doi:10.16183/j.cnki.jsjtu.2021.027

Abstract

Abstract:

Aimed at the uncertainty of charging starting and ending point caused by incomplete charging and discharging in practical applications of lithium-ion battery, an estimation method of battery health based on dual charging state factors is proposed. A battery aging experiment bench is built, and eight nickel-cobalt-manganese lithium-ion batteries are subjected to aging test. Different from the traditional single state factor estimation, the average value of equal time difference current at the front end of constant voltage charging curve and the equal amplitude voltage charging time at the end of constant current charging curve are selected under different aging conditions to construct health factors. The corresponding relationship between state of charge (SOC) and open circuit voltage (OCV) of the experimental battery in different aging states is analyzed and the correctness of health factor is proved by theoretical deduction and experimental results. An improved support vector regression model with a strong generalization ability is established, and the hyperparameters of the model are optimized through the particle swarm optimization algorithm. The results show that the proposed dual-charging health factor is closely related to battery capacity aging and attenuation. The improved support vector regression model can estimate the health status in different aging states in real time, and has the ability to characterize local capacity rebound change, which can be used as an effective method for estimating the state of health of an embedded battery management system.

Key words: lithium-ion battery, state of health estimation, support vector regression, dual charging state, aging experiment

CLC Number:

TM912

LU Dihua, CHEN Ziqiang. [J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 342-352.

Figures/Tables 11

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References 20

[1]	周诗尧, 陈自强, 黄德扬, 等. 轻度混合动力船舶储能系统研究[J]. 装备环境工程, 2018, 15(12):55-59.
	ZHOU Shiyao, CHEN Ziqiang, HUANG Deyang, et al. Energy storage system of mild hybrid ship[J]. Equipment Environmental Engineering, 2018, 15(12):55-59.
[2]	CHENG Y J, LU C, LI T Y, et al. Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach[J]. Energy, 2015, 90:1983-1993. doi: 10.1016/j.energy.2015.07.022 URL
[3]	REMMLINGER J, BUCHHOLZ M, MEILER M, et al. State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation[J]. Journal of Power Sources, 2011, 196(12):5357-5363. doi: 10.1016/j.jpowsour.2010.08.035 URL
[4]	刘江波. 基于内阻检测的锂电池健康状态估计研究[D]. 武汉: 武汉理工大学, 2015.
	LIU Jiangbo. Research on SOH estimation of Li-ion battery based on internal resistance[D]. Wuhan: Wuhan University of Technology, 2015.
[5]	LIU D T, ZHOU J B, LIAO H T, et al. A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(6):915-928. doi: 10.1109/TSMC.2015.2389757 URL
[6]	ZHOU Y P, HUANG M H, CHEN Y P, et al. A novel health indicator for on-line lithium-ion batteries remaining useful life prediction[J]. Journal of Power Sources, 2016, 321:1-10. doi: 10.1016/j.jpowsour.2016.04.119 URL
[7]	HU X S, JIANG J C, CAO D P, et al. Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling[J]. IEEE Transactions on Industrial Electronics, 2016, 63(4):2645-2656.
[8]	LI J F, WANG L X, LYU C, et al. A method of remaining capacity estimation for lithium-ion battery[J]. Advances in Mechanical Engineering, 2013, 5:154831. doi: 10.1155/2013/154831 URL
[9]	孙培坤. 电动汽车动力电池健康状态估计方法研究[D]. 北京: 北京理工大学, 2016.
	SUN Peikun. Research of state of health estimation method for electric vehicle lithium-ion power battery[D]. Beijing: Beijing Institute of Technology, 2016.
[10]	郭永芳, 黄凯, 李志刚. 基于短时搁置端电压压降的快速锂离子电池健康状态预测[J]. 电工技术学报, 2019, 34(19):3968-3978.
	GUO Yongfang, HUANG Kai, LI Zhigang. Fast state of health prediction of lithium-ion battery based on terminal voltage drop during rest for short time[J]. Transactions of China Electrotechnical Society, 2019, 34(19):3968-3978.
[11]	潘海鸿, 吕治强, 付兵, 等. 采用极限学习机实现锂离子电池健康状态在线估算[J]. 汽车工程, 2017, 39(12):1375-1381.
	PAN Haihong, LÜ Zhiqiang, FU Bing, et al. Online estimation of lithium-ion battery’s state of health using extreme learning machine[J]. Automotive Engineering, 2017, 39(12):1375-1381.
[12]	MENG J H, CAI L, STROE D I, et al. Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles[J]. Energy, 2019, 185:1054-1062. doi: 10.1016/j.energy.2019.07.127 URL
[13]	BIAN X L, LIU L C, YAN J Y. A model for state-of-health estimation of lithium ion batteries based on charging profiles[J]. Energy, 2019, 177:57-65. doi: 10.1016/j.energy.2019.04.070 URL
[14]	WENG C H, CUI Y J, SUN J, et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression[J]. Journal of Power Sources, 2013, 235:36-44. doi: 10.1016/j.jpowsour.2013.02.012 URL
[15]	刘健, 陈自强, 黄德扬, 等. 基于等压差充电时间的锂离子电池寿命预测[J]. 上海交通大学学报, 2019, 53(9):1058-1065.
	LIU Jian, CHEN Ziqiang, HUANG Deyang, et al. Remaining useful life prediction for lithium-ion batteries based on time interval of equal charging voltage difference[J]. Journal of Shanghai Jiao Tong University, 2019, 53(9):1058-1065.
[16]	YANG J F, XIA B, HUANG W X, et al. Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis[J]. Applied Energy, 2018, 212:1589-1600. doi: 10.1016/j.apenergy.2018.01.010 URL
[17]	HUANG D Y, CHEN Z Q, ZHENG C W, et al. A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature[J]. Energy, 2019, 185:847-861. doi: 10.1016/j.energy.2019.07.063 URL
[18]	刘轶鑫, 张頔, 李雪, 等. 基于SOC-OCV曲线特征的SOH估计方法研究[J]. 汽车工程, 2019, 41(10):1158-1163. doi: 10.19562/j.chinasae.qcgc.2019.010.008
	LIU Yixin, ZHANG Di, LI Xue, et al. A research on SOH estimation method based on SOC-OCV curve characteristics[J]. Automotive Engineering, 2019, 41(10):1158-1163.
[19]	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
[20]	LI X Y, WANG Z P, ZHANG L, et al. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis[J]. Journal of Power Sources, 2019, 410/411:106-114. doi: 10.1016/j.jpowsour.2018.10.069 URL

组合关系	Pearson 相关系数	Spearman 相关系数
H1&容量	-0.9927	-0.9913
H2&容量	0.9956	0.9926

电池编号	起始循环	优化算法	RMSE	MAPE
B5	第111次	PSO_SVR	0.0053	0.0052
B5	第111次	GA_SVR	0.0084	0.0107
B6	第111次	PSO_SVR	0.0151	0.0205
B6	第111次	GA_SVR	0.0338	0.0473
B7	第111次	PSO_SVR	0.0061	0.0062
B7	第111次	GA_SVR	0.0177	0.0206
B18	第91次	PSO_SVR	0.0106	0.0075
B18	第91次	GA_SVR	0.0142	0.0183

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