基于改进最小二乘支持向量机的锂离子电池健康状态快速估计方法*

doi:10.11985/2022.04.003

电气工程学报 ›› 2022, Vol. 17 ›› Issue (4): 11-19.doi: 10.11985/2022.04.003

• 特邀专栏：电化学储能系统安全管理与运维 • 上一篇下一篇

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基于改进最小二乘支持向量机的锂离子电池健康状态快速估计方法^*

徐彬翔¹^,²(), 郑林锋¹^,²(), 黄乙恒¹^,², 肖志能¹^,², 王新月¹^,²

1.暨南大学国际能源学院珠海 519070
2.暨南大学轨道交通研究院珠海 519070

收稿日期:2022-08-24 修回日期:2022-10-09 出版日期:2022-12-25 发布日期:2023-02-03
通讯作者: 郑林锋，男，1989年生，博士，副教授。主要研究方向为动力/储能电池在新能源汽车和电网储能系统中的应用。E-mail：lfzheng@jnu.edu.cn
作者简介:徐彬翔，男，2001年生，硕士研究生。主要研究方向为电池管理系统。E-mail：binxiangxu@163.com
基金资助:
*广东省科技创新战略专项资金(“攀登计划”专项资金)(pdjh2021b0056);广东省基础与应用基础研究基金(2019A1515012210);广州市科技计划(202201010601)

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

XU Binxiang¹^,²(), ZHENG Linfeng¹^,²(), HUANG Yiheng¹^,², XIAO Zhineng¹^,², WANG Xinyue¹^,²

1. International Energy College, Jinan University, Zhuhai 519070
2. Institute of Rail Transportation, Jinan University, Zhuhai 519070

Received:2022-08-24 Revised:2022-10-09 Online:2022-12-25 Published:2023-02-03
Contact: ZHENG Linfeng, E-mail：lfzheng@jnu.edu.cn

摘要/Abstract

摘要：

锂离子电池健康状态(State of health, SOH)是电池系统安全管理与运维的主要参数之一，准确快速的SOH估计对提高电池应用的安全性有着重要意义。针对目前存在的电池SOH估计速度与精度难以兼顾的问题，提出一种基于改进最小二乘支持向量机(Improved least squares support vector machine, ILS-SVM)的SOH快速准确估计方法。通过对LS-SVM算法设定合适的临界系数，舍弃部分支持向量，削弱边界样本对算法的影响，从而提高了算法的鲁棒性与运行速度，形成改进的LS-SVM算法。通过分析电池电压特性，选取特征电压数据区间进行估计，有效避免电池的完全充放电测量，提高SOH的估计效率。验证结果表明，所提出的电池SOH估计方法估计精度较高，大部分估计误差小于1%，且所提算法相比于改进之前的算法，运行速度提升最高可达20%。

关键词: 锂离子电池, 健康状态估算, 最小二乘支持向量机, 数据驱动

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

中图分类号:

U469

徐彬翔, 郑林锋, 黄乙恒, 肖志能, 王新月. 基于改进最小二乘支持向量机的锂离子电池健康状态快速估计方法^*[J]. 电气工程学报, 2022, 17(4): 11-19.

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.

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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

基于改进最小二乘支持向量机的锂离子电池健康状态快速估计方法^*

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

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