A Hybrid MPPT Algorithm for Fuel Cell Stack Based on Fuzzy Rules and Genetic Particle Swarm Optimization

Abstract

ABSTRACT Proton exchange membrane fuel cells (PEMFCs) serve as crucial components in renewable energy systems, owing to their exceptional efficiency and minimal emissions. This research presents a fused maximum power point tracking (MPPT) approach for a 6 kW PEMFC system, integrating fuzzy logic control (FLC) with genetic algorithm‐augmented particle swarm optimization (GA‐PSO). A rigorous mathematical model of the PEMFC is formulated and experimentally corroborated. A fuzzy logic controller addresses system nonlinearity, while the GA‐PSO algorithm dynamically optimizes the fuzzy rule base to adapt to varying operating conditions. Simulations compare the Mamdani controller and the GA‐PSO hybrid controller interfaced with a DC–DC boost converter (45–85.34 V). Results demonstrate that the GA‐PSO controller achieves 99.7% tracking efficiency (vs. 96.33% for Mamdani) with shorter rise time (5.15 vs. 5.03 ms), lower overshoot (9.17% vs. 10.34%), and enhanced robustness. The hybrid controller effectively tracks maximum power under dynamic loads and environmental changes, proving its superiority in steady‐state accuracy and adaptability. This work provides a practical solution for optimizing PEMFC energy output.

Key findings

• ▸The GA-PSO controller achieves 99.7% tracking efficiency, outperforming the Mamdani controller with 96.33% efficiency
• ▸The hybrid controller exhibits shorter rise time (5.15 ms) and lower overshoot (9.17%) compared to the Mamdani controller
• ▸The algorithm demonstrates enhanced robustness and adaptability to varying operating conditions

Keywords

Identifiers

DOI

10.1002/fuce.70035

Journal

Fuel Cells

Year

2025