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Updated: Jun 4, 2026

Combustion Characterization and Model Fuel Development for Micro-tubular Flame-assisted Fuel Cells
08:16

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Published on: October 2, 2016

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
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.