Interval Estimation of State of Health for Lithium Batteries Considering Different Charging Strategies

doi:10.16183/j.cnki.jsjtu.2022.347

Abstract

Abstract:

State of health (SOH) estimation of lithium-ion (Li-ion) batteries is of great importance for battery use, maintenance, management, and economic evaluation. However, the current SOH estimation methods for Li-ion batteries are mainly targeted at specific charging strategies by using deterministic estimation models, which cannot reflect uncertain information such as randomness and fuzziness in the battery degradation process. To this end, a method for estimating the SOH interval of Li-ion batteries applicable to different charging strategies is proposed, which extracts multiple feature parameters from the cyclic charging and discharging data of batteries with different charging strategies, and automatically selects the optimal combination of feature parameters for a specific charging strategy by using the cross-validation method. In addition, considering the limited number of cycles in the whole life cycle of Li-ion batteries as a small sample, support vector quantile regression (SVQR), which integrates the advantages of support vector regression and quantile regression, is proposed for the estimation of SOH interval of lithium-ion batteries. Li-ion battery charge/discharge cycle data with deep discharge degree is selected as the training set for offline training of the SVQR model, and the trained model is used for online estimation of the SOH of Li-ion batteries of different charging strategies. The proposed method is validated using three datasets with different charging strategies. The experimental results show that the proposed method is applicable to different charging strategies and the estimation results are better than those of quantile regression, quantile regression neural network and Gaussian process regression.

Key words: lithium-ion battery, state of health (SOH), interval estimation, charging strategy, support vector quantile regression (SVQR)

CLC Number:

TM912

ZHANG Xiaoyuan, ZHANG Jinhao, YANG Lixin. Interval Estimation of State of Health for Lithium Batteries Considering Different Charging Strategies[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 273-284.

