IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v166y2019icp1013-1024.html
   My bibliography  Save this article

Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters

Author

Listed:
  • Zhang, Zhendong
  • Kong, Xiangdong
  • Zheng, Yuejiu
  • Zhou, Long
  • Lai, Xin

Abstract

The fast diagnosis of micro-short circuit cells is crucial for the safety of battery packs. Based on the difference between the “median cell” and other cells in a battery pack, we propose a method that can identify micro-short circuit cells under dynamic conditions in real time. We model cell differences and analyze the model from the perspective of its low frequency variation characteristics. We find that approximate open-circuit voltage differences can be obtained when terminal voltage differences are passed through low-pass filters. Then approximate electric quantity differences can be obtained by utilizing the open-circuit voltage differences and the data smoothing function of low-pass filters. For onboard applications of diagnosis method, the recursive least square is adopted to estimate micro-short circuit currents and resistances utilizing the change of electric quantity differences. We verify and analyze the feasibility of the diagnosis method by using simulation data when the cells in a battery pack have temperature, state of charge, capacity, and internal resistance inconsistency, respectively. Finally, the effectiveness of the diagnosis method is further verified by the triggering experiments of micro-short circuits for real battery packs.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:1013-1024
    DOI: 10.1016/j.energy.2018.10.160
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218321571
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.10.160?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. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
    2. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    3. Fu, Yangyang & Lu, Song & Shi, Long & Cheng, Xudong & Zhang, Heping, 2018. "Ignition and combustion characteristics of lithium ion batteries under low atmospheric pressure," Energy, Elsevier, vol. 161(C), pages 38-45.
    4. 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.
    5. Nicholas Williard & Wei He & Christopher Hendricks & Michael Pecht, 2013. "Lessons Learned from the 787 Dreamliner Issue on Lithium-Ion Battery Reliability," Energies, MDPI, vol. 6(9), pages 1-14, September.
    6. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2017. "Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes," Energy, Elsevier, vol. 135(C), pages 257-268.
    7. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    8. Feng, Xuning & Lu, Languang & Ouyang, Minggao & Li, Jiangqiu & He, Xiangming, 2016. "A 3D thermal runaway propagation model for a large format lithium ion battery module," Energy, Elsevier, vol. 115(P1), pages 194-208.
    9. Zahid, Taimoor & Xu, Kun & Li, Weimin & Li, Chenming & Li, Hongzhe, 2018. "State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles," Energy, Elsevier, vol. 162(C), pages 871-882.
    10. Ling, Ziye & Wen, Xiaoyan & Zhang, Zhengguo & Fang, Xiaoming & Gao, Xuenong, 2018. "Thermal management performance of phase change materials with different thermal conductivities for Li-ion battery packs operated at low temperatures," Energy, Elsevier, vol. 144(C), pages 977-983.
    11. Zhao, Rui & Liu, Jie & Gu, Junjie, 2017. "A comprehensive study on Li-ion battery nail penetrations and the possible solutions," Energy, Elsevier, vol. 123(C), pages 392-401.
    12. 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.
    13. Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.
    14. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    15. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    16. Mehigan, L. & Deane, J.P. & Gallachóir, B.P.Ó. & Bertsch, V., 2018. "A review of the role of distributed generation (DG) in future electricity systems," Energy, Elsevier, vol. 163(C), pages 822-836.
    17. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    18. Eddahech, Akram & Briat, Olivier & Vinassa, Jean-Michel, 2015. "Performance comparison of four lithium–ion battery technologies under calendar aging," Energy, Elsevier, vol. 84(C), pages 542-550.
    19. Uddin, Kotub & Jackson, Tim & Widanage, Widanalage D. & Chouchelamane, Gael & Jennings, Paul A. & Marco, James, 2017. "On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system," Energy, Elsevier, vol. 133(C), pages 710-722.
    20. Kvasha, Andriy & Gutiérrez, César & Osa, Urtzi & de Meatza, Iratxe & Blazquez, J. Alberto & Macicior, Haritz & Urdampilleta, Idoia, 2018. "A comparative study of thermal runaway of commercial lithium ion cells," Energy, Elsevier, vol. 159(C), pages 547-557.
    21. Li, Maobing & Xu, Hui & Li, Weimin & Liu, Yin & Li, Fade & Hu, Yue & Liu, Li, 2016. "The structure and control method of hybrid power source for electric vehicle," Energy, Elsevier, vol. 112(C), pages 1273-1285.
    22. Chen, Kunlong & Zheng, Fangdan & Jiang, Jiuchun & Zhang, Weige & Jiang, Yan & Chen, Kunjin, 2017. "Practical failure recognition model of lithium-ion batteries based on partial charging process," Energy, Elsevier, vol. 138(C), pages 1199-1208.
    23. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    24. 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.
    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. 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).
    2. Dong, Cheng & Yang, Haowei & Yang, Zhe & Cao, Wenjian & Miao, Zhengwei & Ren, Lei & Guo, Yacong & Huang, Chenguang & Tu, Huan & Wei, Yanpeng, 2024. "Protective performance of shear thickening gel modified epoxy sealant on lithium-ion batteries under mechanical abuse," Energy, Elsevier, vol. 296(C).
    3. 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).
    4. 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).
    5. 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).
    6. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Xiangli, Kang & Meng, Dean, 2024. "A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis," Energy, Elsevier, vol. 293(C).
    7. 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.
    8. 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).

