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Updated: May 28, 2025

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells
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Revolutionizing proton exchange membrane fuel cell modeling through hybrid aquila optimizer and arithmetic algorithm

Manish Kumar Singla1,2, S A Muhammed Ali3, Ramesh Kumar4

  • 1Fuel Cell Institute, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia. msingla0509@gmail.com.

Scientific Reports
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

A new hybrid algorithm, Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO), accurately identifies Proton Exchange Membrane Fuel Cell (PEMFC) parameters. This method enhances fuel cell modeling and forecasting with superior speed and precision.

Keywords:
Hybrid algorithmMachine learning-inspired optimizationMetaheuristicParameter estimationProton exchange membrane fuel cell

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Area of Science:

  • Electrochemistry
  • Computational modeling
  • Optimization algorithms

Background:

  • Accurate Proton Exchange Membrane Fuel Cell (PEMFC) modeling requires identifying parameters not found in datasheets.
  • Optimization algorithms are crucial for determining these unknown variables to predict fuel cell performance.

Purpose of the Study:

  • To introduce a novel hybrid algorithm, Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO), for enhanced PEMFC parameter identification.
  • To improve the efficiency and accuracy of PEMFC modeling using a new mutation strategy within the AOAAO algorithm.

Main Methods:

  • Developed the Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO) hybrid algorithm.
  • Utilized AOAAO to determine seven unknown PEMFC parameters by minimizing the sum square error (SSE) between predicted and measured cell voltages.
  • Validated AOAAO performance across six commercial PEMFC models and twelve operational case studies.

Main Results:

  • AOAAO achieved a minimum SSE, outperforming other algorithms in accuracy for PEMFC parameter identification.
  • The algorithm demonstrated high accuracy and robustness in predicting Current-Voltage (I/V) and Power-Voltage (P/V) characteristics.
  • AOAAO exhibited a significant computational efficiency improvement of approximately 98% compared to existing methods.

Conclusions:

  • AOAAO is a highly accurate, robust, and time-efficient algorithm for real-time PEMFC modeling.
  • The novel mutation strategy enhances optimization capabilities for complex parameter identification tasks.
  • AOAAO offers a significant advancement in computational efficiency for fuel cell research and development.