Implementation of Rule-Based Controllers on an Electric Vehicle Thermal Management System Model in Simulink

Electric vehicles (EVs) are coming into usage quickly because of the environmental advantages and technological innovations. But among the most important issues in EV operation is effectively handling thermal loads, especially in the mobile air-conditioning (MAC) system. As opposed to internal combustion engine (ICE) vehicles, which have access to engine waste heat to use for climate control, EVs depend solely on the battery for propulsion and auxiliary systems. This renders the MAC system one of the primary energy consumers and directly influences vehicle range and overall efficiency. While MAC systems are inherently designed for energy efficiency, this study focuses on an addition to the controller-level optimization, providing an additional pathway to improve thermal management performance in existing EV architectures. The work uniquely implements and compares five rule-based supervisory controllers (RBCs) on an open-source Simulink-based electric vehicle thermal management (EVTM) model, demonstrating a simple and computationally efficient approach to compressor control. Five different RBC strategies are formulated, each of which controls the compressor depending on factors such as ambient temperature, cabin temperature variation, and battery thermal load. The controllers are tested over three varied driving cycles to determine their robustness: the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) Class 2 cycle, the New European Driving Cycle (NEDC), and Bangalore Drive Cycle. These varied test cycles allow for examination over different traffic patterns, speed profiles, and environmental conditions. Simulation results show the optimum RBC delivers an optimal compressor power saving of 4.38% compared to a baseline control strategy.