Data Driven Model for a Fuel Cell stack development in a complex Multi-source Hybrid Renewable Energy System
Fuel cells based on polymer electrolyte membrane are considered as the most hopeful clean power technology. The operating principles of polymer electrolyte membrane fuel cells (PEMFC) system involve electrochemistry, thermodynamics and hydrodynamics theory for which it is difficult to establish a ma...
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| Veröffentlicht in: | RE&PQJ 2024-01, Vol.8 (1) |
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| creator | G. Napoli M. Ferraro G. Brunaccini G. Dispenza V. Antonucci |
| description | Fuel cells based on polymer electrolyte
membrane are considered as the most hopeful clean
power technology. The operating principles of polymer
electrolyte membrane fuel cells (PEMFC) system involve
electrochemistry, thermodynamics and hydrodynamics
theory for which it is difficult to establish a mathematical
model. In this paper a nonlinear data driven model of a
PEMFC stack is developed using Neural Networks
(NNs). The model presented is a black-box model, based
on a set of measurable exogenous inputs and is able to
predict the output voltage and cathode temperature of a
high power module working at the CNR- ITAE. A 5 kW
PEM fuel cell stack is employed to experimentally
investigate the dynamic behaviour and to reveal the most
influential factors. |
| doi_str_mv | 10.24084/repqj08.549 |
| format | Article |
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membrane are considered as the most hopeful clean
power technology. The operating principles of polymer
electrolyte membrane fuel cells (PEMFC) system involve
electrochemistry, thermodynamics and hydrodynamics
theory for which it is difficult to establish a mathematical
model. In this paper a nonlinear data driven model of a
PEMFC stack is developed using Neural Networks
(NNs). The model presented is a black-box model, based
on a set of measurable exogenous inputs and is able to
predict the output voltage and cathode temperature of a
high power module working at the CNR- ITAE. A 5 kW
PEM fuel cell stack is employed to experimentally
investigate the dynamic behaviour and to reveal the most
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power technology. The operating principles of polymer
electrolyte membrane fuel cells (PEMFC) system involve
electrochemistry, thermodynamics and hydrodynamics
theory for which it is difficult to establish a mathematical
model. In this paper a nonlinear data driven model of a
PEMFC stack is developed using Neural Networks
(NNs). The model presented is a black-box model, based
on a set of measurable exogenous inputs and is able to
predict the output voltage and cathode temperature of a
high power module working at the CNR- ITAE. A 5 kW
PEM fuel cell stack is employed to experimentally
investigate the dynamic behaviour and to reveal the most
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membrane are considered as the most hopeful clean
power technology. The operating principles of polymer
electrolyte membrane fuel cells (PEMFC) system involve
electrochemistry, thermodynamics and hydrodynamics
theory for which it is difficult to establish a mathematical
model. In this paper a nonlinear data driven model of a
PEMFC stack is developed using Neural Networks
(NNs). The model presented is a black-box model, based
on a set of measurable exogenous inputs and is able to
predict the output voltage and cathode temperature of a
high power module working at the CNR- ITAE. A 5 kW
PEM fuel cell stack is employed to experimentally
investigate the dynamic behaviour and to reveal the most
influential factors.</abstract><doi>10.24084/repqj08.549</doi><oa>free_for_read</oa></addata></record> |
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| title | Data Driven Model for a Fuel Cell stack development in a complex Multi-source Hybrid Renewable Energy System |
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