A Backstepping Controller for Interleaved Boost DC–DC Converter Improving Fuel Cell Voltage Regulation

2020 ◽  
pp. 751-762
Author(s):  
Ali Dali ◽  
Samir Abdelmalek ◽  
Maamar Bettayeb
Keyword(s):  
Fuel Cell ◽  
Cell Voltage ◽  
Voltage Regulation ◽  
Backstepping Controller

scholarly journals A new effective robust nonlinear controller based on PSO for interleaved DC–DC boost converters for fuel cell voltage regulation

2020 ◽  
Vol 24 (22) ◽  
pp. 17051-17064 ◽  
Author(s):  
Samir Abdelmalek ◽  
Ali Dali ◽  
Maamar Bettayeb ◽  
Azzeddine Bakdi
Keyword(s):  
Fuel Cell ◽  
Cell Voltage ◽  
Voltage Regulation ◽  
Nonlinear Controller ◽  
Boost Converters ◽  
Robust Nonlinear Controller

GA-based robust LQR controller for interleaved boost DC–DC converter improving fuel cell voltage regulation

2017 ◽  
Vol 152 ◽  
pp. 438-456 ◽  
Author(s):  
Mustapha Habib ◽  
Farid Khoucha ◽  
Abdelghani Harrag
Keyword(s):  
Fuel Cell ◽  
Cell Voltage ◽  
Voltage Regulation ◽  
Lqr Controller

Development of the On-Line Monitoring System for Fuel Cell Voltage

2011 ◽  
Vol 219-220 ◽  
pp. 383-386
Author(s):  
Jing Li ◽  
Hong Pan ◽  
Shu Juan Zhang ◽  
Ling Fang Sun
Keyword(s):  
Fuel Cell ◽  
Monitoring System ◽  
Software Design ◽  
Output Voltage ◽  
Cell Voltage ◽  
Development Environment ◽  
Fuel Cell Stack ◽  
Actual Application ◽  
On Line ◽  
And Storage

According to the single battery's series structure in the fuel cell stack, we develop an on-line fuel cell voltage monitoring system, and realize VISA library functions’ call and operation data acquisition and storage successfully in the Delphi development environment. It’s introduced mainly that the monitoring principle, hardware structure, software design and the main feature. The actual application proves that this system has realized high-precision and real-time monitoring of the output voltage of the fuel cell for multi-channel, and has multi-condition operation by setting original parameters easily, thereby, the system has more applicability and well reliability.

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.

Research on fuel cell voltage monitoring system with negative voltage detection function

2021 ◽  
Author(s):  
Shuai Ding ◽  
Haijun Meng ◽  
Jun Huang ◽  
Haitao Chen ◽  
Xiaobin He
Keyword(s):  
Fuel Cell ◽  
Monitoring System ◽  
Cell Voltage ◽  
Detection Function ◽  
Negative Voltage

scholarly journals Exergy Analysis of Polymer Electrolyte Fuel Cell Systems Using Methanol

2003 ◽  
Author(s):  
Akimitsu Ishihara ◽  
Shigenori Mitsushima ◽  
Nobuyuki Kamiya ◽  
Ken-Ichiro Ota
Keyword(s):  
Fuel Cell ◽  
Polymer Electrolyte ◽  
Steam Reforming ◽  
Waste Heat ◽  
Material Balance ◽  
Cell Voltage ◽  
Exergy Efficiency ◽  
Exergy Loss ◽  
Polymer Electrolyte Fuel Cell ◽  
The Difference

An exergy (available energy) analysis has been conducted on a typical polymer electrolyte fuel cell (PEFC) system using methanol. The material balance and enthalpy balance were calculated for the PEFC system using methanol steam reforming, and the exergy flow was obtained. Based on these results, the exergy loss in each unit was obtained, and the difference between the enthalpy and exergy was discussed. The exergy loss in this system was calculated to be 178kJ/mole MeOH for the steam reforming process of methanol. Although the enthalpy efficiency approached unity as the recovery rate of the waste heat from the cell approached unity, the exergy efficiency remained around 0.45 since the cell’s operating temperature of 80°C is low. It was also found that the cell voltage should exceed 0.82V in order to obtain the exergy efficiency of 0.5 or higher. A direct methanol fuel cell (DMFC) was analyzed using the exergy and compared with the methanol reforming PEFC. In order to obtain the exergy efficiency higher than that of PEFC with steam reforming, the cell voltage of the DMFC should be 0.48V or greater at the current density of 600mA/cm2.

Operating Condition Optimization of PEMFC via Visualization Technique

2013 ◽  
Vol 393 ◽  
pp. 787-792 ◽  
Author(s):  
Khairul Imran Sainan ◽  
Wan Ahmad Najmi Wan Mohamed ◽  
Firdaus Mohamad ◽  
Norhisyam Jenal
Keyword(s):  
Fuel Cell ◽  
Liquid Water ◽  
Cell Voltage ◽  
Water Flooding ◽  
Root Cause ◽  
Condition Optimization ◽  
Dry Conditions ◽  
Voltage Performance ◽  
Stable Voltage ◽  
Membrane Dehydration

Fuel cell water management has two conflicting requirements; too less water causing membrane dehydration and too much water causing liquid water flooding. Both phenomena resulting in significantly instability voltage performance because of imbalance water presence. Therefore, it is vital to analyze and understand the root cause of the problem hence a 96cm2 transparent fuel cell was analyzed experimentally. The fuel cell allows clear visualization of flow channels, thus making it practical to analyze the transportation of reactants and products behavior. The experimental analyses were conducted under different reactant flow rate and inlet humidification variations. Highest cell performance was obtained under both reactant inlets humidification with largest air flow rates. On the other hand, when fuel and air in dry conditions, relatively lower cell voltage was obtained. Meanwhile, stable voltage was obtained under anode humidified and cathode non-humidified conditions with correct air to fuel ratio. Images of liquid water and voltage behavior are presented graphically corresponding to the changes in performance.

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