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A hybrid data-driven and physics-based model for temperature prediction of automotive exterior components
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  • Zhuoqian Chen,
  • houbao XU,
  • Yang Bai,
  • Xiongjun Liu,
  • Yingjing Yong,
  • Huixia Huo,
  • Yiran Zhao
Zhuoqian Chen
Beijing Institute of Technology
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houbao XU
Beijing Institute of Technology

Corresponding Author:[email protected]

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Yang Bai
Beijing Jinghang Institute of Computing and Communication
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Xiongjun Liu
Beijing Jinghang Institute of Computing and Communication
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Yingjing Yong
Beijing Institute of Technology
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Huixia Huo
Beijing Institute of Technology
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Yiran Zhao
Beijing Institute of Technology
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Abstract

This paper constructs a hybrid data-driven and physics-based model for temperature prediction of automotive exterior components based on insufficient natural exposure experimental data. The hybrid model consists of the physical part on heat conduction and the data-driven part on artificial intelligence algorithms. The physical part is developed by the heat balance equation, which ensures the generalization ability of the model. The data-driven part is developed based on BP neural network to calculate the unknown parameters and estimate the missing weather data, which are necessary for the physical part. At the end of the paper, the model performance is evaluated by the experiment data. The numerical results illustrate that the hybrid model not only has accurate predictive ability, but also possesses good generalization capability.