Robust parameter identification strategy of solid oxide fuel cells using bald eagle search optimization algorithm

Abstract

Summary Enhancing the solid oxide fuel cell (SOFC) is an attractive topic for academic research. One of the investigated solutions is to enhance the modeling accuracy. This paper presents a robust strategy based on a bald eagle search optimization (BES) algorithm to determine the best parameters of the solid oxide fuel cell (SOFC) model. Six unknown parameters to be determined are E, A, Io, RΩ, B, and Imax. The sum of mean squared error (SMSE) between the measured and estimated stack voltages is minimized. Two scenarios of the SOFC operation mode are considered. The first is a steady‐state model, and the second is the transient/dynamic‐state model. In a dynamic state‐based model, a demand disturbance is implemented, and the performance of the constructed model is examined. Furthermore, a comparison with previously reported approaches and other programmed algorithms has been performed to approve the proposed strategy. The high performance of the BES algorithm in terms of accuracy and robustness makes it an excellent algorithm for this application. To approve the performance of the proposed strategy, it will be investigated for several operating temperatures with multiple algorithms. The relevant results are auspicious where the final fitness, which expresses the identification accuracy, has been minimized by the BES to 2.6906 × 10−6 for 1073 K, 6.3942 × 10−6 for 1213 K 1.0401 for 1273 K.

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“…Also, MCFC “molten carbonate fuel cell” is modeled using artificial neural networks with an accuracy of 2.4% to 4.6% 33 . In a recent study, Rezk et al, 34 investigated the accuracy of the modeling process of a SOFC. The authors depicted that BES “bald eagle search” optimization algorithm is suitable for modeling SOFC in terms of the model's accuracy 34 .…”

“…The authors depicted that BES "bald eagle search" optimization algorithm is suitable for modeling SOFC in terms of the model's accuracy. 34 Modeling and optimization of a low-temperature PEMFC using ANFIS integrated GWO "grey wolf optimizer" was carried out to improve the performance. The proposed strategy improved the performance by more than 30%.…”

“…In a recent study, Rezk et al, 34 investigated the accuracy of the modeling process of a SOFC. The authors depicted that BES “bald eagle search” optimization algorithm is suitable for modeling SOFC in terms of the model's accuracy 34 . Modeling and optimization of a low‐temperature PEMFC using ANFIS integrated GWO “grey wolf optimizer” was carried out to improve the performance.…”