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Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system

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  • Zhang, Xiaoxi
  • Pan, Yongjun
  • Xiong, Yue
  • Zhang, Yongzhi
  • Tang, Mao
  • Dai, Wei
  • Liu, Binghe
  • Hou, Liang

Abstract

The primary concerns of the automotive industry are structural integrity and battery-pack system (BPS) reliability. To ascertain the appropriate thickness of critical BPS components (e.g., the long bracket, crossbeam, and bottom shell), engineers are required to perform several simulations of finite element (FE) analysis. This procedure is laborious and requires a significant amount of time. This study introduces a very effective approach for predicting vibration-induced stress and fatigue life, intending to enhance dependability in the design process. Firstly, a three-layer lithium battery model is utilized to investigate the impact of vibration on the maximal Mises stress at various states of charge. This information is used to model the BPS, including the batteries. The nonlinear FE model of the BPS is validated using the modal test results. Secondly, a sensitivity analysis is performed to ascertain the impact of component thickness on the maximum Mises stress. For vibration simulations, multiple design variables are selected to collect data. Next, the nonlinear relationship between inputs (thicknesses of critical components) and outputs (maximum Mises stress and minimum fatigue life) is described using a deep learning (DL) modeling framework with forward and backward propagation. Finally, the accuracy of the DL model is assessed by measuring error functions and comparing its performance to six commonly employed methods. Furthermore, the inclusion of Gaussian noise is employed to assess the model’s robustness and ability to generalize. Additionally, the establishment of fatigue-life and stress boundaries serves to offer designers valuable insights. The results indicate that the proposed method for predicting vibration stress and fatigue life is highly efficient and cost-effective, making it useful for the design of a robust and reliable BPS.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018457
    DOI: 10.1016/j.apenergy.2023.122481
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    as
    1. Jie Deng & Chulheung Bae & James Marcicki & Alvaro Masias & Theodore Miller, 2018. "Safety modelling and testing of lithium-ion batteries in electrified vehicles," Nature Energy, Nature, vol. 3(4), pages 261-266, April.
    2. 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.
    3. Chen, Dan & Meng, Jinhao & Huang, Huanyang & Wu, Ji & Liu, Ping & Lu, Jiwu & Liu, Tianqi, 2022. "An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving," Energy, Elsevier, vol. 245(C).
    4. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
    5. Ling, Ziye & Cao, Jiahao & Zhang, Wenbo & Zhang, Zhengguo & Fang, Xiaoming & Gao, Xuenong, 2018. "Compact liquid cooling strategy with phase change materials for Li-ion batteries optimized using response surface methodology," Applied Energy, Elsevier, vol. 228(C), pages 777-788.
    6. 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).
    7. Su, Shaosen & Li, Wei & Garg, Akhil & Gao, Liang, 2022. "An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization," Applied Energy, Elsevier, vol. 312(C).
    8. 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).
    9. Gandoman, Foad H. & Ahmadi, Abdollah & Bossche, Peter Van den & Van Mierlo, Joeri & Omar, Noshin & Nezhad, Ali Esmaeel & Mavalizadeh, Hani & Mayet, Clément, 2019. "Status and future perspectives of reliability assessment for electric vehicles," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 1-16.
    10. Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2022. "Techno-economic assessment of battery storage integrated into a grid-connected and solar-powered residential building under different battery ageing models," Applied Energy, Elsevier, vol. 318(C).
    11. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    12. 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).
    13. 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.
    14. Maitane Berecibar, 2019. "Machine-learning techniques used to accurately predict battery life," Nature, Nature, vol. 568(7752), pages 325-326, April.
    15. Anselma, Pier Giuseppe & Kollmeyer, Phillip & Lempert, Jeremy & Zhao, Ziyu & Belingardi, Giovanni & Emadi, Ali, 2021. "Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation," Applied Energy, Elsevier, vol. 285(C).
    16. Tan, P. & Jiang, H.R. & Zhu, X.B. & An, L. & Jung, C.Y. & Wu, M.C. & Shi, L. & Shyy, W. & Zhao, T.S., 2017. "Advances and challenges in lithium-air batteries," Applied Energy, Elsevier, vol. 204(C), pages 780-806.
    17. Thelen, Adam & Li, Meng & Hu, Chao & Bekyarova, Elena & Kalinin, Sergey & Sanghadasa, Mohan, 2022. "Augmented model-based framework for battery remaining useful life prediction," Applied Energy, Elsevier, vol. 324(C).
    18. Che, Yunhong & Zheng, Yusheng & Wu, Yue & Sui, Xin & Bharadwaj, Pallavi & Stroe, Daniel-Ioan & Yang, Yalian & Hu, Xiaosong & Teodorescu, Remus, 2022. "Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network," Applied Energy, Elsevier, vol. 323(C).
    19. Solai, Elie & Guadagnini, Maxime & Beaugendre, Héloïse & Daccord, Rémi & Congedo, Pietro, 2022. "Validation of a data-driven fast numerical model to simulate the immersion cooling of a lithium-ion battery pack," Energy, Elsevier, vol. 249(C).
    20. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
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