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.

1.
Lunghi
,
P.
, and
Ubertini
,
U.
, 2001, “
Solid Oxide Fuel Cells and Regenerated Gas Turbines Hybrid Systems: A Feasible Solution for Future Ultra High Efficiency Power Plants
,”
Proceedings of the Seventh International Symposium on Solid Oxide Fuel Cells (SOFC-VII)
,
Tsukuba, Ibaraki, Japan
, Jun. 3–8, pp.
254
264
.
2.
Bove
,
R.
,
Lunghi
,
P.
, and
Sammes
,
N. M.
, 2005, “
SOFC Mathematic Model for Systems Simulations–Part 2: Definition of an Analytical Model
,”
Int. J. Hydrogen Energy
0360-3199,
30
(
2
), pp.
189
200
.
3.
Nehter
,
P.
, 2006, “
Two-Dimensional Transient Model of a Cascaded Micro-Tubular Solid Oxide Fuel Cell Fed With Methane
,”
J. Power Sources
0378-7753,
157
(
1
), pp.
325
334
.
4.
Recknagle
,
K. P.
,
Williford
,
R. E.
,
Chick
,
L. A.
, and
Rector
,
D. R.
, 2003, “
Three-Dimensional Thermo-Fluid Electrochemical Modeling of Planar SOFC Stacks
,”
J. Power Sources
0378-7753,
113
(
1
), pp.
109
114
.
5.
Arriagada
,
J.
,
Olausson
,
P.
, and
Selimovic
,
A.
, 2002, “
Artificial Neural Network Simulator for SOFC Performance Prediction
,”
J. Power Sources
0378-7753,
112
, pp.
54
60
.
6.
Wu
,
X.-J.
,
Zhu
,
X.-J.
,
Cao
,
G.-Y.
, and
Tu
,
H.-Y.
, 2008, “
Nonlinear Modeling of a SOFC Stack Based on ANFIS Identification
,”
Simulation Modeling Practice and Theory
,
16
, pp.
399
409
.
7.
Wu
,
X.-J.
,
Zhu
,
X.-J.
,
Cao
,
G.-Y.
, and
Tu
,
H.-Y.
, 2008, “
Dynamic Modeling of SOFC Based on a T-S Fuzzy Model
,”
Simulation Modeling Practice and Theory
,
16
, pp.
494
504
.
8.
Costamagna
,
P.
,
Magistri
,
L.
, and
Massardo
,
A. F.
, 2001, Design and Part-Load Performance of a Hybrid System Based on a Solid Oxide Fuel Cell Reactor and a Microgas Turbine,
J. Power Sources
0378-7753,
96
, pp.
352
368
.
9.
Wu
,
X.-J.
,
Zhu
,
X.-J.
,
Cao
,
G.-Y.
, and
Tu
,
H.-Y.
, 2007, “
Modeling a SOFC Stack Based on GA-RBF Neural Networks Identification. Journal of Power Sources
,”
J. Power Sources
0378-7753,
167
(
1
),
145
150
.
10.
Warwick
,
K.
, 1996, “
An Introduction to Radial Basis Functions for System Identification: A Comparison With Other Neural Networks Methods
,”
Proceedings of the 35th Conference on Decision and Control
,
Kobe, Japan
, Dec., pp.
464
469
.
11.
Sjoberg
,
J.
,
Zhang
,
Q. H.
,
Ljung
,
L.
,
Benveniste
,
A.
,
Deylon
,
B.
,
Glorennec
,
P.
,
Hjalmarsson
,
H.
, and
Juditsky
,
A.
, 1995, “
Nonlinear Black-Box Modeling in System Identification: A Unified Overview
,”
Automatica
0005-1098,
31
(
12
), pp.
1691
1724
.
12.
EG&G Technical Services and Science Applications International Corp., 2002,
Fuel Cell Handbook
, 6th ed., U.S. Department of Energy.
13.
Sedghisigarchi
,
K.
, and
Feliachi
,
A.
, 2004, “
Dynamic and Transient Analysis of Power Distribution Systems With Fuel Cells—Part I: Fuel-Cell Dynamic Model
,”
IEEE Trans. Energy Convers.
0885-8969,
19
(
2
),
423
428
.
You do not currently have access to this content.