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Article: Neural network-based residual capacity indicator for nickel-metal hydride batteries in electric vehicles

TitleNeural network-based residual capacity indicator for nickel-metal hydride batteries in electric vehicles
Authors
KeywordsBattery residual capacity (BPC)
Electric vehicle (EV)
Indicator
Neural network (NN)
Nickel-metal hydride (Ni-MH)
Issue Date2005
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25
Citation
Ieee Transactions On Vehicular Technology, 2005, v. 54 n. 5, p. 1705-1712 How to Cite?
AbstractThis paper presents a new estimation approach for the battery residual capacity (BRC) indicator in electric vehicles (EVs). The key of this approach is to model the EV battery by using a neural network (NN) with a newly defined output and newly proposed inputs. The inputs are the discharged and regenerative capacity distribution and the temperature. The output is the state of available capacity (SOAC) which represents the BRC. Various SOACs of the nickel-metal hydride (Ni-MH) battery are experimentally investigated under different EV discharge current profiles and temperatures. The corresponding data are recorded to train and verify the proposed NN. The results indicate that the NN can provide an accurate and effective estimation of the BRC. Moreover, this NN can be easily implemented as the BRC indicator or estimator for EVs by using a low-cost microcontroller. © 2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/44810
ISSN
2015 Impact Factor: 2.243
2015 SCImago Journal Rankings: 1.203
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorShen, WXen_HK
dc.contributor.authorChau, KTen_HK
dc.contributor.authorChan, CCen_HK
dc.contributor.authorLo, EWCen_HK
dc.date.accessioned2007-10-30T06:10:43Z-
dc.date.available2007-10-30T06:10:43Z-
dc.date.issued2005en_HK
dc.identifier.citationIeee Transactions On Vehicular Technology, 2005, v. 54 n. 5, p. 1705-1712en_HK
dc.identifier.issn0018-9545en_HK
dc.identifier.urihttp://hdl.handle.net/10722/44810-
dc.description.abstractThis paper presents a new estimation approach for the battery residual capacity (BRC) indicator in electric vehicles (EVs). The key of this approach is to model the EV battery by using a neural network (NN) with a newly defined output and newly proposed inputs. The inputs are the discharged and regenerative capacity distribution and the temperature. The output is the state of available capacity (SOAC) which represents the BRC. Various SOACs of the nickel-metal hydride (Ni-MH) battery are experimentally investigated under different EV discharge current profiles and temperatures. The corresponding data are recorded to train and verify the proposed NN. The results indicate that the NN can provide an accurate and effective estimation of the BRC. Moreover, this NN can be easily implemented as the BRC indicator or estimator for EVs by using a low-cost microcontroller. © 2005 IEEE.en_HK
dc.format.extent281801 bytes-
dc.format.extent455168 bytes-
dc.format.extent4045 bytes-
dc.format.extent15593 bytes-
dc.format.extent2902 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25en_HK
dc.relation.ispartofIEEE Transactions on Vehicular Technologyen_HK
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBattery residual capacity (BPC)en_HK
dc.subjectElectric vehicle (EV)en_HK
dc.subjectIndicatoren_HK
dc.subjectNeural network (NN)en_HK
dc.subjectNickel-metal hydride (Ni-MH)en_HK
dc.titleNeural network-based residual capacity indicator for nickel-metal hydride batteries in electric vehiclesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0018-9545&volume=54&issue=5&spage=1705&epage=1712&date=2005&atitle=Neural+network-based+residual+capacity+indicator+for+nickel-metal+hydride+batteries+in+electric+vehiclesen_HK
dc.identifier.emailChau, KT:ktchau@eee.hku.hken_HK
dc.identifier.authorityChau, KT=rp00096en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TVT.2005.853448en_HK
dc.identifier.scopuseid_2-s2.0-29044450593en_HK
dc.identifier.hkuros118908-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-29044450593&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume54en_HK
dc.identifier.issue5en_HK
dc.identifier.spage1705en_HK
dc.identifier.epage1712en_HK
dc.identifier.isiWOS:000233436000016-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridShen, WX=15756297100en_HK
dc.identifier.scopusauthoridChau, KT=7202674641en_HK
dc.identifier.scopusauthoridChan, CC=7404813179en_HK
dc.identifier.scopusauthoridLo, EWC=7101706013en_HK

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