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

doi:10.19799/j.cnki.2095-4239.2023.0186

Abstract

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

CLC Number:

TM 912

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.

Figures/Tables 12

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

References 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]	Jiaxing YANG, Hengyun ZHANG, Yidong XU. Heat generation analysis for lithium-ion battery components using electrochemical and thermal coupled model [J]. Energy Storage Science and Technology, 2023, 12(8): 2615-2625.
[2]	Jilu ZHANG, Yuchen DONG, Qiang SONG, Siming YUAN, Xiaodong GUO. Controllable synthesis and electrochemical mechanism related to polycrystalline and single-crystalline Ni-rich layered LiNi_0.9Co_0.05Mn_0.05O₂ cathode materials [J]. Energy Storage Science and Technology, 2023, 12(8): 2382-2389.
[3]	Anhao ZUO, Ruqing FANG, Zhe LI. Kinetic characterization of electrode materials for lithium-ion batteries via single-particle microelectrodes [J]. Energy Storage Science and Technology, 2023, 12(8): 2457-2481.
[4]	Yun DI, Zhengzhu ZHOU, Huihong DANG, Zhihao GE. Modeling and verification of electric-thermal coupling in batteries based on ECM [J]. Energy Storage Science and Technology, 2023, 12(8): 2638-2648.
[5]	Shuqin LIU, Xiaoyan WANG, Zhendong ZHANG, Zhenxia DUAN. Experimental and simulation research on liquid-cooling system of lithium-ion battery packs [J]. Energy Storage Science and Technology, 2023, 12(7): 2155-2165.
[6]	Maosong FAN, Mengmeng GENG, Guangjin ZHAO, Kai YANG, Fangfang WANG, Hao LIU. Research on battery sorting technology for echelon utilization based on multifrequency impedance [J]. Energy Storage Science and Technology, 2023, 12(7): 2202-2210.
[7]	Yi WANG, Xuebing CHEN, Yuanxi WANG, Jieyun ZHENG, Xiaosong LIU, Hong LI. Overview of multilevel failure mechanism and analysis technology of energy storage lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(7): 2079-2094.
[8]	Zhiwei CHEN, Weige ZHANG, Junwei ZHANG, Yanru ZHANG. Comprehensive health assessment and screening method of power battery pack based on visual characteristics of charge curves [J]. Energy Storage Science and Technology, 2023, 12(7): 2211-2219.
[9]	Yubo ZHANG, Youyuan WANG, Dongning HUANG, Ziyi WANG, Weigen CHEN. Prognostic method of lithium-ion battery lifetime degradation under various working conditions [J]. Energy Storage Science and Technology, 2023, 12(7): 2238-2245.
[10]	Qinpei CHEN, Xuehui WANG, Wenzhong MI. Experiential study on the toxic and explosive characteristics of thermal runaway gas generated in electric-vehicle lithium-ion battery systems [J]. Energy Storage Science and Technology, 2023, 12(7): 2256-2262.
[11]	Wenda ZAN, Rui ZHANG, Fei DING. Development and application of electrochemical models for lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(7): 2302-2318.
[12]	Hongsheng GUAN, Cheng QIAN, Binghui XU, Bo SUN, Yi REN. SAM-GRU-based fusion neural network for SOC estimation in lithium-ion batteries under a wide range of operating conditions [J]. Energy Storage Science and Technology, 2023, 12(7): 2229-2237.
[13]	Qingsong ZHANG, Fangwei BAO, Jiangjao NIU. Risk analysis method of thermal runaway gas explosion in lithium-ion batteries [J]. Energy Storage Science and Technology, 2023, 12(7): 2263-2270.
[14]	Birong TAN, Jianhua DU, Xianghu YE, Xin CAO, Chang QU. Overview of SOC estimation methods for lithium-ion batteries based on model [J]. Energy Storage Science and Technology, 2023, 12(6): 1995-2010.
[15]	Fang LI, Yongjun MIN, Yong ZHANG. Review of key technology research on the reliability of power lithium batteries based on big data [J]. Energy Storage Science and Technology, 2023, 12(6): 1981-1994.