Parameter extraction of proton exchange membrane fuel cell based on artificial rabbits' optimization algorithm and...

Area of Science:

• Renewable Energy Systems
• Electrochemical Engineering
• Computational Intelligence

Background:

• Accurate parameter extraction is crucial for modeling and controlling Proton Exchange Membrane Fuel Cells (PEMFCs).
• Existing optimization algorithms may not sufficiently capture the complex electrochemical dynamics of PEMFCs.

Purpose of the Study:

• To propose and evaluate the Artificial Rabbit Optimization (ARO) algorithm for PEMFC parameter extraction.
• To enhance the accuracy of PEMFC electrochemical models through optimized parameter identification.

Main Methods:

• The Artificial Rabbit Optimization (ARO) algorithm, inspired by rabbit survival strategies, is employed.
• Parameter extraction is framed as an optimization problem minimizing the sum of squared errors between measured and model-predicted voltages.
• Experimental data from a Scribner 850e fuel cell test system is used for validation.

Main Results:

• The ARO algorithm successfully identified optimal parameters for the PEMFC electrochemical model.
• Simulation results demonstrated the superior performance of ARO compared to Grey Wolf Optimization, Particle Swarm Optimization, Salp Swarm Algorithm, and Sine Cosine Algorithm.
• Experimental validation confirmed the effectiveness of the ARO-based parameter extraction.

Conclusions:

• The proposed ARO algorithm offers a robust and efficient method for PEMFC parameter extraction.
• ARO enhances the accuracy of PEMFC models, contributing to improved renewable energy system design and control.
• ARO presents a promising alternative to existing optimization techniques for complex electrochemical systems.