Sorting of retired lithium-ion batteries based on SOM+SVM

doi:10.19799/j.cnki.2095-4239.2022.0336

Abstract

Abstract:

A retired lithium-ion battery-sorting approach based on SOM (self-organizing feature mapping network)+SVM (support vector machine) is proposed to address the shortage of sorting methods for retired power batteries. A battery test was performed on retired batteries. Battery test systems were used to record the changes in battery current, voltage, temperature, and discharge capacity. The battery PNGV (new generation automotive partner) model parameters were identified. Furthermore, the batteries were grouped and sorted according to the characteristics and model parameters, including battery capacity and constant voltage drop time. The retired battery cells in the sorting and classification results were reorganized in parallel, the consistency experiment was performed, and the experimental results were compared and analyzed. The results revealed that the consistency of polarization internal resistance, residual capacity, constant pressure drop time, and temperature conversion rate changed slightly after the recombination cycle operation of the retired battery parameters under this method, and the dispersion of internal ohmic resistance decreased significantly, which is of practical significance in the sorting of retired batteries.

Key words: SOM, SVM, PNGV model, retired battery, battery sorting

CLC Number:

TM 911.1

Lu WANG, Feng WANG, Jing XU, Yanpeng ZHAO, Wei LI, Yanyan WANG, Yingbiao WANG. Sorting of retired lithium-ion batteries based on SOM+SVM[J]. Energy Storage Science and Technology, 2022, 11(11): 3623-3630.

Figures/Tables 14

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 1

Fig. 7

Fig. 8

Table 2

Fig. 9

Fig. 10

Table 3

Table 4

References 17

1	孟高军, 苏令, 孙玉坤, 等. 退役电池梯次利用的一致性管理研究综述[J]. 电源技术, 2021, 45(10): 1376-1379.
	MENG G J, SU L, SUN Y K, et al. Review on consistency management of echelon use of retired battery[J]. Chinese Journal of Power Sources, 2021, 45(10): 1376-1379.
2	李建林, 李雅欣, 吕超, 等. 退役动力电池梯次利用关键技术及现状分析[J]. 电力系统自动化, 2020, 44(13): 172-183.
	LI J L, LI Y X, LYU C, et al. Key technology and research status of cascaded utilization in decommissioned power battery[J]. Automation of Electric Power Systems, 2020, 44(13): 172-183.
3	王东梅, 冯伟峰, 白红燕, 等. 基于电压平台的电池聚类分选算法研究[J]. 电源技术, 2016, 40(5): 994-996, 1007.
	WANG D M, FENG W F, BAI H Y, et al. Research on clustering algorithm based on voltage platform for cells classification[J]. Chinese Journal of Power Sources, 2016, 40(5): 994-996, 1007.
4	FANG K Z, CHEN S, MU D B, et al. Investigation of nickel-metal hydride battery sorting based on charging thermal behavior[J]. Journal of Power Sources, 2013, 224: 120-124.
5	沈景凤, 瞿亚浩, 李富生. 一种基于PLC的新型锂离子电池分选机开发[J]. 电子科技, 2015, 28(12): 14-17, 21.
	SHEN J F, QU Y H, LI F S. A new lithium-ion battery separator based on PLC[J]. Electronic Science and Technology, 2015, 28(12): 14-17, 21.
6	ZHU Z W, ZHENGB Y J, LAI X, et al. A novel fast estimation and regroup method of retired lithium‐ion battery cells[J]. International Journal of Energy Research, 2020, 44(14): 11985-11997.
7	薛金花, 陶以彬, 杨波, 等. 基于老化机理分析退役磷酸铁锂电池分选方法[J]. 电源技术, 2020, 44(5): 660-665.
	XUE J H, TAO Y B, YANG B, et al. Screening method of retired LiFePO₄ batteries based on aging mechanism analysis[J]. Chinese Journal of Power Sources, 2020, 44(5): 660-665.
8	王焰辉, 黄志福, 林宇星, 等. 基于聚类分析算法的锂离子电芯一致性分选[J]. 电池, 2021, 51(3): 247-251.
	WANG Y H, HUANG Z F, LIN Y X, et al. Consistency sorting of Li-ion cell based on clustering analysis algorithm[J]. Battery Bimonthly, 2021, 51(3): 247-251.
9	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.
10	KOHONEN T. Self-organized formation of topologically correct feature maps[J]. Biological Cybernetics, 1982, 43(1): 59-69.
11	胡乙丹, 姜吉祥, 董霞. 基于结合聚类与SVM参数寻优的短期电力负荷预测方法[J]. 电力信息与通信技术, 2022, 20(5): 54-60.
	HU Y D, JIANG J X, DONG X. A short-term power load forecasting method based on combining clustering and SVM parameter optimization[J]. Electric Power Information and Communication Technology, 2022, 20(5): 54-60.
12	INEEL, United States Idaho National Engineering & Environmental Laboratory. Freedom CAR battery test manual [M]. USA: INEEL, 2001: 6-30.
13	杨阳, 汤桃峰, 秦大同, 等. 电动汽车锂电池PNGV等效电路模型与SOC估算方法[J]. 系统仿真学报, 2012, 24(4): 938-942.
	YANG Y, TANG T F, QIN D T, et al. PNGV equivalent circuit model and SOC estimation algorithm of lithium batteries for electric vehicle[J]. Journal of System Simulation, 2012, 24(4): 938-942.
14	谌虹静. 锂离子动力电池剩余寿命预测与退役电池分选方法研究[D]. 南京: 东南大学, 2019.
	CHEN H J. Research on remaining useful life prediction of lithium-ion battery and screening method of recycling battery[D]. Nanjing: Southeast University, 2019.
15	黄保帅, 张巍. 基于单体一致性对动力锂电池性能的影响研究[J]. 电源技术, 2018, 42(9): 1310-1311, 1320.
	HUANG B S, ZHANG W. Influence of cell consistency on performance of power lithium battery[J]. Chinese Journal of Power Sources, 2018, 42(9): 1310-1311, 1320.
16	高洋. 三元材料锂离子电池老化诊断、评估与建模方法[D]. 北京: 北京交通大学, 2019.
	GAO Y. Aging diagnosis, evaluation and modeling method of ternary lithium ion battery[D]. Beijing: Beijing Jiaotong University, 2019.
17	张朝龙, 赵筛筛, 章博. 基于因子分析与K-means聚类的退役动力电池快速分选方法[J]. 电力系统保护与控制, 2021, 49(12): 41-47.
	ZHANG C L, ZHAO S S, ZHANG B. A fast classification method based on factor analysis and K-means clustering for retired electric vehicle batteries[J]. Power System Protection and Control, 2021, 49(12): 41-47.

