IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v243y2022ics0360544221033314.html
   My bibliography  Save this article

Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method

Author

Listed:
  • Qiao, Dongdong
  • Wang, Xueyuan
  • Lai, Xin
  • Zheng, Yuejiu
  • Wei, Xuezhe
  • Dai, Haifeng

Abstract

Early internal short circuit (ISC) diagnosis is critical for a battery management system (BMS) to prevent the thermal runaway of lithium-ion batteries. However, it is difficult to be diagnosed owing to few obvious electric and thermoelectric characteristics in the early stage of ISC. In this study, a novel ISC diagnosis method based on the incremental capacity (IC) curves is proposed. Different charging rates on four ISC situations are carried out on a cell to verify the proposed method. The leakage current of the ISC battery can be obtained by the area difference between the normal cell and the ISC cell, and it can be converted into the ISC resistance. The experiments of different initial charging states of charge (SOC) in a series-connected battery pack are conducted to verify the method in the real EVs working environment. The diagnosis results of the battery cell and battery pack indicate the proposed method is feasible and effective to quantitatively diagnose the ISC.

Suggested Citation

  • Qiao, Dongdong & Wang, Xueyuan & Lai, Xin & Zheng, Yuejiu & Wei, Xuezhe & Dai, Haifeng, 2022. "Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method," Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221033314
    DOI: 10.1016/j.energy.2021.123082
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221033314
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.123082?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Feng, Xuning & Weng, Caihao & Ouyang, Minggao & Sun, Jing, 2016. "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Elsevier, vol. 161(C), pages 168-180.
    2. M. Armand & J.-M. Tarascon, 2008. "Building better batteries," Nature, Nature, vol. 451(7179), pages 652-657, February.
    3. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
    4. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
    5. Dai, Haifeng & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan & Gu, Weijun, 2012. "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Elsevier, vol. 95(C), pages 227-237.
    6. Zhao, Rui & Liu, Jie & Gu, Junjie, 2016. "Simulation and experimental study on lithium ion battery short circuit," Applied Energy, Elsevier, vol. 173(C), pages 29-39.
    7. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    8. Zheng, Yuejiu & Wang, Jingjing & Qin, Chao & Lu, Languang & Han, Xuebing & Ouyang, Minggao, 2019. "A novel capacity estimation method based on charging curve sections for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 185(C), pages 361-371.
    9. Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Yangying Zhu & Jin Xie & Allen Pei & Bofei Liu & Yecun Wu & Dingchang Lin & Jun Li & Hansen Wang & Hao Chen & Jinwei Xu & Ankun Yang & Chun-Lan Wu & Hongxia Wang & Wei Chen & Yi Cui, 2019. "Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    11. Zhang, Zhendong & Kong, Xiangdong & Zheng, Yuejiu & Zhou, Long & Lai, Xin, 2019. "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters," Energy, Elsevier, vol. 166(C), pages 1013-1024.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tian, Jiaqiang & Liu, Xinghua & Li, Siqi & Wei, Zhongbao & Zhang, Xu & Xiao, Gaoxi & Wang, Peng, 2023. "Lithium-ion battery health estimation with real-world data for electric vehicles," Energy, Elsevier, vol. 270(C).
    2. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian, 2023. "Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training," Energy, Elsevier, vol. 266(C).
    3. Wei, Gang & Huang, Ranjun & Zhang, Guangxu & Jiang, Bo & Zhu, Jiangong & Guo, Yangyang & Han, Guangshuai & Wei, Xuezhe & Dai, Haifeng, 2023. "A comprehensive insight into the thermal runaway issues in the view of lithium-ion battery intrinsic safety performance and venting gas explosion hazards," Applied Energy, Elsevier, vol. 349(C).
    4. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    5. Anwei Zhang & You Zhou & Chengyun Wang & Shoutong Liu & Peifeng Huang & Hao Yan & Zhonghao Bai, 2023. "Probing Fault Features of Lithium-Ion Battery Modules under Mechanical Deformation Loading," Sustainability, MDPI, vol. 15(15), pages 1-13, August.
    6. Yang, Qifan & Sun, Jinlei & Kang, Yongzhe & Ma, Hongzhong & Duan, Dawei, 2023. "Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model," Energy, Elsevier, vol. 276(C).
