The two-stage sorting method for retired power battery modules based on multistate coupling

doi:10.19799/j.cnki.2095-4239.2024.0843

Abstract

Abstract:

To effectively reduce the lifetime loss of retired power batteries and improve the consistency of functional states during battery module operation, this study proposes a multistate coupled battery module screening method that considers the state of energy (SOE), state of health (SOH), and state of power (SOP). First, key electrical performance parameters of retired power batteries, such as capacity, voltage, and internal resistance, are extracted. Using this data, a multistate coupling characterization model is developed to represent the SOE, SOH, and SOP of battery modules. Next, the SOE characteristics of the battery modules are estimated, and the consistency of SOH across the modules is predicted to enable hierarchical utilization. An improved K-means clustering algorithm is used for the first stage of dynamic sorting of battery modules. Finally, a multiparameter SOP characterization model is constructed to estimate the SOP deviation among batteries within each module. The battery module is dynamically sorted in the second stage. Simulation case analysis demonstrates the effectiveness of this method. It significantly improves the consistency of battery modules during their second use, reduces system operational life loss, and establishes a theoretical foundation for large-scale secondary utilization in energy storage systems.

Key words: multistate coupling, decommissioned power battery modules, two-stage sorting, improved K-means clustering, dynamic consistency

CLC Number:

TK 01

Chunsheng LI, Shengchun WANG, Chi SONG, Lihai LIU, Ning YAN. The two-stage sorting method for retired power battery modules based on multistate coupling[J]. Energy Storage Science and Technology, 2025, 14(2): 822-830.

