Home > Publications database > A least-squares support vector machine method for modeling transient voltage in polymer electrolyte fuel cells > print

001     890001
005     20240709082108.0
024 7 _ |a 10.1016/j.apenergy.2020.115092
|2 doi
024 7 _ |a 0306-2619
|2 ISSN
024 7 _ |a 1872-9118
|2 ISSN
024 7 _ |a 2128/27178
|2 Handle
024 7 _ |a altmetric:82862999
|2 altmetric
024 7 _ |a WOS:000540436500005
|2 WOS
037 _ _ |a FZJ-2021-00595
082 _ _ |a 620
100 1 _ |a Zou, Wei
|0 P:(DE-Juel1)171395
|b 0
|e Corresponding author
245 _ _ |a A least-squares support vector machine method for modeling transient voltage in polymer electrolyte fuel cells
260 _ _ |a Amsterdam [u.a.]
|c 2020
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1613481467_16605
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a An investigation into the credibility and suitability of a transient voltage model that characterizes the dynamic behavior of polymer electrolyte fuel cells was carried out by means of quantitative and qualitative validations. The least squares support vector machine method was then used to construct a transient voltage model of a fuel cell in the first phase, including a validation based on experimental data obtained from a test rig. In the second phase, a thorough discussion of the effect of the fuel cell’s operating conditions and the exterior load changes on the model’s performance was implemented. For this phase, the influences of the sampling interval and ramp ratio are discussed and determined following a large number of tests under a variety of operating conditions. The results show that sampling with short time intervals is an effective way to improve the model’s performance, and a smoother change to the exterior load is more likely to be approximated by the least squares support vector machine model. Moreover, the voltage model is sensitive to the ramp value by comparison to the ramp time. Suggestions for future applications of the transient voltage models are also provided. For a given combination of load changes, the sampling interval should be managed within a range to reach the demand data that satisfies the voltage accuracy. On the other hand, for a determinate sampling interval, the dynamic change of the load should be restricted within a limit to ensure that the model error is lower than the demand value.
536 _ _ |a 135 - Fuel Cells (POF3-135)
|0 G:(DE-HGF)POF3-135
|c POF3-135
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Froning, Dieter
|0 P:(DE-Juel1)5106
|b 1
700 1 _ |a Shi, Yan
|0 P:(DE-Juel1)186729
|b 2
700 1 _ |a Lehnert, Werner
|0 P:(DE-Juel1)129883
|b 3
773 _ _ |a 10.1016/j.apenergy.2020.115092
|g Vol. 271, p. 115092 -
|0 PERI:(DE-600)2000772-3
|p 115092 -
|t Applied energy
|v 271
|y 2020
|x 0306-2619
856 4 _ |u https://juser.fz-juelich.de/record/890001/files/Zou_Wei_A%20least-squares%20support%20vector%20machine%20medthod%20for%20modeling%20transient%20voltage%20in%20polymer%20electrolyte%20fuel%20cells.pdf
|y Published on 2020-05-26. Available in OpenAccess from 2022-05-26.
909 C O |o oai:juser.fz-juelich.de:890001
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)171395
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 0
|6 P:(DE-Juel1)171395
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)5106
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)186729
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)129883
910 1 _ |a RWTH Aachen
|0 I:(DE-588b)36225-6
|k RWTH
|b 3
|6 P:(DE-Juel1)129883
913 0 _ |a DE-HGF
|b Energie
|l Speicher und vernetzte Infrastrukturen
|1 G:(DE-HGF)POF3-130
|0 G:(DE-HGF)POF3-135
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-100
|4 G:(DE-HGF)POF
|v Fuel Cells
|x 0
913 1 _ |a DE-HGF
|b Forschungsbereich Energie
|l Materialien und Technologien für die Energiewende (MTET)
|1 G:(DE-HGF)POF4-120
|0 G:(DE-HGF)POF4-123
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-100
|4 G:(DE-HGF)POF
|v Chemische Energieträger
|9 G:(DE-HGF)POF4-1231
|x 0
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-09-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-09-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1160
|2 StatID
|b Current Contents - Engineering, Computing and Technology
|d 2020-09-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-09-11
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a Embargoed OpenAccess
|0 StatID:(DE-HGF)0530
|2 StatID
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b APPL ENERG : 2018
|d 2020-09-11
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2020-09-11
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-09-11
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b APPL ENERG : 2018
|d 2020-09-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-09-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-09-11
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-09-11
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IEK-14-20191129
|k IEK-14
|l Elektrochemische Verfahrenstechnik
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IEK-14-20191129
981 _ _ |a I:(DE-Juel1)IET-4-20191129

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21