Thermally robust maximum power point tracking of proton exchange membrane fuel cells using hybrid Moth flame–Mayfly optimization

Proton exchange membrane fuel cells offer the potential as clean-energy power sources for electric vehicles and renewable microgrids, due to their high-power density, high dynamic response, and zero tailpipe emissions. But they are nonlinear in voltage–current characteristics and are sensitive to temperature, reactant pressure, and load changes, to make reliable maximum power extraction challenging. The conventional maximum power point tracking techniques, such as Perturb and Observe and Incremental Conductance, suffer from slow convergence and continuous oscillations near the maximum power point. This article proposes a hybrid Moth flame–Mayfly optimization method for thermally robust maximum power point tracking in a 6 kW proton exchange membrane fuel cell system with a boost converter. The novelty of the introduced approach is an iteration-dependent fusion strategy of ranked flame-guided global exploration and individual best and global best Mayfly refinement. The fusion coefficient is reduced from 0.8 to 0.2 which moves the search progressively from the global exploration to an accurate local exploitation. To assess the proposed method, it was simulated at different reactant pressures (1.0 and 2.0 bar) at different temperatures (313, 328, and 343 K) and under simulated load changes and compared with four implemented hybrid bio-inspired optimization methods. The proposed methodology is achieved efficiencies of 97.0%, 97.3%, and 95.2% at 313, 328, and 343K, respectively. The efficiency was kept between 97.2% and 97.4% under pressure variation. The settling time was 0.05 s, which is 28.6% to 68.8% less than the existing methods, and the efficiency gain was 0.5 to 4.5 percentage point. The standard deviation ( σ ) of the efficiency varied in 30 independent runs between 0.04% and 0.06%. Waveform analysis revealed smoother duty cycle adjustment and lower current, voltage, and power fluctuations.