Identification for automotive air-conditioning system using Particle Swarm Optimization

This paper present the representation of the dynamic model of the temperature an automotive air conditioning system (AAC) as the speed of the air conditioning compressor is varied. The performance of system identification of an AAC system using Recursive Least Squares (RLS) and Particle Swarm Optimization (PSO) techniques measured and discussed. The input - output data are collected through an experimental study using an AAC system integrated with air duct system experimental rig complete with data acquisition and instrumentation system. The single input single output dynamic model was established by using Autoregressive with exogenous input (ARX) model. Recursive Least Squares and Genetic Algorithms were validated using one step-ahead prediction (OSA), mean squared error (MSE) and correlation tests. The comparison results between these parameter estimation optimization techniques were highlighted. It was found that the estimated models using these two methods proposed are comparable, acceptable and possible to be used as a platform of new controller development and evaluation the performance of AAC system in the future work. Amongst all, it was found that the Particle Swarm optimization method produce the best ARX model with the lowest prediction MSE value of 8.5472×10^-5 as compared to the Recursive Least Squares performance.