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Article: A new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system

TitleA new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference system
Authors
KeywordsAdaptive Neuro-Fuzzy Inference System
Battery Residual Capacity
Electric Vehicles
Nickel-Metal Hydride Battery
State Of Available Capacity
Issue Date2003
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/enconman
Citation
Energy Conversion And Management, 2003, v. 44 n. 13, p. 2059-2071 How to Cite?
AbstractThis paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents. © 2002 Elsevier Science Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/155187
ISSN
2015 Impact Factor: 4.801
2015 SCImago Journal Rankings: 2.156
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChau, KTen_US
dc.contributor.authorWu, KCen_US
dc.contributor.authorChan, CCen_US
dc.contributor.authorShen, WXen_US
dc.date.accessioned2012-08-08T08:32:15Z-
dc.date.available2012-08-08T08:32:15Z-
dc.date.issued2003en_US
dc.identifier.citationEnergy Conversion And Management, 2003, v. 44 n. 13, p. 2059-2071en_US
dc.identifier.issn0196-8904en_US
dc.identifier.urihttp://hdl.handle.net/10722/155187-
dc.description.abstractThis paper describes a new approach to estimate accurately the battery residual capacity (BRC) of the nickel-metal hydride (Ni-MH) battery for modern electric vehicles (EVs). The key to this approach is to model the Ni-MH battery in EVs by using the adaptive neuro-fuzzy inference system (ANFIS) with newly defined inputs and output. The inputs are the temperature and the discharged capacity distribution describing the discharge current profile, while the output is the state of available capacity (SOAC) representing the BRC. The estimated SOAC from ANFIS model and the measured SOAC from experiments are compared, and the results confirm that the proposed approach can provide an accurate estimation of the SOAC under variable discharge currents. © 2002 Elsevier Science Ltd. All rights reserved.en_US
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/enconmanen_US
dc.relation.ispartofEnergy Conversion and Managementen_US
dc.subjectAdaptive Neuro-Fuzzy Inference Systemen_US
dc.subjectBattery Residual Capacityen_US
dc.subjectElectric Vehiclesen_US
dc.subjectNickel-Metal Hydride Batteryen_US
dc.subjectState Of Available Capacityen_US
dc.titleA new battery capacity indicator for nickel-metal hydride battery powered electric vehicles using adaptive neuro-fuzzy inference systemen_US
dc.typeArticleen_US
dc.identifier.emailChau, KT:ktchau@eee.hku.hken_US
dc.identifier.authorityChau, KT=rp00096en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/S0196-8904(02)00249-2en_US
dc.identifier.scopuseid_2-s2.0-0037411634en_US
dc.identifier.hkuros90129-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037411634&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume44en_US
dc.identifier.issue13en_US
dc.identifier.spage2059en_US
dc.identifier.epage2071en_US
dc.identifier.isiWOS:000181955600001-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridChau, KT=7202674641en_US
dc.identifier.scopusauthoridWu, KC=7404512320en_US
dc.identifier.scopusauthoridChan, CC=7404813179en_US
dc.identifier.scopusauthoridShen, WX=15756297100en_US

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