Deep Q-Network-Based Controller for Cabin Cooling System of Electric Vehicles
Wan-Sik Choi, Changsun Ahn
Abstract
The efficiency of the thermal management system of electric vehicles is important because the thermal management system requires a significant amount of electric energy. Therefore, controllers of the thermal management system should be designed considering the efficiency. This paper proposes a deep Q-network-based controller for the thermal management system in electric vehicles. The deep Q-networks were designed to control each actuator and the observation signals, the action signals, and the reward function were designed to achieve requirements. The controller regulates cabin and evaporator air temperature by controlling the compressor and cooling fan speed while minimizing energy consumption and adhering to system constraints. Unlike previous studies, this design process considers practical implementation, including a high-fidelity plant model, essential constraint conditions, and multiple objectives. Test results showed lower energy consumption and better temperature regulation performance than a heuristically designed rule-based controller. This method can optimize thermal management system performance in electric vehicles, which have increased complexity and number of thermal loads that conventional control methods cannot adequately address.