Control of off-board bidirectional plug-in electric vehicle charger using a hybrid optimization approach

In the modern era, Electric Vehicles (EVs) have gained immense consideration in developed nations due to diminished usage of fossil fuels as well as lessening the release of gases that cause global warming. Radiation of Greenhouse gases in the atmosphere can be diminished by the utilization of EVs. The charging component is essential for PEVs, as they require electricity from the power grid to charge their batteries. With the appropriate control, the existing charger’s converter architecture can offer additional functionalities. The expansion of Off-board fast-CS is the primary cause of the widespread utilization of EVs. The demand for load increases when the EV battery is charged from the grid. This study examines the application of a V2G-enabled bidirectional off-board EV battery charger using the HBIAO and AIChOa algorithm. The HBIAO algorithm inherits the characteristics and features of Honey Badger and Aquila Optimization Algorithm, while AIChOa algorithm inherits the features of Sooty Tern Optimization Algorithm and Chimp Optimization Algorithm (ChOA). The hybrid optimization algorithms assist in the achievement of global optimal solutions, neglecting the local optimal solutions. The proposed EV charger is modeled and simulated in MATLAB/Simulink, and the controller’s efficiency is validated. The simulation findings show that the system can function efficiently compared to various traditional methodologies. With different simulation and testing outcomes, quick dynamic reactions and excellent steady-state system performances can be demonstrated.