Dynamic model development for vehicle air conditioners based on physics-guided deep learning

Abstract

Air conditioners (AC) are responsible for the largest portion of energy use among all auxiliary components of vehicles. Most white-box models developed for dynamic modeling of vapor compression systems (VCS) are not adequate to model vehicle ACs due to stronger dynamics and disturbances caused by frequent door open/close, random numbers of passengers entering/leaving the vehicles as well as intermittent shading effect on roads. In this study, a novel physics-guided deep learning method is proposed for dynamic modeling of vehicle ACs based on both domain knowledge and historical operational data. To maximize the practical values of the model in control and diagnosis of ACs, this research aims at developing an integrated VCS model consisting of individual models of major AC components rather than a black-box model of the entire system. Domain knowledge guides the determination of model inputs and outputs, design of the model structure, and understanding of temporal relationship in developing dynamic heat exchanger models. A newly developed NARX-LSTM-MLP neural network is proposed for heat exchange process modelling. The compressor is modelled by a multiple-layer perception (MLP). Component models are integrated by referring to the physical system structure. Field AC operation data from a city bus collected by IoT sensors are used in this study. Validation results indicate good accordance between measurements and simulation results. The model developed is less expensive, more convenient, and more feasible for health monitoring of numerous vehicle ACs at city level in the context of IoT.