IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v276y2023ics0360544223009490.html
   My bibliography  Save this article

Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model

Author

Listed:
  • Yang, Qifan
  • Sun, Jinlei
  • Kang, Yongzhe
  • Ma, Hongzhong
  • Duan, Dawei

Abstract

Internal short circuit (ISC) is a serious safety hazard for lithium-ion battery packs. How to comprehensively detect and evaluate ISC in battery packs remains a challenging problem. Motivated by this, this paper proposes an ISC detection method based on the transformation matrix and an ISC resistance calculation method based on an improved state-space model. Specifically, benefiting from the descriptive ability of the transformation matrix for curve changes, the shear element in the transformation matrix is used to capture the skewed and downward voltage pattern of the ISC cell in the battery pack. An online detection flow is designed based on the opposite variation relationship between the shear elements calculated from adjacent cells. Moreover, an improved state-space model is developed to directly estimate the ISC current, which ensures that the weight of the ISC current in the model is unrestricted. A dual extended Kalman filter with separated time scales is deployed to obtain accurate state estimations. Experimental results validate the effectiveness and advantages of the ISC detection method, as well as the high accuracy of the ISC resistance calculation method.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223009490
    DOI: 10.1016/j.energy.2023.127555
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223009490
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.127555?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. Xu, Jun & Wang, Haitao & Shi, Hu & Mei, Xuesong, 2020. "Multi-scale short circuit resistance estimation method for series connected battery strings," Energy, Elsevier, vol. 202(C).
    2. Wang, Zhenpo & Hong, Jichao & Liu, Peng & Zhang, Lei, 2017. "Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles," Applied Energy, Elsevier, vol. 196(C), pages 289-302.
    3. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    4. 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.
    5. 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).
    6. 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).
    7. Minhwan Seo & Taedong Goh & Minjun Park & Gyogwon Koo & Sang Woo Kim, 2017. "Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method," Energies, MDPI, vol. 10(1), pages 1-13, January.
    8. 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).
    9. 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.
    10. Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    11. Dai, Haifeng & Xu, Tianjiao & Zhu, Letao & Wei, Xuezhe & Sun, Zechang, 2016. "Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales," Applied Energy, Elsevier, vol. 184(C), pages 119-131.
    12. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    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. 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).

    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. 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).
    2. Sun, Zhenyu & Han, Yang & Wang, Zhenpo & Chen, Yong & Liu, Peng & Qin, Zian & Zhang, Zhaosheng & Wu, Zhiqiang & Song, Chunbao, 2022. "Detection of voltage fault in the battery system of electric vehicles using statistical analysis," Applied Energy, Elsevier, vol. 307(C).
    3. Bosong Zou & Lisheng Zhang & Xiaoqing Xue & Rui Tan & Pengchang Jiang & Bin Ma & Zehua Song & Wei Hua, 2023. "A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles," Energies, MDPI, vol. 16(14), pages 1-19, July.
    4. Ren, Song & Sun, Jing, 2024. "Multi-fault diagnosis strategy based on a non-redundant interleaved measurement circuit and improved fuzzy entropy for the battery system," Energy, Elsevier, vol. 292(C).
    5. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    6. 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).
    7. 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).
    8. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    9. 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).
    10. 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.
    11. Xu, Yiming & Ge, Xiaohua & Shen, Weixiang, 2024. "Multi-objective nonlinear observer design for multi-fault detection of lithium-ion battery in electric vehicles," Applied Energy, Elsevier, vol. 362(C).
    12. 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).
    13. Wei, Peng & Li, Han-Xiong, 2022. "Multiscale dynamic construction for abnormality detection and localization of Li-ion batteries," Applied Energy, Elsevier, vol. 325(C).
    14. 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).
    15. 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).
    16. Balakumar Balasingam & Mostafa Ahmed & Krishna Pattipati, 2020. "Battery Management Systems—Challenges and Some Solutions," Energies, MDPI, vol. 13(11), pages 1-19, June.
    17. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Meng, Dean, 2023. "Online diagnosis and prediction of power battery voltage comprehensive faults for electric vehicles based on multi-parameter characterization and improved K-means method," Energy, Elsevier, vol. 283(C).
    18. Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    19. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    20. Fan Zhang & Xiao Zheng & Zixuan Xing & Minghu Wu, 2024. "Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features," Energies, MDPI, vol. 17(7), pages 1-21, March.

    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:276:y:2023:i:c:s0360544223009490. 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.