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Forward and reverse design of adhesive in batteries via dynamics and machine learning algorithms for enhanced mechanical safety

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  • Zhang, Xiaoxi
  • Pan, Yongjun
  • Zhou, Junxiao
  • Li, Zhixiong
  • Liao, Tianjun
  • Li, Jie

Abstract

The growing popularity of electric vehicles brings opportunities and challenges to the battery industry. Designers need to develop reliable battery packs to ensure the safety of consumers’ property and passengers’ lives. Due to the complex structure of the battery pack, the traditional finite element analysis design consumes a lot of computational resources. The utilization of multibody system dynamics (MSD) and machine learning (ML) methods can assist developers in the efficient design of reliable battery packs. In this work, an MSD model of a battery pack was constructed based on the recursive idea, which can characterize the state information of each cell, such as velocity, acceleration, deformation, etc., during extrusion. By utilizing ML techniques, it is possible to achieve both the forward and reverse design of the adhesive for the battery pack. This enables accurate prediction of battery deformation under various adhesive stiffness and damping coefficients, as well as different battery SOCs. Consequently, the design of the battery adhesive can be guided, resulting in minimal distortion of the battery pack during extrusion and reducing the risk of internal short circuits. This method enables efficient battery pack design and provides ideas for future reliable battery pack designs.

Suggested Citation

  • Zhang, Xiaoxi & Pan, Yongjun & Zhou, Junxiao & Li, Zhixiong & Liao, Tianjun & Li, Jie, 2024. "Forward and reverse design of adhesive in batteries via dynamics and machine learning algorithms for enhanced mechanical safety," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:reensy:v:247:y:2024:i:c:s0951832024002151
    DOI: 10.1016/j.ress.2024.110141
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    References listed on IDEAS

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    1. 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.
    2. Xu, Jun & Liu, Binghe & Wang, Xinyi & Hu, Dayong, 2016. "Computational model of 18650 lithium-ion battery with coupled strain rate and SOC dependencies," Applied Energy, Elsevier, vol. 172(C), pages 180-189.
    3. Pan, Yongjun & Zhang, Xiaoxi & Liu, Yue & Wang, Huacui & Cao, Yangzheng & Liu, Xin & Liu, Binghe, 2022. "Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design," Applied Energy, Elsevier, vol. 322(C).
    4. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Wenwei, Wang & Yiding, Li & Cheng, Lin & Yuefeng, Su & Sheng, Yang, 2019. "State of charge-dependent failure prediction model for cylindrical lithium-ion batteries under mechanical abuse," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Zhang, Xiaoxi & Xiong, Yue & Pan, Yongjun & Xu, Dongxin & Kawsar, Ibna & Liu, Binghe & Hou, Liang, 2023. "Deep-learning-based inverse structural design of a battery-pack system," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    7. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    8. 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).
    9. Li, Zai-Wei & Zhou, Yun-Lai & Liu, Xiao-Zhou & Abdel Wahab, Magd, 2023. "Service reliability assessment of ballastless track in high speed railway via improved response surface method," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    10. Pan, Yongjun & Sun, Yu & Li, Zhixiong & Gardoni, Paolo, 2023. "Machine learning approaches to estimate suspension parameters for performance degradation assessment using accurate dynamic simulations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
    12. Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, İ. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    14. Neves Costa, João & Ambrósio, Jorge & Andrade, António R. & Frey, Daniel, 2023. "Safety assessment using computer experiments and surrogate modeling: Railway vehicle safety and track quality indices," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    Full references (including those not matched with items on IDEAS)

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