基于电热耦合模型的锂离子电池故障诊断技术

doi:10.3969/j.issn.2097-0706.2025.08.002

综合智慧能源 ›› 2025, Vol. 47 ›› Issue (8): 10-20.doi: 10.3969/j.issn.2097-0706.2025.08.002

基于电热耦合模型的锂离子电池故障诊断技术

喻子逸¹(), 潘庭龙¹^,^*(), 葛科²(), 窦真兰³(), 许德智⁴()

1.江南大学物联网工程学院，江苏无锡 214122
2.江苏海基新能源股份有限公司，江苏无锡 214422
3.国网上海市电力公司，上海 200122
4.东南大学电气工程学院，南京 210018

收稿日期:2024-09-19 修回日期:2024-10-21 出版日期:2025-06-03
通讯作者: *潘庭龙（1976），男，教授，博士生导师，博士，从事微电网控制技术、功率变换技术及应用和电气传动系统及其先进控制技术等方面的研究，tlpan@jiangnan.edu.cn。
作者简介:喻子逸（2000），男，硕士生，从事锂离子电池故障诊断方面的研究，6221915027@stu.jiangnan.edu.cn；
葛科（1983），男，高级工程师，硕士，从事动力及储能电芯研发、设计、生产等方面的研究，geke123@163.com；
窦真兰（1980），女，高级工程师，博士，从事综合能源系统、能源互联网、风力发电、氢能、储能、微网技术等方面的研究，douzhl@126.com；
许德智（1985），男，教授，博士生导师，博士，从事储能系统、运动体与电机控制和故障诊断与容错等方面的研究，xudezhi@seu.edu.cn。
基金资助:
国家自然科学基金项目(62222307)

Fault diagnosis technology for lithium-ion batteries based on electro-thermal coupling model

YU Ziyi¹(), PAN Tinglong¹^,^*(), GE Ke²(), DOU Zhenlan³(), XU Dezhi⁴()

1. School of Internet of Things Engineering， Jiangnan University， Wuxi 214122， China
2. Jiangsu Haiji New Energy Company Limited， Wuxi 214422， China
3. State Grid Shanghai Power Supply Company， Shanghai 200122， China
4. School of Electrical Engineering， Southeast University， Nanjing 210018， China

Received:2024-09-19 Revised:2024-10-21 Published:2025-06-03
Supported by:
National Natural Science Foundation of China(62222307)

摘要/Abstract

摘要：

随着新能源汽车的快速发展，锂离子电池作为动力电池的核心组成部分，其安全性至关重要。基于此背景，提出了一种基于电热耦合模型的锂离子电池故障诊断技术。将锂离子电池的电学与热学特性相结合，建立电热耦合模型，该模型的电压与表面温度的相对误差均小于1%，能更精确地描述电池的性能表现。该模型将二阶Thevenin等效电路模型与集总参数热模型相结合，能够动态反映电流、电压对温度的影响，同时也考虑了温度对电气参数的反向影响。通过含有遗忘因子的递推最小二乘法对模型参数进行辨识，并采用自适应扩展卡尔曼滤波器（AEKF）进行状态估计，再利用测量值与估计值的差异实现对电池故障的精确诊断。试验表明，该技术能够在不同故障条件下对电池的状态进行监测，通过电压和温度的联合监控，成功实现了故障的识别与诊断。

关键词: 锂离子电池, 电热耦合模型, 参数辨识, 状态估计, 故障诊断

Abstract:

With the rapid development of new energy vehicles， the safety of lithium-ion batteries， a core component of power batteries， has become increasingly crucial. Therefore， a fault diagnosis technology for lithium-ion batteries based on an electro-thermal coupling model was proposed. By integrating the electrical and thermal characteristics of lithium-ion batteries， an electro-thermal coupling model was established. The relative errors of the voltage and surface temperature predicted by the model were both less than 1%， providing a more accurate description of battery performance. The model combined a second-order Thevenin equivalent circuit model with a lumped parameter thermal model， dynamically reflecting the influence of current and voltage on temperature while accounting for the feedback effects of temperature on electrical parameters. The model parameters were identified using the Forgetting Factor Recursive Least Squares （FFRLS） algorithm， and state estimation was conducted with an Adaptive Extended Kalman Filter （AEKF）. The differences between measured and estimated values enabled accurate fault diagnosis of the batteries. Simulation results demonstrated that the proposed method successfully monitored battery states under various fault conditions and identified and diagnosed faults through combined voltage and temperature monitoring.

