PEM fuel cell model parameters extraction based on moth-flame optimization

Abstract Accurate modelling of fuel cells (FC) is essential to better control their operation. In this article, we applied the butterfly flame optimization algorithm (MFO) taking into account the measurement uncertainty to estimate the parameters of the proton exchange membrane fuel cell (PEMFC) for their electrical equations based on current-voltage characteristics (I-V). To evaluate the performance of the proposed algorithm, three commercial PEMFCs with their experimental data (I-V) are considered such as 250 W, NedSstack PS6 and BCS 500-W. The cases of the models with 6, 7 and 11 parameters unknown and the learning technique have been treated. The performance analysis of the proposed method is carried out by applying the two sum squared errors (SSE) and root mean square error (RMSE) between the estimated and experimental data, the proposed approach is affirmed by its great superiority compared to the other methods recently published in Literature.

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