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Updated: Jan 16, 2026

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

Published on: December 11, 2019

Extraction of PEM fuel cell variables based on modified hippopotamus optimization algorithm.

Eman Abdullah Aldakheel¹, Alaa A K Ismaeel², Ali M El-Rifaie³

¹Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports

|September 29, 2025

Summary

This summary is machine-generated.

A modified Hippopotamus Optimization (MHO) algorithm accurately identifies key parameters for Proton Exchange Membrane Fuel Cells (PEMFCs), improving performance forecasting and enabling digital twin development.

Keywords:

Fuel cell Modified hippopotamus optimization Parameter identification

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Area of Science:

Electrochemistry
Computational Optimization

Background:

Proton Exchange Membrane Fuel Cells (PEMFCs) require precise parameter identification for accurate performance modeling.
Existing optimization algorithms like Hippopotamus Optimization (HO) suffer from local optima and slow convergence.
Manufacturer datasheets often lack crucial parameters needed for fuel cell performance forecasting.

Purpose of the Study:

To develop a modified Hippopotamus Optimization (MHO) algorithm to overcome the limitations of traditional HO.
To accurately identify unknown parameters for PEMFC performance forecasting models.
To compare the effectiveness of MHO against other optimization algorithms.

Main Methods:

A novel exploitation mechanism and Enhanced Solution Quality method were integrated into the HO algorithm to create MHO.
Five optimization algorithms (MHO, GWO, HO, ChOA, SCA) were employed to determine six unknown PEMFC parameters.
The Sum Square Error (SSE) between estimated and measured cell voltages served as the fitness function for minimization.

Main Results:

The MHO algorithm achieved the lowest Sum Square Error (SSE) of 1.748996055.
MHO demonstrated superior performance compared to HO, GWO, ChOA, and SCA in parameter identification.
MHO exhibited faster convergence rates than the other tested optimization techniques.

Conclusions:

The MHO algorithm is highly effective for parameter identification in PEMFCs, leading to accurate performance prediction.
MHO's accuracy and speed make it suitable for developing digital twins and control systems in the automotive industry.
The proposed MHO algorithm offers a significant advancement over existing swarm-based optimization methods for fuel cell applications.