一种退役锂电池健康状态估计方法

王思亮; 武小兰; 白志峰

doi:10.13433/j.cnki.1003-8728.20200576

一种退役锂电池健康状态估计方法

doi: 10.13433/j.cnki.1003-8728.20200576

王思亮^{1, 2,},
武小兰^{1, 2},
白志峰^{1, 3, ,}

1.
西安建筑科技大学机电工程学院, 西安 710055
2.
陕西省纳米材料与技术重点实验室, 西安 710055
3.
西安市清洁能源重点实验室, 西安 710055

基金项目:

陕西省教育厅重点实验室项目 20JS065

陕西省应用技术研发项目 GX2012

西安市清洁能源重点实验室项目 2019219914SYS014CG036

详细信息

作者简介:
王思亮(1996-)，硕士研究生，研究方向为车辆新型动力传动技术及节能控制，w1271902867@163.com

通讯作者:
白志峰，讲师，博士，zhifeng.bai@xauat.edu.cn

中图分类号: TM912
计量
- 文章访问数: 115
- HTML全文浏览量: 84
- PDF下载量: 28
- 被引次数: 0
出版历程
- 收稿日期: 2021-02-14
- 刊出日期: 2023-01-25

A Method for Estimating State of Health of Retired Lithium Battery

WANG Siliang^{1, 2
,},
WU Xiaolan^{1, 2},
BAI Zhifeng^{1, 3
, ,}

1.
School of Mechanical and Electrical Engneering, Xi′an University of Architecture and Technology, Xi′an 710055, China
2.
Shaanxi Key Laboratory of Nanomaterials and Nanotechnology, Xi′an 710055, China
3.
Xi′an Key Laboratory of Clean Energy, Xi′an 710055, China

摘要

摘要: 针对退役锂电池健康状态估计效率较低的现状, 提出一种快速、有效的估计方法。首先采用3阶RC等效电路模型描述电池特性得出状态方程, 确保电池模型精确性, 同时引入电池荷电状态SOC(State of charge)和欧姆内阻(R₀)作为状态方程参数。其次利用区域概念, 计算出特定的区域容量与区域电压, 减少电池参数估计所需要的数据、时间。然后通过扩展卡尔曼滤波(Extended kalman filtering)算法估计电池参数SOC和R₀, 进而对电池健康状态(State of health, SOH)进行估计。最后, 利用电池测试设备(Arbin-BT2000)对18650电池进行充放电实验, 验证该方法的可行性。实验结果证明SOH估计所需参数明显减少, 使得电池数据测量所需时间明显缩短, 并且估计误差不超过4%, 误差较小, 说明所提出方法能快速、有效地估算出电池SOH。
- 三阶RC等效电路模型 /
- 区域容量 /
- 区域电压 /
- 扩展卡尔曼滤波算法
Abstract: Because the efficiency for estimating the state of health (SOH) of a retired lithium battery is low, a fast and efficient estimation method is proposed. Firstly, the third-order RC equivalent circuit model is used to describe the characteristics and obtain the state equation in order to ensure the accuracy of the battery model. At the same time, the state of charge (SOC) and ohmic internal resistance (R₀) are introduced as the parameters of the state equation. Secondly, the region concept was used to calculate the specific region capacity and region voltage so as to reduce the data and time needed for battery parameter estimation. The battery parameters SOC and R₀ were estimated with the extended Kalman filtering (EKF) algorithm, and then the SOH of the battery was estimated. Finally, a battery test device (Arbin-BT2000) was used to conduct charging and discharging experiments on the 18650 battery to verify the feasibility of the method proposed in the paper. The experimental results show that the parameters required for SOH estimation are significantly reduced, that the time required for battery data measurement is significantly shortened and that the estimation error with the method is less than 4%, indicating that the method can quickly and effectively estimate the SOH of the lithium battery.
- third-order RC equivalent circuit model /
- region capacity /
- region voltage /
- extended Kalman filtering algorithm

