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

scholarly journals Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 192087-192100
Author(s):  
Hesham M. Fekry ◽  
Azza Ahmed Eldesouky ◽  
Ahmed M. Kassem ◽  
Almoataz Y. Abdelaziz
Keyword(s):  
Power Management ◽  
Fuzzy Inference System ◽  
Management Strategy ◽  
Fuzzy Inference ◽  
Inference System ◽  
Power Management Strategy ◽  
Neuro Fuzzy ◽  
Ac Microgrid

POWER MANAGEMENT STRATEGY BASED ON ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR AC MICROGRID

2021 ◽  
Vol 1 (10) ◽  
pp. 288-291
Author(s):  
Kartik Katiyar ◽  
Ayush Ayush ◽  
Deep Singh ◽  
Supriya Sharma
Keyword(s):  
Power Management ◽  
Management Strategy ◽  
Fuzzy Inference ◽  
Climatic Conditions ◽  
Electrical Networks ◽  
Renewable Energy Resources ◽  
Inference System ◽  
Power Management Strategy ◽  
Neuro Fuzzy ◽  
Micro Grids

Micro grids increase the efficiency and resiliency of electrical networks. However, the uncertain nature of renewable energy resources integrated into the MGs usually results in different problems. We are trying to achieve a stable system which can power microgrids and can be of commercial use. This would be achieved by simulation of the system under different climatic conditions. Keywords: Microgrid Optimization, ANFIS, Neural Network, Fuzzy Logic

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.

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