Model Of Direct Methanol Fuel Cell Research Articles

Sustainable Development of a Direct Methanol Fuel Cell Using the Enhanced LSHADE Algorithm and Newton Raphson Method

This paper presents a mathematical model for stacks of direct methanol fuel cells (DMFCs) using an optimised method. In order to reduce the sum of squared errors (SSE) in calculating the polarisation profile, the suggested technique makes use of simulated experimental data. Given that DMFC is one of the viable fuel cell choices, developing an appropriate model is essential for cost reduction. However, resolving this issue has proven difficult due to its complex and highly nonlinear character, particularly when adjusting the DMFC model to various operating temperatures. By combining the algorithm and the objective function, the current work introduces a novel method called LSHADE (ELSHADE) for determining the parameters of the DMFC model. This technique seeks to accurately identify DMFCs’ characteristics. The ELSHADE method consists of two stages, the first of which is controlled by a reliable mutation process and the latter by a chaotic approach. The study also recommends an improved Newton–Raphson (INR) approach to deal with the chaotic nature of the I-V curve equation. The findings show that, when used on actual experimental data, the ELSHADE-INR technique outperforms existing algorithms in a variety of statistical metrics for accurately identifying global solutions.

Maximization of Power Density of Direct Methanol Fuel Cell for Greener Energy Generation Using Beetle Antennae Search Algorithm and Fuzzy Modeling.

Direct methanol fuel cells (DMFCs) are promising form of energy conversion technology that have the potential to take the role of lithium-ion batteries in portable electronics and electric cars. To increase the efficiency of DMFCs, many operating conditions ought to be optimized. Developing a reliable fuzzy model to simulate DMFCs is a major objective. To increase the power output of a DMFC, three process variables are considered: temperature, methanol concentration, and oxygen flow rate. First, a fuzzy model of the DMFC was developed using experimental data. The best operational circumstances to increase power density were then determined using the beetle antennae search (BAS) method. The RMSE values for the fuzzy DMFC model are 0.1982 and 1.5460 for the training and testing data. For training and testing, the coefficient of determination (R2) values were 0.9977 and 0.89, respectively. Thanks to fuzzy logic, the RMSE was reduced by 88% compared to ANOVA. It decreased from 7.29 (using ANOVA) to 0.8628 (using fuzzy). The fuzzy model's low RMSE and high R2 values show that the modeling phase was successful. In comparison with the measured data and RSM, the combination of fuzzy modeling and the BAS algorithm increased the power density of the DMFC by 8.88% and 7.5%, respectively, and 75 °C, 1.2 M, and 400 mL/min were the ideal values for temperature, methanol concentration, and oxygen flow rate, respectively.

Investigation on voltage loss mechanism for direct methanol fuel cell

Direct methanol fuel cell (DMFC) is a device which converts chemical potential energy into electrical energy through the electrochemical reaction that involves oxidation of methanol. This study investigates the underlying voltage loss mechanism and determines how a change in process conditions will affects the resulting voltage loss, through the development of a one-dimensional mathematical DMFC model in software (MATLAB). The one-dimensional mathematical model has adopted a modelling approach that is simple and relatively easy to be constructed, thus providing a method for simple yet accurate estimation of the DMFC voltage loss curve. The study is conducting in three stages, which include the construction of preliminary model, model parameter fitting and model simulation. Based on the developed DMFC model, the polarization curve of a DMFC are dividing into three regions, including activation polarization-controlled region, ohmic drop controlled region and concentration polarization-controlled region. In each of these regions, one of the voltage loss mechanisms is the dominant mechanism that causes the greatest voltage loss. Voltage loss from all three mechanisms are finding to be reduced at higher temperature. The simulated DMFC can operate at a maximum power density of 0.15 W/cm2, with a voltage efficiency of 33.9%. This model operates at temperature of 343K and uses 2M methanol concentration.