HTML全文

图 1 3阶RC等效电路模型

下载: 全尺寸图片幻灯片

图 2 恒流放电工况

下载: 全尺寸图片幻灯片

图 3 电池恒流放电端电压波形

下载: 全尺寸图片幻灯片

图 4 OCV-SOC曲线

下载: 全尺寸图片幻灯片

图 5 区域容量和区域电压的提取

下载: 全尺寸图片幻灯片

图 6 EKF估计SOC流程图

下载: 全尺寸图片幻灯片

图 7 算法流程图

下载: 全尺寸图片幻灯片

图 8 EKF结合区域概念估计电池参数

下载: 全尺寸图片幻灯片

图 9 EKF结合区域概念估计电池参数误差

下载: 全尺寸图片幻灯片

图 10 退役锂电池SOC与内阻关系图

下载: 全尺寸图片幻灯片

图 11 EKF估计R₀所产生的误差和结合区域概念估计所产生的误差对比图

下载: 全尺寸图片幻灯片

图 12 SOC与SOH关系图

下载: 全尺寸图片幻灯片

图 13 EKF结合区域概念估计SOH所产生的误差

下载: 全尺寸图片幻灯片

图 14 实验环境

下载: 全尺寸图片幻灯片

图 15 恒定工况循环

下载: 全尺寸图片幻灯片

图 16 在EKF结合区域概念下估计电池SOC与R₀关系图

下载: 全尺寸图片幻灯片

图 17 在EKF结合区域概念下估计电池SOC与SOH关系图

下载: 全尺寸图片幻灯片

图 18 EKF结合区域概念估计SOH所产生的误差

下载: 全尺寸图片幻灯片

表 1 等效电路模型参数辨识

R₁/mΩ R₂/mΩ R₃/mΩ C₁/kF C₂/kF C₃/kF

1.966 2.511 6.160 4.751 2.703 1.230

下载: 导出CSV

表 2 复合模型参数

K₀ K₁ K₂ K₃ K₄

0.438 7 0.381 6 0.765 5 0.795 2 0.186 9

下载: 导出CSV

参考文献(20)