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References 32

[1]	孙丙香, 任鹏博, 陈育哲, 等. 锂离子电池在不同区间下的衰退影响因素分析及任意区间的老化趋势预测[J]. 电工技术学报, 2021, 36(3): 666-674.
	SUN Bingxiang, REN Pengbo, CHEN Yuzhe, et al. Analysis of influencing factors of degradation under different interval stress and prediction of aging trend in any interval for lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 666-674.
[2]	杨胜杰, 罗冰洋, 王菁, 等. 基于容量增量曲线峰值区间特征参数的锂离子电池健康状态估算[J]. 电工技术学报, 2021, 36(11): 2277-2287.
	YANG Shengjie, LUO Bingyang, WANG Jing, et al. State of health estimation for lithium-ion batteries based on peak region feature parameters of incremental capacity curve[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2277-2287.
[3]	卢地华, 陈自强. 基于双充电状态的锂离子电池健康状态估计[J]. 上海交通大学学报, 2022, 56(3): 342-352. doi: 10.16183/j.cnki.jsjtu.2021.027
	LU Dihua, CHEN Ziqiang. State of health estimation of lithium-ion batteries based on dual charging state[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 342-352.
[4]	李建林, 李雅欣, 陈光, 等. 退役动力电池健康状态特征提取及评估方法综述[J]. 中国电机工程学报, 2022, 42(4): 1332-1347.
	LI Jianlin, LI Yaxin, CHEN Guang, et al. Research on feature extraction and SOH evaluation methods for retired power battery[J]. Proceedings of the CSEE, 2022, 42(4): 1332-1347.
[5]	张立强. 锂离子电池多物理模型参数辨识及健康特征提取[D]. 哈尔滨: 哈尔滨工业大学, 2015.
	ZHANG Liqiang. Parameter identification and health feature extraction of multi-physical model of lithium-ion battery[D]. Harbin: Harbin Institute of Technology, 2015.
[6]	王震坡, 王秋诗, 刘鹏, 等. 大数据驱动的动力电池健康状态估计方法综述[J]. 机械工程学报, 2023, 59(2): 151-168. doi: 10.3901/JME.2023.02.151
	WANG Zhenpo, WANG Qiushi, LIU Peng, et al. Review on techniques for power battery state of health estimation driven by big data methods[J]. Journal of Mechanical Engineering, 2023, 59(2): 151-168. doi: 10.3901/JME.2023.02.151
[7]	李凯铨, 汪玉洁. 锂离子电池的健康状态估计综述[C]// 系统仿真技术及其应用. 安徽: 中国科学技术大学, 2021: 330-333.
	LI Kaiquan, WANG Yujie. A review of the estimation of the state of health of lithium-ion batteries[C]// System Simulation Technology & Its Application. Anhui, China: University of Science and Technology of China, 2021: 330-333.
[8]	OSPINA AGUDELO B, ZAMBONI W, MONMASSON E. Application domain extension of incremental capacity-based battery SoH indicators[J]. Energy, 2021, 234: 121224. doi: 10.1016/j.energy.2021.121224 URL
[9]	SHE C Q, WANG Z P, SUN F C, et al. Battery aging assessment for real-world electric buses based on incremental capacity analysis and radial basis function neural network[J]. IEEE Transactions on Industrial Informatics, 2020, 16(5): 3345-3354. doi: 10.1109/TII.9424 URL
[10]	徐宏东, 高海波, 徐晓滨, 等. 基于证据推理规则CS-SVR模型的锂离子电池SOH估算[J]. 上海交通大学学报, 2022, 56(4): 413-421. doi: 10.16183/j.cnki.jsjtu.2021.345
	XU Hongdong, GAO Haibo, XU Xiaobin, et al. State of health estimation of lithium-ion battery using a CS-SVR model based on evidence reasoning rule[J]. Journal of Shanghai Jiao Tong University, 2022, 56(4): 413-421.
[11]	WANG Z K, ZENG S K, GUO J B, et al. State of health estimation of lithium-ion batteries based on the constant voltage charging curve[J]. Energy, 2019, 167: 661-669. doi: 10.1016/j.energy.2018.11.008 URL
[12]	石伟杰, 王海民. 基于锂离子电池热特性的SOH在线诊断模型研究[J]. 仪器仪表学报, 2020, 41(8): 206-216.
	SHI Weijie, WANG Haimin. On-line diagnosis model of SOH based on thermal characteristics of lithium-ion battery[J]. Chinese Journal of Scientific Instrument, 2020, 41(8): 206-216.
[13]	王萍, 弓清瑞, 张吉昂, 等. 一种基于数据驱动与经验模型组合的锂电池在线健康状态预测方法[J]. 电工技术学报, 2021, 36(24): 5201-5212.
	WANG Ping, GONG Qingrui, ZHANG Ji'ang, et al. An online state of health prediction method for lithium batteries based on combination of data-driven and empirical model[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5201-5212.
[14]	刘金枝, 杨鹏, 李练兵. 一种基于能量建模的锂离子电池电量估算方法[J]. 电工技术学报, 2015, 30(13): 100-107.
	LIU Jinzhi, YANG Peng, LI Lianbing. A method to estimate the capacity of the lithium-ion battery based on energy model[J]. Transactions of China Electrotechnical Society, 2015, 30(13): 100-107.
[15]	ROMAN D, SAXENA S, ROBU V, et al. Machine learning pipeline for battery state-of-health estimation[J]. Nature Machine Intelligence, 2021, 3(5): 447-456. doi: 10.1038/s42256-021-00312-3
[16]	LI K W, WANG R, LEI H T, et al. Interval prediction of solar power using an improved bootstrap method[J]. Solar Energy, 2018, 159: 97-112. doi: 10.1016/j.solener.2017.10.051 URL
[17]	LI H, PAN D H, PHILIP CHEN C L. Intelligent prognostics for battery health monitoring using the mean entropy and relevance vector machine[J]. IEEE Transactions on Systems, Man, & Cybernetics: Systems, 2014, 44(7): 851-862.
[18]	曹孟达. 卫星电源部件在轨退化状态评估方法研究[D]. 长沙: 国防科技大学, 2019.
	CAO Mengda. Research on evaluation method of on-orbit degradation state of satellite power components[D]. Changsha: National University of Defense Technology, 2019.
[19]	魏中宝, 钟浩, 何洪文. 基于多物理过程约束的锂离子电池优化充电方法[J]. 机械工程学报, 2023, 59(2): 223-232. doi: 10.3901/JME.2023.02.223
	WEI Zhongbao, ZHONG Hao, HE Hongwen. Multiphysics-constrained optimal charging of lithium-ion battery[J]. Journal of Mechanical Engineering, 2023, 59(2): 223-232. doi: 10.3901/JME.2023.02.223
[20]	吴晓刚, 崔智昊, 孙一钊, 等. 电动汽车大功率充电过程动力电池充电策略与热管理技术综述[J]. 储能科学与技术, 2021, 10(6): 2218-2234. doi: 10.19799/j.cnki.2095-4239.2021.0267
	WU Xiaogang, CUI Zhihao, SUN Yizhao, et al. Summary of charging strategy and thermal management technology of power battery in high-power charging process of electric vehicle[J]. Energy Storage Science & Technology, 2021, 10(6): 2218-2234.
[21]	郭剑成, 王惜慧, 岑海林. 一种基于温度变化的动力电池智能充电策略[J]. 电源技术, 2022, 46(5): 518-522.
	GUO Jiancheng, WANG Xihui, CEN Hailin. An intelligent charging strategy of power battery based on temperature change[J]. Chinese Journal of Power Sources, 2022, 46(5): 518-522.
[22]	KOENKER R, BASSETT G. Regression quantiles[J]. Econometrica, 1978, 46(1): 33-50. doi: 10.2307/1913643 URL
[23]	CRISTIANINI N, SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge: Cambridge University Press, 2000.
[24]	TAKEUCHI I, FURUHASHI T. Non-crossing quantile regressions by SVM[C]// 2004 IEEE International Joint Conference on Neural Networks. Budapest, Hungary: IEEE, 2004: 401-406.
[25]	SHIM J, KIM Y, LEE J, et al. Estimating value at risk with semiparametric support vector quantile regression[J]. Computational Statistics, 2012, 27(4): 685-700. doi: 10.1007/s00180-011-0283-z URL
[26]	YUAN M. GACV for quantile smoothing splines[J]. Computational Statistics & Data Analysis, 2006, 50(3): 813-829. doi: 10.1016/j.csda.2004.10.008 URL
[27]	HE W, WILLIARD N, OSTERMAN M, et al. Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method[J]. Journal of Power Sources, 2011, 196(23): 10314-10321. doi: 10.1016/j.jpowsour.2011.08.040 URL
[28]	BIRKL C. Diagnosis and prognosis of degradation in lithium-ion batteries[D]. Oxford, UK: University of Oxford, 2017.
[29]	SEVERSON K A, ATTIA P M, JIN N, et al. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4(5): 383-391. doi: 10.1038/s41560-019-0356-8
[30]	LIAN C, ZHU L Z, ZENG Z G, et al. Constructing prediction intervals for landslide displacement using bootstrapping random vector functional link networks selective ensemble with neural networks switched[J]. Neurocomputing, 2018, 291: 1-10. doi: 10.1016/j.neucom.2018.02.046 URL
[31]	LIAN C, ZENG Z G, WANG X P, et al. Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization[J]. Neural Networks, 2020, 130: 286-296. doi: S0893-6080(20)30261-6 pmid: 32717458
[32]	何耀耀, 刘瑞, 撖奥洋. 基于实时电价与支持向量分位数回归的短期电力负荷概率密度预测方法[J]. 中国电机工程学报, 2017, 37(3): 768-775.
	HE Yaoyao, LIU Rui, HAN Aoyang. Short-term power load probability density forecasting method based on real time price and support vector quantile regression[J]. Proceedings of the CSEE, 2017, 37(3): 768-775.