Key words: lithium-ion battery, electro-thermal coupling model, parameter identification, state estimation, fault diagnosis

中图分类号:

TK01：TM911

喻子逸, 潘庭龙, 葛科, 窦真兰, 许德智. 基于电热耦合模型的锂离子电池故障诊断技术[J]. 综合智慧能源, 2025, 47(8): 10-20.

YU Ziyi, PAN Tinglong, GE Ke, DOU Zhenlan, XU Dezhi. Fault diagnosis technology for lithium-ion batteries based on electro-thermal coupling model[J]. Integrated Intelligent Energy, 2025, 47(8): 10-20.

图/表 15

图1

图2

图3

图4

表1

图5

表2

图6

图7

图8

表3

故障等级分类

故障等级	电压RMSE/V	表面温度RMSE/K
正常状态	$<$ 0.01	$<$ 0.07
轻度故障	[0.01,0.05)	[0.07,0.50)
重度故障	$≥$ 0.05	$≥$ 0.50

表3

图9

图10

图11

表4

参考文献 30

[1]	黄晓凡, 李佳瑞, 刘晖, 等. 梯次利用动力电池储能系统综合效益分析[J]. 综合智慧能源, 2024, 46(7): 63-73. doi: 10.3969/j.issn.2097-0706.2024.07.008
	HUANG Xiaofan, LI Jiarui, LIU Hui, et al. Comprehensive benefit analysis on the cascade utilization of a power battery system[J]. Integrated Intelligent Energy, 2024, 46(7): 63-73. doi: 10.3969/j.issn.2097-0706.2024.07.008
[2]	杨帅, 张金换, 钱占伟, 等. 汽车安全多领域融合的研究与展望[J]. 汽车安全与节能学报, 2022, 13(1): 29-47.
	YANG Shuai, ZHANG Jinhuan, QIAN Zhanwei, et al. Research and prospect of multi domain integration of automobile safety[J]. Journal of Automotive Safety and Energy, 2022, 13(1): 29-47.
[3]	吕杰, 王敬翰, 宋文吉, 等. 储能用锂离子电池电热耦合模型研究进展[J]. 电池, 2023, 53(6): 668-672.
	LYU Jie, WANG Jinghan, SONG Wenji, et al. Advances in electro-thermal coupling models for energy storage Li-ion battery[J]. Battery Bimonthly, 2023, 53(6): 668-672.
[4]	苏伟, 钟国彬, 沈佳妮, 等. 锂离子电池故障诊断技术进展[J]. 储能科学与技术, 2019, 8(2):225-236. doi: 10.12028/j.issn.2095-4239.2018.0195
	SU Wei, ZHONG Guobin, SHEN Jiani, et al. The progress in fault diagnosis techniques for lithium-ion batteries[J]. Energy Storage Science and Technology, 2019, 8(2): 225-236. doi: 10.12028/j.issn.2095-4239.2018.0195
[5]	LIU C C, WU T, HE C. Research on fault diagnosis of external short circuit of lithium battery for electric vehicle[J]. IOP Conference Series: Earth and Environmental Science, 2020, 440(3): 032106.
[6]	魏婧雯. 储能锂电池系统状态估计与热故障诊断研究[D]. 合肥: 中国科学技术大学, 2019.
	WEI Jingwen. Research on states estimation and thermal fault diagnostics of lithium-ion battery based energy storage system[D]. Hefei: University of Science and Technology of China, 2019.
[7]	汪玉洁. 动力锂电池的建模、状态估计及管理策略研究[D]. 合肥: 中国科学技术大学, 2017.
	WANG Yujie. Research on modeling, state estimation and management strategy of power lithium-ion batteries[D]. Hefei: University of Science and Technology of China, 2017.
[8]	康永哲. 锂离子电池组容量估计与故障诊断方法研究[D]. 济南: 山东大学, 2021.
	KANG Yongzhe. Research on the capacity estimation and fault diagnosis of lithium-ion batteries[D]. Jinan: Shandong University, 2021.
[9]	LIN T T, CHEN Z Q, ZHENG C W, et al. Fault diagnosis of lithium-ion battery pack based on hybrid system and dual extended Kalman filter algorithm[J]. IEEE Transactions on Transportation Electrification, 2021, 7(1): 26-36.
[10]	刘思佳, 代高强, 周迅, 等. 基于扰动观测器的锂电池荷电状态估算方法[J]. 电源技术, 2021, 45(10): 1256-1259. doi: 10.3969/j.issn.1002-087X.2021.10.007
