An Oriented Control Framework for Realizing Intelligent and Predictive Thermal Management in Electric Vehicles

Abstract

Thermal management plays an important role in the power consumption of electric vehicles (EVs). An efficient control framework of the thermal management system can significantly improve energy efficiency and reduce the battery power consumption in EVs. This paper presents a novel control-oriented framework for realizing the intelligent and predictive thermal management system in EVs. This proposed control-oriented framework integrated the advantage of the traditional PID control with the intelligent MPC control to achieve feasible and efficient control. The control-oriented model can balance the accuracy and complexity of the MPC control model, which has a simple structure but produces relatively reliable prediction results. In this developed control framework, the MPC can directly optimize the cabin air mass flow rate, coolant mass flow rate, and compressor speed to minimize power consumption while the PID controller is used to adjust the EEV opening of the cabin and chiller loop based on the set refrigerant superheat. The control-oriented model is validated using a high-fidelity physical model built in Dymola. The results showed that the control framework effectively decreased the total power consumption by 6.3% for the blower, pump, and compressor in a specific scenario where cabin and battery temperature requirements were well maintained. The study indicated that the proposed control framework could be an adequate control foundation to achieve efficient and intelligent control for the thermal management system in EVs.

Keywords: Thermal management, Electric Vehicle, control-oriented framework, MPC model, energy efficiency