A review of optimization algorithms for the modeling of proton exchange membrane fuel cell

Abstract

The main aim of this paper is to review different optimization techniques and to describe the importance of the Honey Bee Mating Optimization (HBMO) Algorithm for the modeling of fuel cells. HBMO algorithm is a nature inspired one that simulates the process of real Honey Bee Mating and the natural foraging behavior of honey bee has been discussed. The objective function has been framed for the static modeling of proton exchange membrane fuel cell by considering certain constraints. HBMO is implemented to optimize the appropriate parameter and it is mainly dependent upon the parameter decisions; therefore, it results in improved computation. The proposed HBMO is compared with the conventional algorithms like simulated annealing, pattern search, particle swarm, and genetic algorithm reported in the literature. By using the HBMO technique, the electrical, fuel processor, hydrogen, oxygen, and water response times are analyzed and compared with the other algorithms. The results are validated. The proposed HBMO algorithm surpasses the above mentioned optimizers in terms of accuracy.