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<p>Proton exchange membrane fuel cells (PEMFCs) are vital for sustainable energy applications due to their efficiency, low emissions, and quiet operation. Accurate optimization of design variables is essential for their performance enhancement, but existing algorithms like WR-BBO, TB-BBO, and HBBOS struggle with issues like slow convergence, local optima entrapment, and parameter sensitivity. The hybrid grouping biogeography-based optimization (HG-BBO) algorithm uses migration and mutation operators for local and global search, respectively. HG-BBO was applied to optimize the design variables of 12 PEMFC stacks: BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W. Comparative analysis with algorithms such as BBO-M, DE-BBO, Lx-BBO, BHCS, BLPSO, and HBBOS revealed that HG-BBO outperforms them in terms of accuracy, convergence speed, and robustness. The objective function sum of squared error (SSE) for stack voltage is minimized using different algorithms for comparative analysis. Simulation results for <i>I</i>-<i>V</i> and <i>V</i>-<i>P</i> characteristics aligned closely with experimental data under varying temperature and pressure conditions. These findings highlight HG-BBO’s theoretical significance in solving nonlinear optimization problems and its practical utility in enhancing PEMFC design and operational reliability. Future work will focus on real-time optimization, algorithm hybridization, and scaling to larger energy systems, offering a robust tool for advancing PEMFC technology.</p>

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Parameter estimation of proton exchange membrane fuel cell using hybrid grouping biogeography optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Ramesh Kumar,
  • Manish Kumar Singla,
  • Muhammed Ali S.A.,
  • Jyoti Gupta,
  • Pradeep Jangir,
  • Arpita,
  • EI-Sayed M. EI-Kenawy,
  • Amal H. Alharbi,
  • Reena Jangid

摘要

Proton exchange membrane fuel cells (PEMFCs) are vital for sustainable energy applications due to their efficiency, low emissions, and quiet operation. Accurate optimization of design variables is essential for their performance enhancement, but existing algorithms like WR-BBO, TB-BBO, and HBBOS struggle with issues like slow convergence, local optima entrapment, and parameter sensitivity. The hybrid grouping biogeography-based optimization (HG-BBO) algorithm uses migration and mutation operators for local and global search, respectively. HG-BBO was applied to optimize the design variables of 12 PEMFC stacks: BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W. Comparative analysis with algorithms such as BBO-M, DE-BBO, Lx-BBO, BHCS, BLPSO, and HBBOS revealed that HG-BBO outperforms them in terms of accuracy, convergence speed, and robustness. The objective function sum of squared error (SSE) for stack voltage is minimized using different algorithms for comparative analysis. Simulation results for I-V and V-P characteristics aligned closely with experimental data under varying temperature and pressure conditions. These findings highlight HG-BBO’s theoretical significance in solving nonlinear optimization problems and its practical utility in enhancing PEMFC design and operational reliability. Future work will focus on real-time optimization, algorithm hybridization, and scaling to larger energy systems, offering a robust tool for advancing PEMFC technology.