Abstract

Despite its high energy density and zero-carbon emissions, the path to large commercialization for Proton Exchange Membrane Fuel Cell (PEMFC) systems faces challenges related to cost reduction, efficiency enhancement, and durability improvement. One key strategy to address these hurdles is the development of reliable control algorithms that ensure operation at the Maximum Efficiency Point (MEP) of PEMFC systems. This optimization is essential for achieving high efficiency, a crucial factor for PEMFC technology commercial viability. This work explores the experimental design, implementation, and evaluation of Maximum Efficiency Point Tracking (MEPT) control algorithms for a PEMFC system. The investigated methods span across five distinct categories: conventional, data-driven, metaheuristics, online model identification, and filter-based approaches. To assess these methods, a real-time small-scale PEMFC system test bench was built. The setup consists of 200 W open-cathode PEMFC, industrial step-up DC–DC converter, programmable load and a cost-effective measurement and monitoring unit. Merits and demerits of each method are then discussed based on different performance metrics such as tracking speed, hydrogen consumption and power stress. Comparative analysis of the results reveals that, while all the algorithms achieved a maximum electrical efficiency of about 48%, data-driven methods exhibited a superior trade-off between performance metrics. This ultimately leads to improved PEMFC durability.

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