Abstract
This paper presents a smart electric vehicle (EV) charging management system that integrates Reinforcement Learning intelligence on a Field-Programmable Gate Array (FPGA) platform. The system is based on the Q-learning algorithm, where the RL agent perceives environmental conditions, captured through hardware sensors such as current, voltage, and priority indicators, and makes optimal charging decisions to address grid stress and prioritize charging needs. The FPGA implementation leverages hardware design strategies to ensure efficient operation and real-time response within a limited amount of required energy, allowing for its implementation in embedded applications and possibly enabling the use of an energy harvesting power source, like a small solar panel. The proposed design effectively manages multiple EV chargers by dynamically allocating current and prioritizing charging tasks to maintain service quality. Through intelligent decision making, informed by continuous sensor feedback, the system adapts to fluctuating grid conditions and optimizes energy distribution. Key findings highlight the system's ability to maintain stable operation under varying demand conditions, improving power efficiency, safety, and service reliability. Moreover, the design is scalable, enabling seamless expansion for larger installations by following consistent architectural guidelines. This FPGA-based solution combines RL intelligence, sensor-based environmental perception, and robust hardware design, offering a practical framework for an efficient EV charging infrastructure in modern smart grid environments.