Bacterial Foraging Optimization of Electric Vehicle Charging Systems

This research study proposes an optimized technique for vehicle -to-grid (V2G) and dynamically wireless charged (DMC) systems applying Bacterial Foraging Optimization (BFO), using MATLAB, to improve key attributes, as energy transfer rate, charging efficiency, system's stability, and renewable energy integration that allows Electric Vehicles (EV) to provide stability to grid by delivering stored energy at the time of peak demands. Although, maximizing efficacy of the systems is quite challenging owing to the nonlinearity amongst variables. The BFO, is inspired by bacterial nutrient acquisition, adjusting the charging attributes to discover optimal settings that reduces power loss while improving energy transfer and enhance reliability. Results show that the technique attains a significant improvement in performance criteria, delivering a viable solution in order to optimize EV charging supporting sustainable grid. This research work applies the bio-inspired advanced grid systems, exhibiting BFO's ability to handle complicated and complex, multi-objective optimization.