Parameter design on the multi-objectives of PEM fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms

2014 ◽  
Vol 39 (9) ◽  
pp. 4502-4515 ◽  
Author(s):  
Chuan-Tien Li ◽  
Sheng-Ju Wu ◽  
Wei-Lung Yu
Keyword(s):  
Genetic Algorithms ◽  
Fuel Cell ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pem Fuel Cell ◽  
Parameter Design ◽  
Inference System ◽  
Fuel Cell Stack ◽  
Neuro Fuzzy

Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Genetic Algorithms ◽  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Vibration Data ◽  
Neuro Fuzzy ◽  
Machine Fault ◽  
The Time Domain ◽  
Selection Of

A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The results of fault detection are compared with GA based artificial neural network (ANN), termed here as GA-ANN. In GA-ANN, the number of hidden nodes and the selection of input features are optimized using GAs. For each trial, the GA-ANFIS and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GA-ANFIS and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers (ANFIS and ANN) with GA based selection of features and classifier parameters.

scholarly journals Dynamic Modeling of a Reformed Methanol Fuel Cell System Using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

2013 ◽  
Vol 11 (2) ◽  
Author(s):  
Kristian K. Justesen ◽  
Søren Juhl Andreasen ◽  
Hamid Reza Shaker
Keyword(s):  
Fuel Cell ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cell Model ◽  
Empirical Methods ◽  
Inference System ◽  
System Models ◽  
Neuro Fuzzy ◽  
The Individual ◽  
Methanol Fuel Cell

In this work, a dynamic matlab Simulink model of an H3-350 reformed methanol fuel cell (RMFC) stand-alone battery charger produced by Serenergy® is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous hydrogen, which is difficult and energy-consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and adaptive neuro-fuzzy inference system models of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other's output. The models take this into account using an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model, which is adapted to fit the measured performance of the H3-350 module. All of the individual parts of the model are verified and fine-tuned through a series of experiments and are found to have mean absolute errors between 0.4% and 6.4% but typically below 3%. After a comparison between the performance of the combined model and the experimental setup, the model is deemed to be valid for control design and optimization purposes.

scholarly journals Dynamic Modeling of a Reformed Methanol Fuel Cell System Using Empirical Data and Adaptive Neuro-Fuzzy Inference System Models

2013 ◽  
Author(s):  
Kristian K. Justesen ◽  
Søren J. Andreasen ◽  
Hamid R. Shaker
Keyword(s):  
Fuel Cell ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cell Model ◽  
Empirical Methods ◽  
Inference System ◽  
System Models ◽  
Neuro Fuzzy ◽  
The Individual ◽  
Methanol Fuel Cell

In this work, a dynamic MATLAB Simulink model of a H3-350 Reformed Methanol Fuel Cell (RMFC) stand-alone battery charger produced by Serenergy® is developed on the basis of theoretical and empirical methods. The advantage of RMFC systems is that they use liquid methanol as a fuel instead of gaseous hydrogen, which is difficult and energy consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and Adaptive Neuro-Fuzzy Inference System models of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified and fine-tuned through a series of experiments and are found to have mean absolute errors between 0.4% and 6.4% but typically below 3%. After a comparison between the performance of the combined model and the experimental setup, the model is deemed to be valid for control design and optimization purposes.

Power management strategy based on adaptive neuro-fuzzy inference system for fuel cell-battery hybrid vehicle

2012 ◽  
Vol 4 (1) ◽  
pp. 013106 ◽  
Author(s):  
Qi Li ◽  
Weirong Chen ◽  
Shukui Liu ◽  
Zhiyu You ◽  
Shiyong Tao ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Power Management ◽  
Fuzzy Inference System ◽  
Management Strategy ◽  
Fuzzy Inference ◽  
Hybrid Vehicle ◽  
Inference System ◽  
Power Management Strategy ◽  
Neuro Fuzzy

Application of Adaptive Neuro-Fuzzy Inference System Techniques to Predict Water Activity in Proton Exchange Membrane Fuel Cell

2018 ◽  
Vol 15 (4) ◽  
Author(s):  
Khaled Mammar ◽  
Slimane Laribi
Keyword(s):  
Fuel Cell ◽  
Water Activity ◽  
Fuzzy Inference System ◽  
Proton Exchange Membrane ◽  
Fuzzy Inference ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Exchange Membrane

This work defines and implements a technique to predict water activity in proton exchange membrane fuel cell. This technique is based on the electrochemical impedance spectroscopy (EIS) as sensor and adaptive neuro-fuzzy inference system (ANFIS) as estimator. For this purpose, a proton exchange membrane fuel cell (PEMFC) model has been proposed to study the performances of the fuel cell for different operating conditions where the simulation model for water activity behavior is in the proposed structure. The technique based on ANFIS predicts the PEM fuel cell relative humidity (RH) from the EIS. For creation of ANFIS training and checking database, a new method based on factorial design of experimental is used. To check the proposed technique, the ANFIS estimator will be compared with the output humidity relative observation.

Modeling of an Intermediate Temperature Solid Oxide Fuel Cell Using the Adaptive Neuro-Fuzzy Inference System (ANFIS)

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Yasemin Vural ◽  
Diyar Akay ◽  
Mohamed Pourkashanian ◽  
Derek B. Ingham
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Solid Oxide ◽  
Intermediate Temperature ◽  
Physical Models ◽  
Oxide Fuel ◽  
Inference System ◽  
Neuro Fuzzy

The modeling of an intermediate temperature solid oxide fuel cell using an adaptive neuro-fuzzy inference system (ANFIS) has been presented. The results show that a well trained and well tested ANFIS model has the capability to predict the fuel cell performance under varying operational conditions depending on the availability of the data and can be used as an alternative to the physical models in the sense that the results can be produced in a fast and cost effective way. The performance of the model in regions where there is data deficiency has been discussed.

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