Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in...

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Optimization Algorithms

Background:

• Proton exchange membrane (PEM) fuel cells are crucial for green energy but sensitive to environmental changes.
• Efficient operation requires advanced Maximum Power Point Tracking (MPPT) to adapt to dynamic conditions like pressure and temperature fluctuations.

Purpose of the Study:

• To evaluate and compare the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms for MPPT in PEM fuel cells under varying environmental conditions.
• To assess the integration of these MPPT algorithms with boost converters for enhanced system reliability and productivity.

Main Methods:

• Comparative simulation and analysis of FSSO and CS algorithms applied to PEM fuel cell systems.
• Integration of MPPT algorithms with boost converters to optimize power extraction.
• Performance evaluation based on power output, adaptability to environmental changes (pressure, temperature), and convergence speed to the maximum power point (MPP).

Main Results:

• FSSO demonstrated superior performance over CS, achieving an average 12.5% increase in power extraction from PEM fuel cells.
• FSSO exhibited enhanced adaptability to varying operational conditions and a 25% faster convergence rate to the MPP compared to CS.
• The integration with boost converters further improved the reliability and productivity of the PEM fuel cell systems.

Conclusions:

• FSSO is a highly effective and robust MPPT method for optimizing PEM fuel cell performance, outperforming CS in efficiency and speed.
• The study provides quantitative evidence for the potential of FSSO in advancing green energy technologies.
• Further research into hybrid optimization methods for fuel cell systems is recommended.