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PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm.

Rulin Duan1, Defeng Lin2, Gholamreza Fathi3

  • 1School of Computing, Guangdong Vocational Institute of Public Administration, Guangzhou, 510800, Guangdong, China.

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Summary
This summary is machine-generated.

This study introduces an improved deep learning model and optimization algorithm for accurate Proton Exchange Membrane Fuel Cell (PEMFC) modeling. The new method enhances efficiency and accuracy for clean energy applications.

Keywords:
Model identificationModified transient search optimization algorithmOutput voltageProton exchange membrane fuel cellsSqueezenetSum of squared error

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Area of Science:

  • Energy Systems Engineering
  • Computational Science
  • Renewable Energy Technologies

Background:

  • Proton Exchange Membrane Fuel Cells (PEMFCs) are vital for clean energy, but accurate system modeling is crucial for performance and efficiency.
  • Existing PEMFC modeling techniques face challenges like slow convergence, high computational demands, and insufficient accuracy.
  • Effective modeling is essential for optimal design, control, and monitoring of PEMFC systems.

Purpose of the Study:

  • To develop an enhanced approach for accurate PEMFC modeling and parameter identification.
  • To overcome the limitations of existing PEMFC modeling methods.
  • To improve the efficiency and accuracy of PEMFC system analysis.

Main Methods:

  • A modified SqueezeNet deep learning model was employed to reduce computational complexity.
  • A novel Modified Transient Search Optimization (MTSO) Algorithm was introduced to enhance search capabilities.
  • The combined approach was used to model PEMFC output voltage under various operating conditions.

Main Results:

  • The proposed approach demonstrated superior performance compared to Gated Recurrent Unit/Improved Manta Ray Foraging Optimization (GRU/IMRFO) and Grey Neural Network Model/Particle Swarm Optimization (GNNM/PSO).
  • The method achieved the lowest Sum of Squared Errors (SSE), indicating high accuracy in PEMFC voltage modeling.
  • Empirical data validated the effectiveness and superiority of the developed modeling technique.

Conclusions:

  • The enhanced PEMFC modeling approach offers improved accuracy and efficiency over existing methods.
  • This research facilitates better design, control, and monitoring of PEMFC systems.
  • The findings contribute to the advancement of clean and renewable energy technologies.