基于增量容量曲线的锂离子电池微内短路故障诊断方法

doi:10.19799/j.cnki.2095-4239.2023.0186

储能科学与技术 ›› 2023, Vol. 12 ›› Issue (8): 2536-2546.doi: 10.19799/j.cnki.2095-4239.2023.0186

基于增量容量曲线的锂离子电池微内短路故障诊断方法

郭煜¹^,²^,³^,⁴(), 王亦伟²^,³^,⁴, 钟隽⁵, 杜进桥⁵, 田杰⁵, 李艳⁵, 蒋方明¹^,²^,³^,⁴()

^1.中国科学技术大学能源科学与技术学院，安徽合肥 230026
^2.中国科学院广州能源研究所
^3.中国科学院可再生能源重点实验室
^4.广东省新能源和可再生能源研究开发与应用重点实验室，广东广州 510640
^5.深圳供电局有限公司，广东深圳 518001

收稿日期:2023-03-28 修回日期:2023-04-16 出版日期:2023-08-05 发布日期:2023-08-23
通讯作者: 蒋方明 E-mail:guoyu@ms.giec.ac.cn;jiangfm@ms.giec.ac.cn
作者简介:郭煜（1999—），男，硕士研究生，研究方向为锂离子电池故障诊断，E-mail：guoyu@ms.giec.ac.cn；
基金资助:
广州市科技计划(202102080433);南方电网科技项目(090000KK52210140)

Fault diagnosis method for microinternal short circuits in lithium-ion batteries based on incremental capacity curve

Yu GUO¹^,²^,³^,⁴(), Yiwei WANG²^,³^,⁴, Juan ZHONG⁵, Jinqiao DU⁵, Jie TIAN⁵, Yan LI⁵, Fangming JIANG¹^,²^,³^,⁴()

^1.School of Energy Science and Engineering, University of Science and Technology of China, Hefei 230026, Anhui, China
^2.Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences
^3.CAS Key Laboratory of Renewable Energy
^4.Guangdong Provincial Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, Guangdong, China
^5.Shenzhen Power Supply Co. , Ltd, Shenzhen 518001, Guangdong, China

Received:2023-03-28 Revised:2023-04-16 Online:2023-08-05 Published:2023-08-23
Contact: Fangming JIANG E-mail:guoyu@ms.giec.ac.cn;jiangfm@ms.giec.ac.cn

摘要/Abstract

摘要：

锂离子电池内短路极易引发热失控事故，有必要尽早检测出锂离子电池内短路故障。然而锂离子电池在内短路故障发展的初始阶段，短路电阻阻值较大，难以被识别诊断。本工作提出了基于锂离子电池IC曲线的电池微内短路故障诊断方法。当电池发生内短路故障时，部分充电电流会流过短路电阻而不是参与锂离子电池内部电化学反应，因此短路电池与正常电池的IC曲线会存在微小差异，可以通过计算不同短路程度锂离子电池与正常电池IC曲线之间的均方误差(MSE)进一步放大故障电池与正常电池之间的偏差，进而对锂离子电池微内短路故障进行诊断，由短路电池与正常电池在相同电压区间内充电电量的差异，开发了短路电阻的定量计算方法。模拟和实验结果表明，本工作提出的方法可对阻值达到710 Ω的电池微内短路故障进行准确检测，对于短路电阻的最大估算误差为6.1%，对老化电池进行的短路实验表明该算法对于老化电池也有较强的适用性。且该算法计算复杂度低，仅需要电池低倍率充电数据即可进行短路故障诊断，时效性较强，易于实际应用。此外本工作还研究了锂离子电池在发生微内短路故障时的温升效应，结果表明短路电阻为100 Ω时电池表面最大温升为4.1 ℃，短路电阻为710 Ω时最大温升为0.4 ℃，电池发生微内短路故障时温升特征不明显。

关键词: 锂离子电池, 内短路故障, 增量容量曲线, 等效电路模型

Abstract:

Internal short circuits (ISCs) in lithium-ion batteries (LIBs) lead to thermal runaway accidents. Therefore, diagnosing ISC faults in LIBs as early as possible is essential. However, the ISC resistance of LIBs in the early ISC fault stage is large, making it difficult to diagnose the microinternal short circuit (MISC) fault of LIBs. Herein, a fault diagnosis method is proposed for the MISC fault of LIBs based on the incremental capacity (IC) curve. When an MISC fault occurs in LIBs, a part of the charging current generates ohmic heat due to the presence of the short circuit resistance rather than participating in the electrochemical reaction of the LIB to increase the battery voltage. Consequently, the IC value of a short circuit battery is higher than that of a normal battery. Mean square error of the IC values of an MISC battery and a normal battery was calculated to evaluate the deviation, thereby diagnosing the MISC fault. Based on the differences between the charging capacities of MISC and normal batteries in the same voltage range, a quantitative calculation method was developed for MISC resistance. Simulation and experimental results showed that the proposed method could accurately detect MISC faults in batteries with a resistance of 710 Ω, and the maximum estimation error of the short-circuit resistance was 6.1%. Additionally, short-circuit experiments on aging batteries revealed that the proposed method was applicable for such batteries. The algorithm has low computational complexity and can be used for short-circuit fault diagnosis with just the charging data of low-rate batteries, which is easy for practical applications. The thermal effects of MISC faults in LIBs were also studied. The results demonstrated that the maximum temperature rise on the battery surface was 4.1 ℃ when the short circuit resistance was 100 Ω and 0.4 ℃ when it was 710 Ω. Temperature rise was not obvious when the MISC fault occurred in LIBs.

