IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v322y2022ics0306261922008455.html
   My bibliography  Save this article

Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design

Author

Listed:
  • Pan, Yongjun
  • Zhang, Xiaoxi
  • Liu, Yue
  • Wang, Huacui
  • Cao, Yangzheng
  • Liu, Xin
  • Liu, Binghe

Abstract

The structural integrity and crashworthiness of the battery-pack system (BPS) in electric vehicles are an emerging concern of engineers. Therefore, corresponding numerical and experimental investigations have to be carried out. Engineers need to select appropriate thicknesses and materials of main components through multiple finite element analysis (FEA), e.g., upper enclosure and bottom shell. This process is laborious and time-consuming. In this paper, a rapid stress prediction method is proposed to help select components’ thicknesses and materials under crush scenarios. This method is based on historical FEA data and a deep neural network (DNN) algorithm. First, a nonlinear FE model of a BPS that includes battery modules is developed. The FE model is verified via mesh-sensitivity analysis and modal test results. The crush simulations are performed and the FEA data are collected. Second, a DNN framework with forwarding and backward propagations is used to train the FEA data. Therefore, a DNN model that can describe the relationship between the inputs (thicknesses and materials of related components) and outputs (maximum von Mises stresses of modules) is established The established DNN model can effectively predict the modules’ stresses. The accuracy of the DNN model is investigated in terms of error functions. Furthermore, the second-order response surface model, third-order response surface model, and radial basis function neural network model are used to demonstrate the advantages of the DNN model. The proposed crushing behavior prediction method, which can be used in the design of safe and durable BPS, is proven efficient and accurate.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:322:y:2022:i:c:s0306261922008455
    DOI: 10.1016/j.apenergy.2022.119527
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922008455
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119527?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. Zhu, Juner & Zhang, Xiaowei & Luo, Hailing & Sahraei, Elham, 2018. "Investigation of the deformation mechanisms of lithium-ion battery components using in-situ micro tests," Applied Energy, Elsevier, vol. 224(C), pages 251-266.
    2. Liu, Binghe & Yin, Sha & Xu, Jun, 2016. "Integrated computation model of lithium-ion battery subject to nail penetration," Applied Energy, Elsevier, vol. 183(C), pages 278-289.
    3. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    4. Rogers, Daniel J. & Aslett, Louis J.M. & Troffaes, Matthias C.M., 2021. "Modelling of modular battery systems under cell capacity variation and degradation," Applied Energy, Elsevier, vol. 283(C).
    5. Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
    6. 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.
    7. Wang, WenWei & Yang, Sheng & Lin, Cheng, 2017. "Clay-like mechanical properties for the jellyroll of cylindrical Lithium-ion cells," Applied Energy, Elsevier, vol. 196(C), pages 249-258.
    8. 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.
    9. Cheng, Ziqiang & Tenny, Kevin M. & Pizzolato, Alberto & Forner-Cuenca, Antoni & Verda, Vittorio & Chiang, Yet-Ming & Brushett, Fikile R. & Behrou, Reza, 2020. "Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods," Applied Energy, Elsevier, vol. 279(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. 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.
    2. Ren, Dongsheng & Feng, Xuning & Lu, Languang & He, Xiangming & Ouyang, Minggao, 2019. "Overcharge behaviors and failure mechanism of lithium-ion batteries under different test conditions," Applied Energy, Elsevier, vol. 250(C), pages 323-332.
