Abstract
One important part of designing and manufacturing of the fuel cells is their model identification. The present study proposes an optimal method for optimal parameter estimation of the undetermined parameters in Proton Exchange Membrane Fuel Cells (PEMFCs). The method uses a novel modified version of the Moth Search Algorithm, called Converged Moth Search Algorithm (CMSA) to minimize the total of the squared deviations (TSD) between the output voltage and the experimental data. The method is then applied to two different test cases including BCS 500-W PS6 and NedStack PS6. The results show that the suggested CMSA has a TSD and running time equal to 0.012 and 2.96 for BCS and 2.15 and 3.19 for the NedStack that are the minimum values for both case studies toward the other compared algorithms. therefore, the results showed that the suggested method has a good data agreement with the experimental data.
Highlights
With the advancement of technology and increasing the need for energy, the use of fossil fuels exponentially increased (Mirzapour et al, 2019)
The results show that the minimum value for the the squared deviations (TSD) value is obtained by the suggested Converged Moth Search Algorithm (CMSA) again
The present paper proposes an optimal parameter estimation method for Proton exchange membrane fuel cells (PEMFCs)
Summary
With the advancement of technology and increasing the need for energy, the use of fossil fuels exponentially increased (Mirzapour et al, 2019) This reason increased the amount of carbon dioxide in the atmosphere which causes climate changes, environmental pollution, and global warming (Aghajani and Ghadimi, 2018). It may be possible to use fossil fuels in such a way that we first convert them to hydrogen as a healthy and harmless fuel. In these centers, pollutants are converted into simpler and harmless substances during some processes before they enter the air. Hydrogen can be released and converted into electricity with the help of Fuel Cell technology (Selem et al, 2020; El-Fergany et al, 2019; El-Fergany, 2017)
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