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.