Optimal parameters estimation of PEMFCs model using Converged Moth Search Algorithm - Shouqiang Sun; Yumei Su; Chengbo Yin; Kittisak Jermsittiparsert (Energy Reports, 2020)
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.
Keywords: 330; A total of the squared deviations; Moth Search Algorithm; Optimal parameter estimation; PEMFC