Key words: lithium-ion battery, internal short circuit fault, incremental capacity curve, equivalent circuit model

中图分类号:

TM 912

郭煜, 王亦伟, 钟隽, 杜进桥, 田杰, 李艳, 蒋方明. 基于增量容量曲线的锂离子电池微内短路故障诊断方法[J]. 储能科学与技术, 2023, 12(8): 2536-2546.

Yu GUO, Yiwei WANG, Juan ZHONG, Jinqiao DU, Jie TIAN, Yan LI, Fangming JIANG. Fault diagnosis method for microinternal short circuits in lithium-ion batteries based on incremental capacity curve[J]. Energy Storage Science and Technology, 2023, 12(8): 2536-2546.

图/表 12

图1

图2

图3

图4

图 5

图6

图 7

图8

图9

图10

图11

图12

参考文献 37

1	ZHAO G L, BAKER J. Effects on environmental impacts of introducing electric vehicle batteries as storage-A case study of the United Kingdom[J]. Energy Strategy Reviews, 2022, 40: doi: 10.1016/j.esr.2022.100819.
2	曹文炅, 雷博, 史尤杰, 等. 韩国锂离子电池储能电站安全事故的分析及思考[J]. 储能科学与技术, 2020, 9(5): 1539-1547.
	CAO W J, LEI B, SHI Y J, et al. Ponderation over the recent safety accidents of lithium-ion battery energy storage stations in South Korea[J]. Energy Storage Science and Technology, 2020, 9(5): 1539-1547.
3	周洋捷, 王震坡, 洪吉超, 等. 新能源汽车动力电池"过充电-热失控"安全防控技术研究综述[J]. 机械工程学报, 2022, 58(10): 112-135.
	ZHOU Y J, WANG Z P, HONG J C, et al. Review of overcharge-to-thermal runaway and the control strategy for lithium-ion traction batteries in electric vehicles[J]. Journal of Mechanical Engineering, 2022, 58(10): 112-135.
4	刘力硕, 张明轩, 卢兰光, 等. 锂离子电池内短路机理与检测研究进展[J]. 储能科学与技术, 2018, 7(6): 1003-1015.
	LIU L S, ZHANG M X, LU L G, et al. Recent progress on mechanism and detection of internal short circuit in lithium-ion batteries[J]. Energy Storage Science and Technology, 2018, 7(6): 1003-1015.
5	FENG X N, OUYANG M G, LIU X, et al. Thermal runaway mechanism of lithium ion battery for electric vehicles: A review[J]. Energy Storage Materials, 2018, 10: 246-267.
6	OUYANG M G, ZHANG M X, FENG X N, et al. Internal short circuit detection for battery pack using equivalent parameter and consistency method[J]. Journal of Power Sources, 2015, 294: 272-283.
7	MENG J W, BOUKHNIFER M, DELPHA C, et al. Incipient short-circuit fault diagnosis of lithium-ion batteries[J]. Journal of Energy Storage, 2020, 31: doi: 10.1016/j.est.2020.101658.
8	CHANG C, ZHOU X P, JIANG J C, et al. Electric vehicle battery pack micro-short circuit fault diagnosis based on charging voltage ranking evolution[J]. Journal of Power Sources, 2022, 542: doi: 10.1016/j.jpowsour.2022.231733.
9	KONG X D, ZHENG Y J, OUYANG M G, et al. Fault diagnosis and quantitative analysis of micro-short circuits for lithium-ion batteries in battery packs[J]. Journal of Power Sources, 2018, 395: 358-368.
10	QIU Y S, CAO W J, PENG P, et al. A novel entropy-based fault diagnosis and inconsistency evaluation approach for lithium-ion battery energy storage systems[J]. Journal of Energy Storage, 2021, 41: doi: 10.1016/j.est.2021.102852.
11	ZHANG Z D, KONG X D, ZHENG Y J, et al. Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters[J]. Energy, 2019, 166: 1013-1024.
12	FENG X N, WENG C H, OUYANG M G, et al. Online internal short circuit detection for a large format lithium ion battery[J]. Applied Energy, 2016, 161: 168-180.
13	WANG G, SUN Y D, PANG K, et al. Quantitative diagnosis of the soft short circuit for LiFePO₄ battery packs between voltage plateaus[J]. Journal of Energy Storage, 2023, 61: doi: 10.1016/j.est.2023.106683.
14	XIE J L, ZHANG L, YAO T Q, et al. Quantitative diagnosis of internal short circuit for cylindrical Li-ion batteries based on multiclass relevance vector machine[J]. Journal of Energy Storage, 2020, 32: doi: 10.1016/j.est.2020.101957.
15	CHEN Z Y, XIONG R, TIAN J P, et al. Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles[J]. Applied Energy, 2016, 184: 365-374.
16	CHEN Y X, TORRES-CASTRO L, CHEN K H, et al. Operando detection of Li plating during fast charging of Li-ion batteries using incremental capacity analysis[J]. Journal of Power Sources, 2022, 539: doi: 10.1016/j.jpowsour.2022.231601.
