Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/38436
Share/Export:
logo share SHARE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invite to open peer review
Title

Adaptive Predictive Robust Control for Fuel Cells Hybrid Vehicles

AuthorsNieto Deglioumini, Lucas; Zumoffen, David; Basualdo, Marta; Feroldi, Diego CSIC ORCID; Riera, Jordi CSIC
KeywordsPredictive adaptive robust control
Fuel cell hybrid vehicles
Energy management strategies
Energy storage
Issue Date2010
PublisherInstitute of Electrical and Electronics Engineers
CitationIEEE Vehicle Power and Propulsion Conference: 1-6 (2010)
AbstractThe transient behavior of a Polymer Electrolyte Membrane Fuel Cell System (PEMFCS) under an efficient Adaptive Predictive Control with Robust Filter (APCWRF) is analyzed. This control scheme is tested to evaluate its performance when sudden changes in the load occur. It is produced by the demands of the electric motor of a hybrid vehicle, powered by a PEMFC and a supercapacitor bank to fulfil Standard Driving Cycles. The objective of the proposed advanced strategy is to control the oxygen excess ratio in the cathode to improve the system efficiency and to ensure a safe operation for the PEM. Several results through a simulation environment are presented. They are useful for showing the potentiality of the APCWRF for the proposed exigent scenarios.
DescriptionTrabajo presentado al VPPC celebrado en Lille del 1 al 3 de septiembre de 2010.
Publisher version (URL)http://dx.doi.org/10.1109/VPPC.2010.5729254
URIhttp://hdl.handle.net/10261/38436
DOI10.1109/VPPC.2010.5729254
ISBN978-1-4244-8220-7
Appears in Collections:(IRII) Libros y partes de libros




Files in This Item:
File Description SizeFormat
Adaptive Predictive Robust.pdf442,42 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

Page view(s)

304
checked on May 21, 2024

Download(s)

306
checked on May 21, 2024

Google ScholarTM

Check

Altmetric

Altmetric


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.