数据集	充电协议	标称电压/V	标称容量/(mA·h)
CALCE	CC-CV	4.2	1100
Oxford	CC	3.7	740
TRI	2-step	3.3	1100

数据集数据划分	CALCE		Oxford		TRI
数据集数据划分	训练集	测试集	训练集	测试集	训练集	测试集
电池标号	CS36	CS35; CS37; CS38	Cell1	Cell3; Cell7; Cell8	b0c31	b0c32; b0c34; b0c35
循环次数	791	716; 857; 849	78	76; 77; 76	839	683; 683; 640

单体电池	方法	AIS	MPICD	MAPE
CS35	QR	-0.1472	1.679	12.39
	QRNN	-0.1499	3.792	11.05
	SVQR	-0.0582	0.97	6.62
	GPR	-0.0524	3.109	16.32
CS37	QR	-0.1211	1.816	12.07
	QRNN	-0.1262	1.575	10.78
	SVQR	-0.0513	0.707	5.46
	GPR	-0.0416	0.959	14.57
CS38	QR	-0.1256	4.773	12.39
	QRNN	-0.1285	2.773	11.03
	SVQR	-0.0868	1.64	7.29
	GPR	-0.0567	2.232	18.1

单体电池	方法	AIS	MPICD	MAPE
Cell3	QR	-1.946	1.91	105.36
	QRNN	-1.614	1.68	87.19
	SVQR	-0.43	1.34	19.892
	GPR	-0.698	2.49	32.63
Cell7	QR	-1.904	11.19	104.17
	QRNN	-1.567	3.96	84.75
	SVQR	-0.402	3.75	18.99
	GPR	-0.651	2.12	32.48
Cell8	QR	-1.929	2.709	105.35
	QRNN	-1.611	2.57	87.51
	SVQR	-0.4	0.322	20.54
	GPR	-0.705	3.218	32.56

单体电池	方法	AIS	MPICD	MAPE
b0c32	QR	-0.0312	1.934	12.39
	QRNN	-0.0283	1.2	11.05
	SVQR	-0.0234	1.811	6.62
	GPR	-0.0411	0.035	16.326
b0c34	QR	-0.0304	2.274	12.07
	QRNN	-0.0273	1.51	10.78
	SVQR	-0.0198	2.67	5.46
	GPR	-0.0368	0.416	14.57
b0c35	QR	-0.0362	3.49	12.39
	QRNN	-0.034	2.561	11.03
	SVQR	-0.0331	3.972	7.29
	GPR	-0.0508	1.13	18.1