17	OSPINA AGUDELO B, ZAMBONI W, MONMASSON E. Application domain extension of incremental capacity-based battery SoH indicators[J]. Energy, 2021, 234: doi: 10.1016/j.energy.2021.121224.
18	TANG X P, LIU K L, LU J Y, et al. Battery incremental capacity curve extraction by a two-dimensional Luenberger-Gaussian-moving-average filter[J]. Applied Energy, 2020, 280: doi: 10.1016/j.apenergy.2020.115895.
19	NOELLE D J, WANG M, LE A V, et al. Internal resistance and polarization dynamics of lithium-ion batteries upon internal shorting[J]. Applied Energy, 2018, 212: 796-808.
20	QIAO D D, WANG X Y, LAI X, et al. Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method[J]. Energy, 2022, 243: doi: 10.1016/j.energy. 2021.123082.
21	BHAT A, RICHARDSON I, KANNANGARA S. A new perceptual quality metric for compressed video based on mean squared error[J]. Signal Processing: Image Communication, 2010, 25(8): 588-596.
22	JIANG B, DAI H F, WEI X Z. Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition[J]. Applied Energy, 2020, 269: doi: 10.1016/j.apenergy.2020.115074.
23	PAN W J, LUO X S, ZHU M T, et al. A health indicator extraction and optimization for capacity estimation of Li-ion battery using incremental capacity curves[J]. Journal of Energy Storage, 2021, 42: doi: 10.1016/j.est.2021.103072.
24	CHEN Z H, DENG Y L, LI H L, et al. An efficient regrouping method of retired lithium-ion iron phosphate batteries based on incremental capacity curve feature extraction for echelon utilization[J]. Journal of Energy Storage, 2022, 56: doi: 10.1016/j.est.2022. 105917.
25	LI Y, ABDEL-MONEM M, GOPALAKRISHNAN R, et al. A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter[J]. Journal of Power Sources, 2018, 373: 40-53.
26	MA M N, WANG Y, DUAN Q L, et al. Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis[J]. Energy, 2018, 164: 745-756.
27	WU M Y, QIN L L, WU G. State of power estimation of power lithium-ion battery based on an equivalent circuit model[J]. Journal of Energy Storage, 2022, 51: doi: 10.1016/j.est.2022.104538.
28	FENG X N, PAN Y, HE X M, et al. Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm[J]. Journal of Energy Storage, 2018, 18: 26-39.
29	SINGH A, IZADIAN A, ANWAR S. Model based condition monitoring in lithium-ion batteries[J]. Journal of Power Sources, 2014, 268: 459-468.
30	HU X S, LI S B, PENG H E. A comparative study of equivalent circuit models for Li-ion batteries[J]. Journal of Power Sources, 2012, 198: 359-367.
31	何晋, 马睿飞, 蔡琦琳, 等. 基于递推最小二乘法的锂电池内短路全寿命周期辨识[J]. 机械工程学报, 2022, 58(17): 96-104.
	HE J, MA R F, CAI Q L, et al. Life cycle identification of internal short circuit in lithium battery based on recursive least square method[J]. Journal of Mechanical Engineering, 2022, 58(17): 96-104.
32	JIAQIANG E, ZHANG B, ZENG Y, et al. Effects analysis on active equalization control of lithium-ion batteries based on intelligent estimation of the state-of-charge[J]. Energy, 2022, 238: doi: 10.1016/j.energy.2021.121822.
33	RAMADASS P, FANG W F, ZHANG Z M. Study of internal short in a Li-ion cell I. Test method development using infra-red imaging technique[J]. Journal of Power Sources, 2014, 248: 769-776.
34	LAMB J, ORENDORFF C J. Evaluation of mechanical abuse techniques in lithium ion batteries[J]. Journal of Power Sources, 2014, 247: 189-196.
35	ORENDORFF C J, ROTH E P, NAGASUBRAMANIAN G. Experimental triggers for internal short circuits in lithium-ion cells[J]. Journal of Power Sources, 2011, 196(15): 6554-6558.
36	ZHANG M X, DU J Y, LIU L S, et al. Internal short circuit trigger method for lithium-ion battery based on shape memory alloy[J]. Journal of the Electrochemical Society, 2017, 164(13): doi: 10.1149/2.0731713jes.
37	GUO R, LU L G, OUYANG M G, et al. Mechanism of the entire overdischarge process and overdischarge-induced internal short circuit in lithium-ion batteries[J]. Scientific Reports, 2016, 6: doi: 10.1038/srep30248.

