Maximization of Power Density of Direct Methanol Fuel Cell for Greener Energy Generation Using Beetle Antennae Search...

Area of Science:

• Energy Conversion
• Fuel Cell Technology
• Computational Modeling

Background:

• Direct methanol fuel cells (DMFCs) are a promising alternative to lithium-ion batteries for portable electronics and electric vehicles.
• Optimizing operating conditions is crucial for enhancing DMFC efficiency and power output.
• Accurate modeling is essential for simulating DMFC performance and identifying optimal parameters.

Purpose of the Study:

• To develop a reliable fuzzy model for simulating DMFC performance.
• To optimize DMFC operating conditions for increased power density.
• To compare the effectiveness of fuzzy logic modeling against traditional methods like ANOVA and RSM.

Main Methods:

• A fuzzy model of the DMFC was constructed using experimental data.
• The Beetle Antennae Search (BAS) algorithm was employed to find optimal operating parameters.
• Model performance was evaluated using Root Mean Square Error (RMSE) and coefficient of determination (R²).

Main Results:

• The fuzzy DMFC model achieved low RMSE (0.1982 for training, 1.5460 for testing) and high R² (0.9977 for training, 0.89 for testing).
• Fuzzy logic reduced RMSE by 88% compared to ANOVA, decreasing it from 7.29 to 0.8628.
• The combined fuzzy modeling and BAS approach increased DMFC power density by 8.88% over measured data and 7.5% over RSM.

Conclusions:

• The developed fuzzy model accurately simulates DMFC performance, indicated by low RMSE and high R² values.
• Optimal operating conditions were identified as 75 °C, 1.2 M methanol concentration, and 400 mL/min oxygen flow rate.
• The integration of fuzzy logic and BAS offers a superior method for enhancing DMFC power density and efficiency.