A Multi-Fluid Model for Water and Methanol Transport in a Direct Methanol Fuel Cell

Direct-methanol fuel cell (DMFC) systems are comparatively simple, sometimes just requiring a fuel cartridge and a fuel cell stack with appropriate control devices. The key challenge in these systems is the accurate determination and control of the flow rates and the appropriate mixture of methanol and water, and fundamental understanding can be gained by computational fluid dynamics. In this work, a three-dimensional, steady-state, two-phase, multi-component and non-isothermal DMFC model is presented. The model is based on the Eulerian approach, and it can account for gas and liquid transport in porous media subject to mixed wettability, i.e., the simultaneous presence of hydrophilic and hydrophobic pores. Other phenomena considered are variations in surface tension due to water–methanol mixing and the capillary pressure at the gas diffusion layer–channel interface. Another important aspect of DMFC modeling is the transport of methanol and water across the membrane. In this model, non-equilibrium sorption–desorption, diffusion and electro-osmotic drag of both species are included. The DMFC model is validated against experimental measurements, and it is used to study the interaction between volume porosity of the anode gas diffusion layer and the capillary pressure boundary condition at the anode, and how it affects performance and limiting current density.

Modeling the Output Power of Direct Methanol Fuel Cells Using SVR

The output power of Direct Methanol Fuel Cells (DMFC) is one of the most important elements which limit the performance of DMFC. In order to enhance performance of DMFC, it is necessary to have model to modeling the output power of DMFC. In this paper, a novel model base on Support Vector Regression (SVR) to modeling the output power of DMFC base on output current (I) and operating temperature (T). The test result is shown that the generalization ability of SVR model is high accuracy. This investigation suggests that SVR is quite satisfied used to developing a DMFC model and can be used for controlling, optimal designing and feasibility study of the DMFC system.

Studies on Influence of Cell Temperature in Direct Methanol Fuel Cell Operation

Directmethanol fuel cells (DMFCs) offer one of the most promising alternatives for the replacement of fossil fuels. A DMFC that had an active Membrane Electrode Assembly (MEA) area of 45 cm2, a squoval-shaped manifold hole design, and a Pt-Ru/C catalyst combination at the anode was taken for analysis in simulation and real-time experimentation. A mathematical model was developed using dynamic equations of a DMFC. Simulation of a DMFC model using MATLAB software was carried out to identify the most influencing process variables, namely cell temperature, methanol flow rate and methanol concentration during a DMFC operation. Simulation results were recorded and analyzed. It was observed from the results that the cell temperature was the most influencing process variable in the DMFC operation, more so than the methanol flow rate and the methanol concentration. In the DMFC, real-time experimentation was carried out at different cell temperatures to find out the optimum temperature at which maximum power density was obtained. The results obtained in simulation and the experiment were compared and it was concluded that the temperature was the most influencing process variable and 333K was the optimum operating temperature required to achieve the most productive performance in power density of the DMFC.

Investigation of the effects of battery types and power management algorithms on drive cycle simulation for a range-extended electric vehicle powertrain

ABSTRACTFuel cell (FC) hybrid vehicle power trains are an attractive technology especially for automotive applications because of their higher efficiency and lower emissions compared to conventional vehicles. This study focuses on the design of an FC hybrid power train system and evaluation of its simulations for a given speed profile through two alternative power management algorithms (PMAs). Parameters suitable for a small vehicle were taken into consideration in the mathematical model of the vehicle. The proposed hybrid power train consists of an energy storage system, composed of a 4-kg battery pack (either lithium-ion (Li-ion) battery, nickel metal hydride, or nickel–cadmium) and a direct methanol fuel cell (DMFC) as the range extender. The PMAs basically aim to fulfill the power requirements of the vehicle and decide how to command the power split between the battery and the FC. The model comprising a DMFC, a battery, and PMAs was developed in MATLAB/Simulink environment. The polarization curve of the FC was obtained using a one-dimensional DMFC model. Vehicle power requirements for a drive cycle were calculated using the equations of longitudinal dynamics of vehicle, and the results were integrated into MATLAB/Simulink model. As a result of the simulations, methanol consumption, state of charge of the battery, and power output of the FC were compared for the PMAs. This comparison shows the effect of PMAs on the hybrid vehicle performance for three battery types. The results indicate that the vehicle range could be increased when proper strategy is used as PMA.

