Human thermal sensation and its algorithmic modelization under dynamic environmental thermal characteristics of vehicle cabin.

Thermal conditions are strongly changeable in a vehicle cabin, where passengers could suffer consecutive self-thermoregulation to such dynamic changing thermal stresses, though its HVAC system works well. To observe human overall and local thermal sensations in dynamic thermal conditions, a series of experiments under various conditions were carried out in a cabin-like climate chamber. The results showed that the head, chest, back, and hands during hot exposure are warmer leading to the overall thermal sensation being hot. The thermal sensation of the head was warmer than the overall thermal sensation. During cold exposure, arms, hands, legs, and feet were the main areas causing coldness. In a dynamic thermal environment, the previous skin temperature state and thermal sensation form a thermal sensation overshoot, causing a shift in the body's neutral temperature point. This study proposes a thermal sensation model for the prediction of human thermal sensation local and overall based on skin temperature changes in a dynamic environment. Considering the airflow characteristics in the cabin, the human body is set into seven local parts in the local thermal sensation model. To compensate for sensation overshoot from this, defining recovery points rp for local parts differentiate temperature setpoints according to the experienced thermal state so that the effect resulting from the dynamic condition is integrated into the model algorithm. The model provides a scientific basis for guiding design optimization and intelligent regulation in the dynamic environment of the vehicle cabin, so as to achieve efficient energy utilization.