Fuel cell hybrid model for predicting hydrogen inflow through energy demand
Use este enlace para citar
http://hdl.handle.net/2183/24448
Excepto si se señala otra cosa, la licencia del ítem se describe como Creative Commons Attribution (CC BY 4.0)
Colecciones
- GI-CTC - Artigos [83]
Metadatos
Mostrar el registro completo del ítemTítulo
Fuel cell hybrid model for predicting hydrogen inflow through energy demandAutor(es)
Fecha
2019-11-10Cita bibliográfica
Casteleiro-Roca, J.-L.; Barragán, A.J.; Manzano, F.S.; Calvo-Rolle, J.L.; Andújar, J.M. Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand. Electronics 2019, 8, 1325.
Resumen
[Abstract]: Hydrogen-based energy storage and generation is an increasingly used technology, especially in renewable systems because they are non-polluting devices. Fuel cells are complex nonlinear systems, so a good model is required to establish efficient control strategies. This paper presents a hybrid model to predict the variation of H2 flow of a hydrogen fuel cell. This model combining clusters’ techniques to get multiple Artificial Neural Networks models whose results are merged by Polynomial Regression algorithms to obtain a more accurate estimate. The model proposed in this article use the power generated by the fuel cell, the hydrogen inlet flow, and the desired power variation, to predict the necessary variation of the hydrogen flow that allows the stack to reach the desired working point. The proposed algorithm has been tested on a real proton exchange membrane fuel cell, and the results show a great precision of the model, so that it can be very useful to improve the efficiency of the fuel cell system.
Palabras clave
Fuel Cell
Hydrogen energy
Intelligent systems
Hybrid systems
Artificial neural networks
Power management
Pila de combustible
Energía del hidrógeno
Sistemas inteligentes
Sistemas híbridos
Redes neuronales artificiales
Gestión de potencia
Hydrogen energy
Intelligent systems
Hybrid systems
Artificial neural networks
Power management
Pila de combustible
Energía del hidrógeno
Sistemas inteligentes
Sistemas híbridos
Redes neuronales artificiales
Gestión de potencia
Versión del editor
Derechos
Creative Commons Attribution (CC BY 4.0)
ISSN
2079-9292