A reliable starfish optimization algorithm for establishing equivalent circuit of proton exchange membrane fuel cell

Area of Science:

• * Renewable Energy Systems
• * Electrochemical Engineering
• * Computational Modeling

Background:

• * Accurate modeling of proton exchange membrane fuel cells (PEMFCs) is crucial for effective simulation, management, and control.
• * Nonlinear characteristics and data limitations in fuel cells (FCs) present significant challenges for parameter identification.
• * Existing optimization algorithms may struggle with local optima and efficiency in complex PEMFC parameter estimation.

Purpose of the Study:

• * To propose a novel starfish optimization algorithm (SFOA) for identifying unknown parameters in PEMFC equivalent circuit models.
• * To evaluate the efficiency and accuracy of SFOA in parameter estimation using experimental data from diverse PEMFC stacks.
• * To compare the performance of SFOA against several established optimization algorithms.

Main Methods:

• * Development and application of the starfish optimization algorithm (SFOA) for PEMFC parameter estimation.
• * Utilizing experimental data from five distinct PEMFC stacks: Horizon_H12, Horizon H-1000XP, Temasek_1kw, SR-12PEM 500-W, and BCS-500W.
• * Comparative analysis of SFOA against algorithms including SOA, ESC, GRO, TSA, BWO, SHIO, ESTO, and NRBO.
• * Validation through dynamic simulation in Matlab/Simulink under step load disruption.

Main Results:

• * SFOA achieved minimal sum square errors (SSE) for all tested PEMFCs, demonstrating high accuracy in parameter identification.
• * Achieved SSE values include 4.66E-04 (Horizon_H12), 3.95E-01 (Horizon H-1000XP), 5.43E-01 (Temasek_1kw), 1.06 (SR-12PEM 500-W), and 1.17E-02 (BCS-500W).
• * SFOA outperformed other tested algorithms in minimizing SSE, indicating superior exploitation and exploration balance.
• * Matlab/Simulink simulations confirmed the reliability of SFOA-derived PEMFC models under dynamic conditions.

Conclusions:

• * The proposed starfish optimization algorithm (SFOA) is a highly effective and reliable method for PEMFC parameter identification.
• * SFOA successfully overcomes the challenges posed by nonlinearities and data scarcity in fuel cell modeling.
• * The developed SFOA-based models provide accurate dynamic simulations, enhancing PEMFC management and control capabilities.