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<p>Proper modeling, control and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs) depends on the correct extraction of parameters. This is a nonlinear identification problem that is complex and involves the estimation of seven interdependent parameters using empirical voltage-current data. The paper suggests a new metaheuristic approach, the Starfish Optimization Algorithm (SFOA) based on the regenerative and coordinated feeding behaviors of starfish. The algorithm is strictly tested by reducing the sum-square error (SSE) of the model predictions and experimental data of twelve different PEMFC stacks, whose power was 12&#xa0;W to 6&#xa0;kW. The effectiveness of SFOA is proved by the comparative analysis with nine state-of-the-art algorithms (GJO, COA, RSA, PO, SO, YDSE, AOA, RIME, DE). The results consistently indicate that SFOA has the most accuracy with the smallest mean error of 0.0255 being the lowest, the smallest SSE as well as being very robust with a small standard deviation of 1.69E-05. Moreover, SFOA is computationally highly efficient and can reach the optimal solution in 0.38&#xa0;s, much faster than any of the benchmarked algorithms. The reliability and accuracy of SFOA are validated statistically through convergence curves, box plot analysis and Friedman ranking tests. This study has made SFOA a powerful new instrument in improving the accuracy of fuel cell models, which is essential to create real-time control schemes, system design optimization, and critical monitoring processes.</p>

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Enhanced mathematical modeling of PEM fuel cells using the starfish optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • Manish Kumar Singla,
  • Mohammad Aljaidi,
  • Jyoti Gupta,
  • Pradeep Jangir,
  • Arpita,
  • Ramesh Kumar,
  • Reena Jangid,
  • EI-Sayed M. EI-Kenawy,
  • Amal H. Alharbi

摘要

Proper modeling, control and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs) depends on the correct extraction of parameters. This is a nonlinear identification problem that is complex and involves the estimation of seven interdependent parameters using empirical voltage-current data. The paper suggests a new metaheuristic approach, the Starfish Optimization Algorithm (SFOA) based on the regenerative and coordinated feeding behaviors of starfish. The algorithm is strictly tested by reducing the sum-square error (SSE) of the model predictions and experimental data of twelve different PEMFC stacks, whose power was 12 W to 6 kW. The effectiveness of SFOA is proved by the comparative analysis with nine state-of-the-art algorithms (GJO, COA, RSA, PO, SO, YDSE, AOA, RIME, DE). The results consistently indicate that SFOA has the most accuracy with the smallest mean error of 0.0255 being the lowest, the smallest SSE as well as being very robust with a small standard deviation of 1.69E-05. Moreover, SFOA is computationally highly efficient and can reach the optimal solution in 0.38 s, much faster than any of the benchmarked algorithms. The reliability and accuracy of SFOA are validated statistically through convergence curves, box plot analysis and Friedman ranking tests. This study has made SFOA a powerful new instrument in improving the accuracy of fuel cell models, which is essential to create real-time control schemes, system design optimization, and critical monitoring processes.