scholarly journals State-of-the-Art Fuel Cell Voltage Durability Status (Presentation)

2012 ◽  
Author(s):  
Jennifer Kurtz ◽  
Sam Sprik ◽  
Genevieve Saur
Keyword(s):  
Fuel Cell ◽  
State Of The Art ◽  
Cell Voltage

scholarly journals State-of-the-Art Fuel Cell Voltage Durability and Cost Status: 2018 Composite Data Products

2018 ◽  
Author(s):  
Genevieve Saur ◽  
Jennifer M. Kurtz ◽  
Huyen N. Dinh ◽  
Christopher D. Ainscough ◽  
Shaun Onorato
Keyword(s):  
Fuel Cell ◽  
State Of The Art ◽  
Cell Voltage ◽  
Data Products ◽  
Composite Data

Spontaneous formation of nanoparticles on electrospun fibers for fuel cell electrodes

2018 ◽  
Author(s):  
Norbert Radacsi ◽  
Fernando Diaz Campos ◽  
Calum Chisholm ◽  
Konstantinos P. Giapis
Keyword(s):  
Fuel Cell ◽  
Surface Area ◽  
State Of The Art ◽  
High Surface ◽  
High Surface Area ◽  
Cell Voltage ◽  
Dihydrogen Phosphate ◽  
Large Area ◽  
Fuel Cell Electrodes ◽  
Electrolyte Surface

Nanofibers spontaneously decorated with nanoparticles were synthesized by nozzle-free electrospinning, showcasing the latter as a novel, inexpensive and scalable method for depositing high-surface area composites. Layers of nanofibers of the intermediate-temperature proton conducting electrolyte cesium dihydrogen phosphate, (CsH2PO4, CDP), were deposited from homogeneous undersaturated solutions of CDP and polyvinylpyrrolidone (PVP), uniformly over large area substrates. Under certain conditions, the nanofibers develop CDP nanoparticles on their surface, which increases the exposed electrolyte surface area and ultimately enhances electrocatalytic performance. Indeed, fuel cell tests on cathodes made of processed nanoparticle-decorated CDP nanofibers produced higher cell voltage, as compared to state-of-the-art electrodes.

scholarly journals State-of-the-Art Fuel Cell Voltage Durability Status: Spring 2013 Composite Data Products

2013 ◽  
Author(s):  
J. Kurtz ◽  
S. Sprik ◽  
G. Saur ◽  
M. Peters ◽  
M. Post ◽  
...  
Keyword(s):  
Fuel Cell ◽  
State Of The Art ◽  
Cell Voltage ◽  
Data Products ◽  
Composite Data

scholarly journals State-of-the-art Fuel Cell Voltage Durability Status: 2015 Composite Data Products

2015 ◽  
Author(s):  
Jennfier Kurtz ◽  
Huyen Dinh ◽  
Chris Ainscough ◽  
Genevieve Saur
Keyword(s):  
Fuel Cell ◽  
State Of The Art ◽  
Cell Voltage ◽  
Data Products ◽  
Composite Data

Development of the On-Line Monitoring System for Fuel Cell Voltage

2011 ◽  
Vol 219-220 ◽  
pp. 383-386
Author(s):  
Jing Li ◽  
Hong Pan ◽  
Shu Juan Zhang ◽  
Ling Fang Sun
Keyword(s):  
Fuel Cell ◽  
Monitoring System ◽  
Software Design ◽  
Output Voltage ◽  
Cell Voltage ◽  
Development Environment ◽  
Fuel Cell Stack ◽  
Actual Application ◽  
On Line ◽  
And Storage

According to the single battery's series structure in the fuel cell stack, we develop an on-line fuel cell voltage monitoring system, and realize VISA library functions’ call and operation data acquisition and storage successfully in the Delphi development environment. It’s introduced mainly that the monitoring principle, hardware structure, software design and the main feature. The actual application proves that this system has realized high-precision and real-time monitoring of the output voltage of the fuel cell for multi-channel, and has multi-condition operation by setting original parameters easily, thereby, the system has more applicability and well reliability.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

SUPPORT VECTOR REGRESSION MODEL FOR DIRECT METHANOL FUEL CELL

2012 ◽  
Vol 23 (07) ◽  
pp. 1250055 ◽  
Author(s):  
J. L. TANG ◽  
C. Z. CAI ◽  
T. T. XIAO ◽  
S. J. HUANG
Keyword(s):  
Fuel Cell ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Percentage Error ◽  
Support Vector ◽  
Absolute Percentage Error ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The purpose of this paper is to establish a direct methanol fuel cell (DMFC) prediction model by using the support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter selection. Two variables, cell temperature and cell current density were employed as input variables, cell voltage value of DMFC acted as output variable. Using leave-one-out cross-validation (LOOCV) test on 21 samples, the maximum absolute percentage error (APE) yields 5.66%, the mean absolute percentage error (MAPE) is only 0.93% and the correlation coefficient (R2) as high as 0.995. Compared with the result of artificial neural network (ANN) approach, it is shown that the modeling ability of SVR surpasses that of ANN. These suggest that SVR prediction model can be a good predictor to estimate the cell voltage for DMFC system.

Research on fuel cell voltage monitoring system with negative voltage detection function

2021 ◽  
Author(s):  
Shuai Ding ◽  
Haijun Meng ◽  
Jun Huang ◽  
Haitao Chen ◽  
Xiaobin He
Keyword(s):  
Fuel Cell ◽  
Monitoring System ◽  
Cell Voltage ◽  
Detection Function ◽  
Negative Voltage

scholarly journals Exergy Analysis of Polymer Electrolyte Fuel Cell Systems Using Methanol

2003 ◽  
Author(s):  
Akimitsu Ishihara ◽  
Shigenori Mitsushima ◽  
Nobuyuki Kamiya ◽  
Ken-Ichiro Ota
Keyword(s):  
Fuel Cell ◽  
Polymer Electrolyte ◽  
Steam Reforming ◽  
Waste Heat ◽  
Material Balance ◽  
Cell Voltage ◽  
Exergy Efficiency ◽  
Exergy Loss ◽  
Polymer Electrolyte Fuel Cell ◽  
The Difference

An exergy (available energy) analysis has been conducted on a typical polymer electrolyte fuel cell (PEFC) system using methanol. The material balance and enthalpy balance were calculated for the PEFC system using methanol steam reforming, and the exergy flow was obtained. Based on these results, the exergy loss in each unit was obtained, and the difference between the enthalpy and exergy was discussed. The exergy loss in this system was calculated to be 178kJ/mole MeOH for the steam reforming process of methanol. Although the enthalpy efficiency approached unity as the recovery rate of the waste heat from the cell approached unity, the exergy efficiency remained around 0.45 since the cell’s operating temperature of 80°C is low. It was also found that the cell voltage should exceed 0.82V in order to obtain the exergy efficiency of 0.5 or higher. A direct methanol fuel cell (DMFC) was analyzed using the exergy and compared with the methanol reforming PEFC. In order to obtain the exergy efficiency higher than that of PEFC with steam reforming, the cell voltage of the DMFC should be 0.48V or greater at the current density of 600mA/cm2.

scholarly journals End-of-life of fuel cell and hydrogen products: A state of the art

2019 ◽  
Vol 44 (25) ◽  
pp. 12872-12879 ◽  
Author(s):  
Ana María Férriz ◽  
Alfonso Bernad ◽  
Mitja Mori ◽  
Sabina Fiorot
Keyword(s):  
Fuel Cell ◽  
End Of Life ◽  
State Of The Art
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