Advanced ANN-Controlled System with Hybrid Energy Sources Enabling Vehicle-to-Grid and Gridto-Vehicle Operations

Global power shortages are forcing people to look for other sources of energy to combat the impending power crisis. Grid-synchronized electric vehicles (EVs) are one possible way to do this. Inequalities in supply and demand, voltage, and frequency are brought about when different EVs are connected to the grid. This affects the overall power system. In order to allay these worries, we have integrated solar and wind energy. Energy exchange between the electric vehicle and the grid is made possible by vehicle to grid (V2G) technology, one of the developments in smart grids. The hybrid energy source with intelligent controller for EV charging is proposed in this proposal. Here, the EV battery is charged using electricity from the PV panel, wind turbine, and AC generator. Any extra power is then used to power the next household's linear and non-linear demands. Reducing the number of panels further reduces the system's size and cost through the use of a high gain Single ended primary inductor (SEPIC) converter. In this study, the voltage for the EV battery is adjusted using a bidirectional converter. For efficient power conversion, this bidirectional converter is controlled by an Artificial Neural Network (ANN) control unit. Electricity from EVs that are not in use may be sent back into the grid for additional purposes. Simulink and MATLAB 2021a are used to simulate and evaluate the proposed method. With a voltage gain of 1:8, and an efficiency of 92%, the proposed SEPIC converter is quite effective.