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Structural performance prediction based on the digital twin model: A battery bracket example

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  • He, Wenbin
  • Mao, Jianxu
  • Song, Kai
  • Li, Zhe
  • Su, Yulong
  • Wang, Yaonan
  • Pan, Xiangcheng

Abstract

Battery bracket for new energy commercial vehicles is subjected to variable loads and battery temperature changes both during the design road test phase and in-service operation. Therefore, their structural performance must be evaluated in real-time for reliability design and health monitoring. With the rapid development of industrial digitization, the digital twin has become an indispensable technology. This paper proposes a digital twin approach for predictive monitoring of the performance of mechanical structures. Taking the structural performance for the battery bracket of new energy commercial vehicles as an example, this paper builds a unit-level digital twin model—DTMAR. It comprises the numerical model, NN-RSR model, and hybrid machine learning model. The results reveal that the DTMAR model can efficiently and accurately calculate and predict the structural performance. This can not only provide constructive guidance for optimal design of the next generation product structure, but also aid in evaluating the structural reliability of the battery bracket of new energy commercial vehicles and improve their driving safety.

Suggested Citation

  • He, Wenbin & Mao, Jianxu & Song, Kai & Li, Zhe & Su, Yulong & Wang, Yaonan & Pan, Xiangcheng, 2023. "Structural performance prediction based on the digital twin model: A battery bracket example," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004914
    DOI: 10.1016/j.ress.2022.108874
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    References listed on IDEAS

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    Cited by:

    1. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    3. Giannakeas, Ilias N. & Mazaheri, Fatemeh & Bacarreza, Omar & Khodaei, Zahra Sharif & Aliabadi, Ferri M.H., 2023. "Probabilistic residual strength assessment of smart composite aircraft panels using guided waves," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. D'Urso, Diego & Chiacchio, Ferdinando & Cavalieri, Salvatore & Gambadoro, Salvatore & Khodayee, Soheyl Moheb, 2024. "Predictive maintenance of standalone steel industrial components powered by a dynamic reliability digital twin model with artificial intelligence," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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