	LIU Sijia, DAI Gaoqiang, ZHOU Xun, et al. SOC estimation method of Li-ion battery based on disturbance observer[J]. Chinese Journal of Power Sources, 2021, 45(10): 1256-1259. doi: 10.3969/j.issn.1002-087X.2021.10.007
[11]	朱景哲, 张希, 高一钊, 等. 数据驱动的锂离子电池智能故障诊断算法[J]. 电池, 2022, 52(4):401-405.
	ZHU Jingzhe, ZHANG Xi, GAO Yizhao, et al. Data driven intelligent fault diagnosis algorithm for Li-ion battery[J]. Battery Bimonthly, 2022, 52(4): 401-405.
[12]	胡言庆, 杨斌, 王宇作, 等. 不同工况下功率型锂离子电池的热特性与仿真研究[J]. 电工电能新技术, 2023, 42(1):21-28. doi: 10.12067/ATEEE2204052
	HU Yanqing, YANG Bin, WANG Yuzuo, et al. Thermal characteristics and simulation of power lithium-ion batteries under different operating conditions[J]. Advanced Technology of Electrical Engineering and Energy, 2023, 42(1): 21-28. doi: 10.12067/ATEEE2204052
[13]	张扬, 李晓杰, 马兹林, 等. 锂离子电池故障诊断算法研究综述[J]. 重庆理工大学学报(自然科学), 2023, 37(9): 49-61.
	ZHANG Yang, LI Xiaojie, MA Zilin, et al. A review of fault diagnosis algorithms for lithium-ion batteries[J]. Journal of Chongqing University of Technology (Natural Science), 2023, 37(9): 49-61.
[14]	YU Y B, HUANG T F, MIN H T, et al. Co-estimation of state of charge and internal temperature of pouch lithium battery based on multi-parameter time-varying electrothermal coupling model[J]. Journal of Energy Storage, 2023, 66: 107411.
[15]	HE H W, ZHANG X W, XIONG R, et al. Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles[J]. Energy, 2012, 39(1): 310-318.
[16]	安诺静. 基于EKF的电动汽车用锂离子电池SOC估计方法研究[D]. 西安: 长安大学, 2020.
	AN Nuojing. Li-ion battery modelling and SOC estimation for electric vehicles[D]. Xi'an: Chang'an University, 2020.
[17]	HE H W, XIONG R, FAN J X. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach[J]. Energies, 2011, 4(4): 582-598.
[18]	FORGEZ C, DO D V, FRIEDRICH G, et al. Thermal modeling of a cylindrical LiFePO₄/graphite lithium-ion battery[J]. Journal of Power Sources, 2010, 195(9): 2961-2968.
[19]	RAO L, NEWMAN J. Heat-generation rate and general energy balance for insertion battery systems[J]. Journal of the Electrochemical Society, 1997, 144(8): 2697-2704.
[20]	孙丙香, 刘莹, 赵鑫泽, 等. 基于电热耦合模型的宽温域锂离子电池SOC/SOP联合估计[J/OL]. 储能科学与技术: 1-13 (2024-08-15)[2024-09-15]. https://link.cnki.net/doi/10.19799/j.cnki.2095-4239.2024.0659.
	SUN Bingxiang, LIU Ying, ZHAO Xinze, et al. Joint estimation of SOC/SOP for lithium-ion battery under wide temperature range based on electro-thermal coupling model[J/OL]. Energy Storage Science and Technology: 1-13 (2024-08-15)[2024-09-15]. https://link.cnki.net/doi/10.19799/j.cnki.2095-4239.2024.0659.
[21]	KVIDAL C, PANASONIC K P J. 18650PF li-ion battery data and example FNN and LSTM neural network SOC estimator training script[J]. Mendeley Data, 2021.
[22]	袁赛, 邓志刚, 帅孟超. 大容量锂电池在线参数辨识及SOC联合估计[J]. 电气开关, 2019, 57(2): 7-11, 20.