Figures/Tables 11

Fig. 1

Fig. 2

Fig. 3

Table 1

Fig. 4

Table 2

Fig. 5

Fig. 6

Table 3

Fig. 7

Table 4

References 17

1	马天翼, 王伟哲, 林春景, 等. 磷酸铁锂电池多阶段利用可行性[J]. 储能科学与技术, 2022, 11(1): 119-126. DOI: 10.19799/j.cnki.2095-4239.2021.0317.
	MA T Y, WANG W Z, LIN C J, et al. Feasibility of the multistage reuse of commercial LiFePO₄/C batteries[J]. Energy Storage Science and Technology, 2022, 11(1): 119-126. DOI: 10.19799/j.cnki.2095-4239.2021.0317.
2	工业和信息化部. 新能源汽车动力蓄电池回收利用管理暂行办法[EB/OL]. http://www.miit.gov.cn/n1146295/n1652858/n1652930/n3757016/c6068823/content.html.
	Ministry of Industry and Information Technology. Interim measures for the management of recycling and utilization of new energy vehicle power batteries[EB/OL]. http://www.miit.gov.cn/n1146295/n1652858/n1652930/n3757016/c6068823/contenthtml.
3	颜宁, 李相俊, 钟瑶, 等. 基于静动态一致性的退役动力电池模组阶梯式筛选方法[J]. 中国电机工程学报, 2023, 43(5): 2060-2069. DOI: 10.13334/j.0258-8013.pcsee.213256.
	YAN N, LI X J, ZHONG Y, et al. Stepwise screening method for retired power battery modules based on static-dynamic consistency[J]. Proceedings of the CSEE, 2023, 43(5): 2060-2069. DOI: 10.13334/j.0258-8013.pcsee.213256.
4	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. DOI: 10.19799/j.cnki.2095-4239.2023.0333.
	FAN M S, GENG M M, ZHAO G J, et al. Research on battery sorting technology for echelon utilization based on multifrequency impedance[J]. Energy Storage Science and Technology, 2023, 12(7): 2202-2210. DOI: 10.19799/j.cnki.2095-4239.2023.0333.
5	LIN M Q, WU J, ZOU J Q, et al. Retired battery screening based on Markov transition field and swin transformer[J]. IEEE Transactions on Transportation Electrification, 2024, 10(2): 4217-4227. DOI: 10.1109/TTE.2023.3306393.
6	刘征宇, 郭乐凯, 孟辉, 等. 基于改进DBSCAN的退役动力电池分选方法[J]. 电工技术学报, 2023, 38(11): 3073-3083. DOI: 10.19595/j.cnki.1000-6753.tces.220187
	LIU Z Y, GUO L K, MENG H, et al. A sorting method of retired power battery based on improved DBSCAN[J]. Transactions of China Electrotechnical Society, 2023, 38(11): 3073-3083. DOI: 10.19595/j.cnki.1000-6753.tces.220187
7	李建林, 王哲, 李雅欣, 等. 退役动力电池梯级利用分选技术研究[J]. 太阳能学报, 2023, 44(1): 418-425. DOI: 10.19912/j.0254-0096.tynxb.2021-0859.
	LI J L, WANG Z, LI Y X, et al. Research on sorting technology of cascade utilization of retired power batteries[J]. Acta Energiae Solaris Sinica, 2023, 44(1): 418-425. DOI: 10.19912/j.0254-0096.tynxb.2021-0859.
8	王帅, 尹忠东, 郑重, 等. 基于电压曲线的退役电池模组分选方法[J]. 中国电机工程学报, 2020, 40(8): 2691-2704. DOI: 10.13334/j.0258-8013.pcsee.190659.
	WANG S, YIN Z D, ZHENG Z, et al. A sorting method for retired battery modules based on voltage curves[J]. Proceedings of the CSEE, 2020, 40(8): 2691-2704. DOI: 10.13334/j.0258-8013.pcsee.190659.
9	于会群, 胡哲豪, 彭道刚, 等. 退役动力电池回收及其在储能系统中梯次利用关键技术[J]. 储能科学与技术, 2023, 12(5): 1675-1685. DOI: 10.19799/j.cnki.2095-4239.2023.0036
	YU H Q, HU Z H, PENG D G, et al. Key technologies for retired power battery recovery and its cascade utilization in energy storage systems[J]. Energy Storage Science and Technology, 2023, 12(5): 1675-1685. DOI: 10.19799/j.cnki.2095-4239.2023.0036
10	ZHOU Z H, RAN A H, CHEN S X, et al. A fast screening framework for second-life batteries based on an improved bisecting K-means algorithm combined with fast pulse test[J]. Journal of Energy Storage, 2020, 31: 101739. DOI: 10.1016/j.est.2020.101739.
11	杨昌海, 徐逸扬, 杨婷婷, 等. 改进SPBO优化BIRCH算法的退役动力电池等级划分[J]. 电源技术, 2023, 47(4): 469-473. DOI: 10.3969/j.issn.1002-087X.2023.04.012.
	YANG C H, XU Y Y, YANG T T, et al. Improved SPBO optimized BIRCH algorithm for retired power battery grade classification[J]. Chinese Journal of Power Sources, 2023, 47(4): 469-473. DOI: 10.3969/j.issn.1002-087X.2023.04.012.
12	张彦龙, 朱华炳, 刘征宇, 等. 基于DBSCAN聚类的退役动力电池深度配组方法[J]. 电源技术, 2023, 47(4): 462-468. DOI: 10.3969/j.issn.1002-087X.2023.04.011.
	ZHANG Y L, ZHU H B, LIU Z Y, et al. A reutilization grouping method for retired power batteries based on DBSCAN clustering[J]. Chinese Journal of Power Sources, 2023, 47(4): 462-468. DOI: 10.3969/j.issn.1002-087X.2023.04.011.
13	XU Z C, WANG J, LUND P D, et al. A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries[J]. Journal of Energy Storage, 2020, 29: 101303. DOI: 10.1016/j.est.2020.101303.
14	颜宁, 武中立, 李相俊, 等. 基于数字孪生技术的退役电池模组动态SOF筛选方法研究[J/OL]. 中国电机工程学报, 1-10[2024-08-13]. https://doi.org/10.13334/j.0258-8013.pcsee.232601.
	YAN N, WU Z L, LI X J, et al. Research on the screening method for retired battery modules based on digital twin technology and SOF dynamic characteristics[J/OL]. Proceedings of the CSEE, 1-10[2024-08-13]. https://doi.org/10.13334/j.0258-8013.pcsee.232601.
15	陈剑, 肖振锋, 刘顺成, 等. 基于EKF-SVSF的锂离子电池SOC和SOH准确估计[J]. 电源技术, 2020, 44(10): 1483-1487. DOI: 10.3969/j.issn.1002-087X.2020.10.021.
	CHEN J, XIAO Z F, LIU S C, et al. Accurate estimation of SOC and SOH of Li-ion battery based on EKF-SVSF[J]. Chinese Journal of Power Sources, 2020, 44(10): 1483-1487. DOI: 10.3969/j.issn.1002-087X.2020.10.021.
16	刘子豪, 张雪松, 林达, 等. 基于扩展卡尔曼滤波的储能电池能量和功率状态联合估计方法[J]. 储能科学与技术, 2023, 12(3): 913-922. DOI: 10.19799/j.cnki.2095-4239.2022.0637.
	LIU Z H, ZHANG X S, LIN D, et al. Joint energy and power state estimation method for energy storage battery based on extended Kalman filter[J]. Energy Storage Science and Technology, 2023, 12(3): 913-922. DOI: 10.19799/j.cnki.2095-4239.2022.0637.
17	ZHENG K Y, ZHANG W G, WU X Z, et al. Partial-power conversion for increased energy storage capability of Li-ion battery energy storage system[J]. IEEE Transactions on Industrial Electronics, 2024, 71(5): 4742-4752. DOI: 10.1109/TIE.2023.3283710.