A three-dimensional multi-phase numerical model of DMFC utilizing Eulerian-Eulerian model

A three-dimensional multiphase model of DMFC (direct methanol fuel cell) is developed, in which the Eulerian-Eulerian model is adopted to treat the gas and liquid two-phase flow in channel. Meanwhile, the multiphase flow in porous electrodes is solved with the help of gas and liquid pressure conservation equations to reflect the liquid saturation jump phenomenon at two different porous electrodes (e.g. DL (diffusion layer) and CL (catalyst layer)). The effects of current density, methanol concentration and temperature on gas and liquid two-phase flow in channel and porous electrodes are investigated in detail. It is found that the carbon dioxide in anode channel gradually increases along flow direction and is mainly accumulated at the interface of anode channel and DL. Meanwhile, the carbon dioxide produced in ACL (anode catalyst layer) is likely to accumulate under the inlet region and then increases along flow direction gradually. Moreover, the higher the temperature, the more methanol crossover and the less carbon dioxide produced in DMFC because of the dissolution.

High-performance direct methanol fuel cell with optimized membrane electrode assembly: A theoretical approach

Methanol/water crossover through proton-exchange membranes and low cell voltage resulting from sluggish catalytic reactions are major issues that must be overcome to commercialize direct methanol fuel cells (DMFCs). Three key performance indices (low methanol crossover rate (MCO), low water–transfer coefficient, and high cell voltage) can be improved by optimizing the design of the DMFC membrane electrode assembly for the cell operating conditions. Using computational techniques with a one-dimensional DMFC model, the present study proposes guidelines for constructing a high-performance DMFC with a focus on membrane electrode assembly (MEA). First, the membrane thickness is determined by balancing the MCO and ionic conductivity for both the fluorocarbon (FC) membrane and the hydrocarbon (HC) membrane. Second, the material composition of the cathode catalyst layer (cCL) is optimized by balancing charge transfer and mass transfer. Finally, the anode diffusion layer (aDL) is optimized according to the cell operating conditions. Compared to the referenced baseline cell, this optimization process is predicted to improve the cell voltage by 19% and decrease methanol crossover by 64%.

Numerical investigation of spatial variation of hydrophobicity in diffusion media along the through-plane direction in direct methanol fuel cells

The wetting characteristics of the diffusion media (DM) of direct methanol fuel cells (DMFCs) are studied using a one-dimensional (1-D) DMFC model. The model, which is based on the capillary transport theory for porous media, facilitates investigation of the impact of spatial variation of hydrophobicity in the DM on methanol transport/crossover and the resultant cell performance. The 1-D DMFC simulation results were compared with the empirical data acquired at a methanol feed concentration of 8 M. The theoretical simulations agree well with the experimental data and also indicate that methanol crossover from the anode to the cathode can be decreased by employing an anode DM design in which the hydrophobicity increases toward the anode catalyst layer. Under the anode DM design, a maximum power density of 73.15 mW cm−2 was predicted by the model at a methanol feed concentration of 8 M, i.e. sufficiently close to the experimental measurement (71.17 mW cm−2). Detailed simulation results clearly show that the reduced methanol crossover is mainly derived from liquid transport characteristics; the rate of liquid transport through the DM is reduced as the liquid flows from the relatively hydrophilic region to the relatively hydrophobic one. This numerical study demonstrates that the present DMFC model is a valuable tool for the design and optimization of DM with spatial wettability variation to effectively control the methanol and water transport in DMFCs.

Analyzing the effects of fluctuating methanol feed concentration in active-type direct methanol fuel cell (DMFC) systems

In active-type direct methanol fuel cell (DMFC) systems, the methanol feed concentration inevitably fluctuates for various reasons, such as inaccuracy in the methanol sensors or the complexity of injecting additional methanol and/or water into the anode fuel recirculation loop. In this study, we use theoretical computations to examine the effects of fluctuating methanol feed concentration on the time-dependent behavior and resultant cell performance of DMFCs. Using a one-dimensional (1-D) two-phase transient thermal DMFC model, we conducted 1-D transient simulations under various DMFC operating conditions with different anode/cathode stoichiometries, cell operating temperatures, and cathode inlet humidification conditions. The simulation results clearly address the influences of these operating parameters on the evolution of cell voltage, cell temperature, methanol crossover, and overall efficiency, highlighting that fluctuation in methanol concentration on the DMFC performance induces fluctuations in these key profiles. Finally, we propose an optimum strategy to mitigate the influence of the fluctuating methanol feed concentration, i.e., purposely fluctuating the anode or cathode stoichiometry. The simulation results clearly show that when a fluctuating methanol feed concentration is present, a constant cell voltage of approximately 340 mV was successfully maintained by fluctuating the cathode stoichiometry between 3.0 and 4.0.