	YUAN Sai, DENG Zhigang, SHUAI Mengchao. Online parameter identification and SOC joint estimation of large capacity lithium batteries[J]. Electric Switchgear, 2019, 57(2): 7-11, 20.
[23]	封居强, 伍龙, 黄凯峰, 等. 基于FFRLS和AEKF的锂离子电池SOC在线估计研究[J]. 储能科学与技术, 2021, 10(1): 242-249. doi: 10.19799/j.cnki.2095-4239.2020.0296
	FENG Juqiang, WU Long, HUANG Kaifeng, et al. Online SOC estimation of a lithium-ion battery based on FFRLS and AEKF[J]. Energy Storage Science and Technology, 2021, 10(1): 242-249. doi: 10.19799/j.cnki.2095-4239.2020.0296
[24]	田茂飞, 安治国, 陈星, 等. 基于在线参数辨识和AEKF的锂电池SOC估计[J]. 储能科学与技术, 2019, 8(4): 745-750. doi: 10.12028/j.issn.2095-4239.2019.0077
	TIAN Maofei, AN Zhiguo, CHEN Xing, et al. SOC estimation of lithium battery based online parameter identification and AEKF[J]. Energy Storage Science and Technology, 2019, 8(4): 745-750. doi: 10.12028/j.issn.2095-4239.2019.0077
[25]	ZHANG H T, ZHOU M, LAN X D. State of charge estimation algorithm for unmanned aerial vehicle power-type lithium battery packs based on the extended Kalman filter[J]. Energies, 2019, 12(20): 3960.
[26]	梁奇. 基于无迹卡尔曼滤波的锂电池SOC估算[D]. 绵阳: 西南科技大学, 2018.
	LIANG Qi. Lithium battery SOC estimation based on unscented Kalman filter[D]. Mianyang: Southwest University of Science and Technology, 2018.
[27]	GUO Y F, ZHAO Z S, HUANG L M. SoC estimation of lithium battery based on AEKF algorithm[J]. Energy Procedia, 2017, 105: 4146-4152.
[28]	乔家璐, 王顺利, 于春梅, 等. 基于加权多新息AEKF的锂电池SOC在线估算[J]. 储能科学与技术, 2021, 10(6): 2318-2325. doi: 10.19799/j.cnki.2095-4239.2021.0242
	QIAO Jialu, WANG Shunli, YU Chunmei, et al. Novel multiple weighted-AEKF method for online state-of-charge estimation of lithium-ion batteries[J]. Energy Storage Science and Technology, 2021, 10(6): 2318-2325. doi: 10.19799/j.cnki.2095-4239.2021.0242
[29]	王祥, 苏建徽, 赖纪东, 等. 基于AEKF的锂离子电池SOC估算[J]. 电子技术应用, 2023, 49(4): 57-62.
	WANG Xiang, SU Jianhui, LAI Jidong, et al. SOC estimation of lithium-ion battery based on AEKF[J]. Application of Electronic Technique, 2023, 49(4): 57-62.
[30]	徐智帆, 李华森, 李文院, 等. 基于递归小脑模型神经网络和卡尔曼滤波器的锂电池荷电状态预测[J]. 综合智慧能源, 2024, 46(7): 81-86. doi: 10.3969/j.issn.2097-0706.2024.07.010
	XU Zhifan, LI Huasen, LI Wenyuan, et al. State of charge prediction for lithium-ion batteries based on KF-RCMNN[J]. Integrated Intelligent Energy, 2024, 46(7): 81-86. doi: 10.3969/j.issn.2097-0706.2024.07.010

拟合参数	温度/℃
拟合参数	-20	-10	0	10	25
P₁	1.044 0	2.880 0	4.031 0	14.520 0	11.370 0
P₂	0.047 4	-4.067 0	-0.775 0	-41.560 0	-31.170 0
P₃	-3.195 0	-1.877 0	1.132 0	42.530 0	27.210 0
P₄	3.042 0	5.739 0	5.956 0	-17.700 0	-6.102 0
P₅	-0.939 1	-2.919 0	-4.066 0	2.066 0	-2.695 0
P₆	0.631 2	1.057 0	1.471 0	0.957 3	1.991 0
P₇	3.439 0	3.414 0	3.357 0	3.325 0	3.220 0
R²	0.999 6	0.999 9	0.999 9	0.999 6	0.999 7

电池	端电压RMSE/V	表面温度RMSE/K
^#1	0.017 6	0.106 6
^#2	0.071 5	0.797 2
^#3	0.001 5	0.008 6

基于电热耦合模型的锂离子电池故障诊断技术

Fault diagnosis technology for lithium-ion batteries based on electro-thermal coupling model

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