Genetic algorithm identification for automotive air-conditioning system

Abstract

In this study, system identification of an automotive air conditioning (AAC) system with different parameter estimation methods were conducted. The AAC experiment rig which comes complete with an air duct system to simulate the actual behaviour of AAC system was used to acquire the input and output datasets for the identification of the system. The single input single output dynamic model was established by using Autoregressive with exogenous input (ARX) model. Recursive Least Square (RLS) and Genetic Algorithm (GA) were used to optimize the ARX model and hence to obtain the dynamic model of AAC system based on one-step-ahead (OSA) prediction. The performances of the models were validated using statistical analysis based on the mean squares of error (MSE) between the actual and predicted output responses of the models. The comparison results between these parameter estimation optimization techniques were highlighted. The GA optimization method produce the best ARX model with the lowest prediction MSE value of 0.0015059 and it was proposed to be used to represent the AAC system for further development of the controller strategy.