Artificial neural network analysis of an automobile air conditioning system

This study deals with the applicability of artificial neural networks (ANNs) to predict the performance of automotive air conditioning (AAC) systems using HFC134a as the refrigerant. For this aim, an experimental plant consisting of original components from the air conditioning system of a compact size passenger vehicle was developed. The experimental system was operated at steady state conditions while varying the compressor speed, cooling capacity and condensing temperature. Then, with the use of some experimental data for training, an ANN model for the system, based on the standard back propagation algorithm was developed. The model was used for predicting various performance parameters of the system, namely the compressor power, heat rejection rate in the condenser, refrigerant mass flow rate, compressor discharge temperature and coefficient of performance. The ANN predictions for these parameters usually agreed well with the experimental values with correlation coefficients in the range of 0.968–0.999, mean relative errors in the range of 1.52–2.51% and very low root mean square errors. This study shows that air conditioning systems, even those employing a variable speed compressor, such as AAC systems, can alternatively be modelled using ANNs with a high degree of accuracy.

[1]  E. Arcaklioğlu.  Performance comparison of CFCs with their substitutes using artificial neural network , 2004 .

[2]  Mahmoud Ghodbane,et al.  An Investigation of R152a and Hydrocarbon Refrigerants in Mobile Air Conditioning , 1999 .

[3]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[4]  Vojislav Kecman,et al.  New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks , 2001 .

[5]  Martin T. Hagan,et al.  Neural network design , 1995 .

[6]  M. S. Bhatti.  Enhancement of R-134a automotive air-conditioning system , 1999 .

[7]  J. Jabardo,et al.  Modeling and experimental evaluation of an automotive air conditioning system with a variable capacity compressor , 2002 .

[8]  Murat Hosoz,et al.  Artificial neural network analysis of a refrigeration system with an evaporative condenser , 2006 .

[9]  Yasar Islamoglu,et al.  Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach , 2005 .

[10]  M. Hosoz,et al.  Performance evaluation of an integrated automotive air conditioning and heat pump system , 2006 .

[11]  Dongsoo Jung,et al.  Evaluation of supplementary/retrofit refrigerants for automobile air-conditioners charged with CFC12 , 1999 .

[12]  Khalid A. Joudi,et al.  Experimental and computer performance study of an automotive air conditioning system with alternative refrigerants , 2003 .

[13]  Omar M. Al-Rabghi,et al.  Retrofitting R-12 car air conditioner with R-134a refrigerant , 2000 .

[14]  Rodney L. McClain,et al.  Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data , 2001 .

[15]  M. M Prieto,et al.  Power plant condenser performance forecasting using a non-fully connected artificial neural network , 2001 .

[16]  Adnan Sözen,et al.  A new approach to thermodynamic analysis of ejector–absorption cycle: artificial neural networks , 2003 .

[17]  Xianting Li,et al.  Numerical simulation on performance band of automotive air conditioning system with a variable displacement compressor , 2005 .

[18]  Derk J. Swider,et al.  A comparison of empirically based steady-state models for vapor-compression liquid chillers , 2003 .

[19]  D. Richon,et al.  Modeling of thermodynamic properties using neural networks: Application to refrigerants , 2002 .

[20]  Vojislav Kecman,et al.  Neural networks—a new approach to model vapour‐compression heat pumps , 2001 .

[21]  J. Yoo,et al.  Performance analysis and simulation of automobile air conditioning system , 2000 .

[22]  O. Kaynakli,et al.  An experimental analysis of automotive air conditioning system , 2003 .

[23]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

[24]  Piotr A. Domanski,et al.  Comparitive analysis of an automotive air conditioning systems operating with CO2 and R134a , 2002 .

[25]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[26]  Eric B. Ratts,et al.  An experimental analysis of the effect of refrigerant charge level on an automotive refrigeration system , 2000 .

[27]  E. Arcaklioğlu,et al.  Artificial neural network analysis of heat pumps using refrigerant mixtures , 2004 .

[28]  Carl G. Looney,et al.  Pattern recognition using neural networks: theory and algorithms for engineers and scientists , 1997 .