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.

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 200W PEM Fuel Cell Stack with Online Model-Based Monitoring System

2014 ◽  
Vol 18 (4) ◽  
pp. 13-26 ◽  
Author(s):  
Pittaya Khanungkhid ◽  
Pornpote Piumsomboon
Keyword(s):  
Fuel Cell ◽  
Monitoring System ◽  
Pem Fuel Cell ◽  
Fuel Cell Stack ◽  
Model Based

A robust cell voltage monitoring system for analysis and diagnosis of fuel cell or battery systems

2010 ◽  
Vol 195 (24) ◽  
pp. 8006-8012 ◽  
Author(s):  
Doug Brunner ◽  
Ajay K. Prasad ◽  
Suresh G. Advani ◽  
Brian W. Peticolas
Keyword(s):  
Fuel Cell ◽  
Monitoring System ◽  
Cell Voltage ◽  
Battery Systems

T-S Fuzzy Model-Based Fault Diagnosis of PEM Fuel Cell Engine

2010 ◽  
Vol 34-35 ◽  
pp. 92-97
Author(s):  
Rui Quan ◽  
Shu Hai Quan ◽  
Liang Huang
Keyword(s):  
Fuel Cell ◽  
Fault Diagnosis ◽  
Proton Exchange Membrane ◽  
Fuzzy Model ◽  
Cell Voltage ◽  
Proton Exchange ◽  
Safety And Reliability ◽  
Group A ◽  
On Line ◽  
Exchange Membrane

Proton exchange membrane fuel cell(PEMFC) technology has been greatly promoted in recent years, but the fault diagnosis and predictive maintenance are unneglectable issues in practical work. According to the safety and reliability requirement of 60kW automotive fuel cell engine designed by our group, a fault diagnosis method based on T-S fuzzy model which is tuned and optimized thanks to particle swarm optimization is put forward in this paper. Its inputs include voltage, the lowest single cell voltage, current, temperature and air pressure, by setting the output threshold of T-S fuzzy model at 0.85,when the healthy degree and its variety rate are below 0.85 and 0.05 respectively, the flooding fault is distinguished, if the healthy degree is below 0.85 but its variety rate is above 0.05,drying of the proton membrane is on-line diagnosed successfully, which can provide a guidance to its real-time monitoring and optimized control in future.

scholarly journals An Adaptive Digital Neural Network-Like-PID Control Law Design for Fuel Cell System Based on FPGA Technique

2020 ◽  
Vol 26 (9) ◽  
pp. 24-44 ◽  
Author(s):  
Wajdi T. Joudah Al-Rubaye ◽  
Ahmed S. Al-Araji ◽  
Hayder A. Dhahad
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Partial Pressure ◽  
Pid Control ◽  
Output Voltage ◽  
Cell System ◽  
Fuel Cell System ◽  
Digital Pid ◽  
Optimal Value ◽  
On Line

This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fuel cell system and to achieve the stability of the desired output voltage of fuel cell. The numerical simulation results (MATLAB) package along with the schematic design experimental work using Spartan-3E xc3s500e-4fg320 board with the Xilinx development tool Integrated Software Environment (ISE) version 14.7 and using Verilog hardware description language for design testing are illustrated the performance enhancement of the proposed an adaptive intelligent FPGA-PID-NN controller in terms of error voltage reduction and generating optimal value of the hydrogen partial pressure action (PH2) without oscillation in the output and no saturation state when these results are compared with other controllers.

scholarly journals PEM Fuel Cell Voltage Neural Control Based on Hydrogen Pressure Regulation

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 434 ◽  
Author(s):  
Andrés Morán-Durán ◽  
Albino Martínez-Sibaja ◽  
José Pastor Rodríguez-Jarquin ◽  
Rubén Posada-Gómez ◽  
Oscar Sandoval González
Keyword(s):  
Fuel Cell ◽  
Output Voltage ◽  
Neural Control ◽  
Principal Component ◽  
Cell Voltage ◽  
Cell Performance ◽  
Fuel Cell Performance ◽  
Model Control ◽  
And Control ◽  
The Impact

Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is proposed a hybrid scheme to model and control fuel cell systems using neural networks. Several feature selection algorithms were tested for dimensionality reduction, aiming to eliminate non-significant variables with respect to the control objective. Principal component analysis (PCA) obtained better results than other algorithms. Based on these variables, an inverse neural network model was developed to emulate and control the fuel cell output voltage under transient conditions. The results showed that fuel cell performance does not only depend on the supply of the reactants. A single neuro-proportional–integral–derivative (neuro-PID) controller is not able to stabilize the output voltage without the support of an inverse model control that includes the impact of the other variables on the fuel cell performance. This practical data-driven approach is reliably able to reduce the cost of the control system by the elimination of non-significant measures.

Parameterization of Fuel Cell Stack Voltage: Issues on Sensitivity, Cell-to Cell Variation, and Transient Response

2006 ◽  
Author(s):  
Arlette L. Schilter ◽  
Denise A. McKay ◽  
Anna G. Stefanopoulou
Keyword(s):  
Fuel Cell ◽  
Cell Voltage ◽  
Cell System ◽  
Cell Polarization ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Water Dynamics ◽  
Lumped Parameter Model ◽  
Experimental Methodology ◽  
Fuel Cell Stack

We present here a calibrated and experimentally validated lumped parameter model of fuel cell polarization for a hydrogen fed multi-cell, low-pressure, proton exchange membrane (PEM) fuel cell stack. The experimental methodology devised for calibrating the model was completed on a 24 cell, 300 cm2 stack with GORE™ PRIMERA® Series 5620 membranes. The predicted cell voltage is a static function of current density, stack temperature, reactant partial pressures, and membrane water content. The maximum prediction error associated with the sensor resolutions used for the calibration is determined along with a discussion of the model sensitivity to physical variables. The expected standard deviation due to the cell-to-cell voltage variation is also modelled. In contrast to other voltage models that match the observed dynamic voltage behavior by adding unreasonably large double layer capacitor effects or by artificially adding dynamics to the voltage equation, we show that a static model can be used when combined with dynamically resolved variables. The developed static voltage model is then connected with a dynamic fuel cell system model that includes gas filling dynamics, diffusion and water dynamics and we demonstrate the ability of the static voltage equation to predict important transients such as reactant depletion and electrode flooding. It is shown that the model can qualitatively predict the observed stack voltage under various operating conditions including step changes in current, temperature variations, and anode purging.

An On-Line Monitoring System of Temperature Field in Tire

2013 ◽  
Vol 300-301 ◽  
pp. 551-555
Author(s):  
Deng Chao Li ◽  
Jian Hua Wang ◽  
Yong Sheng Yang
Keyword(s):  
Temperature Field ◽  
Temperature Measurement ◽  
Monitoring System ◽  
Computer Software ◽  
Difficult Problem ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
On Line ◽  
Upper Computer ◽  
And Storage

The internal high temperature of tire not only reduces the service life of the tire, but also increases fuel consumption. An on-line monitoring system of temperature field in tire is designed. The temperature measurement module of lower computer is made up of temperature measurement network of thermocouple, single chip microcomputer and other modules. The upper computer software is designed under the environment of VC ++, making use of the serial port to realize communication. The functions such as temperature collection, analysis, display and storage capabilities were realized. The system has the advantages of convenient use, reliable operation, solving the difficult problem of obtaining tire actual temperature.

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