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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm.

Premkumar Manoharan1,2, Sowmya Ravichandran3, S Kavitha4

  • 1Department of Electrical and Electronics Engineering, College of Engineering, Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Putrajaya, 43000, Kajang, Selangor, Malaysia.

Scientific Reports
|September 9, 2024
PubMed
Summary
This summary is machine-generated.

A new GOOSE algorithm accurately estimates parameters for proton exchange membrane fuel cells (PEMFCs). This method improves fuel cell modeling and simulation for better performance and technological applications.

Keywords:
EnergyFuel cellsGOOSE algorithmOrthogonal learningPEMFC parameterRoot mean square error

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Area of Science:

  • Energy Systems
  • Electrochemical Engineering
  • Computational Modeling

Background:

  • Proton exchange membrane fuel cells (PEMFCs) require accurate modeling for optimal performance and simulation.
  • Complex non-linear behaviors in fuel cells necessitate precise parameter determination.
  • Effective fuel cell design is critical for various technological applications.

Purpose of the Study:

  • To develop an improved parameter estimation method for PEMFCs.
  • To enhance the accuracy and robustness of fuel cell modeling and simulation.
  • To introduce a novel algorithm inspired by natural adaptive behaviors.

Main Methods:

  • Proposed an enhanced GOOSE algorithm with an orthogonal learning mechanism.
  • Utilized the root mean squared error as the objective function for parameter optimization.
  • Validated the algorithm through experiments using diverse datasets and comparison with state-of-the-art methods.

Main Results:

  • The enhanced GOOSE algorithm demonstrated superior performance compared to existing algorithms.
  • The proposed method achieved promising results in estimating PEMFC parameters.
  • The algorithm effectively simulates complex systems, enhancing simulation tool adaptability.

Conclusions:

  • The enhanced GOOSE algorithm offers a robust and adaptable approach for PEMFC parameter estimation.
  • This method facilitates more accurate and effective advancements in fuel cell technology.
  • The study highlights the potential of bio-inspired algorithms in complex system modeling.