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Optimal Parameter Estimation Methodology of Solid Oxide Fuel Cell Using Modern Optimization

Author

Listed:
  • Hesham Alhumade

    (Department of Chemical and Materials Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Center of Research Excellence in Renewable Energy and Power systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ahmed Fathy

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
    Electrical Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Abdulrahim Al-Zahrani

    (Department of Chemical and Materials Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Muhyaddin Jamal Rawa

    (Center of Research Excellence in Renewable Energy and Power systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11911, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61517, Egypt)

Abstract

An optimal parameter estimation methodology of solid oxide fuel cell (SOFC) using modern optimization is proposed in this paper. An equilibrium optimizer (EO) has been used to identify the unidentified parameters of the SOFC equivalent circuit with the assistance of experimental results. This is presented via formulating the modeling process as an optimization problem considering the sum mean squared error (SMSE) between the observed and computed voltages as the target. Two modes of the SOFC-based model are investigated under variable operating conditions, namely, the steady-state and the dynamic-state based models. The proposed EO results are compared to those obtained via the Archimedes optimization algorithm (AOA), Heap-based optimizer (HBO), Seagull Optimization Algorithm (SOA), Student Psychology Based Optimization Algorithm (SPBO), Marine predator algorithm (MPA), Manta ray foraging optimization (MRFO), and comprehensive learning dynamic multi-swarm marine predators algorithm. The minimum fitness function at the steady-state model is obtained via the proposed EO with value of 1.5527 × 10 −6 at 1173 K. In the dynamic based model, the minimum SMSE is 1.0406. The obtained results confirmed the reliability and superiority of the proposed EO in constructing a reliable model of SOFC.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:9:p:1066-:d:551282
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    References listed on IDEAS

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