    3. Li, Honggang & Zhou, Dian & Zhang, Meihe & Liu, Binghe & Zhang, Chao, 2023. "Multi-field interpretation of internal short circuit and thermal runaway behavior for lithium-ion batteries under mechanical abuse," Energy, Elsevier, vol. 263(PE).
    4. Yiding, Li & Wenwei, Wang & Cheng, Lin & Xiaoguang, Yang & Fenghao, Zuo, 2021. "A safety performance estimation model of lithium-ion batteries for electric vehicles under dynamic compression," Energy, Elsevier, vol. 215(PA).
    5. Sheng Yang & Wenwei Wang & Cheng Lin & Weixiang Shen & Yiding Li, 2019. "Investigation of Internal Short Circuits of Lithium-Ion Batteries under Mechanical Abusive Conditions," Energies, MDPI, vol. 12(10), pages 1-16, May.
    6. 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).
    7. Liu, Lishuo & Feng, Xuning & Zhang, Mingxuan & Lu, Languang & Han, Xuebing & He, Xiangming & Ouyang, Minggao, 2020. "Comparative study on substitute triggering approaches for internal short circuit in lithium-ion batteries," Applied Energy, Elsevier, vol. 259(C).
    8. Ren, Dongsheng & Liu, Xiang & Feng, Xuning & Lu, Languang & Ouyang, Minggao & Li, Jianqiu & He, Xiangming, 2018. "Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components," Applied Energy, Elsevier, vol. 228(C), pages 633-644.
    9. Zhu, Juner & Zhang, Xiaowei & Luo, Hailing & Sahraei, Elham, 2018. "Investigation of the deformation mechanisms of lithium-ion battery components using in-situ micro tests," Applied Energy, Elsevier, vol. 224(C), pages 251-266.
    10. Yang, Sheng & Wang, Wenwei & Lin, Cheng & Shen, Weixiang & Li, Yiding, 2019. "Improved constitutive model of the jellyroll for cylindrical lithium ion batteries considering microscopic damage," Energy, Elsevier, vol. 185(C), pages 202-212.
    11. Qingxia Yang & Jun Xu & Binggang Cao & Xiuqing Li, 2017. "A simplified fractional order impedance model and parameter identification method for lithium-ion batteries," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.
    12. Xiao, Feiyu & Xing, Bobin & Kong, Lingzhao & Xia, Yong, 2021. "Impedance-based diagnosis of internal mechanical damage for large-format lithium-ion batteries," Energy, Elsevier, vol. 230(C).
    13. Jingyi Chen & Genwei Wang & Hui Song & Bin Wang & Guiying Wu & Jianyin Lei, 2022. "Stress and Displacement of Cylindrical Lithium-Ion Power Battery during Charging and Discharging," Energies, MDPI, vol. 15(21), pages 1-22, November.
    14. Genwei Wang & Xuanfu Guo & Jingyi Chen & Pengfei Han & Qiliang Su & Meiqing Guo & Bin Wang & Hui Song, 2023. "Safety Performance and Failure Criteria of Lithium-Ion Batteries under Mechanical Abuse," Energies, MDPI, vol. 16(17), pages 1-25, September.
    15. Li, Xiaoyu & Zhang, Zuguang & Wang, Wenhui & Tian, Yong & Li, Dong & Tian, Jindong, 2020. "Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis," Applied Energy, Elsevier, vol. 262(C).
    16. Yasir Ali & Imran Shah & Tariq Amin Khan & Noman Iqbal, 2023. "A Multiphysics-Multiscale Model for Particle–Binder Interactions in Electrode of Lithium-Ion Batteries," Energies, MDPI, vol. 16(15), pages 1-15, August.
    17. Krishnan, Syam G. & Arulraj, Arunachalam & Khalid, Mohammad & Reddy, M.V. & Jose, Rajan, 2021. "Energy storage in metal cobaltite electrodes: Opportunities & challenges in magnesium cobalt oxide," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    18. Akash Samanta & Sheldon S. Williamson, 2021. "A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems," Energies, MDPI, vol. 14(18), pages 1-25, September.
    19. Ma, Chen & Chang, Long & Cui, Naxin & Duan, Bin & Zhang, Yulong & Yu, Zhihao, 2022. "Statistical relationships between numerous retired lithium-ion cells and packs with random sampling for echelon utilization," Energy, Elsevier, vol. 257(C).
    20. Kim, Seongyoon & Choi, Yun Young & Choi, Jung-Il, 2022. "Impedance-based capacity estimation for lithium-ion batteries using generative adversarial network," Applied Energy, Elsevier, vol. 308(C).

    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:appene:v:322:y:2022:i:c:s0306261922008455. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.