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Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell

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  • Rezk, Hegazy
  • Olabi, A.G.
  • Ferahtia, Seydali
  • Sayed, Enas Taha

Abstract

One of the essential phases in delivering a precise simulation of the fuel cell system behavior is estimating the model parameters. A novel identification approach based on the bald eagle search algorithm (BES) is suggested in this study to reliably extract the best PEM fuel cell (PEMFC) characteristics. BES is a modern metaheuristic algorithm with outstanding performance in various applications. Furthermore, this method delivers an accurate result because of its unique convergence mechanism. The paremeter identification of PEMFC is an optimization problem to minimize the sum square error (SSE) of the model and measurements. The unknown parameters are used as decision variables. To assess the effectiveness of the suggested method, two different types of PEMFCs were used: BCS 500 W and NedStack PS6. To approve the excellence of BES, a comparison was performed to those achieved using various optimizers, including gravitational search algorithm (GSA), grey wolf optimizer (GWO), differential evolution (DE), sine cosine algorithm (SCA), RSA encryption algorithm, and arithmetic optimization algorithm (AOA). The achieved results approve the advantage of BES in comparison with other algorithms. The SSE for NedStack PS6 employing the proposed method has been reduced to 2.07974 and 0.01136 for the BCS 500 W type. An accurate fitting for the measured datasets with the least deviation between the estimated and the experimental one.

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  • Rezk, Hegazy & Olabi, A.G. & Ferahtia, Seydali & Sayed, Enas Taha, 2022. "Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222013573
    DOI: 10.1016/j.energy.2022.124454
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    2. Hassan Ali, Hossam & Fathy, Ahmed, 2024. "Reliable exponential distribution optimizer-based methodology for modeling proton exchange membrane fuel cells at different conditions," Energy, Elsevier, vol. 292(C).
    3. Zhang, Bo & Wang, Rongjie & Jiang, Desong & Wang, Yichun & lin, Anhui & Wang, Jianfeng & Ruan, Bingcong, 2023. "Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm," Energy, Elsevier, vol. 283(C).
    4. Alaa A. Zaky & Rania M. Ghoniem & F. Selim, 2023. "Precise Modeling of Proton Exchange Membrane Fuel Cell Using the Modified Bald Eagle Optimization Algorithm," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    5. Hegazy Rezk & A. G. Olabi & Tabbi Wilberforce & Enas Taha Sayed, 2023. "A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    6. Yang, Fan & Li, Yuehua & Chen, Dongfang & Hu, Song & Xu, Xiaoming, 2024. "Parameter identification of PEMFC steady-state model based on p-dimensional extremum seeking via simplex tuning optimization method," Energy, Elsevier, vol. 292(C).
    7. Li, Ruiheng & Tian, Hao & Di, Yi & Hossain, Sarmistha, 2024. "Nonlinear simulating of the Proton Exchange Membrane Fuel Cells utilizing Ridgelet Neural Network optimized using a hybrid form of Northern Goshawk Optimizer," Applied Energy, Elsevier, vol. 360(C).
    8. Abdel-Basset, Mohamed & Mohamed, Reda & Abouhawwash, Mohamed, 2023. "On the facile and accurate determination of the highly accurate recent methods to optimize the parameters of different fuel cells: Simulations and analysis," Energy, Elsevier, vol. 272(C).

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