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Parameter estimation of proton exchange membrane fuel cell using a novel meta-heuristic algorithm.

Manish Kumar Singla1, Parag Nijhawan2, Amandeep Singh Oberoi3

  • 1Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India. msingla60_phd18@thapar.edu.

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A new Black Widow Optimization (BWO) algorithm effectively models proton exchange membrane fuel cells (PEMFCs). BWO outperforms other methods in parameter optimization for reliable renewable energy systems.

Keywords:
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Area of Science:

  • Renewable Energy Systems
  • Electrochemical Engineering
  • Computational Intelligence

Background:

  • Proton exchange membrane fuel cells (PEMFCs) are crucial for reliable renewable energy, offering pollution-free operation with hydrogen and air.
  • Accurate modeling and parameter optimization of PEMFCs are essential due to their complex behavior under varying conditions.
  • Existing literature lacks exact PEMFC models, necessitating advanced optimization techniques.

Purpose of the Study:

  • To introduce and evaluate a novel Black Widow Optimization (BWO) algorithm for PEMFC parameter extraction.
  • To assess the performance of BWO in optimizing PEMFC models across different operating temperatures.
  • To demonstrate the superiority of BWO compared to established optimization algorithms.

Main Methods:

  • Development of the Black Widow Optimization (BWO) algorithm.
  • Validation of BWO using complex benchmark functions.
  • Application of BWO for PEMFC parameter identification under various temperatures.
  • Comparative analysis of BWO against Particle Swarm Optimization (PSO), Multi-Verse Optimizer (MVO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Grey Wolf Optimization (GWO).
  • Error analysis using two PEMFC datasets and non-parametric testing.

Main Results:

  • The proposed BWO algorithm demonstrated superior performance in PEMFC parameter optimization.
  • BWO achieved better accuracy and efficiency compared to PSO, MVO, SCA, WOA, and GWO.
  • Error analysis and non-parametric tests confirmed the robustness and effectiveness of BWO.

Conclusions:

  • The Black Widow Optimization algorithm is a highly effective tool for the parameter optimization of proton exchange membrane fuel cells.
  • BWO offers significant advantages over existing evolutionary algorithms for PEMFC modeling.
  • This research contributes to enhancing the reliability and performance of renewable energy systems through improved PEMFC modeling.