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An efficient framework for proton exchange membrane fuel cell parameter estimation using numerous MH algorithms

Author

Listed:
  • Rathod, Asmita Ajay
  • Sharma, Pankaj
  • Choudhary, Arun
  • Raju, Saravanakumar
  • Subramanian, Balaji

Abstract

Proton Exchange Membrane Fuel Cells (PEMFCs) are essential for the progress of environmentally friendly hydrogen automobiles. Due to their ability to transform hydrogen into power, they are very promising alternatives for replacing traditional engines. Fuel cell (FC) systems have a complex and non-linear structure. Therefore, it is essential to accurately model the system for the purpose of simulation, design, as well as analysis. The primary aim of this paper is to provide a better meta-heuristic (MH) algorithm for estimating the values of unknown variables in the PEMFC model. In this paper, 25 state of art MH algorithms are utilized to calculate the unknown parameters of the PEMFC stacks (Ballard Mark V, BCS 500 W, Stack 250 W, NedStack PS6, Horizon H-12 as well as Temasek stack). The objective is to minimize the sum of square error (SSE) between the estimated data obtained using the MH algorithms and the actual data. Also, the obtained results are compared with each other to validate their effectiveness. Furthermore, qualitative assessments such as statistical, convergence characteristics, box plots, correlation, and radar chart analysis are carried out to evaluate the effectiveness of the 25 state-of-the-art MH algorithms. Additionally, the (I-V and I-P) polarization curves obtained from the applied 25 MH algorithms exactly match the manufacturing polarization curves across all case study outcomes. The findings enhance the understanding of the MH algorithms and offer significant insights for the effective design of PEMFC.

Suggested Citation

  • Rathod, Asmita Ajay & Sharma, Pankaj & Choudhary, Arun & Raju, Saravanakumar & Subramanian, Balaji, 2025. "An efficient framework for proton exchange membrane fuel cell parameter estimation using numerous MH algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:rensus:v:216:y:2025:i:c:s136403212500276x
    DOI: 10.1016/j.rser.2025.115603
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