<![CDATA[This study proposes a Maximum Power Point Tracking (MPPT) method based on Particle Swarm Optimization (PSO) to enhance power extraction performance in Proton Exchange Membrane Fuel Cell (PEMFC) systems operating under dynamic and varying environmental conditions. The proposed method is designed to optimize the system's operating point to ensure that the power generated by the fuel cell is fully utilized. In this research, the PSO algorithm is integrated with an Interleaved Boost–Buck Converter (IBBC), which functions to reduce current ripple and improve the stability of the output power in the energy conversion system. This integration is expected to provide a faster and more efficient system response in adjusting to changes in operational conditions. One of the main challenges in implementing MPPT in PEMFC-based systems is the nonlinear characteristics of PEMFC itself, which are highly influenced by various external parameters such as operating temperature, hydrogen pressure, and membrane water content. Variations in these parameters can cause fluctuations in output voltage and current, making the maximum power point tracking process more complex. Therefore, an adaptive optimization method with good convergence ability is required. Simulation results demonstrate that the proposed PSO method achieves a tracking accuracy of up to 99.96% with a settling time of 2 seconds under varying membrane water content conditions. The PSO approach also outperforms the Fuzzy Logic and Cuckoo Search Algorithm (CSA) methods. These findings confirm that the integration of PSO and IBBC significantly improves both the accuracy and speed of MPP tracking in PEMFC systems.]]>
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