Smart Electric Vehicle Charging Management Using Reinforcement Learning on FPGA Platforms.

This project improves electric‑vehicle (EV) charging by running reinforcement‑learning (RL) algorithms on a Field‑Programmable Gate Array (FPGA). As more people buy EVs, local power grids face higher loads. Smart charging systems are needed to balance demand, lower charging costs, and keep the grid from overloading. Software‑only controllers often lag because they must handle many real‑time decisions at once. To fix this, the work moves the RL‑based scheduling logic onto hardware. By implementing the neural network or Q‑learning model directly in the FPGA, the system can make decisions quickly and with high throughput. The framework treats a charging station as a changing environment. The RL agent learns the best charging rates

by looking at real‑time electricity prices, grid limits, and driver preferences. A hardware‑software co‑design splits the work: the FPGA handles the heavy policy calculations, while a host processor manages overall coordination. The result is a scalable, energy‑efficient solution for modern smart‑grid integration. Tests show the FPGA version runs much faster and uses far less power than traditional microcontroller‑based designs.

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