액티브형 직접메탄올연료전지 시스템의 메탄올 농도 변동이 성능에 미치는 영향성에 대한 수치적 연구

In this study, we develop a one-dimensional (1-D), two-phase, transient-thermal DMFC model to investigate the effect of methanol concentration fluctuation that usually occurs in active-type direct methanol fuel cell (DMFC) systems. 1-D transient simulations are conducted and time-dependent behaviors of DMFCs are analyzed under various DMFC operating conditions such as anode/cathode stoichiometry, cell temperature, and cathode inlet humidification. The simulation results indicate that the effect of methanol concentration fluctuation on DMFC performance can be mitigated by proper control of anode/cathode stoichiometry, providing a guideline to optimize operating conditions of active DMFC systems.

Modeling of Direct Methanol Fuel Cell Using the Artificial Neural Network

The performance of a direct methanol fuel cell (DMFC) has complex nonlinear characteristics. In this paper, the performance of a DMFC has been modeled using a neural network approach. The input parameters of the DMFC model include cell geometrical and operational parameters such as the cell temperature, oxygen flow rate, channel depth of the bipolar plate, methanol concentration, cathode back pressure, and current density and the output parameter is the cell voltage. In order to predict the performance of a DMFC single cell, two types of artificial neural network (ANN) have been developed to correlate the input parameters of the DMFC to the cell voltage. The performance of the networks was investigated by varying the number of neurons, number of layers, and transfer function of the ANNs and the best one is selected based on the mean square error. The results indicated that the neural network models can predict the cell voltage with an acceptable accuracy.

Non-isothermal multi-dimensional direct methanol fuel cell model with micro-porous layers mitigating water/methanol crossover

A two-phase three-dimensional direct methanol fuel cell (DMFC) model with the capability of saturation jump is developed in order to investigate the effect of micro-porous layers (MPL) on the water/methanol crossover in a DMFC. It is found that hydrophobic MPL on the cathode side helps cathode catalyst layer reserve water whereas hydrophobic MPL on the anode side blocks liquid flow to the cathode side, which effectively reduces water/methanol crossover. As the water transport in a DMFC occurs in two-phase, energy equation considering the heat-pipe effect with latent heat is solved together. Calculation result revealed thermal conductivity of gas diffusion layer has a strong influence on both temperature and water transport. Finally, a large-scale simulation of 28 cm2 cell is conducted to examine current density and fuel concentration distribution.

Water management in a passive direct methanol fuel cell

SUMMARY Passive direct methanol fuel cells (DMFCs) are under development for use in portable applications because of their enhanced energy density in comparison with other fuel cell types. The most significant obstacles for DMFC development are methanol and water crossover because methanol diffuses through the membrane generating heat but no power. The presence of a large amount of water floods the cathode and reduces cell performance. The present study was carried out to understand the performance of passive DMFCs, focused on the water crossover through the membrane from the anode to the cathode side. The water crossover behaviour in passive DMFCs was studied analytically with the results of a developed model for passive DMFCs. The model was validated with an in-house designed passive DMFC. The effect of methanol concentration, membrane thickness, gas diffusion layer material and thickness and catalyst loading on fuel cell performance and water crossover is presented. Water crossover was lowered with reduction on methanol concentration, reduction of membrane thickness and increase on anode diffusion layer thickness and anode and cathode catalyst layer thickness. It was found that these conditions also reduced methanol crossover rate. A membrane electrode assembly was proposed to achieve low methanol and water crossover and high power density, operating at high methanol concentrations. The results presented provide very useful and actual information for future passive DMFC systems using high concentration or pure methanol. Copyright © 2012 John Wiley & Sons, Ltd.

