Fast Estimating the State of Health of Lithium-ion Batteries Based on Improved Least Squares Support Vector Machine

doi:10.11985/2022.04.003

Abstract

Abstract:

State of health(SOH) is one of the core parameters for the safe management and operation of battery systems. Accurate and rapid estimation of SOH is of great significance to the safe utilization of batteries. To address the issue that conventional SOH estimation algorithms are difficult to involve both high running speed and high accuracy, a fast estimation method of battery SOH using an improved least squares support vector machine(ILS-SVM) is proposed. The robustness and running speed of the algorithm can be effectively improved by setting appropriate critical parameters, which discards some support vectors and weakens the influence of boundary samples on the algorithm. The ILS-SVM is then employed to fast estimate SOH of different battery data sets. By analyzing the operation data of batteries, a specific voltage data interval is used for data preprocessing to avoid the complete charging and discharging measurement of the battery, thus improving the efficiency of battery SOH estimation. The verified results show that accurate estimates can be achieved, and most of estimation errors of battery SOH are less than 1%. Compared with the original LS-SVM algorithm, the running speed of the ILS-SVM can be improved by up to 20%.

Key words: Lithium-ion battery, SOH estimation, least squares support vector machine(LS-SVM), data driven

CLC Number:

U469

XU Binxiang, ZHENG Linfeng, HUANG Yiheng, XIAO Zhineng, WANG Xinyue. Fast Estimating the State of Health of Lithium-ion Batteries Based on Improved Least Squares Support Vector Machine[J]. Journal of Electrical Engineering, 2022, 17(4): 11-19.

Figures/Tables 12

References 29

[1]	缪平, 姚祯, LEMMON J, 等. 电池储能技术研究进展及展望[J]. 储能科学与技术, 2020, 9(3):670-678.
	MIAO Ping, YAO Zhen, LEMMON J, et al. Current situations and prospects of energy storage batteries[J]. Energy Storage Science and Technology, 2020, 9(3):670-678.
[2]	欧阳明高. 能源革命与新能源智能汽车[J]. 中国工业和信息化, 2019(11):21-24.
	OUYANG Minggao. Energy revolution and new energy intelligent vehicles[J]. China Industry and Information Technology, 2019(11):21-24.
[3]	LIN C P, CABRERA J, YANG F, et al. Battery state of health modeling and remaining useful life prediction through time series model[J]. Applied Energy, 2020, 275:115338. doi: 10.1016/j.apenergy.2020.115338
[4]	陈猛, 乌江, 焦朝勇, 等. 锂离子电池健康状态多因子在线估计方法[J]. 西安交通大学学报, 2020, 54(1):169-175.
	CHEN Meng, WU Jiang, JIAO Chaoyong, et al. Multi-factor online estimation method for health status of lithium-ion battery[J]. Journal of Xi’an Jiaotong University, 2020, 54(1):169-175.
[5]	戴海峰, 魏学哲, 孙泽昌. 基于等效电路的内阻自适应锂离子电池模型[J]. 同济大学学报, 2010, 38(1):98-102.
	DAI Haifeng, WEI Xuezhe, SUN Zechang. An inner resistance adaptive model based on equivalent circuit of lithium-ion batteries[J]. Journal of Tongji University, 2010, 38(1):98-102.
[6]	魏克新, 陈峭岩. 基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J]. 中国电机工程学报, 2014, 34(3):445-452.
	WEI Kexin, CHEN Qiaoyan. States estimation of Li-ion power batteries based on adaptive unscented Kalman filters[J]. Proceedings of the CSEE, 2014, 34(3):445-452.
[7]	韦海燕, 陈孝杰, 吕治强, 等. 灰色神经网络模型在线估算锂离子电池SOH[J]. 电网技术, 2017, 41(12):4038-4044.
	WEI Haiyan, CHEN Xiaojie, LÜ Zhiqiang, et al. Online estimation of lithium-ion battery state of health using grey neural network[J]. Power System Technology, 2017, 41(12):4038-4044.
[8]	SIHVO J, ROINILA T, STROE D I. SOH analysis of Li-ion battery based on ECM parameters and broadband impedance measurements[C]// IECON2020:The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020:1923-1928.
[9]	SANKARASUBRAMANIAN S, KRISHNAMURTHY B. A capacity fade model for lithium-ion batteries including diffusion and kinetics[J]. Electrochimica Acta, 2012, 70:248-254. doi: 10.1016/j.electacta.2012.03.063
[10]	GAO Y Z, ZHANG X, YANG J, et al. Estimation of state-of-charge and state-of-health for lithium-ion degraded battery considering side reactions[J]. Journal of the Electrochemical Society, 2018, 165(16):A4018-A4026. doi: 10.1149/2.0981816jes
[11]	LOTFI N, LI J, LANDERS R G, et al. Li-ion battery state of health estimation based on an improved single particle model[C]// 2017 American Contrl. Conference, 2017:86-91.
[12]	ALLAM A, ONORI S. Online capacity estimation for lithium-ion battery cells via an electrochemical model-based adaptive interconnected observer[J]. IEEE Transactions on Control Systems Technology, 2021, 29(4):1639-1651.
[13]	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):1375-1381,1396.
	PAN Haihong, LÜ Zhiqiang, FU Bing, et al. Online estimation of lithium battery’s state of health using extreme learning machine[J]. Automotive Engineering, 2017, 39(12):1375-1381,1396.
[14]	SHEN S, SADOUGHI M, CHEN X, et al. A deep learning method for online capacity estimation of lithium-ion batteries[J]. The Journal of Energy Storage, 2019, 25:100817. doi: 10.1016/j.est.2019.100817
[15]	GUO P, CHENG Z, YANG L. A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction[J]. Journal of Power Sources, 2019, 412:442-450. doi: 10.1016/j.jpowsour.2018.11.072
[16]	SHU X, LI G, SHEN J, et al. An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation[J]. Journal of Power Sources, 2020, 462:228132. doi: 10.1016/j.jpowsour.2020.228132
[17]	贾俊, 胡晓松, 邓忠伟, 等. 数据驱动的锂离子电池健康状态综合评分及异常电池筛选[J]. 机械工程学报, 2021, 57(14):141-149,159. doi: 10.3901/JME.2021.14.141
	JIA Jun, HU Xiaosong, DENG Zhongwei, et al. Data-driven comprehensive evaluation of lithium-ion battery state of health and abnormal battery screening[J]. Journal of Mechanical Engineering, 2021, 57(14):141-149,159. doi: 10.3901/JME.2021.14.141
[18]	HU X, CHE Y, LIN X, et al. Health prognosis for electric vehicle battery packs:A data-driven approach[J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(6):2622-2632. doi: 10.1109/TMECH.2020.2986364
[19]	WANG Z P, MA J, ZHANG L. State-of-health estimation for lithium-ion batteries based on the multi-island genetic algorithm and the Gaussian process regression[J]. IEEE Access, 2017, 5:21286-21295. doi: 10.1109/ACCESS.2017.2759094
[20]	KATTERNBORN T, LEITLOFF J, SCHIEFER F, et al. Review on convolutional neural networks (CNN) in vegetation remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 173:24-49. doi: 10.1016/j.isprsjprs.2020.12.010
[21]	LI Y, LIU K L, FOLEY A M, et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries:A review[J]. Renewable and Sustainable Energy Reviews, 2019, 113:109254. doi: 10.1016/j.rser.2019.109254
[22]	LEI Y G, LI N P, GUO L, et al. Machinery health prognostics:A systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104:799-834. doi: 10.1016/j.ymssp.2017.11.016
[23]	叶美盈, 汪晓东, 张浩然. 基于在线最小二乘支持向量机回归的混沌时间序列预测[J]. 物理学报, 2005, 54(6):2568-2573.
	YE Meiying, WANG Xiaodong, ZHANG Haoran, et al. Chaotic time series prediction based on online least squares support vector machine regression[J]. Acta Physica Sinica, 2005, 54(6):2568-2573. doi: 10.7498/aps.54.2568
[24]	ZHAO Y P, WANG J J, LI X Y, et al. Extended least squares support vector machine with applications to fault diagnosis of aircraft engine[J]. ISA Transactions, 2020, 97:189-201. doi: 10.1016/j.isatra.2019.08.036
[25]	YANG D, ZHANG X, PAN R, et al. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources, 2018, 384:387-395. doi: 10.1016/j.jpowsour.2018.03.015
[26]	SEVERSON K A, ATTIA P M, JIN N, et al. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4:383-391. doi: 10.1038/s41560-019-0356-8
[27]	BOLE B, KULKARNI C S, DAIGLE M. Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use[C]// Annual Conference of the Prognostics and Health Management Society, 2014, 6(1):2490.
[28]	阎威武, 邵惠鹤. 支持向量机和最小二乘支持向量机的比较及应用研究[J]. 控制与决策, 2003(3):358-360.
	YAN Weiwu, SHAO Huihe. Application of support vector machines and least squares support vector machines to heart disease diagnoses[J]. Control and Decision, 2003(3):358-360.
[29]	顾燕萍, 赵文杰, 吴占松. 最小二乘支持向量机的算法研究[J]. 清华大学学报, 2010, 50(7):1063-1066,1071.
	GU Yanping, ZHAO Wenjie, WU Zhansong. Least squares support vector machine algorithm[J]. Journal of Tsinghua University, 2010, 50(7):1063-1066,1071.

