Hybrid Grey Wolf Optimization and Cuckoo Search Algorithm for Extracting Unknown Parameters of PEM Fuel Cell

ABSTRACT Recently fuel cell becomes more popular as renewable energy source to produce electrical energy using hydrogen gas. Optimal modelling of fuel cell is important to extract specified power. In this paper precise calibration of proton exchange membrane fuel cell (PEMFC) is analyzed using optimization process. Hybrid grey wolf optimization‐cuckoo search (hGWO‐CS) algorithm is employed to minimize squared error between measured and simulated terminal voltage. Performance of the algorithm is analyzed for six different bench mark functions. Different optimization algorithms and published results are compared in this paper for Ballad Mark V50 KW, BCS 500 W PEMFC, and NedStack PS6 stacks fuel cell. Among these algorithms with hGWO‐CS lower SSD is achieved for three cases. The effectiveness of the proposed method is also validated comparing theoretical and experimental simulations. Further, computational time and statistical indices like mean, minimum, standard deviation, maximum value RMSE, MAE of SSD for hybrid method specify smallest value amongst all other algorithms which clarifies hybrid method as more robust and effective. Moreover, convergence curves and non‐parametric test additionally confirm sturdiness and consistency of hGWO‐CS in detecting unidentified parameters of PEMFC. Sensitive analysis with variation in optimized parameters are performed to provide insights for optimizing the PEMFC performance. Major contribution of the paper is to design hGWO‐CS algorithm accurately optimizing PEMFC parameter which can improve overall performance of fuel cell.