scholarly journals Performance Prediction of Tubular Solid Oxide Fuel Cell in Consideration of 2 Dimensional Gas Diffusion

1992 ◽  
Vol 112 (3) ◽  
pp. 267-275
Author(s):  
Susumu Nagata ◽  
Yasuhiro Kasuga
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Performance Prediction ◽  
Gas Diffusion ◽  
Solid Oxide ◽  
Oxide Fuel

Deposition of Porous Anode Electrode of a Solid Oxide Fuel Cell by Ultrasonic Spray Pyrolysis

2010 ◽  
Author(s):  
Lin Liu ◽  
Gap-Yong Kim ◽  
Abhijit Chandra
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Spray Pyrolysis ◽  
Precursor Solution ◽  
Gas Diffusion ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Anode Electrode ◽  
Porous Anode ◽  
Anode Film

A modified spray pyrolysis approach has been utilized to fabricate anode electrode of a Solid Oxide Fuel Cell (SOFC). It was designed to control the anode microstructure to achieve large triple phase boundaries (TPBs) and high gas diffusion capability, which are critical in enhancing the performance of a SOFC. Deposition of porous anode film of Nickel and Ce0.9Gd0.1O1.95 on dense 8 mol.% yttria stabilized zirconia (YSZ) substrate was carried out using the modified spray pyrolysis. Effects of precursor solution feed rates, precursor solution concentrations and deposition temperatures on the TPB formation and porosity were investigated. The composition of the deposited anode film was evaluated by energy dispersive X-ray spectroscopy (EDS). Scanning electron microscope (SEM) examinations revealed that the deposition temperature and precursor solution concentration were the most critical parameters that influenced the morphology, porosity and the particle size of the anode film.

scholarly journals Performance Prediction Model of Solid Oxide Fuel Cell Stack Using Deep Neural Network Technique

2020 ◽  
Vol 31 (5) ◽  
pp. 436-443
Author(s):  
JAEYOON LEE ◽  
ISRAEL TORRES PINEDA ◽  
VAN-TIEN GIAP ◽  
DONGKEUN LEE ◽  
YOUNG SANG KIM ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Prediction Model ◽  
Solid Oxide Fuel Cell ◽  
Performance Prediction ◽  
Deep Neural Network ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Fuel Cell Stack

Air flow modeling and performance prediction of the integrated-planar solid oxide fuel cell IP-SOFC

2013 ◽  
Vol 7 ◽  
pp. 4775-4788
Author(s):  
Mounir Hamid ◽  
Belaiche Mohamed ◽  
El Marjani Abdellatif ◽  
Mohamed Karim Ettouhami
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Performance Prediction ◽  
Air Flow ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Flow Modeling ◽  
And Performance

Impedance Spectroscopy Study and System Identification of a Solid-Oxide Fuel Cell Stack With Hammerstein–Wiener Model

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
M. Y. Abdollahzadeh Jamalabadi
Keyword(s):  
Fuel Cell ◽  
Impedance Spectroscopy ◽  
Solid Oxide Fuel Cell ◽  
Electrochemical Impedance ◽  
Gas Diffusion ◽  
Solid Oxide ◽  
Dynamic Responses ◽  
Current Response ◽  
Oxide Fuel ◽  
Wiener Model

In this paper, the electrochemical impedance spectroscopy (EIS) method is applied through a transient in solid oxide fuel cell (SOFC) to obtain the dynamic modeling. Instead of measuring the current response of a fuel cell to a small sinusoidal perturbation in voltage at each frequency, the Hammerstein–Wiener model identification method is applied through a one transient who leads to the significant decrease of computational costs. Dynamic responses are determined as the solutions of coupled partial differential equations derived from conservation laws of charges, mass, momentum, and energy with electrochemical kinetics by using Butler–Volmer model and gas diffusion on the extended Maxwell-Stefan species equations or dusty gas model (DGM). Because the system consisted of electrical and mechanical components, the behavior of the system was nonlinear. The obtained results are in good qualitative agreement with experimental data published in literatures shown the effectiveness of the propose model. Finally, a parametric study based on the obtained model is performed to study the effects of channel length, inlet H2 concentration, inlet velocity, and cell temperature in Nyquist plots and the voltage responses to step changes in the fuel concentration and load current. The model can be useful as a benchmark for illustrating different designs and control schemes.

scholarly journals Performance Prediction Model of Solid Oxide Fuel Cell System Based on Neural Network Autoregressive with External Input Method

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 828
Author(s):  
Shan-Jen Cheng ◽  
Jing-Kai Lin
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Prediction Model ◽  
Solid Oxide Fuel Cell ◽  
Performance Prediction ◽  
Optimal Parameter ◽  
Cell System ◽  
External Input ◽  
Solid Oxide ◽  
Oxide Fuel

An accurate performance prediction model for the solid oxide fuel cell (SOFC) system not only contributes to the realization of the operating condition but also plays a role in long-term prediction performance. Accordingly, a research study has been developed to suitably deal with the time-series model and accurately build the performance prediction model of SOFC system based on neural network autoregressive with external input (NNARX) method. The architecture regressor parameters of the NNARX model were efficiently determined using the Taguchi orthogonal array (OA) method for optimal sets. The identified and evaluated optimal parameter levels were used to conduct an analysis of variance (ANOVA) to prove correctness. Moreover, a series of statistics criteria and multi-step prediction were also employed for investigating the uncertainty of the predicted model and solve the overfitting and under fitting problems; further. These criteria were also used to determine the performance of the proposed model architecture. The predicted results of the current study indicated that the developed optimal model level parameters consistently had the least statistics errors and reduced workload of the trial-and-error processes.

Three-Dimensional Simulation of Planar Solid Oxide Fuel Cell Stack

2008 ◽  
Vol 128 (2) ◽  
pp. 459-466 ◽  
Author(s):  
Yoshitaka Inui ◽  
Tadashi Tanaka ◽  
Tomoyoshi Kanno
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Three Dimensional ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Fuel Cell Stack ◽  
Dimensional Simulation
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