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.

车辆已逐渐被视为人们花费大量时间的另一个重要室内环境。随着自动驾驶汽车的加速发展，优化汽车使用者的热舒适性越来越受到关注。与建筑物相比，车辆具有不均匀的室内环境，在太阳辐射和一年四季的影响下动态变化。考虑到显着影响车辆室内条件的各种环境因素，估计车辆用户的热舒适度非常具有挑战性，尤其是当车辆在室外时。许多舒适模型是基于使用放置在气候室中的车辆进行的主题实验而开发的。然而，由于此类实验室实验只考虑非常有限的条件，无法实现模拟车辆使用者在真实驾驶条件下实际情况的动态场景。为了解决这个问题，本研究利用了长期的户外实验。基于从实验中获得的数据，我们推导出了在瞬态和稳定条件下预测女性车辆用户的整体热感觉 (OTS) 的方程。田间试验进行了三个季节，总共考虑了 80 名年龄在 20-30 岁之间的女性受试者。所有的实验都是在一座七层楼的屋顶上使用实验车进行的，那里的阳光没有被屏蔽。环境条件（气温、相对湿度、太阳辐射、和空气速度）在车辆内外进行了测量。实验过程中受试者的心理和生理反应也被记录下来。生理反应包括 16 个局部身体部位的皮肤温度，而心理反应包括局部和整体热感觉和舒适度。实验中随机选择的 60 名受试者的数据用于推导出用于预测女性车辆用户 OTS 的最优多元回归方程，而其他 20 名受试者的数据用于验证所提出的方程。车辆非均匀和均匀状态的 OTS 预测模型的两个导出方程由简单明了的环境指标组成，例如室外和室内空气温度，室外和室内空气温度之间的差异，以及太阳辐射。发现实际 OTS 和从方程预测的 OTS 之间存在很强的相关性，表明开发的 OTS 预测模型的可行性。