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Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm

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  • El-Hay, E.A.
  • El-Hameed, M.A.
  • El-Fergany, A.A.

Abstract

A novel application of interior search optimizer (ISO) to define the necessary parameters to model solid-oxide fuel cells (SOFCs) for further studies is presented. Sum of mean squared error (SMSE) is used to formulate the objective function to be optimized by the ISO subject to the validity of predefined constraints. The current study is carried out into two phases: i) under steady-state; various case studies under various operating conditions are demonstrated, and ii) at later stage, scenarios for transient performance of a SOFC system are investigated. In the same context, MATLAB/SIMULINK is used to implement the proposed ISO-based method. A standard proportional-integral (PI)-controller is engaged to the dynamic model to improve its performance during transient disturbances. Transient responses of the stack current and voltage are analyzed due to load changes. Additionally, the hydrogen and oxygen flow rates along hydrogen utilization are investigated. For all test cases, detailed comparisons to other competing recent algorithms such as satin bowerbird algorithm, grasshopper optimizer and genetic algorithm are made to validate the numerical results. It can be emphasized that the comparisons along other demonstrations indicate the viability of the proposed ISO-based method in defining the unknown parameters of the SOFCs efficiently.

Suggested Citation

  • El-Hay, E.A. & El-Hameed, M.A. & El-Fergany, A.A., 2019. "Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm," Energy, Elsevier, vol. 166(C), pages 451-461.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:451-461
    DOI: 10.1016/j.energy.2018.10.038
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    2. Fathy, Ahmed & Rezk, Hegazy & Mohamed Ramadan, Haitham Saad, 2020. "Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process," Energy, Elsevier, vol. 207(C).
    3. Hachana, Oussama & El-Fergany, Attia A., 2022. "Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer," Energy, Elsevier, vol. 250(C).
    4. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    5. Wang, Jian & Xu, Yi-Peng & She, Chen & Xu, Ping & Bagal, Hamid Asadi, 2022. "Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm," Energy, Elsevier, vol. 240(C).
    6. Yang, Bo & Guo, Zhengxun & Yang, Yi & Chen, Yijun & Zhang, Rui & Su, Keyi & Shu, Hongchun & Yu, Tao & Zhang, Xiaoshun, 2021. "Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells," Applied Energy, Elsevier, vol. 303(C).
    7. Wang, Nan & Wang, Dongxuan & Xing, Yazhou & Shao, Limin & Afzal, Sadegh, 2020. "Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model," Renewable Energy, Elsevier, vol. 150(C), pages 221-233.
    8. Liu, Lijun & Qian, Jin & Hua, Li & Zhang, Bin, 2022. "System estimation of the SOFCs using fractional-order social network search algorithm," Energy, Elsevier, vol. 255(C).
    9. Hesham Alhumade & Ahmed Fathy & Abdulrahim Al-Zahrani & Muhyaddin Jamal Rawa & Hegazy Rezk, 2021. "Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization," Mathematics, MDPI, vol. 9(9), pages 1-19, May.
    10. Mohamed Louzazni & Sameer Al-Dahidi & Marco Mussetta, 2020. "Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique," Sustainability, MDPI, vol. 12(19), pages 1-23, October.
    11. Fathy, Ahmed & Rezk, Hegazy, 2022. "Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models," Energy, Elsevier, vol. 238(PC).
    12. Abdel-Basset, Mohamed & Mohamed, Reda & El-Fergany, Attia & Chakrabortty, Ripon K. & Ryan, Michael J., 2021. "Adaptive and efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis," Energy, Elsevier, vol. 233(C).

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