Development of Semi-Empirical Cell Voltage Model for Direct Methanol Fuel Cell

2019 ◽  
Vol 2 (24) ◽  
pp. 31-43
Author(s):  
Ji Eun Choi ◽  
Young Chan Bae ◽  
Dong Lae Kim
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Direct Methanol ◽  
Semi Empirical ◽  
Methanol Fuel Cell

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.

Effect of Anode and Cathode Current-Collector Structure on Performance of a Passive Direct Methanol Fuel Cell

2011 ◽  
Vol 347-353 ◽  
pp. 3275-3280 ◽  
Author(s):  
Xian Qi Cao ◽  
Ji Tian Han ◽  
Ze Ting Yu ◽  
Pei Pei Chen
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Current Collector ◽  
Cell Voltage ◽  
Methanol Concentration ◽  
Membrane Area ◽  
Fuel Cell Performance ◽  
Direct Methanol ◽  
Cathode Current ◽  
Methanol Fuel Cell

In this work, the effect of the current-collector structure on the performance of a passive direct methanol fuel cell (DMFC) was investigated. Parallel current-collector (PACC) and other two kinds of perforated current collectors (PECC) were designed, fabricated and tested. The studies were conducted in a passive DMFC with active membrane area of 9 cm2, working at ambient temperature and pressure. Two kinds of methanol solution of 2 M and 4 M were used. Results showed that the PACC as anode current-collector has a positive effect on cell voltage and power. For the cathode current-collector structure, the methanol concentration of 2 M for PECC-2 (higher open ratio 50.27 %) increased performance of DMFC. But the methanol concentration of 4 M led to an enhancement of fuel cell performance that used PACC or PECC-2 as cathode current-collector.

Cell voltage transients of a gas-fed direct methanol fuel cell

2004 ◽  
Vol 127 (1-2) ◽  
pp. 181-186 ◽  
Author(s):  
Josef Kallo ◽  
Jim Kamara ◽  
Werner Lehnert ◽  
Rittmar von Helmolt
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Direct Methanol ◽  
Voltage Transients ◽  
Methanol Fuel Cell

Design and Performance Analysis of Direct Methanol Fuel Cell

2011 ◽  
Vol 403-408 ◽  
pp. 4030-4035
Author(s):  
Ling Zhi Cao ◽  
Zhi Li Su ◽  
Chun Wen Li
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Network Model ◽  
Neural Network Model ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Direct Methanol ◽  
Different Temperatures ◽  
And Performance ◽  
Methanol Fuel Cell

It is necessary to built an accurate model of direct methanol fuel cell (DMFC) as a complex nonlinear multi-input multi-output system .In this paper .the methanol concentrations, temperature and current were considered as inputs, the cell voltage was taken as output, and the performance of a direct methanol fuel cell was modeled by neural network model. This model is based on Matlab / Simulink software .The simulation shows that the neural network model has better accuracy than the physical model, while analyzing the performance of the DMFC in different temperatures and concentrations.

A Semi-Empirical Method for Electrically Modeling of Fuel Cell: Executed On a Direct Methanol Fuel Cell

2019 ◽  
Vol 12 (1) ◽  
pp. 221-237
Author(s):  
Yang Wang ◽  
Jim P. Zheng ◽  
George Au ◽  
Edward J. Plichta
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Empirical Method ◽  
Direct Methanol ◽  
Semi Empirical ◽  
Methanol Fuel Cell

scholarly journals Effects of operating parameters on performance of a single direct methanol fuel cell

2010 ◽  
Vol 14 (2) ◽  
pp. 469-477 ◽  
Author(s):  
Ebrahim Alizadeh ◽  
Mousa Farhadi ◽  
Kurosh Sedighi ◽  
Mohsen Shakeri
Keyword(s):  
Fuel Cell ◽  
Flow Rate ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Oxygen Flow Rate ◽  
Methanol Concentration ◽  
Cell Performance ◽  
Oxygen Flow ◽  
Direct Methanol ◽  
Methanol Fuel Cell

In this study the effect of various operating conditions on 10 cm ?10 cm active area of in-house fabricated direct methanol fuel cell was investigated experimentally. The effect of the cell temperature, methanol concentration, and oxygen flow rate on cell performance was studied. The study reveals that current density is not monotonous function of temperature, but has an optimum operating condition for each cell voltage. The experiments also indicate that the cell performance increases with an increased of oxygen flow rate up to a certain value and then further increase has no significant effect. Furthermore, for methanol concentration greater than 1.5 M, a reduction of cell voltage was indicated which is due to an increase of methanol cross over.

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