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

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

Reliability Engineering and System Safety, 2023, vol. 229, issue C

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.

Keywords: Predictive monitoring; Structural reliability of battery bracket; Digital twin; Machine learning; Response surface; Finite element simulation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004914

DOI: 10.1016/j.ress.2022.108874

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