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Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System

Author

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  • Duwon Choi

    (Department of Mechanical Engineering, Ajou University, 206 World cup-ro, Yeongtong-gu, Suwon 16499, Gyeonggi, Korea
    Vehicle Calibration Team, Tenergy, 145 Gwanggyo-ro, Yeongtong-gu, Suwon 16229, Gyeonggi, Korea
    These authors contributed equally to this work.)

  • Youngkuk An

    (Department of Mechanical Engineering, Ajou University, 206 World cup-ro, Yeongtong-gu, Suwon 16499, Gyeonggi, Korea
    These authors contributed equally to this work.)

  • Nankyu Lee

    (Vehicle Calibration Team, Tenergy, 145 Gwanggyo-ro, Yeongtong-gu, Suwon 16229, Gyeonggi, Korea)

  • Jinil Park

    (Department of Mechanical Engineering, Ajou University, 206 World cup-ro, Yeongtong-gu, Suwon 16499, Gyeonggi, Korea)

  • Jonghwa Lee

    (Department of Mechanical Engineering, Ajou University, 206 World cup-ro, Yeongtong-gu, Suwon 16499, Gyeonggi, Korea)

Abstract

Vehicle integrated thermal management system (VTMS) is an important technology used for improving the energy efficiency of vehicles. Physics-based modeling is widely used to predict the energy flow in such systems. However, physics-based modeling requires several experimental approaches to get the required parameters. The experimental approach to obtain these parameters is expensive and requires great effort to configure a separate experimental device and conduct the experiment. Therefore, in this study, a neural network (NN) approach is applied to reduce the cost and effort necessary to develop a VTMS. The physics-based modeling is also analyzed and compared with recent NN techniques, such as ConvLSTM and temporal convolutional network (TCN), to confirm the feasibility of the NN approach at EPA Federal Test Procedure (FTP-75), Highway Fuel Economy Test cycle (HWFET), Worldwide harmonized Light duty driving Test Cycle (WLTC) and actual on-road driving conditions. TCN performed the best among the tested models and was easier to build than physics-based modeling. For validating the two different approaches, the physical properties of a 1 L class passenger car with an electric control valve are measured. The NN model proved to be effective in predicting the characteristics of a vehicle cooling system. The proposed method will reduce research costs in the field of predictive control and VTMS design.

Suggested Citation

  • Duwon Choi & Youngkuk An & Nankyu Lee & Jinil Park & Jonghwa Lee, 2020. "Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System," Energies, MDPI, vol. 13(20), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5301-:d:426648
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    References listed on IDEAS

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    1. Briggs, Ian & Murtagh, Martin & Kee, Robert & McCulloug, Geoffrey & Douglas, Roy, 2017. "Sustainable non-automotive vehicles: The simulation challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 840-851.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Zahid, Taimoor & Xu, Kun & Li, Weimin & Li, Chenming & Li, Hongzhe, 2018. "State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles," Energy, Elsevier, vol. 162(C), pages 871-882.
    4. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting," Energies, MDPI, vol. 11(1), pages 1-13, January.
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    Cited by:

    1. Wenbin Su & Wei Ren & Hui Sun & Canjie Liu & Xuhao Lu & Yingli Hua & Hongbo Wei & Han Jia, 2022. "Data-Based Flow Rate Prediction Models for Independent Metering Hydraulic Valve," Energies, MDPI, vol. 15(20), pages 1-12, October.
    2. Tong-Bou Chang & Jer-Jia Sheu & Jhong-Wei Huang, 2020. "High-Efficiency HVAC System with Defog/Dehumidification Function for Electric Vehicles," Energies, MDPI, vol. 14(1), pages 1-12, December.
    3. Kibok Kim & Jinil Park & Jonghwa Lee, 2021. "Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning," Energies, MDPI, vol. 14(15), pages 1-13, July.

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