电池单体		电池模组
参数	数值	参数	数值
电池类型	方形钢壳电池	电压范围/V	2.0~3.8
电压范围/V	2.0~3.8	标称电压/V	3.2
标称电压/V	3.2	额定容量/Ah	80
额定容量/Ah	80	正极材料	磷酸铁锂
能量密度/(Wh/kg)	192	负极材料	石墨

筛选方法	集群离散度/%
筛选方法	集群1	集群2	集群3
K-means聚类	2.34	1.76	0.79
改进K-means聚类	2.06	1.19	0.66
离散提升度/%	11.97	32.39	16.46

误差区间	分组	电池模组编号
[0, 1.0%]	D1	C-2、C-3、C-4、C-5、C-7
[1.0%, 2.0%]	D2	C-1、C-6、C-9、C-10
超过2.0%	D3	C-8、C-11、C-12、C-13

考虑因素		循环次数
考虑因素		200	400
考虑SOP一致性/%	H1	77.29	66.67
	H2	77.75	66.53
	H3	78.37	66.21
不考虑SOP一致性/%		77.25	65.50

[1]	Cheng CHEN, Shili LIN, Anxin HU, Xianyong ZHANG. Research on energy management strategy optimization of CCHP system with composite energy storage in grid-connected and power export modes [J]. Energy Storage Science and Technology, 2024, 13(11): 3981-3992.
[2]	Junmei WANG, Xiaonan WU, Bairui LIAO. Multi-season optimization of integrated energy systems considering environmental penalties [J]. Energy Storage Science and Technology, 2024, 13(10): 3720-3729.
[3]	Xi ZHAO. Research on smart energy design of wind and solar energy storage integration in the context of Internet plus [J]. Energy Storage Science and Technology, 2024, 13(10): 3566-3568.
[4]	Siyuan HUANG, Chen WANG, Ting LIANG, Zhu JIANG, Jiajing LI, Xiaohui SHE, Xiaosong ZHANG. Research on optimal configuration for integrated energy system with liquid air energy storage combined heat and power supply [J]. Energy Storage Science and Technology, 2024, 13(6): 1929-1939.
[5]	Hongpei NIU. Research on the application of phase change energy storage materials in energy saving building design [J]. Energy Storage Science and Technology, 2024, 13(3): 847-849.
[6]	Haifeng MA, Chunyan XU, Chuanbo ZHANG, Tong YU, Jian JIAO. The application of computer technology in the integrated smart energy design of wind， solar， and energy storage [J]. Energy Storage Science and Technology, 2024, 13(3): 946-948.
[7]	Xixiang XU, Yue ZHAO, Mingyue RUAN, Qiang LI. CoO lithium storage mechanism revealed based on magnetic measurement [J]. Energy Storage Science and Technology, 2024, 13(1): 12-23.
[8]	Jie JU, Ruifang CHEN, Gang WEI. Application of new phase change energy storage materials in building engineering [J]. Energy Storage Science and Technology, 2023, 12(12): 3883-3885.
[9]	Xin ZHANG, Zuoxia XING, Qitong FU, Chao ZHANG, Libing JIANG. Multiphysics study of induction heating for solid electric heat storage devices [J]. Energy Storage Science and Technology, 2023, 12(12): 3761-3769.
[10]	Hongjian WANG, Yongchun LAI, Xianjin SU, Chunbao ZENG, Linyi XU. Solutions for new energy construction projects in extreme operating environments and liquid cooled energy storage [J]. Energy Storage Science and Technology, 2023, 12(7): 2349-2354.
[11]	Xin ZHENG, Hao YU, Xiaoyu GUO, Ying ZHOU, Yuanjie ZUO, Yujia LIU. Design optimization of integrated energy system using liquid flow battery and heating and cooling storage energy system [J]. Energy Storage Science and Technology, 2023, 12(3): 870-877.
[12]	Jin CHAI, Jun WANG, Qiqiang NI. Experiment on heat transfer performance of phase change materials strengthened by nanoparticles and fins [J]. Energy Storage Science and Technology, 2022, 11(10): 3161-3170.
[13]	Jian LIU. Key Issues and Policy Mechanisms for the Development of New Energy Storage in China [J]. Energy Storage Science and Technology, 2025, (): 1-9.