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Article: A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system

TitleA new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system
Authors
KeywordsAdaptive neuro-fuzzy inference system
Battery residual capacity
Electric vehicles
Lithium-ion battery
State of available capacity
Issue Date2004
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/enconman
Citation
Energy Conversion And Management, 2004, v. 45 n. 11-12, p. 1681-1692 How to Cite?
AbstractThis paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles. © 2003 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73544
ISSN
2015 Impact Factor: 4.801
2015 SCImago Journal Rankings: 2.156
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChau, KTen_HK
dc.contributor.authorWu, KCen_HK
dc.contributor.authorChan, CCen_HK
dc.date.accessioned2010-09-06T06:52:22Z-
dc.date.available2010-09-06T06:52:22Z-
dc.date.issued2004en_HK
dc.identifier.citationEnergy Conversion And Management, 2004, v. 45 n. 11-12, p. 1681-1692en_HK
dc.identifier.issn0196-8904en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73544-
dc.description.abstractThis paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output, which represents the BRC. Moreover, realistic EV discharge current profiles are newly used to formulate the proposed model. The accuracy of the estimated SOAC obtained from the model is verified by experiments under various EV discharge current profiles. © 2003 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/enconmanen_HK
dc.relation.ispartofEnergy Conversion and Managementen_HK
dc.subjectAdaptive neuro-fuzzy inference systemen_HK
dc.subjectBattery residual capacityen_HK
dc.subjectElectric vehiclesen_HK
dc.subjectLithium-ion batteryen_HK
dc.subjectState of available capacityen_HK
dc.titleA new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference systemen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0196-8904&volume=45&issue=11-12&spage=1681&epage=1692&date=2004&atitle=A+new+battery+capacity+indicator+for+lithium-ion+battery+powered+electric+vehicles+using+adaptive+neuro-fuzzy+inference+systemen_HK
dc.identifier.emailChau, KT:ktchau@eee.hku.hken_HK
dc.identifier.authorityChau, KT=rp00096en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.enconman.2003.09.031en_HK
dc.identifier.scopuseid_2-s2.0-1542538895en_HK
dc.identifier.hkuros90136en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-1542538895&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume45en_HK
dc.identifier.issue11-12en_HK
dc.identifier.spage1681en_HK
dc.identifier.epage1692en_HK
dc.identifier.isiWOS:000220531500005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChau, KT=7202674641en_HK
dc.identifier.scopusauthoridWu, KC=7404512320en_HK
dc.identifier.scopusauthoridChan, CC=7404813179en_HK

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