SOH estimation based on distribution of relaxation times for the retired power lithium-ion battery

doi:10.19799/j.cnki.2095-4239.2024.0749

Abstract

Abstract:

Retired batteries should undergo rigorous testing and evaluation to ascertain their performance before they are deployed in echelons utilization to ensure that appropriate application scenarios are selected based on the performance of the batteries. An accurate assessment of the State of Health (SOH) is fundamental to determine whether a power battery possesses the value of echelons utilization. In view of the low accuracy of SOH evaluation of retired power batteries, the relaxation time distribution method was used to analyze the electrochemical impedance spectroscopy in this study. This was done to obtain the characteristic frequency that can accurately reflect the health state of the battery. The impedance data corresponding to the characteristic frequency was used as the characteristic input parameters, and the extreme learning machine model optimized using the sparrow algorithm served as input to realize the SOH evaluation of decommissioned power batteries. To verify the effectiveness of the evaluation method, seven retired prismatic lithium-iron-phosphate batteries were subjected to cyclic aging experiments, and electrochemical impedance spectroscopy tests were performed on the batteries after each cycle. Actual electrochemical impedance spectroscopy of the decommissioned power batteries was used for analysis and modeling to evaluate the SOH, and the results were compared with actual SOH data obtained using traditional SOH evaluation methods. Our findings demonstrate that the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) associated with the relaxation time distribution method are lower than those of other methods. Compared with the unoptimized extreme learning machine model, the values of the MSE and MAPE reduced by 47.1% and 60.5%, respectively, indicating that the SOH evaluation method employed in this study is relatively highly accurate and less prone to error, indicating its applicability in practical echelons utilization.

Key words: retired lithium-ion power battery, state of health, electrochemical impedance spectroscopy, distribution of relaxation times, extreme learning machine

CLC Number:

TM 912

Ziheng ZHANG, Mengmeng GENG, Maosong FAN, Yuhong JIN, Jingbing LIU, Kai YANG, Hao WANG. SOH estimation based on distribution of relaxation times for the retired power lithium-ion battery[J]. Energy Storage Science and Technology, 2025, 14(2): 770-778.

