Experimental study of data-driven model predictive control on transcritical CO2 thermal system in electric vehicles

Abstract

The transcritical CO₂ thermal system has been considered an effective and completable candidate for providing space/battery cooling and heating in electric vehicles. For a realistic system, the optimal performance relies on an effective control strategy. This paper presents a model predictive control approach to optimize the real-time operation of the transcritical CO₂ thermal system and conduct a complete experimental investigation. A data-driven control-oriented model is first developed to predict the next steps in system behaviours in a finite time domain. The model predictive controller is designed to provide the optimal inputs based on the control-oriented model and the designed objective function, in which optimal system COP can be achieved provided that the cooling/heating capacity is maintained. Then, a complete test rig is built in a psychrometric test room to experimentally investigate the operating performance using the proposed model predictive control strategy. The experiments are conducted under fixed and variable ambient temperatures for both cooling and heating conditions. The experimental results indicate that the model predictive control strategy can accurately forecast system states and determine optimal control inputs for the transcritical CO₂ thermal system to achieve the highest operating COP with the required cooling/heating capacity in electric vehicles.