Adaptive fuzzy PI control for the dynamic operation of the transcritical CO2 thermal system in electric vehicles

Abstract

Process control is essential for ensuring the stable and efficient operation of EVs’ transcritical CO2 thermal management systems. Traditional PI control generally shows an insufficient dynamic response for the variable and complex operating conditions and then causes fluctuant process control. This study proposed an adaptive fuzzy PI control strategy to provide real-time varying PI gains for fast-tracking response and fluctuation suppression, considering the compressor discharge pressure, coolant, and air outlet temperature. A high-fidelity transcritical CO2 thermal model in EVs was first built in the Amesim platform to simulate real-time operation, and the adaptive fuzzy PI control algorithm was then developed to optimize the process control. The co-simulation is conducted to validate the effectiveness and adaptability of the proposed control methodology by comparing it with a benchmark traditional PI control strategy. The results indicated that the proposed adaptive fuzzy PI could provide a 9.22 % overshoot suppression for discharge pressure, a maximal 22.29 % overshoot inhibition and 86 s settling time reduction for the air outlet temperature, and a 25 % settling time reduction for the coolant temperature control. In addition, the fuzzy PI improved the start-up characteristics by avoiding excessive response. Such findings demonstrate that the proposed fuzzy PI control algorithm can effectively optimize the dynamic operation of the transcritical CO2 thermal system in EVs.