Local entropy generation analysis on passive high-concentration DMFCs (direct methanol fuel cell) with different cell structures

In this paper, the local entropy generation analysis has been conducted based on a two-dimensional, two-phase, non-isothermal DMFC (direct methanol fuel cell) model, the entropy generation contributed by the chemical reactions, heat transfer, mass diffusion, and viscous dissipation is investigated. Then, the performance of fuel cells with different methanol barrier layers and electrolyte membranes have been studied based on the local entropy generation analysis. Results indicate that the entropy generation during cell operation is mainly caused by the irreversible electrochemical reactions, and that the entropy generated by mass diffusion and viscous dissipation can be considered negligible. The entropy generated by heat transfer is about two magnitudes less than the entropy generated by the electrochemical reactions in the passive DMFCs operating near room temperature. The overall entropy generation rate in a DMFC can be decreased by increasing the thickness of the methanol barrier layer and decreasing the thickness of the electrolyte membrane.

Comparison of numerical simulation results with experimental current density and methanol-crossover data for direct methanol fuel cells

The simulation results of a one-dimensional (1D) direct methanol fuel cell (DMFC) model are compared with the current density and methanol-crossover data that are experimentally measured under several different cell designs and operating conditions. No fitting parameters are employed for the comparison and model input parameters obtained from the literature are consistently used for all the cases of comparison. The numerical predictions agree well with the experimental data and the 1D DMFC model successfully captures key experimental trends that are observed in the cell current density and methanol-crossover data. This clearly illustrates that the present DMFC model can be applicable for optimizing DMFC component designs and operating conditions. In addition, the model simulations further indicate that the reduction of the methanol concentration in the anode catalyst layer is critical to simultaneously suppress both the electro-osmotic drag (EOD) and the diffusion aspects of methanol crossover.

A global transient, one-dimensional, two-phase model for direct methanol fuel cells (DMFCs) – Part II: Analysis of the time-dependent thermal behavior of DMFCs

Abstract A transient-thermal model based on a lumped system is newly developed and implemented in a one-dimensional (1D), two-phase rigorous direct methanol fuel cell (DMFC) model presented in Part I. In this model, the main focus lies on the investigation of the transient thermal behavior of DMFCs and its influence on methanol crossover, cell performance, and efficiency. 1D simulations are carried out and the time-dependent thermal behaviors of DMFCs are analyzed for various methanol-feed concentrations and external heat-transfer conditions. Predicting the close interactions between the evolution of the transient temperature, methanol crossover, cell voltage, and efficiency during DMFC operations, the simulations of transient behavior indicate that the insufficient cooling of DMFCs finally lead to thermal runaway, particularly under high methanol-feed concentrations. Therefore, it is concluded that an efficient cooling system is greatly needed to safeguard DMFC operations and enhance the performance of DMFCs. The present 1D DMFC model is a useful tool for attaining a better understanding of complicated physical phenomena in DMFCs, which assists in optimizing the operating conditions of such cells and material/design parameters.

Experimental Validation of a One Dimensional, Two-Phase Direct Methanol Fuel Cell Model under a Wide Range of Operating Temperatures and Methanol Feed Concentrations

not Available.

High concentration methanol fuel cells: Design and theory

Use of highly concentrated methanol fuel is required for direct methanol fuel cells (DMFCs) to compete with the energy density of Li-ion batteries. Because one mole of H 2O is needed to oxidize one mole of methanol (CH 3OH) in the anode, low water crossover to the cathode or even water back flow from the cathode into the anode is a prerequisite for using highly concentrated methanol. It has previously been demonstrated that low or negative water crossover can be realized by the incorporation of a low-α membrane electrode assembly (MEA), which is essentially an MEA designed for optimal water management, using, e.g. hydrophobic anode and cathode microporous layers (aMPL and cMPL). In this paper we extend the low-α MEA concept to include an anode transport barrier (aTB) between the backing layer and hydrophobic aMPL. The main role of the aTB is to act as a barrier to CH 3OH and H 2O diffusion between a water-rich anode catalyst layer (aCL) and a methanol-rich fuel feed. The primary role of the hydrophobic aMPL in this MEA is to facilitate a low (or negative) water crossover to the cathode. Using a previously developed 1D, two-phase DMFC model, we show that this novel design yields a cell with low methanol crossover (i.e. high fuel efficiency, ∼80%, at a typical operating current density of ∼80–90% of the cell limiting current density), while directly feeding high concentration methanol fuel into the anode. The physics of how the aTB and aMPL work together to accomplish this is fully elucidated. We further show that a thicker, more hydrophilic, more permeable aTB, and thicker, more hydrophobic, and less permeable aMPL are most effective in accomplishing low CH 3OH and H 2O crossover.