    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. 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.
    2. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    3. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    4. 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).
    5. Zhu, Xiaoqing & Wang, Zhenpo & Wang, Yituo & Wang, Hsin & Wang, Cong & Tong, Lei & Yi, Mi, 2019. "Overcharge investigation of large format lithium-ion pouch cells with Li(Ni0.6Co0.2Mn0.2)O2 cathode for electric vehicles: Thermal runaway features and safety management method," Energy, Elsevier, vol. 169(C), pages 868-880.
    6. Huang, Peifeng & Yao, Caixia & Mao, Binbin & Wang, Qingsong & Sun, Jinhua & Bai, Zhonghao, 2020. "The critical characteristics and transition process of lithium-ion battery thermal runaway," Energy, Elsevier, vol. 213(C).
    7. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    8. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    9. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2018. "Successive-approximation algorithm for estimating capacity of Li-ion batteries," Energy, Elsevier, vol. 159(C), pages 61-73.
    10. Noelle, Daniel J. & Wang, Meng & Le, Anh V. & Shi, Yang & Qiao, Yu, 2018. "Internal resistance and polarization dynamics of lithium-ion batteries upon internal shorting," Applied Energy, Elsevier, vol. 212(C), pages 796-808.
    11. Qian, Cheng & Xu, Binghui & Chang, Liang & Sun, Bo & Feng, Qiang & Yang, Dezhen & Ren, Yi & Wang, Zili, 2021. "Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries," Energy, Elsevier, vol. 227(C).
    12. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation," Energy, Elsevier, vol. 207(C).
    13. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    14. Shu, Xing & Li, Guang & Shen, Jiangwei & Lei, Zhenzhen & Chen, Zheng & Liu, Yonggang, 2020. "A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization," Energy, Elsevier, vol. 204(C).
    15. 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).
    16. Gandoman, Foad H. & Jaguemont, Joris & Goutam, Shovon & Gopalakrishnan, Rahul & Firouz, Yousef & Kalogiannis, Theodoros & Omar, Noshin & Van Mierlo, Joeri, 2019. "Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: Basics, progress, and challenges," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    17. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    18. 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).
    19. 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).
    20. Wang, Shun-Li & Fernandez, Carlos & Zou, Chuan-Yun & Yu, Chun-Mei & Chen, Lei & Zhang, Li, 2019. "A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction," Energy, Elsevier, vol. 171(C), pages 444-455.

    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:166:y:2019:i:c:p:1013-1024. 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.