Nonlinear dynamic modeling for a SOFC stack by using a Hammerstein model

Abstract

Solid oxide fuel cell (SOFC) is a kind of nonlinear, multi-input–multi-output (MIMO) system that is hard to model by the traditional methodologies. For the purpose of dynamic simulation and control, this paper reports a dynamic modeling study of SOFC stack using a Hammerstein model. The static nonlinear part of the Hammerstein model is modeled by a radial basis function neural network (RBFNN), and the linear part is modeled by an autoregressive with exogenous input (ARX) model. To estimate the hidden centers, the radial basis function widths and the connection weights of the RBFNN, a new gradient descent algorithm is derived in the study. On the other hand, the least squares (LS) algorithm and Akaike Information Criteria (AIC) are used to estimate the parameters and the orders of the ARX model, respectively. The applicability of the proposed Hammerstein model in modeling the nonlinear dynamic properties of the SOFC is illustrated by the simulation. At the same time, the experimental comparisons between the Hammerstein model and the RBFNN model are provided which show a substantially better performance for the Hammerstein model. Furthermore, based on this Hammerstein model, some control schemes such as predictive control, robust control can be developed.