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Machine learning-based personal thermal comfort model for electric vehicles with local infrared radiant warmers

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Abstract

Thermal comfort of occupants in conventional vehicles driven by an internal combustion engine is controlled by heating, ventilation, and air conditioning (HVAC) system. However, the operation of a conventional HVAC system decreases the mileage of electric vehicle (EV) in the heating mode by nearly 50 %. Thus, local radiant heating was proposed as a heating strategy to reduce electric energy consumption while providing reasonable thermal comfort. In this work, we developed a personalized overall thermal sensation (OS) model using machine learning to evaluate the thermopsychological effect of local radiant heating and simulate the OS of occupants in EVs. Data were obtained from a real EV that went through a cold environmental chamber and were evaluated using random forest algorithm. By considering individual thermal preferences of occupants, we predicted the OS for each subject with higher accuracy by a factor of 2.6 compared with the prediction performed using the weighted average method. Total energy consumption was reduced by approximately 10 % in the EV equipped with local infrared radiant warmers while providing OS that was comparable with that of the HVAC system.

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Acknowledgments

This work was supported by grants from the Korea Evaluation Institute of Industrial Technology (KEIT) (20011377) funded by the Ministry of Trade, Industry, and Energy and the National Research Foundation (NRF) (NRF-2019R1A2C2088973, NRF-2019R1A2C1009501) funded by the Ministry of Science and ICT, Republic of Korea. The authors thank the members of the Thermal Management Research Lab, Hyundai Motor Company for providing the environmental chamber facility and participating in the real car experiment.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Graduate School, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Korea

    Yein Lee

  2. School of Mechanical Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul, 02707, Korea

    Hyunjin Lee, Byung Ha Kang & Jung Kyung Kim

Authors
  1. Yein Lee
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  2. Hyunjin Lee
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  4. Jung Kyung Kim
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Corresponding author

Correspondence to Jung Kyung Kim.

Additional information

Yein Lee entered the Kookmin University in Seoul, Korea in 2014. She received a B.S. degree in Mechanical Systems Engineering and a M.S. degree in Mechanical Engineering in 2018 and 2020, respectively. Her research interests include mathematical modeling, numerical analysis, and human thermoregulation.

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Lee, Y., Lee, H., Kang, B.H. et al. Machine learning-based personal thermal comfort model for electric vehicles with local infrared radiant warmers. J Mech Sci Technol 35, 3239–3247 (2021). https://doi.org/10.1007/s12206-021-0644-7

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  • DOI: https://doi.org/10.1007/s12206-021-0644-7

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