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  1. Home
  3. Modeling Of Solid Oxide Fuel Cells And Optimal Parameter Extraction At Various Operating Data Using An Optimization Method.
  1. Home
  3. Modeling Of Solid Oxide Fuel Cells And Optimal Parameter Extraction At Various Operating Data Using An Optimization Method.

Related Experiment Video

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Modeling of solid oxide fuel cells and optimal parameter extraction at various operating data using an optimization

Amlak Abaza1, Ragab A El-Sehiemy1,2, Rania M Ghoniem3

  • 1Electrical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, Egypt.

Plos One
|June 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new Puma Optimization Algorithm (POA) enhances solid oxide fuel cell (SOFC) models by accurately predicting performance. This bio-inspired method offers superior parameter optimization for clean energy applications.

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Area of Science:

  • Energy Conversion and Storage
  • Computational Optimization
  • Materials Science

Background:

  • Solid Oxide Fuel Cells (SOFCs) are a promising clean energy technology for mobile and stationary power.
  • Accurate modeling of SOFC stacks is crucial for optimizing their design and performance.
  • Existing optimization algorithms may not fully capture the complex nonlinear dynamics of SOFCs.

Purpose of the Study:

  • To develop an optimal design model for SOFC stacks using a novel optimization algorithm.
  • To extract unknown parameters of the SOFC stack through a dimensional nonlinear optimization problem.
  • To evaluate the performance of the proposed algorithm against established optimization techniques.

Main Methods:

  • Development of the Puma Optimization Algorithm (POA), inspired by predator-prey dynamics.
  • Implementation of a phase change hyper-heuristic intelligent mechanism within POA.
  • Testing the SOFC stack model under four different operating conditions (temperatures 923-1073 K, 3 bar).

    • Main Results:

    • POA demonstrated superior performance compared to Marine Predator Algorithm (MPA), Moth Flame Algorithm (MFA), Sine Cosine Algorithm (SCA), and Grey Wolf Optimizer (GWO).
    • Computed polarization curves (V-I, P-I) closely matched measured datasets across various operating conditions.
    • Statistical analysis and ANOVA tests confirmed the robustness and viability of POA.

    Conclusions:

    • The proposed POA effectively optimizes SOFC stack parameters, yielding significant improvements.
    • POA exhibits good convergence rates, making it suitable for diverse SOFC operating conditions.
    • This bio-inspired optimization approach advances the development of efficient SOFC technology.