[1]	ZHOU X, PAN Z Q, HAN X B, et al. An easy-to-implement multi-point impedance technique for monitoring aging of lithium ion batteries[J]. Journal of Power Sources, 2019, 417: 188-192 doi: 10.1016/j.jpowsour.2018.11.087
[2]	GUO P Y, 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
[3]	CHEN Z Y, XIONG R, CAO J Y. Particle swarm optimization- based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions[J]. Energy, 2016, 96: 197-208 doi: 10.1016/j.energy.2015.12.071
[4]	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
[5]	杨杰, 解晶莹, 晏莉琴, 等. 锂离子电池健康状态估计方法[J]. 电池, 2019, 49(3): 247-250 doi: 10.19535/j.1001-1579.2019.03.017 YANG J, XIE J Y, YAN L Q, et al. The state of health estimation methods for Li-ion battery[J]. Battery Bimonthly, 2019, 49(3): 247-250 (in Chinese) doi: 10.19535/j.1001-1579.2019.03.017
[6]	SCIPIONI R, JϕRGENSEN P S, STROE D I, et al. Complementary analyses of aging in a commercial LiFePO₄/Graphite 26650 cell[J]. Electrochimica Acta, 2018, 284: 454-468 doi: 10.1016/j.electacta.2018.07.124
[7]	COUTO L D, SCHORSCH J, JOB N, et al. State of health estimation for lithium ion batteries based on an equivalent-hydraulic model: an iron phosphate application[J]. Journal of Energy Storage, 2019, 21: 259-271 doi: 10.1016/j.est.2018.11.001
[8]	TANG X P, ZOU C F, YAO K, et al. A fast estimation algorithm for lithium-ion battery state of health[J]. Journal of Power Sources, 2018, 396: 453-458 doi: 10.1016/j.jpowsour.2018.06.036
[9]	TRAN N T, KHAN A B, CHOI W. State of charge and state of health estimation of AGM VRLA batteries by employing a dual extended Kalman filter and an ARX model for online parameter estimation[J]. Energies, 2017, 10(1): 137 doi: 10.3390/en10010137
[10]	李嘉波, 魏孟, 李忠玉, 等. 基于自适应扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 储能科学与技术, 2020, 9(4): 1147-1152 https://www.cnki.com.cn/Article/CJFDTOTAL-CNKX202004016.htm LI J B, WEI M, LI Z Y, et al. State of charge estimation of Li-ion battery based on adaptive extended Kalman filter[J]. Energy Storage Science and Technology, 2020, 9(4): 1147-1152 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CNKX202004016.htm
[11]	秦鹏, 王振新, 康健强, 等. 实时辨识锂离子电池参数并基于改进AEKF估算SOC[J]. 电子测量技术, 2020, 43(10): 30-35 https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL202010006.htm QIN P, WANG Z X, KANG J Q, et al. Real-time identification of lithium-ion battery parameters and estimation of SOC based on improved AEKF[J]. Electronic Measurement Technology, 2020, 43(10): 30-35 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL202010006.htm
[12]	王维强, 张吉, 张力, 等. 基于三阶RC网络的等效电路电池模型[J]. 电池, 2019, 49(3): 212-216 https://www.cnki.com.cn/Article/CJFDTOTAL-DACI201903011.htm WANG W Q, ZHANG J, ZHANG L, et al. Equivalent circuit model for battery based on third-order RC network[J]. Battery Bimonthly, 2019, 49(3): 212-216 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DACI201903011.htm
[13]	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 https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201910008.htm LIU Y X, ZHANG D, LI X, et al. A research on SOH estimation method based on SOC-OCV curve characteristics[J]. Automotive Engineering, 2019, 41(10): 1158-1163 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC201910008.htm
[14]	程泽, 孙幸勉, 程思璐. 一种锂离子电池荷电状态估计与功率预测方法[J]. 电工技术学报, 2017, 32(15): 180-189 https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201715021.htm CHENG Z, SUN X M, CHENG S L. Method for estimation of state of charge and power prediction of lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2017, 32(15): 180-189 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201715021.htm
[15]	刘毅, 谭国俊, 何晓群. 优化电池模型的自适应Sigma卡尔曼荷电状态估算[J]. 电工技术学报, 2017, 32(2): 108-118 https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201702013.htm LIU Y, TAN G J, HE X Q. Optimized battery model based adaptive sigma Kalman filter for state of charge estimation[J]. Transactions of China Electrotechnical Society, 2017, 32(2): 108-118 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201702013.htm
[16]	张昊. 基于IC曲线特征参数的锂离子电池SOH估计及DSP实现[D]. 北京: 北京交通大学, 2018 ZHANG H. SOH estimation and DSP implementation of lithium ion battery based on characteristic parameters of IC curve[D]. Beijing: Beijing Jiaotong University, 2018 (in Chinese)
[17]	王中鲜, 赵魁, 徐建东, 等. MATLAB建模与仿真应用教程[M]. 2版. 北京: 机械工业出版社, 2014 WANG Z X, ZHAO K, XU J D, et al. MATLAB modeling and simulation application tutorial[M]. 2nd ed. Beijing: Machinery Industry Press, 2014 (in Chinese)
[18]	黄小平, 王岩. 卡尔曼滤波原理及应用-MATLAB仿真[M]. 北京: 电子工业出版社, 2015 HUANG X P, WANG Y. Kalman filter principle and application-MATLAB simulation[M]. Beijing: Electronic Industry Press, 2015 (in Chinese)
[19]	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
[20]	KIM I S. A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer[J]. IEEE Transactions on Power Electronics, 2010, 25(4): 1013-1022