0.711 2
CNN	0.765 6	0.646	0.547	0.547
GRU	0.75	0.583 3	0.638	0.594

模型	准确率	精确率	召回率	F1分数
充电数据	0.796 8	0.646 8	0.646 8	0.609 8
EIS数据	0.75	0.667	0.683	0.653
两种数据	0.951 4	0.928	0.95	0.93

Rapid Sorting of Retired Lithium-Ion Batteries by Integrating Clustering and Classification

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 28

Related Articles 15

Metrics

Comments

Recommended 0

[1]	Xiaoyu Li,Shen Zhao,Jun Tian,Songli Zhang,Yanli Zhu. Research on Thermal Runaway Modeling and Safety Boundary of Li-ion Batteries Under Extreme Temperature Shock [J]. Automotive Engineering, 2025, 47(6): 1022-1036.
[2]	Zhaojie Geng,Wenjing Yuan,Rong Huang,Bao Mu,Kangkang Wang,Jingjing Liang. Study on the Influence of Lithium Ion Soft Package Battery Leakage on Electrical Performance and Safety, and Big Data Warning [J]. Automotive Engineering, 2024, 46(9): 1643-1653.
[3]	Zirui Li,Haowen Wang,Jianwei Gong,Lü Chao,Xiaocong Zhao,Meng Wang. Interactive Trajectory Primitives Representation and Extraction Based on Naturalistic Driving Data [J]. Automotive Engineering, 2024, 46(8): 1382-1393.
[4]	Hanxin Yao,Xueyuan Wang,Yongjun Yuan,Haifeng Dai,Xuezhe Wei. Diagnosis for Battery Module Inconsistencies Based on Electrochemical Impedance Spectroscopy [J]. Automotive Engineering, 2024, 46(7): 1167-1176.
[5]	Tao Ye,Jianhua Wu,Linfeng Zheng,Yuxin Zhang,Chaofeng Hong. Study on Lithium Battery Fast Charge Performance with Ripple Charging Current [J]. Automotive Engineering, 2024, 46(6): 1034-1044.
[6]	Nannan Kuang,Botao Hu,Guo Li,Guanglei Zhao,Shuang Feng,Likun Xu. Study on Accurate Modeling and Efficient Simulation of Thermal Runaway Propagation of Battery Modules [J]. Automotive Engineering, 2024, 46(4): 652-661.
[7]	Wanli Liu,Zihan Li,Hongyi Liang,Jiqing Chen,Bingda Mo. Real Vehicle Battery Health State Estimation Based on Similarity Optimization Model Samples [J]. Automotive Engineering, 2024, 46(3): 489-497.
[8]	Zhixiang Li,Danhui Zhu,Jiahuan Zhang. Machine Learning Based Crashworthiness Optimization with Structural Deformation Mode Control [J]. Automotive Engineering, 2024, 46(12): 2220-2231.
[9]	Jinbao Zhang,Yongle Yang,Zhifei Zhang,Liangfeng Peng,Weixiong Lin,Youyuan Zhang,Zhongming Xu. Extrapolation of Load Spectrum Based on KANN-DBSCAN Bandwidth Optimization Kernel Density Estimation [J]. Automotive Engineering, 2024, 46(11): 2100-2109.
[10]	Qiuyan Zhang,Ze Cheng,Xu Liu. RUL Estimation Method for Lithium-ion Batteries Based on Multi-dimensional and Multi-scale Features [J]. Automotive Engineering, 2024, 46(10): 1897-1903.
[11]	Fei Chen,Xiangdong Kong,Yuedong Sun,Xuebing Han,Languang Lu,Yuejiu Zheng,Minggao Ouyang. Progress in Simulation Technology of Lithium-ion Battery Manufacturing Process [J]. Automotive Engineering, 2023, 45(9): 1516-1529.
[12]	Wenchao Guo,Lin Yang,Zhongwei Deng,Jilin Li,Zhixian Fan. Research on Multi-level Fault Warning Method for Lithium-ion Batteries Driven by Cloud Data [J]. Automotive Engineering, 2023, 45(9): 1677-1687.
[13]	Haiqiang Liang,Hongwen He,Kangwei Dai,Bo Pang,Peng Wang. Research on Lithium Ion Battery Life Prediction Method Based on Empirical Aging Model and Mechanism Model for Electric Vehicles [J]. Automotive Engineering, 2023, 45(5): 825-835.
[14]	Yubo Lian,Heping Ling,Qingchan Ma,Qiang Ren,Bin He. Research Progress on Pulse Heating Technology of Lithium-ion Battery for Electric Vehicles [J]. Automotive Engineering, 2023, 45(2): 169-174.
[15]	Jianhao Zhang,Xingqi Gao,Li Zhang. Micro Short Circuit Diagnosis Method of Battery Pack Based on Capacity Increment Curve and Charge Capacity Difference [J]. Automotive Engineering, 2023, 45(2): 191-198.