In this paper, a novel algorithm for estimating clothing insulation is proposed to assess thermal comfort, based on the non-contact and real-time measurements of the face and clothing temperatures by an infrared camera. The proposed method can accurately measure the clothing insulation of various garments under different clothing fit and sitting postures. The proposed estimation method is investigated to be effective to measure its clothing insulation significantly in different seasonal clothing conditions using a paired t-test in 99% confidence interval. Temperatures simulated with the proposed estimated insulation value show closer to the values of actual temperature than those with individual clothing insulation values. Upper clothing’s temperature is more accurate within 3% error and lower clothing’s temperature is more accurate by 3.7%~6.2% error in indoor working scenarios. The proposed algorithm can reflect the effect of air layer which makes insulation different in the calculation to estimate clothing insulation using the temperature of the face and clothing. In future, the proposed method is expected to be applied to evaluate the customized passenger comfort effectively.

While electric vehicles (EVs) continue to draw more attention as an alternative to traditional fossil fuel vehicles, the relatively short driving range of EVs is often pointed out as their biggest drawback. In terms of energy consumption, one of the most energy-intensive systems in EVs is the heating, ventilation, and air conditioning (HVAC) system. Most HVAC systems use On/Off or PID control for the actuators, but these control methods have low efficiency and are difficult to apply in multiple-input multiple-output systems. In this paper, we propose a novel multi-agent deep reinforcement learning (MADRL) method to directly control actuators such as the compressor and electric expansion valve to reach the setpoint temperature while minimizing the work. The proposed method is evaluated with an industrial vehicle simulator and compared with conventional PID control. The experimental results show that the MADRL-based method improves the efficiency of the HVAC system compared to PID control while achieving comparable target convergence performance.