Prediction of thermal comfort of female passengers in a vehicle based on an outdoor experiment

Abstract

Vehicles have come to be regarded as another important indoor environment where people spend a significant amount of time. Along with the accelerated development of self-driving vehicles, more attention has been focused on optimizing the thermal comfort of vehicle users. Compared with buildings, vehicles have a non-uniform indoor environment that dynamically changes under the influence of solar radiation and the season of the year. Considering the various environmental factors that significantly impact the indoor condition of a vehicle, it is very challenging to estimate the thermal comfort of a vehicle user, especially when the vehicle is outdoors. Many comfort models have been developed based on subject experiments conducted using vehicles placed in climatic chambers. However, because such laboratory experiments consider only very limited conditions, a dynamic scene that simulates the actual situation of vehicle users under real driving conditions cannot be implemented. To address this issue, the present study utilized long-term outdoor experiments. Based on the data acquired from the experiments, we derived equations for predicting the overall thermal sensation (OTS) of a female vehicle user under both transient and stable conditions. The field experiments were conducted over three seasons and considered a total of 80 female subjects of ages 20–30 years. All the experiments were performed using an experimental vehicle on the rooftop of a seven-story building, where the sunlight was not shielded. The environmental conditions (air temperature, relative humidity, solar radiation, and air velocity) inside and outside the vehicle were measured. The psychological and physiological responses of the subjects during the experiments were also recorded. The physiological responses consisted of the skin temperature at 16 local body sites, while the psychological responses consisted of the local and overall thermal sensation and comfort. The data of 60 randomly selected subjects of the experiments were used to derive optimal multiple regression equations for predicting the OTS of a female vehicle user, while the data of the other 20 subjects were used to validate the proposed equations. The two derived equations of the OTS prediction model for both non-uniform and uniform state of a vehicle consist of simple and straightforward environmental indicators such as outdoor and indoor air temperature, the difference between the outdoor and indoor air temperature, and the solar radiation. A strong correlation between the actual OTS and predicted OTS from the equations were found, showing the feasibility of the developed OTS prediction model.