Seven-parameter PEMFC model optimization using an battlefield optimization algorithm

Precise modeling of Proton Exchange Membrane Fuel Cells (PEMFCs) requires accurate identification of key parameters, which are often unavailable from manufacturers but crucial for predicting fuel cell performance. The system relies on seven key parameters to determine activation and ohmic and concentration overpotential values through ξ1, ξ2, ξ3, ξ4, λ, Rc, and β. The Battlefield Optimization Algorithm (BfOA) represents a new optimization method that finds these seven essential PEMFC parameters effectively. Using Sum Squared Error (SSE) to minimize the difference between estimated and actual cell voltages, BfOA outperformed other optimization algorithms in determining parameters for six PEMFC models under varying operating conditions. The optimized parameters enabled accurate prediction of I-V and P V curves, closely matching experimental data. BfOA's efficiency and robustness make it well-. suited for real-time fuel cell modeling. Its effectiveness as a method for precise PEMFC device analysis within electronic component simulators is demonstrated. Future development will explore BfOA's compatibility with other fuel cell technologies, incorporate real-time data capabilities, and implement the algorithm in embedded systems for real-time PEMFC monitoring and control. • Application to PEMFCs: The BfOA algorithm is applied to optimize design variables for six PEMFC stacks: BCS 500 W [ 41 ] [ 42 ], SR-12500 W [ 41 , 42 ], STD 250 W [ 41 ] [ 42 ], Nedstack 600 W PS6 [ 43 ], Horizon H-12 [ 44 ], and Ballard Mark V [ 44 ]. • Comparative Analysis: The performance of BfOA is benchmarked against nine state-of-the-art algorithms, including Thermal Exchange Optimization (TEO) [ 45 ], Grey Wolf optimization (GWO) [ 46 ], Rime Optimization (RIME) [ 47 ], Equilibrium Optimizer (EO) [ 48 ], Marine Predators Algorithm (MPA) [ 49 ], Komodo Mlipir Algorithm (KMA) [ 50 ], Self-Adaptive Differential Evolution (SaDE) [ 51 ], White Shark Optimizer (WSO) [ 52 ], and Genetic Algorithm (GA) [ 53 ], across a range of optimization scenarios. • Environmental Impact Assessment: The impact of varying temperature and pressure conditions on PEMFC performance is evaluated, demonstrating the adaptability and reliability of the optimized models. • Validation Against Experimental Data: Simulation results are validated against experimental data for each PEMFC stack, confirming the robustness and accuracy of the BfOA optimized models.