The Model Predictive Control Strategy of the Transcritical Co2 Air Conditioning System Used in Railway Vehicles

Abstract

This paper presented a model predictive control (MPC) strategy to optimize the operation of the transcritical CO 2 air conditioning (TCAC) system used in railway vehicles, which had dual-requirements of passenger comfort and the energy-saving effects. In this study, a multi-variable control technique, MPC strategy, was used to optimize both discharge pressure and evaporator air flow rate (EAFR) without sacrificing comfort. MPC controller can forecast future operation-conditions and calculate the ideal inputs based on the objective function and the predictive model. Based on the nonlinear predictive model, which was proposed in this study combining data and physical laws, the MPC controller was adopted in GT-SUITE platform to realize the real-time maximization of the COP and maintain comfort requirement by adjusting discharge pressure, EAFR and compressor speed. The simulation was conducted under fixed conditions and realistic conditions, which validated that the MPC strategy can be an effective control method for the optimal operation of TCAC system.

Keywords: Model predictive control, transcritical CO2 air conditioning system, optimal discharge pressure, optimal evaporator air flow rate, data-driven model