Nonisothermal Hydrodynamic Modeling of the Flowing Electrolyte Channel in a Flowing Electrolyte–Direct Methanol Fuel Cell

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Eric Duivesteyn ◽  
Cynthia A. Cruickshank ◽  
Edgar Matida
Keyword(s):  
Fluid Dynamics ◽  
Fuel Cell ◽  
Pressure Drop ◽  
Direct Methanol Fuel Cell ◽  
Operating Conditions ◽  
Methanol Crossover ◽  
Material Microstructure ◽  
Direct Methanol ◽  
Porous Domain ◽  
Methanol Fuel Cell

The performance of a direct methanol fuel cell (DMFC) can be significantly reduced by methanol crossover. One method to reduce methanol crossover is to utilize a flowing electrolyte channel. This is known as a flowing electrolyte–direct methanol fuel cell (FE–DMFC). In this study, recommendations for the improvement of the flowing electrolyte channel design and operating conditions are made using previous modeling studies on the fluid dynamics in the porous domain of the flowing electrolyte channel and on the performance of a 1D isothermal FE-DMFC incorporating multiphase flow, in addition to modeling of the nonisothermal effects on the fluid dynamics of the FE-DMFC flowing electrolyte channel. The results of this study indicate that temperature difference between flowing electrolyte inflow and the fuel cell have negligible hydrodynamic implications, except that higher fuel-cell temperatures reduce pressure drop. Reducing porosity and increasing permeability is recommended, with a porosity of around 0.4 and a porous-material microstructure typical dimension around 60–70 μm being potentially suitable values for achieving these goals.

Non-Isothermal Hydrodynamic Modelling of the Flowing Electrolyte Channel in a Flowing Electrolyte-Direct Methanol Fuel Cell

2013 ◽  
Author(s):  
Eric Duivesteyn ◽  
Cynthia A. Cruickshank ◽  
Edgar Matida
Keyword(s):  
Fluid Dynamics ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Operating Conditions ◽  
Methanol Crossover ◽  
Material Microstructure ◽  
Direct Methanol ◽  
Modelling Studies ◽  
Porous Domain ◽  
Methanol Fuel Cell

The performance of a direct methanol fuel cell (DMFC) can be significantly reduced by methanol crossover. One method to reduce methanol crossover is to utilize a flowing electrolyte channel. This is known as a flowing electrolyte-direct methanol fuel cell (FE-DMFC). In this study, recommendations for the improvement of the flowing electrolyte channel design and operating conditions are made using previous modelling studies on the fluid dynamics in the porous domain of the flowing electrolyte channel, and on the performance of a 1D isothermal FE-DMFC incorporating multiphase flow, in addition to modelling of the non-isothermal effects on the fluid dynamics of the FE-DMFC flowing electrolyte channel. The results of this study indicate that temperature difference between flowing electrolyte inflow and the fuel cell have negligible hydrodynamic implications, except that higher fuel cell temperatures reduce pressure drop. Reducing porosity and increasing permeability is recommended, with a porosity of around 0.4 and a porous material microstructure typical dimension around 60–70 μm being potentially suitable values for achieving these goals.

Performance of a Direct Methanol Fuel Cell Operating Close to Room Temperature

2010 ◽  
Vol 8 (1) ◽  
Author(s):  
V. B. Oliveira ◽  
C. M. Rangel ◽  
A. M. F. R. Pinto
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Gas Diffusion ◽  
Operating Conditions ◽  
Cell Performance ◽  
Methanol Crossover ◽  
Gas Diffusion Layers ◽  
Diffusion Layers ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The direct methanol fuel cell (DMFC) is a promising power source for micro- and various portable electronic devices (mobile phones, PDAs, laptops, and multimedia equipment) with the advantages of easy fuel storage, no need for humidification, and simple design. However, a number of issues need to be resolved before DMFC commercialization, such as the methanol crossover and water crossover, which must be minimized in portable DMFCs. In the present work, a detailed experimental study on the performance of an “in-house” developed DMFC with 25 cm2 of active membrane area, working near the ambient conditions is described. The influence on the DMFC performance of the methanol concentration in the fuel feed solution and of both anode and cathode flowrates was studied. Tailored membrane electrode assemblies (MEAs) were designed in order to select optimal working conditions. Different structures and combinations of gas diffusion layers (GDLs) were tested. Under the operating conditions studied it was shown that, as expected, the cell performance significantly increases with the introduction of gas diffusion layers and that carbon cloth is more efficient than carbon paper both for the anode and cathode GDLs. The results reported allow the setup of tailored MEAs enabling the cell operation at high methanol concentrations (high power densities) without sacrificing performance (i.e., achieving low methanol crossover values). The influence of the different parameters on the cell performance is explained under the light of the predictions from a previously developed one-dimensional model, coupling heat and mass transfer effects. The main gain of this work is to report DMFC detailed experimental data at near ambient temperature which are insufficient in literature. This operating condition is of special interest in portable applications.

Experimental Investigation on the Performance of a Formic Acid Electrolyte-Direct Methanol Fuel Cell

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
David Ouellette ◽  
Cynthia Ann Cruickshank ◽  
Edgar Matida
Keyword(s):  
Fuel Cell ◽  
Formic Acid ◽  
Power Output ◽  
Direct Methanol Fuel Cell ◽  
Operating Conditions ◽  
Portable Devices ◽  
Acid Electrolyte ◽  
Optimal Operating ◽  
Direct Methanol ◽  
Methanol Fuel Cell

The performance of 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 investigated. This fuel cell type has the capability of recycling/washing away methanol, without the need of methanol-electrolyte separation. 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 along with a cell temperature of 52.8 °C provided the best performance. Since this fuel cell has a low optimal operating temperature, this fuel cell has potential applications for handheld portable devices.

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.

Direct methanol fuel cell modeling based on the norm optimal iterative learning control

2020 ◽  
Vol 235 (1) ◽  
pp. 68-79
Author(s):  
Nastaran Shakeri ◽  
Zahra Rahmani ◽  
Abolfazl Ranjbar Noei ◽  
Mohammadreza Zamani
Keyword(s):  
Fuel Cell ◽  
Iterative Learning Control ◽  
Direct Methanol Fuel Cell ◽  
Direct Methanol Fuel Cells ◽  
Learning Control ◽  
Iterative Learning ◽  
Operating Conditions ◽  
Primary Model ◽  
Direct Methanol ◽  
Methanol Fuel Cell

Direct methanol fuel cells are one of the most promisingly critical fuel cell technologies for portable applications. Due to the strong dependency between actual operating conditions and electrical power, acquiring an explicit model becomes difficult. In this article, the behavioral model of direct methanol fuel cell is proposed with satisfactory accuracy, using only input/output measurement data. First, using the generated data which are tested on the direct methanol fuel cell, the frequency response of the direct methanol fuel cell is estimated as a primary model in lower accuracy. Then, the norm optimal iterative learning control is used to improve the estimated model of the direct methanol fuel cell with a predictive trial information algorithm. Iterative learning control can be used for controlling systems with imprecise models as it is capable of correcting the input control signal in each trial. The proposed algorithm uses not only the past trial information but also the future trials which are predicted. It is found that better performance, as well as much more convergence speed, can be achieved with the predicted future trials. In addition, applying the norm optimal iterative learning control on the proposed procedure, resulted from the solution of a quadratic optimization problem, leads to the optimal selection of the control inputs. Simulation results demonstrate the effectiveness of the proposed approach by practical data.

Sign in / Sign up

Export Citation Format

Share Document