Artificial neural networks for automotive air-conditioning systems performance prediction

Abstract

In this study, ANN model for a standard air-conditioning system for a passenger car was developed to predict the cooling capacity, compressor power input and the coefficient of performance (COP) of the automotive air-conditioning (AAC) system. This paper describes the development of an experimental rig for generating the required data. The experimental rig was operated at steady-state conditions while varying the compressor speed, air temperature at evaporator inlet, air temperature at condenser inlet and air velocity at evaporator inlet. Using these data, the network using Lavenberg-Marquardt (LM) variant was optimized for 4-3-3 (neurons in input-hidden-output layers) configuration. The developed ANN model for the AAC system shows good performance with an error index in the range of 0.65-1.65%, mean square error (MSE) between 1.09 × 10-5 and 9.05 × 10-5 and the root mean square error (RMSE) in the range of 0.33-0.95%. Moreover, the correlation which relates the predicted outputs of the ANN model to the experimental results has a high coefficient in predicting the AAC system performance.