This paper aims to deliver accurate and robust parameter identification for proton exchange membrane fuel cells (PEMFCs) by enhancing the gorilla troops optimizer (GTO) with physics-consistent search mechanisms and evaluation-aware control.
We propose an improved GTO (IGTO) tailored to PEMFC polarization modeling. The identification problem is formulated as the minimization of the discrepancy between measured and model-predicted voltage–current characteristics, using a weighted sum of squared errors (SSE) objective with an optional Huber loss and light ridge regularization. IGTO integrates (i) vector (parameter-wise) bounds aligned with PEMFC physics, (ii) adaptive schedules for the main control parameters to coordinate exploration and exploitation, (iii) explicit elitism to preserve the best solution, (iv) opposition-based restart with jitter to mitigate stagnation and (v) early stopping under objective stabilization. The method is validated on three commercial PEMFC stacks (Horizon 500 W, BCS 500 W and NedStack PS6) and benchmarked against baseline GTO and representative metaheuristics under matched settings.
Across all stacks, IGTO achieves lower identification errors and improved stability over R = 30 independent runs. The best obtained SSE values are 2.06435 (NedStack PS6), 1.114\times 10-2 (BCS 500 W) and 1.102\times 10-2 (Horizon 500 W), with consistently faster convergence and reduced run-to-run dispersion compared with the baseline.
The method enables routine, reliable PEMFC parameter tuning for diagnostics, control and digital-twin applications, shortening calibration time and reducing computational cost.
More accurate fuel-cell models support higher efficiency, reliability and lifetime, contributing to cleaner energy systems and reduced emissions.
IGTO provides a coherent, PEMFC-driven enhancement of GTO that couples physics-aligned vector bounds, robust identification objectives and evaluation-aware search control (adaptive schedules, elitism, restart and early stopping), enabling reliable parameter extraction in a practical MATLAB workflow.
