Optimized Sliding Mode P&O MPPT Control for Grid-Connected EV Charging Using Genetic Algorithms

Abstract

Power system networks have seen an increase in the use of renewable energy sources in recent years. Due to the fast expansion of civilization and cultural modernization, both the need for complex transportation networks and the frequency of severe climatic change are on the rise. Electric vehicles (EVs) are being promoted by almost every country as a solution to the transmission-related environmental problem. Here is a new way to build an MPPT (maximum power point tracking) controller for photovoltaic (PV) systems that work in environments where the weather is always changing. The optimal sliding mode controller (SLMC) gains, as calculated by the Genetic approach (GAO), are the driving force behind the variable step of the conventional Perturb and observe (Pb&O) approach. As an added bonus, MATLAB/Simulink is used to execute and test a PI controller, a grid that uses a current-controlling topology, and an efficient charging station that uses a GAO-optimized Sliding Mode-based reconfigurable step size Pb&O as an MPPT controller to keep the power at the station under optimal control. This study primarily aims to improve the tracking performance of the controller so that it can reach the maximum power point (MPP) with minimal ripple, low overshoot, and oscillation, and to achieve excellent speed in rapidly changing air turbulence conditions while maintaining a constant supply to the electric vehicle (EV). In addition, when compared to other systems that have been studied in the literature, the created system overall demonstrates excellent effectiveness. Lastly, by including grid integration into the total demand, this suggested solution guarantees a consistent power supply to the charging station, regardless of weather conditions.