Figures/Tables 11

Fig.1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Table 1

Table 2

Fig. 9

References 30

1	王宇胜, 陈德旺, 蔡俊鹏, 等. 基于LSTM-SVR的锂电池健康状态预测研究[J]. 电源技术, 2020, 44(12): 1784-1787. DOI: 10.3969/j.issn.1002-087X.2020.12.019.
	WANG Y S, CHEN D W, CAI J P, et al. Research on lithium battery state of health prediction based on LSTM-SVR[J]. Chinese Journal of Power Sources, 2020, 44(12): 1784-1787. DOI: 10.3969/j.issn.1002-087X.2020.12.019.
2	王思瑜. 新能源汽车发展现状研究综述[J]. 内燃机与配件, 2024(5): 135-137. DOI: 10.19475/j.cnki.issn1674-957x.2024.05.033.
	WANG S Y. Overview of research on the development status of new energy vehicles[J]. Internal Combustion Engine & Parts, 2024(5): 135-137. DOI: 10.19475/j.cnki.issn1674-957x.2024.05.033.
3	TAN Q Y, LI J H, YANG L Y, et al. Cascade use potential of retired traction batteries for renewable energy storage in China under carbon peak vision[J]. Journal of Cleaner Production, 2023, 412: 137379. DOI:10.1016/j.jclepro.2023.137379.
4	何海斌. 磷酸铁锂退役电池储能系统健康度在线评估研究[J]. 上海电气技术, 2022, 15(2): 75-80, 69. DOI: 10.3969/j.issn.1674-540X.2022.02.016.
5	MENG H X, LI Y F. A review on prognostics and health management (PHM) methods of lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews, 2019, 116: 109405. DOI:10.1016/j.rser.2019.109405.
6	HOSSAIN LIPU M S, HANNAN M A, HUSSAIN A, et al. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations[J]. Journal of Cleaner Production, 2018, 205: 115-133. DOI:10.1016/j.jclepro.2018.09.065.
7	XING Y J, MA E W M, TSUI K L, et al. Battery management systems in electric and hybrid vehicles[J]. Energies, 2011, 4(11): 1840-1857. DOI:10.3390/en4111840.
8	熊庆, 邸振国, 汲胜昌. 锂离子电池健康状态估计及寿命预测研究进展综述[J]. 高电压技术, 2024, 50(3): 1182-1195. DOI: 10.13336/j.1003-6520.hve.20221843
	XIONG Q, DI Z G, JI S C. Review on health state estimation and life prediction of lithium-ion batteries[J]. High Voltage Engineering, 2024, 50(3): 1182-1195. DOI: 10.13336/j.1003-6520.hve.20221843
9	赵显赫, 耿光超, 林达, 等. 基于数据驱动的锂离子电池健康状态评估综述[J]. 浙江电力, 2021, 40(7): 65-73. DOI: 10.19585/j.zjdl.202107011.
	ZHAO X H, GENG G C, LIN D, et al. Review of data-driven state of health estimation for lithium-ion battery[J]. Zhejiang Electric Power, 2021, 40(7): 65-73. DOI: 10.19585/j.zjdl.202107011.
10	KHAN N, ULLAH F U M, Afnan, et al. Batteries state of health estimation via efficient neural networks with multiple channel charging profiles[J]. IEEE Access, 2020, 9: 7797-7813. DOI:10.1109/ACCESS.2020.3047732.
11	ZHOU K Q, QIN Y, LAU B P L, et al. Lithium-ion battery state of health estimation based on cycle synchronization using dynamic time warping[C]//IECON 2021-47th Annual Conference of the IEEE Industrial Electronics Society. October 13-16, 2021, Toronto, ON, Canada. IEEE, 2021: 1-6. DOI:10.1109/IECON4 8115.2021.9589504.
12	TIAN J P, XIONG R, SHEN W X. State-of-health estimation based on differential temperature for lithium ion batteries[J]. IEEE Transactions on Power Electronics, 2020, 35(10): 10363-10373. DOI:10.1109/TPEL.2020.2978493.
13	NAGULAPATI V M, LEE H, JUNG D, et al. A novel combined multi-battery dataset based approach for enhanced prediction accuracy of data driven prognostic models in capacity estimation of lithium ion batteries[J]. Energy and AI, 2021, 5: 100089. DOI:10.1016/j.egyai.2021.100089.
14	DENG Z W, HU X S, LI P H, et al. Data-driven battery state of health estimation based on random partial charging data[J]. IEEE Transactions on Power Electronics, 2022, 37(5): 5021-5031. DOI:10.1109/TPEL.2021.3134701.
15	王琛, 闵永军. 基于容量增量曲线与GWO-GPR的锂离子电池SOH估计[J]. 储能科学与技术, 2023, 12(11): 3508-3518. DOI: 10.19799/j.cnki.2095-4239.2023.0458.
	WANG C, MIN Y J. SOH estimation of lithium-ion batteries based on capacity increment curve and GWO-GPR[J]. Energy Storage Science and Technology, 2023, 12(11): 3508-3518. DOI: 10.19799/j.cnki.2095-4239.2023.0458.
