Prediction Models of Overall Thermal Sensation and Comfort in Vehicle Cabin Based on Field Experiments

Abstract

Vehicle thermal comfort has received more attention due to advancements in autonomous driving and intelligent cabin technology. Prediction of thermal comfort is challenging due to the passenger compartment's complex transient non-uniform thermal environment. Many thermal comfort models are primarily based on environmental or human thermal physiology factors, but too many temperature measurements may affect driving behavior. This study analyzed the correlations between local thermal sensation (LTS), local thermal comfort (LTC), the thermal environment in an automobile's cabin, and skin temperature. The optimal combination of influencing factors was established in the prediction model of overall thermal sensation (OTS) and overall thermal comfort (OTC) in the vehicle cabin. The results indicated that breathing air and chest skin surface temperature had the best correlation with subjective human evaluation. The prediction models of OTS and OTC have good prediction performance, and their R² values are 0.77 and 0.51, respectively. Accurately predicting the thermal comfort in the vehicle provides a valuable reference for intelligent cabin thermal environment control and automobile energy savings.