Comparative analysis on parametric estimation of a PEM fuel cell using metaheuristics algorithms

One of the primary issues in the modelling of fuel cell is the determination of specific boundary conditions often deduced from the manufacturer of the fuel cell. Realistically, not all data is available from the manufacturer's data sheet; hence, to improve the accuracy as well as predict the performance of the cell, all these information need to be determined. This investigation however advanced the concept of using five different algorithms (Grey Wolf Optimization(GWO), Particle Swarm Optimization(PSO), Slime Mould Algorithm(SMA), Harris Hawk Optimiser (HHO), artificial ecosystem-based algorithm(AEO)) to ascertaining seven (xi 1, xi 2, xi 3, xi 4,R,B,lambda) unknow parameters that affect the mathematical modelling of the cell. The unknown parameters were used as the modelling variables. A minimum fitness function implied a good correlation between the measured/experimental data and the predicted/modelled data. The study had to rank the performance of the algorithms from the best value to the worse value, average and standard deviation. The artificial ecosystem-based algorithm showed the best results compared to the PSO, SMA, GWO and HHO algorithms.