TWO-STAGE DC-DC ISOLATED CONVERTER FOR EV BATTERY CHARGING WITH AI CONTROLLER

Abstract

The Electric vehicles (EVs) require efficient and reliable charging systems to optimize battery performance and lifespan. Traditional converters, however, face limitations in meeting these demands, especially under dynamic charging conditions. This project presents a novel approach to EV battery charging using an AI-controlled two-stage DC-DC isolated converter, enhanced with an artificial neural network (ANN) for real-time optimization. The proposed system utilizes a two-stage converter architecture, where the first stage boosts the input voltage, and the second isolated stage provides galvanic separation, ensuring safe and efficient power transfer to the EV battery. An ANN- based controller dynamically adjusts the converter parameters based on input voltage, battery state-of-charge (SOC), and output current. This intelligent control minimizes energy losses, improves voltage regulation, and reduces thermal stress on the converter components, leading to enhanced system efficiency. Simulation results indicate that the ANN controller adapts effectively to varying input conditions, maintaining optimal charging rates while safeguarding battery health. This AI-driven approach offers a promising solution for fast, efficient, and adaptive EV battery charging, aligning with the increasing demand for smart and sustainable transportation technologies.