16	EZEMOBI E, TONOLI A, SILVAGNI M. Battery state of health estimation with improved generalization using parallel layer extreme learning machine[J]. Energies, 2021, 14(8): 2243. DOI:10.3390/en14082243.
17	CHEN K, LI J L, LIU K, et al. State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine[J]. Green Energy and Intelligent Transportation, 2024, 3(1): 100151. DOI:10.1016/j.geits.2024.100151.
18	柯学, 洪华伟, 郑鹏, 等. 基于多时间尺度建模自动特征提取和通道注意力机制的锂离子电池健康状态估计[J].储能科学与技术, 2024, 13(9): 3059-3071. DOI:10.19799/j.cnki.2095-4239.2024.0627.
	KE X, HONG H W,ZHENG P, et al. Lithium-ion battery health state estimation based on automatic feature extraction and channel attention mechanism for multi-timescale modeling[J].Energy Storage Science and Technology, 2024, 13(9): 3059-3071. DOI:10.19799/j.cnki.2095-4239.2024.0627.
19	CHEN K, LIAO Q, LIU K, et al. Capacity degradation prediction of lithium-ion battery based on artificial bee colony and multi-kernel support vector regression[J]. Journal of Energy Storage, 2023, 72: 108160. DOI:10.1016/j.est.2023.108160.
20	高治军, 戴瑞, 宁一, 等. 结合特征分析和 V-PSA-LSTM的锂离子电池SOH预估[J/OL].电源学报. [2024-0808]. https://link.cnki.net/urlid/12.1420.tm.20240807.1556.002
21	郭鹏旭, 赵理, 张丰硕. 基于随机充电片段的锂电池健康状态估计方法[J/OL]. 电源学报. [2024-08-02]. https://link.cnki.net/urlid/12.1420.tm.20240819.1312.002.
22	MA B, YANG S, ZHANG L, et al. Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model[J]. iScience, 2022, 25(12): 105638. DOI: 10.1016/j.isci.2022.105638.
23	吴磊, 吕桃林, 陈启忠, 等. 电化学阻抗谱测量与应用研究综述[J]. 电源技术, 2021, 45(9): 1227-1230. DOI: 10.3969/j.issn.1002-087X. 2021.09.035.
	WU L, LV T L, CHEN Q Z, et al. Review of measurement and application of electrochemical impedance spectroscopy[J]. Chinese Journal of Power Sources, 2021, 45(9): 1227-1230. DOI: 10.3969/j.issn.1002-087X.2021.09.035.
24	LIU W, XU Y, FENG X. A hierarchical and flexible data-driven method for online state-of-health estimation of Li-ion battery[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14739-14748. DOI:10.1109/TVT.2020.3037088.
25	FU Y M, XU J, SHI M J, et al. A fast impedance calculation-based battery state-of-health estimation method[J]. IEEE Transactions on Industrial Electronics, 2022, 69(7): 7019-7028. DOI:10.1109/TIE.2021.3097668.
26	邹红波, 柴延辉, 杨钦贺, 等. 基于混合ISSA-LSTM的锂离子电池剩余使用寿命预测[J]. 电力系统保护与控制, 2023, 51(19): 21-31. DOI: 10.19783/j.cnki.pspc.230297.
	ZOU H B, CHAI Y H, YANG Q H, et al. Remaining useful life prediction of lithium-ion batteries based on hybrid ISSA-LSTM[J]. Power System Protection and Control, 2023, 51(19): 21-31. DOI: 10.19783/j.cnki.pspc.230297.
27	XUE J K, SHEN B. A novel swarm intelligence optimization approach: Sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1): 22-34. DOI:10.1080/21642583. 2019.1708830.
28	耿萌萌, 范茂松, 杨凯, 等. 基于EIS和神经网络的退役电池SOH快速估计[J]. 储能科学与技术, 2022, 11(2): 673-678. DOI: 10.19799/j.cnki.2095-4239.2021.0503.
	GENG M M, FAN M S, YANG K, et al. 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. DOI: 10.19799/j.cnki.2095-4239.2021.0503.
29	董明, 范文杰, 刘王泽宇, 等. 基于特征频率阻抗的锂离子电池健康状态评估[J]. 中国电机工程学报, 2022, 42(24): 9094-9105. DOI: 10.13334/j.0258-8013.pcsee.212036.
	DONG M, FAN W J, LIU W, et al. Health assessment of lithium-ion batteries based on characteristic frequency impedance[J]. Proceedings of the CSEE, 2022, 42(24): 9094-9105. DOI: 10.13334/j.0258-8013.pcsee.212036.
30	胡广方. 基于交流阻抗特性的锂离子电池析锂特征及安全性研究[D]. 长沙: 湖南大学, 2021. DOI: 10.27135/d.cnki.ghudu.2021. 004096.
	HU G F. Study on lithium evolution characteristics and safety of lithium-ion batteries based on AC impedance characteristics[D]. Changsha: Hunan University, 2021. DOI: 10.27135/d.cnki.ghudu. 2021.004096.