电池	实际容量 /(A·h)	电压1/V	电压2/V	电压3/V	电压4/V
Cell1	1.089 8	3.500	3.504	3.507	3.512
Cell1	0.981 7	3.500	3.508	3.516	3.523
Cell1	0.869 0	3.500	3.521	3.538	3.551
Cell2	1.054 4	3.500	3.505	3.510	3.515
Cell2	1.016 7	3.500	3.506	3.512	3.517
Cell2	0.845 0	3.500	3.529	3.548	3.561
Cell3	1.074 2	3.500	3.505	3.509	3.513
Cell3	0.957 9	3.500	3.514	3.524	3.532
Cell3	0.890 3	3.500	3.521	3.535	3.547

算法		方均根误差 (RMSE)(%)		运行时间/s
算法		训练集	测试集	运行时间/s
SVM		0.59	1.15	0.151
LS-SVM		0.77	1.11	0.123
ILS-SVM	${{\alpha }_{c}}$=0.01	0.58	1.12	0.109
	${{\alpha }_{c}}$=0.02	0.56	0.84	0.103
	${{\alpha }_{c}}$=0.03	0.62	1.09	0.97
	${{\alpha }_{c}}$=0.04	0.79	1.53	0.95
	${{\alpha }_{c}}$=0.05	0.98	1.97	0.093

电池编号	方均根误差(%)			总运行时间/s
电池编号	ILS-SVM	LS-SVM	提升量	ILS-SVM	LS-SVM	提升量(%)
Cell1	0.184 1	2.579 1	92.86	0.197	0.228	13.596
Cell2	0.143 9	1.110 0	87.83	0.206	0.244	15.574
Cell3	0.430 5	0.874 8	50.79	0.201	0.247	18.623

电池编号	最大误差 (ME)(%)	方均根误差 (RMSE)(%)	总运行时间/s
B1	3.727 3	0.745 2	0.179
B2	2.578 8	0.644 1	0.176
B3	2.019 4	0.574 1	0.154
B4	2.145 5	1.082 4	0.089