参数	R₀₁/Ω	R₀₂/Ω	R₀₃/Ω	C_b1/F	C_b2/F	C_b3/F	R_a1/Ω	R_a2/Ω
数值	0.149～0.024	0.142～0.024	0.139～0.024	1.718～4.221	1.869～5.689	1.748～6.405	0.020～0.075	0.019～0.092
参数	R_a3/Ω	C_a1/F	C_a2/F	C_a3/F	C/Ah	t_D/s	T_R	C_a2/F
数值	0.021～0.126	13.04～1696.66	227.54～1483.42	147.16～1188.76	0.606～0.947	258～1511.9	0.00271～0.00691	227.54～1483.42

特征参数	R₀₁	R₀₂	R₀₃	R_a1	R_a2	R_a3	C	t_D	T_R
循环前离散度	0.195	0.186	0.157	0.182	0.190	0.117	0.480	0.202	0.112
循环后离散度	0.099	0.117	0.133	0.216	0.223	0.112	0.501	0.225	0.128

[1]	Mengmeng GENG, Maosong FAN, Kai YANG, Guangjin ZHAO, Zhen TAN, Fei GAO, Mingjie ZHANG. Fast estimation method for state-of-health of retired batteries based on electrochemical impedance spectroscopy and neural network [J]. Energy Storage Science and Technology, 2022, 11(2): 673-678.
[2]	Wanli CHEN, Mei ZHANG, Tao FENG. The health factor extraction and SOH prediction of lithium battery based on douglas-peucker fusion minkowski distance [J]. Energy Storage Science and Technology, 2022, 11(10): 3306-3315.
[3]	Yuyang LIU, Shunli WANG, Yanxin XIE, Weikang JI, Yixing ZHANG. Research on Li-ion battery modeling and SOC estimation based on online parameter identification and improved 2RC-PNGV model [J]. Energy Storage Science and Technology, 2021, 10(6): 2312-2317.
[4]	LI Jiabo, WEI Meng, LI Zhongyu, YE Min, JIAO Shengjie, XU Xinxin. An improved battery state estimation based on support vector machine regression [J]. Energy Storage Science and Technology, 2020, 9(4): 1200-1205.
[5]	WANG Yuyuan, LI Jiabo, ZHANG Fu. Battery state estimation of least squares support vector machinebased on particle swarm optimization [J]. Energy Storage Science and Technology, 2020, 9(4): 1153-1158.
[6]	LI Jiabo, LI Zhongyu, JIAO Shengjie, YE Min, XU Xinxin. Lithium-ion state estimation based on feedback least square support vector machine [J]. Energy Storage Science and Technology, 2020, 9(3): 951-957.
[7]	LIU Jian. Second use potential of retired EV batteries in power system and associated cost analysis#br# [J]. Energy Storage Science and Technology, 2017, 6(2): 243-249.