Hybrid parameter estimation and sensitivity analysis of PEM fuel cells using Rüppell's fox optimizer and Sobol...

Area of Science:

• * Energy Systems Engineering
• * Electrochemical Engineering
• * Computational Optimization

Background:

• * Proton exchange membrane fuel cells (PEMFCs) are crucial for clean energy, but accurate modeling requires precise parameter estimation.
• * Existing optimization methods face challenges in handling the nonlinearities and multi-parameter nature of PEMFC models.
• * Robust parameter identification is essential for improving PEMFC performance prediction and control.

Purpose of the Study:

• * To introduce and evaluate the novel Rüppell's Fox Optimizer (RFO) for optimizing PEMFC models.
• * To estimate unknown parameters of PEMFCs by minimizing voltage errors between experimental and modeled data.
• * To validate the RFO's effectiveness on established PEMFC benchmark cases under diverse operating conditions.

Main Methods:

• * Application of the Rüppell's Fox Optimizer (RFO) algorithm.
• * Minimization of the sum of squared voltage errors using experimental and modeled data.
• * Validation against three benchmark PEMFC models (Ballard Mark V, Horizon H-12 Stack, Temasek 1 kW) under varying temperatures and pressures.
• * Comparative analysis with existing optimization techniques.

Main Results:

• * The RFO demonstrated high accuracy in estimating PEMFC parameters across different operating conditions.
• * The algorithm showed robust convergence behavior and reliable performance compared to other methods.
• * Precise modeling of experimental data was achieved, confirming the RFO's effectiveness for nonlinear, multi-parameter systems.

Conclusions:

• * Rüppell's Fox Optimizer (RFO) is a highly effective and efficient tool for PEMFC parameter estimation.
• * The RFO provides accurate and stable modeling of PEMFCs, suitable for various applications.
• * This advanced optimization approach offers a promising alternative for complex fuel cell modeling challenges.