Hengyong Tu

Nonlinear modeling of a SOFC stack based on ANFIS identification

Abstract An adaptive neural-fuzzy inference system (ANFIS) model is developed to study different flows effect on the performance of solid oxide fuel cell (SOFC). During the process of modeling, a hybrid learning algorithm combining backpropagation (BP) and least squares estimate (LSE) is adopted to identify linear and nonlinear parameters in the ANFIS. The validity and accuracy of modeling are tested by simulations and the simulation results reveal that the obtained ANFIS model can efficiently approximate the dynamic behavior of the SOFC stack. Thus it is feasible to establish the model of SOFC stack by ANFIS.

Dynamic modeling of SOFC based on a T–S fuzzy model

Abstract The operating temperature and voltage are the key parameters affecting the performance of Solid Oxide Fuel Cell (SOFC). In this article a Takagi–Sugeno (T–S) fuzzy model is proposed to describe the nonlinear temperature and voltage dynamic properties of the SOFC system. During the process of modeling, a Fuzzy Clustering Means (FCM) method is used to determine the nonlinear antecedent parameters, and the linear consequent parameters are identified by a recursive least squares algorithm. The validity and accuracy of modeling are tested by simulations. The simulation results show that it is feasible to establish the dynamic model of SOFC by using the T–S fuzzy identification method.

A Hybrid Experimental Model of a Solid Oxide Fuel Cell Stack

A multivariable hybrid experimental model of a solid oxide fuel cell stack is developed in this paper. The model consists of an improved radial basis function (RBF) neural network model and a pressure-incremental model. The improved RBF model is built to predict the stack voltage with different temperatures and current density. Likewise, the pressure-incremental model is constructed to predict the stack voltage under various hydrogen, oxygen, and water partial pressures. We combine the two models together and make a powerful hybrid multivariable model that can predict the voltage under any current density, temperature, hydrogen, oxygen, and water partial pressure. The validity and accuracy of modeling are tested by simulations, and the simulation results show that it is feasible to build the hybrid multivariable experimental model.