Performance evaluation of a novel self-tuning particle swarm optimization algorithm-based maximum power point tracker for porton exchange membrane fuel cells under different operating conditions

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) and extract the most efficient power from the fuel cell (FC) stack. This article 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 four 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, consistently exceeding 99.602 % and 99.545 %, while also achieving rapid tracking times of no more than 0.366 s and 0.297 s for the two tested scenarios. 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 of approximately 98.94 % with a rapid tracking time of 2.0 s. Additionally, it demonstrates the lowest power fluctuations of about 2.26 %, providing a stable power output.