الفهرس 
Chapter 1Only 14 pages are availabe for public view
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Abstract
The power generated by a proton exchange membrane fuel cell (PEMFC) is heavily impacted by the change in membrane water content (MWC) and cell temperature. Since PEMFC stacks exhibit nonlinear characteristics, it is crucial to employ a controller that can accurately track the maximum power point (MPP) for the fuel cell (FC) stack. This thesis introduces a novel MPP tracking technique, based on a self-tuning particle swarm optimization (ST-PSO) algorithm, to maximize power output from PEMFC under different operational conditions. The performance of the ST-PSO algorithm is evaluated through numerical simulations and compared to well-known metaheuristic algorithms. The results indicate that the proposed ST-PSO-based MPPT technique surpasses the other metaheuristic methods in terms of extracting the maximum power, achieving fast-tracking, and minimizing power fluctuations in various operating conditions. It attained an MPPT efficiency as well achieving rapid tracking time for the two tested scenarios. The mentioned scenarios are the variation of membrane water content (MWC) with constant FC temperature and the change of FC temperature with constant MWC. Moreover, the ST-PSO controller exhibits robustness and consistent tracking of the MPP. Experimental validation of the ST-PSO controller confirms its robustness and superiority over the other tested algorithm, achieving the highest MPPT efficiency with a rapid tracking time. Additionally, it demonstrates the lowest power fluctuations, providing a stable power output.