Design of DC-DC Converter for a Bifacial PV-Powered Stand-Alone Electric Vehicle Charging Station Using Secretary Bird Optimization Algorithm.

In response to the growing emphasis on sustainable mobility, this study presents a renewable-energy-based electric vehicle (EV) charging system incorporating a station battery energy storage (BES). The system employs a bifacial photovoltaic (PV) array with an artificial neural network (ANN)-assisted maximum power point tracking (MPPT) scheme. Additionally, the Secretary Bird Optimization Algorithm (SBOA) is used to optimize the DC-DC converter, filter, and PI controller parameters to minimize THD while maintaining a stable DC-bus voltage. Five EV models were evaluated, including one lead-acid battery-based model (BMW i3) and four lithium-ion battery-based models (Fiat 500e, Mercedes EQA 250, Volkswagen e-Golf, and Hyundai Kona Electric). The proposed MPPT configuration extracts maximum energy from the bifacial PV system while maintaining a stable DC-bus voltage under variable environmental and loading conditions. Validation through MATLAB/Simulink simulations under three operating scenarios (irradiance = 800-1000 W/m2; temperature = 20-25 °C) demonstrates high conversion efficiency, THDs of 2.85%, 2.26%, and 2.23%, and robust power management suitable for off-grid EV charging stations.