Parameter estimation study of polymer electrolyte membrane fuel cell using artificial hummingbird algorithm

Abstract

This study represents a comprehensive investigation of the performance of Artificial Hummingbird Algorithm for parameter estimation for Polymer Electrolyte Membrane Fuel Cell. With this purpose, four commercial fuel cell systems which were widely preferred in the literature such as NedStack PS6 (Case-I), 250 W fuel cell stack (Case-II), Horizon 500 W (Case-III), and BCS 500 W (Case-IV) were chosen. In order to compare the performance of this algorithm, seven well-known optimization techniques including Artificial Bee Colony, Salp Swarm Optimization, Particle Swarm Optimization, Gray Wolf Optimization, Genetic Algorithm, Harris Hawks Optimization, and Whale Optimization Algorithm were used. The sum of the squared errors, computational speed, and statistical measurements were calculated for the performance comparison. In this context, the best SSE values were found as 2.06556, 5.25017, 0.02477, 0.01170 for Case-I, Case-II, Case-III, and Case-IV, respectively. The best standard deviation value was found as 1 e−6 for the Case-III. Based on the obtained results, the Artificial Hummingbird Algorithm established itself as a competitive optimization technique for parameter estimation study of PEMFC in terms of computational speed and robustness.