An efficient approach for mathematical modeling and parameter estimation of PEM fuel based on Young's double-slit...

Area of Science:

• Energy Systems Engineering
• Computational Intelligence
• Electrochemical Engineering

Background:

• Proton Exchange Membrane Fuel Cells (PEMFCs) are crucial for renewable energy, but accurate parameter estimation is challenging.
• Existing metaheuristic algorithms show limitations in optimizing complex PEMFC models.
• Efficient system identification is vital for developing advanced control systems and digital twins for PEMFCs.

Purpose of the Study:

• To introduce and evaluate a novel optimization algorithm, Young's double-slit experiment algorithm (YSDE), for PEMFC parameter estimation.
• To compare the performance of YSDE against established metaheuristic algorithms in terms of accuracy, robustness, and convergence speed.
• To validate the efficacy of YSDE across various PEMFC stack configurations.

Main Methods:

• Integration of the Young's double-slit experiment algorithm (YSDE) with five other metaheuristic techniques (SCA, MFO, HHO, GWO, ChOA).
• Estimation of six critical parameters for PEMFC models.
• Performance evaluation using Sum of Square Error (SSE), standard deviation (SD), and Friedman rank (FRK) metrics over 30 independent runs.
• Statistical validation using Wilcoxon rank-sum and multiple comparison tests.

Main Results:

• YSDE achieved the lowest Sum of Square Error (SSE) of approximately 1.9454, outperforming competing algorithms.
• YSDE demonstrated remarkable consistency and robustness with a low standard deviation (2.21 × 10⁻⁵) and faster convergence.
• Significant improvements were observed: up to 97.8% reduction in average SSE compared to GWO, and up to 70.6% improvement in worst-case SSE over IChOA.
• YSDE maintained a 1000-times lower SD in complex configurations and achieved up to 87% improvement in Friedman ranking scores.

Conclusions:

• The Young's double-slit experiment algorithm (YSDE) is a highly effective and stable solution for Proton Exchange Membrane Fuel Cell (PEMFC) system identification.
• YSDE offers superior accuracy, robustness, and computational efficiency compared to existing metaheuristic methods.
• The algorithm shows significant potential for developing digital twins, control systems in automotive applications, and advancing renewable energy technologies.