With the rapid adoption of electric vehicles (xEVs), the importance of maintaining thermal comfort in vehicles has become more critical. Unlike internal combustion engine vehicles, xEVs require optimized energy efficiency, which is closely related to the operation of HVAC (Heating, Ventilation, and Air Conditioning) systems. This study utilizes the Predicted Mean Vote (PMV) model to quantitatively evaluate in-vehicle thermal comfort based on real-time driving data, including temperature, humidity, and airflow velocity, collected during summer and winter. Using Python's pythermalcomfort module, PMV values were successfully calculated, demonstrating their applicability in dynamic vehicle environments. The results showed that approximately 70% of summer data and 85% of winter data fell within the ISO 7730 comfort range $(-\boldsymbol{0.5}\leq\mathbf{PMV} \leq+\boldsymbol{0.5})$, validating the PMV model as an effective tool for assessing in-vehicle thermal comfort. Additionally, the study identifies areas for improvement in HVAC system operation and suggests potential future directions for personalized comfort evaluation and system optimization.


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    Title :

    PMV-Based Thermal Comfort Assessment Methodology using Driving Data from xEV Vehicles


    Contributors:


    Publication date :

    2025-02-18


    Size :

    404561 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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