The model predictive control strategy of the transcritical CO₂ air conditioning system used in railway vehicles

This paper presented a model predictive control strategy to optimize the operation of the transcritical CO₂ air conditioning system used in railway vehicles, which had to balance the needs of the passengers' comfort with energy-saving effects. In this work, the discharge pressure and evaporator air volume flow rates were optimized while maintaining comfort using a multi-variable control technique called model predictive control strategy. The objective function and the predictive model, which were proposed in this work combining data and physical laws, were the basis for the model predictive controller's ability to foresee future operation-conditions and calculate the optimal inputs. The validation results showed that the prediction error was less than 4.5%. The model predictive controller was adopted in GT-SUITE platform to realize the real-time maximization of the energy efficiency and maintain comfort requirement by adjusting discharge pressure, evaporator air volume flow rate and compressor speed. The simulation was conducted under fixed conditions and realistic conditions. As for the fixed conditions, the coefficient of performance achieved at 2.24, equaling to the maximum value of the map. Under the realistic conditions, the overall energy consumption of the control method using the MPC strategy was lower than that using the PID control strategy, and the average COP was increased by 7.4%. Further, there was a 1% error in coefficient of performance between the extreme value gained by the model predictive control method and the extreme value offered by the map method, which validated that the model predictive control strategy can be an effective control method for the optimal operation of the transcritical CO₂ air conditioning system.