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Fuel cell parameters estimation using optimization techniques

Abstract:

This chapter aims to demonstrate a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), which accurately mimics the characteristics of the real PEMFC stacks at various operating scenarios. In this chapter, models of PEMFC consider semiempirical and contain multiple nonlinear terms which must be identified and estimated precisely to guarantee adequate modeling. The unknown parameters of commercial PEMFC model are estimated using different optimization techniques, namely grey wolf optimizer (GWO), salp swarm algorithm (SSA), and whale optimization algorithm (WOA). The main fitness function used in this model is the minimization between measured output voltages and the computed voltages. The reliability and efficiency of developed PEMFC parameters’ estimation algorithms are tested on three commercial PEMFC stacks. Moreover, the results of these PEMFC stacks that is obtained by the developed algorithms are comprehensively compared with each other. In addition, the dynamic operation of these stacks is studied under the variation of cell temperature and reactants’ pressures. Furthermore, parametric and nonparametric statistical tests are provided to affirm the robustness and superiority of proposed PEMFC parameters’ estimation algorithms.