IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v247y2024ics0951832024002151.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024002151
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110141?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. 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. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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).
    11. 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.
    12. 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).
    13. 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).
    14. 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).
    Full references (including those not matched with items on IDEAS)

    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. Xu, Dongxin & Pan, Yongjun & Zhang, Xiaoxi & Dai, Wei & Liu, Binghe & Shuai, Qi, 2024. "Data-driven modelling and evaluation of a battery-pack system’s mechanical safety against bottom cone impact," Energy, Elsevier, vol. 290(C).
    2. 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).
    3. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    4. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    5. Zhang, Junwei & Zhang, Weige & Sun, Bingxiang & Zhang, Yanru & Fan, Xinyuan & Zhao, Bo, 2024. "A novel method of battery pack energy health estimation based on visual feature learning," Energy, Elsevier, vol. 293(C).
    6. Tian, Yuan & Han, Minghao & Kulkarni, Chetan & Fink, Olga, 2022. "A prescriptive Dirichlet power allocation policy with deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    7. Wang, Lubing & Li, Jianping & Chen, Jiaying & Duan, Xudong & Li, Binqi & Li, Jiani, 2023. "Revealing the internal short circuit mechanisms in lithium-ion batteries upon dynamic loading based on multiphysics simulation," Applied Energy, Elsevier, vol. 351(C).
    8. Shu, Xing & Shen, Jiangwei & Chen, Zheng & Zhang, Yuanjian & Liu, Yonggang & Lin, Yan, 2022. "Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    9. Xu, Tingting & Peng, Zhen & Wu, Lifeng, 2021. "A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current," Energy, Elsevier, vol. 218(C).
    10. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    11. Chen, Zhang & Shen, Wenjing & Chen, Liqun & Wang, Shuqiang, 2022. "Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries," Energy, Elsevier, vol. 248(C).
    12. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    13. Li, Yong & Wang, Liye & Feng, Yanbiao & Liao, Chenglin & Yang, Jue, 2024. "An online state-of-health estimation method for lithium-ion battery based on linear parameter-varying modeling framework," Energy, Elsevier, vol. 298(C).
    14. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    15. Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).
    16. 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).
    17. Li, Haoqian & Wang, Yong & Zeng, Jing & Li, Fansong & Yang, Zhenhuan & Mei, Guiming & Ye, Yunguang, 2024. "Virtual point tracking method for online detection of relative wheel-rail displacement of railway vehicles," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    18. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
    19. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    20. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).

    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:reensy:v:247:y:2024:i:c:s0951832024002151. 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: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.