ANN Modeling for Thermal Load Estimation in a Cabin Vehicle

People’s need for comfort has accelerated the development of air conditioning systems. The cooling system is an essential system to analyze since it affects passenger comfort and the vehicle’s transient responsiveness. In this paper, a thermal load estimation has been developed for the Arteon vehicle cabin travelling in Miskolc. The outside conditions include a minimum and maximum temperature of −3 and 35 ℃, respectively, while the air velocity is between 0.83 and 7.5 m/s. The Arteon’s vehicle air-conditioning system plays a crucial part in providing a comfortable environment. The heat generated in the cabin is a combination of passengers, solar radiation, ambient, exhaust, engine, and ventilation. In this investigation, a negative load was applied to the cabin in order to cool or heat it. The air conditioner (AC) loads have been computed for three distinct time periods, namely three days in each summer and winter month. Based on these data, an Artificial Neural Network (ANN) was developed to predict the heat load in the car during the year. The agreement with the theoretical calculations was good even if we used a seventh month, namely September, for verification. The maximum error between the thermal load and ANN was 3%, 5%, and 11% for August, December and September, respectively.