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Protective performance of shear thickening gel modified epoxy sealant on lithium-ion batteries under mechanical abuse

Author

Listed:
  • Dong, Cheng
  • Yang, Haowei
  • Yang, Zhe
  • Cao, Wenjian
  • Miao, Zhengwei
  • Ren, Lei
  • Guo, Yacong
  • Huang, Chenguang
  • Tu, Huan
  • Wei, Yanpeng

Abstract

The popularity of electric vehicles leads to more attention drawn to the safety of Lithium-ion batteries in traffic crashes. In this study, a novel epoxy-based sealant was proposed by incorporating shear-thickening gel (STG) into the matrix material. The material properties including morphological characteristics and chemical compositions were determined via microscopic analysis techniques. Mechanical properties of the newly proposed material under different loading conditions were determined using corresponding testing methods. The experimental results confirmed the contribution of the STG to dampness, ductility, and toughness. To examine the protective effectiveness of the proposed encapsulating material, indentation tests on individual cell units and drop weight tests on battery packs were carried out. It was found that the modified sealant could improve the failure strength as well as delay the onset of internal short-circuit under lateral compression. In comparison with the conventional sealant, the deformations of cell units and the acceleration at the central region of the battery pack were remarkedly reduced by using the modified material under drop weight impact, which were detected by the strain gauges and accelerometers. The experimental observations and test results in this study could support the potential application of STG-modified epoxy material in battery systems of electric vehicles.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224009253
    DOI: 10.1016/j.energy.2024.131152
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    References listed on IDEAS

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    1. Dai, Haifeng & Yu, Chenchen & Wei, Xuezhe & Sun, Zechang, 2017. "State of charge estimation for lithium-ion pouch batteries based on stress measurement," Energy, Elsevier, vol. 129(C), pages 16-27.
    2. Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Wang, Ju, 2019. "Frequency and time domain modelling and online state of charge monitoring for ultracapacitors," Energy, Elsevier, vol. 176(C), pages 874-887.
    3. 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.
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