A novel Parrot Optimizer for robust and scalable PEMFC parameter optimization

The use of proton exchange membrane fuel cells (PEMFCs) in sustainable energy applications depends on their high efficiency levels along with their ability to produce low emissions and operation without noise. The optimization of PEMFC design variables faces difficulties because of the complex nonlinear relationships which exist between activation overpotential and concentration overpotential and internal resistance. The optimization methods PSO, DE and WOA face three major setbacks which include their delayed convergence rates as well as their sensitiveness to initial parameter settings and their tendency to lock onto sub-optimal solutions. The study presents the Parrot Optimizer (PO) as a new metaheuristic algorithm which derives its inspiration from the adaptive behaviors of Pyrrhura Molinae parrots to overcome current optimization challenges. The PO serves to optimize six PEMFC stack design variables for BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W. The research performs an extensive comparison between nine advanced algorithms to analyze their performance against PSO, DE, WOA, Rabbit Optimization Algorithm (ROA), Flamingo Herd Optimization (FHO), Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO) and Bat Algorithm (BA). The objective function Sum of Squared Error (SSE) for stack voltage is minimized using different algorithms for comparative analysis. Simulation results for I-V and V-P characteristics aligned closely with experimental data under varying temperature and pressure conditions. PO achieved the lowest Mean SSE values across all cases, with values of 0.025519, 0.275211, 0.242413, 0.102915, 0.148632, and 0.283774 for the BCS 500 W, Nedstack 600 W PS6, SR-12 W, Horizon H-12, Ballard Mark V, and STD 250 W stacks, respectively. Additionally, PO demonstrated the fastest runtime (RT) in all cases, with values as low as 0.116855 s for the Horizon H-12 stack. The results indicate that PO delivers better performance than existing algorithms because it reaches the lowest Sum of Squared Error for stack voltage outputs across every test scenario. I-V and V-P characteristic simulations match experimental results across different temperature and pressure values which proves the theoretical value and practical usage of PO in solving nonlinear optimization problems. The study demonstrates PO as a dependable optimization method which improves PEMFC design processes while enhancing operational reliability through future research that includes real-time control and algorithm combination and system scalability.