Artificial Neural Network MPPT method of Solar PV System for Electric Vehicles using Mat Lab Simulink

Abstract

Artificial Intelligence (AI) techniques are increasingly used in various areas due to their capability of handling complex systems specifies. Among the techniques of AI, Artificial Neural Networks (ANN) technique plays an important role. The Power Quality (PQ) of average to short-voltage control transmission power flow continues to be an increasingly significant issue in electric vehicles. In the previous method, charging management, large duration, and low performance are drawbacks of renewable electric vehicles. So in this method, Photovoltaic based on the available irradiance and temperatures with Battery operate charging Condition. The DC-DC converter is a bidirectional flow in order to charge the battery and supply electricity to the demand when the irradiance is high enough to generate the necessary voltage. The battery will provide power to the load using an identical bidirectional DC-DC converter whenever the PV array’s irradiance is insufficient to create a suitable voltage, at which point the battery will drain across the load. A bidirectional power flow is created by connecting buck and boost converters antiparallel to one another. Neural network controllers are designed to produce a particular duty cycle for MOSFET/IGBT. Maximum power point tracking (MPPT) using Artificial Neural Network technique for PWM. Condition to evaluate output power quality by applying convert DC to DC for the Duty cycle. The output result gains a better-charging state and analyses the outcomes of simulations in Mat lab/Simulink is used for improved output results.