不同方法	MAPE	MSE
特征频点	3.44%	0.00173
等效电路	1.97%	0.000710
DRT	1.46%	0.000548

不同方法	MAPE	MSE
BP	1.96%	0.000482
ELM	1.46%	0.000548
SSA-ELM	0.57%	0.000290

[1]	Yingying LIU, Xiaoyuan ZHANG, Mengnan LIU, junzhang SUN, Yan ZHANG. State of health interval estimation for lithium battery via Gaussian process regression with adaptive optimal combination Kernel function [J]. Energy Storage Science and Technology, 2025, 14(1): 346-357.
[2]	Jizhong LU, Simin PENG, Xiaoyu LI. State-of-health estimation of lithium-ion batteries based on multifeature analysis and LSTM-XGBoost model [J]. Energy Storage Science and Technology, 2024, 13(9): 2972-2982.
[3]	Yuan CHEN, Siyuan ZHANG, Yujing CAI, Xiaohe HUANG, Yanzhong LIU. State-of-health estimation of lithium batteries based on polynomial feature extension of the CNN-transformer model [J]. Energy Storage Science and Technology, 2024, 13(9): 2995-3005.
[4]	Xue KE, Huawei HONG, Peng ZHENG, Zhicheng LI, Peixiao FAN, Jun YANG, Yuzheng GUO, Chunguang KUAI. Estimating lithium-ion battery health using automatic feature extraction and channel attention mechanisms for multi-timescale modeling [J]. Energy Storage Science and Technology, 2024, 13(9): 3059-3071.
[5]	Zhifeng HE, Yuanzhe TAO, Yonggang HU, Qicong Wang, Yong YANG. Machine learning-enhanced electrochemical impedance spectroscopy for lithium-ion battery research [J]. Energy Storage Science and Technology, 2024, 13(9): 2933-2951.
[6]	Yufeng HUANG, Huanchao LIANG, Lei XU. Kalman filter optimize Transformer method for state of health prediction on lithium-ion battery [J]. Energy Storage Science and Technology, 2024, 13(8): 2791-2802.
[7]	Jingjing LEI, Zehao LI, Binbin CHEN, Denggao HUANG. Estimation of internal battery temperature based on electrochemical impedance spectroscopy [J]. Energy Storage Science and Technology, 2024, 13(8): 2823-2834.
[8]	Chen LI, Huilin ZHANG, Jianping ZHANG. Estimated state of health for retired lithium batteries using kernel function and hyperparameter optimization [J]. Energy Storage Science and Technology, 2024, 13(6): 2010-2021.
[9]	Yueming MIN, Chuang ZHANG, Wenjie LIU, Suzhen LIU, Zhicheng XU. Study on aging characteristics and failure mechanism of lithium-ion battery under slight-overcharge cycling [J]. Energy Storage Science and Technology, 2024, 13(10): 3343-3356.
[10]	Hangting JIANG, Qianqian ZHANG, Songtong ZHANG, Xiayu ZHU, Wenjie MENG, Jingyi QIU, Hai MING. Internal resistance measurement and condition monitoring strategy for chemical power systems [J]. Energy Storage Science and Technology, 2024, 13(10): 3400-3422.
[11]	Ye XIAO, Lei XU, Chong YAN, Jiaqi HUANG. Design and application of reference electrodes for lithium batteries [J]. Energy Storage Science and Technology, 2024, 13(1): 82-91.
[12]	Zenghui HAO, Xunliang LIU, Yuan MENG, Nan MENG, Zhi WEN. Effect of electrode interface microstructure on the performance of solid-state lithium-ion battery [J]. Energy Storage Science and Technology, 2023, 12(7): 2095-2104.
[13]	Fang LI, Yongjun MIN, Yong ZHANG. Review of key technology research on the reliability of power lithium batteries based on big data [J]. Energy Storage Science and Technology, 2023, 12(6): 1981-1994.
[14]	Linze LI, Xiangwen ZHANG. SOH estimation for lithium-ion batteries based on combination of frequency impedance characteristics [J]. Energy Storage Science and Technology, 2023, 12(5): 1705-1712.
[15]	Feng LIU, Haizhong CHEN. Lithium-ion battery state prediction based on CEEMDAN and ISOA-ELM [J]. Energy Storage Science and Technology, 2023, 12(4): 1244-1256.