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Performance analysis of safety barriers against cascading failures in a battery pack

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  • Xie, Lin
  • Ustolin, Federico
  • Lundteigen, Mary Ann
  • Li, Tian
  • Liu, Yiliu

Abstract

Lithium-ion batteries have been widely employed as the principal power source in electric vehicles and other storage systems. However, some critical issues in a battery pack still exist, such as thermal failures on initial cells that impact the temperatures of the surrounding cells. Such cascading failures may significantly affect battery performance and safety. Thermal barriers, as one kind of safety barrier, are therefore installed to prevent failure propagations. This paper focuses on the situation when the temperature of battery cell increases, but the battery pack still can be used in a degradation mode since the barriers are against cascading failures. An approach is proposed to analyze how the deployment and performance of thermal barriers in a battery pack determine their capabilities against cascading failures. The approach includes thermal propagation models associated with the simulations, degradation models, reliability analysis, and barrier analysis. Its application is illustrated with a practical case study. The battery reliabilities are sensitive to many factors of the barriers, such as temperature differences, failed cells, and performance coefficient. The barriers between parallel cells are found to be more effective in mitigating failure propagation. Such findings can be beneficial for barrier optimization and reliability improvement of battery packs.

Suggested Citation

  • Xie, Lin & Ustolin, Federico & Lundteigen, Mary Ann & Li, Tian & Liu, Yiliu, 2022. "Performance analysis of safety barriers against cascading failures in a battery pack," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022004239
    DOI: 10.1016/j.ress.2022.108804
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    References listed on IDEAS

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    1. He, Jiabei & Tian, Yi & Wu, Lifeng, 2022. "A hybrid data-driven method for rapid prediction of lithium-ion battery capacity," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Coman, Paul T. & Darcy, Eric C. & Veje, Christian T. & White, Ralph E., 2017. "Numerical analysis of heat propagation in a battery pack using a novel technology for triggering thermal runaway," Applied Energy, Elsevier, vol. 203(C), pages 189-200.
    3. Liu, Zhitao & Tan, CherMing & Leng, Feng, 2015. "A reliability-based design concept for lithium-ion battery pack in electric vehicles," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 169-177.
    4. Xia, Quan & Yang, Dezhen & Wang, Zili & Ren, Yi & Sun, Bo & Feng, Qiang & Qian, Cheng, 2020. "Multiphysical modeling for life analysis of lithium-ion battery pack in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Levitin, Gregory & Xing, Liudong, 2010. "Reliability and performance of multi-state systems with propagated failures having selective effect," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 655-661.
    6. Mishra, Madhav & Martinsson, Jesper & Rantatalo, Matti & Goebel, Kai, 2018. "Bayesian hierarchical model-based prognostics for lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 25-35.
    7. Zhou, Zhizuan & Zhou, Xiaodong & Li, Maoyu & Cao, Bei & Liew, K.M. & Yang, Lizhong, 2022. "Experimentally exploring prevention of thermal runaway propagation of large-format prismatic lithium-ion battery module," Applied Energy, Elsevier, vol. 327(C).
    8. Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    9. Downey, Austin & Lui, Yu-Hui & Hu, Chao & Laflamme, Simon & Hu, Shan, 2019. "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 1-12.
    10. Lin Xie & Mary Ann Lundteigen & Yiliu Liu, 2020. "Reliability and barrier assessment of series–parallel systems subject to cascading failures," Journal of Risk and Reliability, , vol. 234(3), pages 455-469, June.
    11. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    12. 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.
    13. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    14. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    15. Xu, Fan & Yang, Fangfang & Fei, Zicheng & Huang, Zhelin & Tsui, Kwok-Leung, 2021. "Life prediction of lithium-ion batteries based on stacked denoising autoencoders," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
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