[1]	黄永浩, 臧国景, 朱霨亚, 廖友好, 李伟善. LiF添加剂改善含锂陶瓷隔膜与4.35 V LiNi_0.8Co_0.1Mn_0.1O₂ 正极的界面稳定性[J]. 储能科学与技术, 2023, 12(8): 2361-2369.
[2]	杨佳兴, 张恒运, 徐屹东. 基于电化学-热耦合模型的锂离子电池组件产热分析[J]. 储能科学与技术, 2023, 12(8): 2615-2625.
[3]	张吉禄, 董育辰, 宋强, 袁思鸣, 郭孝东. 多晶及单晶高镍三元材料LiNi_0.9Co_0.05Mn_0.05O₂ 的可控制备及其电化学储锂特性[J]. 储能科学与技术, 2023, 12(8): 2382-2389.
[4]	左安昊, 方儒卿, 李哲. 锂离子电池单颗粒动力学表征方法综述[J]. 储能科学与技术, 2023, 12(8): 2457-2481.
[5]	陈满, 程志翔, 赵春朋, 彭鹏, 雷旗开, 金凯强, 王青松. 锂离子电池储能集装箱爆炸危害数值模拟[J]. 储能科学与技术, 2023, 12(8): 2594-2605.
[6]	唐程波, 锁要红, 何昭坤. 基于正弦函数的液冷板上流体流向对锂离子电池散热性能的影响[J]. 储能科学与技术, 2023, 12(8): 2547-2555.
[7]	狄云, 周正柱, 党会鸿, 葛志浩. 基于ECM的电芯电-热耦合建模与验证[J]. 储能科学与技术, 2023, 12(8): 2638-2648.
[8]	范茂松, 耿萌萌, 赵光金, 杨凯, 王放放, 刘皓. 基于多频点阻抗的梯次利用电池分选技术研究[J]. 储能科学与技术, 2023, 12(7): 2202-2210.
[9]	李晋, 王青松, 孔得朋, 王晓冬, 俞振华, 乐艳飞, 黄鑫炎, 胡振恺, 吴候福, 方华斌, 曹伟, 张少禹, 卓萍, 陈晔, 李紫婷, 梅文昕, 张越, 赵丽香, 唐亮, 黄宗侯, 陈篪, 刘彦辉, 储玉喜, 许晓元, 张晋, 李贻恺, 冯蓉, 杨标, 户波, 杨晓滢. 锂离子电池储能安全评价研究进展[J]. 储能科学与技术, 2023, 12(7): 2282-2301.
[10]	张佳怡, 翁素婷, 王兆翔, 王雪锋. 石墨负极界面SEI膜与锂离子电池热失控[J]. 储能科学与技术, 2023, 12(7): 2105-2118.
[11]	刘书琴, 王小燕, 张振东, 段振霞. 锂离子电池组液冷式热管理系统的设计及优化[J]. 储能科学与技术, 2023, 12(7): 2155-2165.
[12]	陈育新, 杨家沐, 练成, 刘洪来. 基于相场模型的锂电池电极浆料稳定涂布窗口分析[J]. 储能科学与技术, 2023, 12(7): 2185-2193.
[13]	杲齐新, 赵景腾, 李国兴. 锂离子电池快速充电研究进展[J]. 储能科学与技术, 2023, 12(7): 2166-2184.
[14]	张青松, 包防卫, 牛江昊. 环境压力对锂电池热失控产气及爆炸风险的影响[J]. 储能科学与技术, 2023, 12(7): 2263-2270.
[15]	陈智伟, 张维戈, 张珺玮, 张言茹. 基于视觉特征的动力电池组综合健康评估及分筛方法[J]. 储能科学与技术, 2023, 12(7): 2211-2219.

基于增量容量曲线的锂离子电池微内短路故障诊断方法

Fault diagnosis method for microinternal short circuits in lithium-ion batteries based on incremental capacity curve

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 37

相关文章 15

编辑推荐

Metrics

本文评价