Effect of operating conditions on energy efficiency for a small passive direct methanol fuel cell

2006 ◽  
Vol 51 (26) ◽  
pp. 5829-5835 ◽  
Author(s):  
Deryn Chu ◽  
Rongzhong Jiang
Keyword(s):  
Energy Efficiency ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Methanol Fuel Cell

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.

scholarly journals Optimum Conditions for Maximum Power of a Direct Methanol Fuel Cell

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Mojtaba Tafaoli-Masoule ◽  
Arian Bahrami ◽  
Danial Mohammadrezaei
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Operating Conditions ◽  
Maximum Power ◽  
Effective Parameters ◽  
Cell Temperature ◽  
Isothermal Model ◽  
Direct Methanol ◽  
Methanol Fuel Cell ◽  
Good Agreement

It is well known that anode and cathode pressures and cell temperature are the effective parameters in performance of Direct Methanol Fuel Cell (DMFC). In the present study, the genetic algorithm as one of the most powerful optimization tools is applied to determine operating conditions which result in the maximum power density of a DMFC. A quasi-two-dimensional, isothermal model is presented to determine maximum power of a DMFC. For validation of this model, the results of the model are compared to experimental results and shown to be in good agreement with them.

Systematic Experimental Analysis of a Direct Methanol Fuel Cell

2006 ◽  
Vol 4 (4) ◽  
pp. 418-424 ◽  
Author(s):  
A. Casalegno ◽  
R. Marchesi ◽  
F. Rinaldi
Keyword(s):  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Operating Conditions ◽  
Experimental Results ◽  
Cell Temperature ◽  
Systematic Analysis ◽  
Operating Condition ◽  
Direct Methanol ◽  
Methanol Fuel Cell

Different studies are carried out to compare the performances of different fuel cell constructive materials and operating conditions. In this work, a methodology for the characterization of DMFC experimental results in term of uncertainty and repeatability and for a systematic analysis of operating condition influence on performance is presented. The measurement system (composed of calibrated instruments) and experimental and data elaboration procedures are described. Experimental results, characterized by uncertainty and repeatability, are discussed for different operating conditions: fuel cell temperature, anode flow rate, and methanol concentration. The influence of operating condition history on performance is observed. It arises also from accumulation, both of methanol and carbon dioxide at the anode side; consequently, the operating condition history has to be considered in evaluating direct methanol fuel cell (DMFC) performances and repeatability of measurements. This work confirms that to compare experimental performances of fuel cells, the measurements shall be characterized by traceability, repeatability, reproducibility, and uncertainty.

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