    7. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    8. Pan, Yue & Kong, Xiangdong & Yuan, Yuebo & Sun, Yukun & Han, Xuebing & Yang, Hongxin & Zhang, Jianbiao & Liu, Xiaoan & Gao, Panlong & Li, Yihui & Lu, Languang & Ouyang, Minggao, 2023. "Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses," Energy, Elsevier, vol. 262(PB).
    9. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    10. Chenqiang Luo & Zhendong Zhang & Dongdong Qiao & Xin Lai & Yongying Li & Shunli Wang, 2022. "Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML," Energies, MDPI, vol. 15(13), pages 1-15, June.
    11. Song, Youngbin & Park, Shina & Kim, Sang Woo, 2023. "Model-free quantitative diagnosis of internal short circuit for lithium-ion battery packs under diverse operating conditions," Applied Energy, Elsevier, vol. 352(C).
    12. Wang, Shuhui & Wang, Zhenpo & Cheng, Ximing & Zhang, Zhaosheng, 2023. "A double-layer fault diagnosis strategy for electric vehicle batteries based on Gaussian mixture model," Energy, Elsevier, vol. 281(C).
    13. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    2. Ren, Dongsheng & Liu, Xiang & Feng, Xuning & Lu, Languang & Ouyang, Minggao & Li, Jianqiu & He, Xiangming, 2018. "Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components," Applied Energy, Elsevier, vol. 228(C), pages 633-644.
    3. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    4. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
    5. Ouyang, Minggao & Gao, Shang & Lu, Languang & Feng, Xuning & Ren, Dongsheng & Li, Jianqiu & Zheng, Yuejiu & Shen, Ping, 2016. "Determination of the battery pack capacity considering the estimation error using a Capacity–Quantity diagram," Applied Energy, Elsevier, vol. 177(C), pages 384-392.
    6. Sun, Tao & Chen, Jianguo & Wang, Shaoqing & Chen, Quanwei & Han, Xuebing & Zheng, Yuejiu, 2023. "Aging mechanism analysis and capacity estimation of lithium - ion battery pack based on electric vehicle charging data," Energy, Elsevier, vol. 283(C).
    7. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).
    8. Chen, Jianguo & Han, Xuebing & Sun, Tao & Zheng, Yuejiu, 2024. "Analysis and prediction of battery aging modes based on transfer learning," Applied Energy, Elsevier, vol. 356(C).
    9. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    10. Huang, Peifeng & Ping, Ping & Li, Ke & Chen, Haodong & Wang, Qingsong & Wen, Jennifer & Sun, Jinhua, 2016. "Experimental and modeling analysis of thermal runaway propagation over the large format energy storage battery module with Li4Ti5O12 anode," Applied Energy, Elsevier, vol. 183(C), pages 659-673.
    11. Lai, Xin & Yi, Wei & Cui, Yifan & Qin, Chao & Han, Xuebing & Sun, Tao & Zhou, Long & Zheng, Yuejiu, 2021. "Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter," Energy, Elsevier, vol. 216(C).
    12. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    13. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    14. Chen, Zeyu & Zhang, Bo & Xiong, Rui & Shen, Weixiang & Yu, Quanqing, 2021. "Electro-thermal coupling model of lithium-ion batteries under external short circuit," Applied Energy, Elsevier, vol. 293(C).
    15. Tian, Yong & Dong, Qianyuan & Tian, Jindong & Li, Xiaoyu & Li, Guang & Mehran, Kamyar, 2023. "Capacity estimation of lithium-ion batteries based on optimized charging voltage section and virtual sample generation," Applied Energy, Elsevier, vol. 332(C).
    16. Jinhyeong Park & Munsu Lee & Gunwoo Kim & Seongyun Park & Jonghoon Kim, 2020. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH," Energies, MDPI, vol. 13(9), pages 1-20, April.
    17. Wang, Tianyu & Ma, Zhongjing & Zou, Suli & Chen, Zhan & Wang, Peng, 2024. "Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels," Applied Energy, Elsevier, vol. 355(C).
    18. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    19. Minhwan Seo & Taedong Goh & Minjun Park & Sang Woo Kim, 2018. "Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell," Energies, MDPI, vol. 11(7), pages 1-18, June.
    20. Liu, Lishuo & Feng, Xuning & Zhang, Mingxuan & Lu, Languang & Han, Xuebing & He, Xiangming & Ouyang, Minggao, 2020. "Comparative study on substitute triggering approaches for internal short circuit in lithium-ion batteries," Applied Energy, Elsevier, vol. 259(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:243:y:2022:i:c:s0360544221033314. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.