Stochastic Methods for Parameter Estimation of Multiphysics Models of Fuel Cells

The accurate modeling of complex multiphysical devices and systems is a crucial problem in engineering. Such models are usually characterized by highly nonlinear equations and depend on a high number of parameters, which often cannot be directly measured. In this paper, two stochastic optimization techniques are applied to the solution of such challenging problems in the case of a fuel cell. The algorithms provide satisfactory results, and in particular differential evolution, seldom used in parameter identification for systems of this type, is shown to be powerful and robust.