ANN-Based Interleaved Synchronous Rectifier for Electric Vehicle Charging

Vehicles that run on electricity have advantage of being non-polluting, which makes them an excellent alternative to fuel-powered vehicles. Numerous individuals are making use of EVs as a result of high demand and price of fuels. Normally, to charge these EV vehicles, electricity is essential. For employing in EVs and load systems, this work proposes an entirely novel, highly effective interleaved synchronous rectifier with isolation transformer. To enhance the charging performance, suggested system includes synchronous rectification technique. The high-frequency (HF) full-bridge inverter and HF isolation transformer are the essential components of this EV charger. Isolated transformers are typically required by EV battery chargers to safely isolate power grid from battery system. The use of a pulse-width modulation (PWM) control circuit in design of a single-phase high-frequency inverter minimizes demand for expensive circuitry to produce sinusoidal pulses for transistor switches. Artificial neural networks (ANN) are utilized to control the output of proposed interleaved synchronous rectifier that achieves maximum output to the battery for EVs by reducing HF rectification losses with faster transition response and input noise ejection. The controller accomplishes nonlinear switching converter control and increases converter’s durability. The MATLAB/Simulink environment are employed to analyze the performance of this approach.