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Proton Exchange Membrane Fuel cells (PEMFC) are used in many engineering applications as a power source. The device has a mathematical model by which the output voltage-current (V-I) characteristics can be estimated at various operating conditions. However, this model comprises several nonlinear coupled parameters, and that makes the identification problem as challenging task. Owing to that, some advanced techniques been employed to extract the optimal constants of a PEMFC including meta-heuristic optimization algorithms. At this point, this paper deals with a novel swarm-intelligence optimization method named Whale Optimization Algorithm for the purpose of extracting the exact parameters of the PEMFC. This investigation is conducted to examine the effectiveness of the method in providing better results compared to other methods presented in the literature. A commercial PEMFC from Heliocentris with rated power = 40 W is employed to conduct a series of experiments in the laboratory. Moreover, to confirm the effectuality of the method, the results are compared with Particle-Swarm-Optimization (PSO) algorithm. The proposed method showed a significant enhancement in terms of accuracy and convergence speed compared to PSO, where, the error between the predicted and real data was negligibly small (Mean Absolute Error = 0.0726 V).