File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles

TitleAdaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles
Authors
Shen, WXChan, CCLo, EWCChau, KT
KeywordsAdaptive neuro-fuzzy inference system
Battery modeling
Battery residual capacity
Electric vehicles
Issue Date2002
PublisherI E E E. The Journal's web site is located at http://www.ewh.ieee.org/soc/ies/ties/index.html
Citation
Ieee Transactions On Industrial Electronics, 2002, v. 49 n. 3, p. 677-684 How to Cite?
DOI: http://dx.doi.org/10.1109/TIE.2002.1005395
AbstractThis paper proposes and implements a new method for the estimation of the battery residual capacity (BRC) for electric vehicles (EVs). The key of the proposed method is to model the EV battery by using the adaptive neuro-fuzzy inference system. Different operating profiles of the EV battery are investigated, including the constant current discharge and the random current discharge as well as the standard EV driving cycles in Europe, the U.S., and Japan. The estimated BRCs are directly compared with the actual BRCs, verifying the accuracy and effectiveness of the proposed modeling method. Moreover, this method can be easily implemented by a low-cost microcontroller and can readily be extended to the estimation of the BRC for other types of EV batteries.
Persistent Identifierhttp://hdl.handle.net/10722/42896
ISSN
0278-0046
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 3.395
ISI Accession Number ID
WOS:000175970100020
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorShen, WXen_HK
dc.contributor.authorChan, CCen_HK
dc.contributor.authorLo, EWCen_HK
dc.contributor.authorChau, KTen_HK
dc.date.accessioned2007-03-23T04:34:14Z-
dc.date.available2007-03-23T04:34:14Z-
dc.date.issued2002en_HK
dc.identifier.citationIeee Transactions On Industrial Electronics, 2002, v. 49 n. 3, p. 677-684en_HK
dc.identifier.issn0278-0046en_HK
dc.identifier.urihttp://hdl.handle.net/10722/42896-
dc.description.abstractThis paper proposes and implements a new method for the estimation of the battery residual capacity (BRC) for electric vehicles (EVs). The key of the proposed method is to model the EV battery by using the adaptive neuro-fuzzy inference system. Different operating profiles of the EV battery are investigated, including the constant current discharge and the random current discharge as well as the standard EV driving cycles in Europe, the U.S., and Japan. The estimated BRCs are directly compared with the actual BRCs, verifying the accuracy and effectiveness of the proposed modeling method. Moreover, this method can be easily implemented by a low-cost microcontroller and can readily be extended to the estimation of the BRC for other types of EV batteries.en_HK
dc.format.extent276835 bytes-
dc.format.extent27648 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/msword-
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.ewh.ieee.org/soc/ies/ties/index.htmlen_HK
dc.relation.ispartofIEEE Transactions on Industrial Electronicsen_HK
dc.rights©2002 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.-
dc.subjectAdaptive neuro-fuzzy inference systemen_HK
dc.subjectBattery modelingen_HK
dc.subjectBattery residual capacityen_HK
dc.subjectElectric vehiclesen_HK
dc.titleAdaptive neuro-fuzzy modeling of battery residual capacity for electric vehiclesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0278-0046&volume=49&issue=3&spage=677&epage=684&date=2002&atitle=Adaptive+neuro-fuzzy+modeling+of+battery+residual+capacity+for+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/TIE.2002.1005395en_HK
dc.identifier.scopuseid_2-s2.0-0036610663en_HK
dc.identifier.hkuros70894-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036610663&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume49en_HK
dc.identifier.issue3en_HK
dc.identifier.spage677en_HK
dc.identifier.epage684en_HK
dc.identifier.isiWOS:000175970100020-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridShen, WX=15756297100en_HK
dc.identifier.scopusauthoridChan, CC=7404813179en_HK
dc.identifier.scopusauthoridLo, EWC=7101706013en_HK
dc.identifier.scopusauthoridChau, KT=7202674641en_HK
dc.identifier.issnl0278-0046-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats