Experimental Studies on the Performances of a Direct Methanol Fuel Cell with a Novel Integrated Ultrasonic Atomization Fuel Feeder

Fuel Cells ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 158-165
Author(s):  
C. Wu ◽  
S. Gong ◽  
S. Hu ◽  
Z. Ye
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Experimental Studies ◽  
Ultrasonic Atomization ◽  
Direct Methanol ◽  
Methanol Fuel Cell

scholarly journals Experimental Studies on a New Controller Design and Implementation in Direct Methanol Fuel Cell

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 796
Author(s):  
Ramasamy Govindarasu ◽  
Solaiappan Somasundaram
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Direct Methanol Fuel Cell ◽  
Controller Design ◽  
Experimental Studies ◽  
Set Point ◽  
Point Tracking ◽  
Time Operation ◽  
Direct Methanol ◽  
Methanol Fuel Cell

A dynamic model of a Direct Methanol Fuel Cell is developed in the MATLAB platform. A newly proposed Coefficient Diagram based Proportional Integral Controller (CD-PIC) is designed and its parameters are calculated. The newly designed CD-PIC is implemented in a real time Direct Methanol Fuel Cell (DMFC) experimental setup. Performances in real time operation of the Direct Methanol Fuel Cell (DMFC) are evaluated. The performance of CD-PIC is obtained under tracking of set point changes. In order to evaluate the CD-PIC performances, most popular tuning rules based Conventional PI Controllers (C-PIC) are also designed and analyzed. Set point tracking is carried out for the step changes of ±10% and ±15% at two different operational points. The controller performances are analyzed in terms of Controller Performance Measuring (CPM) indices. The said performance measures indicate that the proposed CD-PIC gives the superior performances for set point changes and found very much robust in controlling DMFC.

scholarly journals Performance of passive Direct Methanol Fuel Cell: modelling and experimental studies

2017 ◽  
Vol 1 (1) ◽  
pp. 89-103 ◽  
Author(s):  
Beatriz A. Berns ◽  
Mariana F. Torres ◽  
Vânia B. Oliveira ◽  
Alexandra M. F. R. Pinto
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Experimental Studies ◽  
Gas Diffusion ◽  
Membrane Electrode ◽  
Membrane Area ◽  
Diffusion Layers ◽  
Electrode Assemblies ◽  
Direct Methanol ◽  
Methanol Fuel Cell

Low methanol and water crossover with high methanol concentrations are essential requirements for a passive Direct Methanol Fuel Cell (DMFC) to be used in portable applications. Therefore, it is extremely important to clearly understand and study the effect of the different operating and configuration parameters on the cell’s performance and both methanol and water crossover. In the present work, a detailed experimental study on the performance of an in-house developed passive DMFC with 25 cm2 of active membrane area is described. Tailored membrane electrode assemblies (MEAs) with different structures and combinations of gas diffusion layers (GDL) and membranes, were tested in order to select optimal working conditions at high methanol concentration levels without sacrificing performance. The experimental polarization curves were successfully compared with the predictions of a steady state, one-dimensional model accounting for coupled heat and mass transfer, along with the electrochemical reactions occurring in the passive DMFC developed by the same authors.

3D Numerical Study of a Flowing Electrolyte – Direct Methanol Fuel Cell With an Implemented Bi-Layered Membrane Electrode Diaphragm Assembly

2011 ◽  
Author(s):  
P. A. Cornellier ◽  
E. Matida ◽  
C. A. Cruickshank
Keyword(s):  
Fuel Cell ◽  
Flow Rate ◽  
Direct Methanol Fuel Cell ◽  
Numerical Study ◽  
Experimental Studies ◽  
Membrane Electrode ◽  
Pressure Losses ◽  
Direct Methanol ◽  
Flow Through ◽  
Methanol Fuel Cell

In the present work, fluid dynamic simulation and experimental studies are compared to assess the validity of using computational fluid dynamics (CFD) to accurately predict the pressure losses experienced across each of the three fluid channels in a flowing electrolyte direct methanol fuel cell: methanol flow through anodic-serpentine channels; air flow through the cathodic-serpentine channels; dilute sulfuric acid flow through the flowing electrolyte (FE) channel located between two membrane-electrode assemblies (MEAs). The methanol flow rate is varied from 5 to 25 mL/min and the airflow is varied from 0.5 to 5 L/min. The flowing electrolyte flow rate is also varied from 5 to 25 mL/min in order to analyze pressure levels within the FE channel, which, according to this analysis, must be larger than the adjacent serpentine channels. This pressure difference is particularly important to maintain the distance (and flow structure) between the MEAs without affecting performance of the fuel cell. Adequately controlling the pressure of each of three fluids disables the MEAs ability to deform without the use of an electrolyte spacer, effectively creating an inter-dependent bi-layered membrane electrode diaphragm assembly (Bi-MEDA). Through CFD simulation, it was observed that pressure equalization through the Bi-MEDA approach supports the elimination of a flowing electrolyte channel spacer from current FE-DMFC designs. The reduction of the spacer is expected to decrease ohmic losses currently experienced in all FE-DMFC designs. Despite several approximations, simulations predicting pressure losses throughout the two serpentine fuel channels are compared against obtained experimental data, showing relatively good agreement for a single cell arrangement.

Experimental Studies of a Formic Acid Electrolyte: Direct Methanol Fuel Cell

2013 ◽  
Author(s):  
David Ouellette ◽  
Cynthia Ann Cruickshank ◽  
Edgar Matida
Keyword(s):  
Fuel Cell ◽  
Formic Acid ◽  
Power Output ◽  
Direct Methanol Fuel Cell ◽  
Experimental Studies ◽  
Operating Conditions ◽  
Acid Electrolyte ◽  
Direct Methanol ◽  
A Cell ◽  
Methanol Fuel Cell

A new methanol fuel cell that utilizes a liquid formic acid electrolyte, named the formic acid electrolyte-direct methanol fuel cell (FAE-DMFC) is experimentally tested. Three fuel cell configurations were examined; a flowing electrolyte and two circulating electrolyte configurations. From these three configurations, the flowing electrolyte and the circulating electrolyte, with the electrolyte outlet routed to the anode inlet, provided the most stable power output; where minimal decay in performance and less than 3 and 5.6 % variation in power output were observed in the respective configurations. The flowing electrolyte configuration also yielded the greatest power output by as much as 34 %. Furthermore, for the flowing electrolyte configuration, several key operating conditions were experimentally tested to determine the optimal operating points. It was found that an inlet concentration of 2.2 M methanol and 6.5 M formic acid, as well as a cell temperature of 52.8 °C provided the best performance.

Prediction of Direct Methanol Fuel Cell Stack Performance Using Artificial Neural Network

2017 ◽  
Vol 14 (3) ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Experimental Studies ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Coefficient Of Determination ◽  
Membrane Electrode ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell (DMFC) converts liquid fuel into electricity to power devices, while operating at relatively low temperatures and producing virtually no greenhouse gases. Since DMFC performance characteristics are inherently complex, it can be postulated that artificial neural networks (NN) represent a marked improvement in prediction capabilities. In this work, an artificial NN is employed to predict the performance of a DMFC under various operating conditions. Input variables for the analysis consist of methanol concentration, temperature, current density, number of cells, and anode flow rate. The addition of the two latter variables allows for a more distinctive model when compared to prior NN models. The key performance indicator of our NN model is cell voltage, which is an average voltage across the stack and ranges from 0 to 0.8 V. Experimental studies were conducted using DMFC stacks with membrane electrode assemblies consisting of an additional unique liquid barrier layer to minimize water loss to atmosphere. To determine the best fit to the experimental data, the model is trained using two second-order training algorithms: OWO-Newton and Levenberg–Marquardt (LM). The topology of OWO-Newton algorithm is slightly different from that of LM algorithm by employing bypass weights. The application of NN shows rapid construction of a predictive model of cell voltage for varying operating conditions with an accuracy on the order of 10−4, which can be comparable to literature. The coefficient of determination of the optimal model results using either algorithm were greater than 0.998.

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