The air conditioning (A/C) system is the largest ancillary load in passenger cars, with significant impact on fuel economy. In order to reduce the energy consumption of A/C systems, model-based optimization and optimal control design tools can be effectively applied to design of a supervisory energy management strategy. Significant challenges however lie in the design of a system model that is accurate enough to represent the nonlinear behavior of the system, yet sufficiently simple to enable the use of model-based control design methods.
This paper presents a low-order, energy-based model of an automotive A/C system that is able to predict the dynamics of the evaporator and condenser pressures and the compressor power consumption during typical thermostatic (on/off) operations. A characterization of the mass and energy transport in the heat exchangers is obtained using a lumped-parameter approximation, leading to a model with reasonable accuracy but greatly reduced complexity, hence for supervisory control design. The model was validated against experimental data obtained on a test vehicle, allowing one to evaluate the accuracy in predicting the pressure states and the power consumption.