boost dc-dc converter design for improved performance and stability of fuel cell using model predictive control and firefly optimization algorithm

Dc-dc converters play a crucial role in fuel cell power generation systems, serving as an interface between the fuel cell and the load. boost converters have gained popularity due to their ability to increase input voltage. however, the performance and efficiency of dc-dc converters in fuel cell power systems have posed significant challenges. this study proposes the use of model predictive control (mpc) and the firefly optimization algorithm (fa) for designing and controlling boost dc-dc converters in the most efficient manner. initially, stability analysis and precise modeling techniques were employed to optimize the characteristics of boost dc-dc converters in fuel cell power generation systems. subsequently, the predictive control method, utilizing the firefly optimization algorithm, was applied to enhance converter performance under diverse conditions. the outcomes of the designed control system were compared with conventional methods. both predictive control and the firefly optimization algorithm were integrated into the design and control processes of boost dc-dc converters in fuel cell. based on the simulation results and stability evaluations, the application of the firefly algorithm and predictive control led to a significant improvement, increasing the system efficiency by approximately 4.7%. these findings highlight the effectiveness of the proposed approach in enhancing the performance of dc-dc boost converters in fuel cell.