Dynamic modeling of a reformed methanol fuel cell system using empirical data and adaptive neuro-fuzzy inference system models

Kristian K. Justesen, Søren Juhl Andreasen, Hamid Reza Shaker

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

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.

Original languageEnglish
Article number021004
JournalJournal of Fuel Cell Science and Technology
Volume11
Issue number2
ISSN1550-624X
DOIs
Publication statusPublished - 1. Apr 2014
Externally publishedYes

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Methanol fuels
Fuzzy inference
Fuel cells
Blowers
Cooling
Ventilation exhausts
Fins (heat exchange)
Reforming reactions
Fuel burners

Cite this

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title = "Dynamic modeling of a reformed methanol fuel cell system using empirical data and adaptive neuro-fuzzy inference system models",
abstract = "In this work, a dynamic matlab Simulink model of an H3-350 reformed methanol fuel cell (RMFC) stand-alone battery charger produced by Serenergy{\circledR} 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.",
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Dynamic modeling of a reformed methanol fuel cell system using empirical data and adaptive neuro-fuzzy inference system models. / Justesen, Kristian K.; Andreasen, Søren Juhl; Shaker, Hamid Reza.

In: Journal of Fuel Cell Science and Technology, Vol. 11, No. 2, 021004, 01.04.2014.

Research output: Contribution to journalJournal articleResearchpeer-review

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