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Article: A new battery available capacity indicator for electric vehicles using neural network

TitleA new battery available capacity indicator for electric vehicles using neural network
Authors
KeywordsBattery available capacity
Electric vehicles
Neural network model
Issue Date2002
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/enconman
Citation
Energy Conversion And Management, 2002, v. 43 n. 6, p. 817-826 How to Cite?
AbstractThe ability to calculate the battery available capacity (BAC) for electric vehicles (EVs) is very important. Knowing the BAC and, thus, the driving range cannot only prevent EVs from being stranding on the road but also optimize the utilization of the battery energy storage in EVs. In order to determine the BAC, this paper presents a new neural network (NN) model of the lead-acid battery, based on the battery discharge current and temperature. Comparisons between the calculated BAC from the NN model and the measured BAC from experiments show good agreement. Furthermore, this new approach can readily be extended to the calculation of the BAC for other types of batteries. © 2002 Elsevier Science Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/73903
ISSN
2015 Impact Factor: 4.801
2015 SCImago Journal Rankings: 2.156
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorShen, WXen_HK
dc.contributor.authorChan, CCen_HK
dc.contributor.authorLo, EWCen_HK
dc.contributor.authorChau, KTen_HK
dc.date.accessioned2010-09-06T06:55:53Z-
dc.date.available2010-09-06T06:55:53Z-
dc.date.issued2002en_HK
dc.identifier.citationEnergy Conversion And Management, 2002, v. 43 n. 6, p. 817-826en_HK
dc.identifier.issn0196-8904en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73903-
dc.description.abstractThe ability to calculate the battery available capacity (BAC) for electric vehicles (EVs) is very important. Knowing the BAC and, thus, the driving range cannot only prevent EVs from being stranding on the road but also optimize the utilization of the battery energy storage in EVs. In order to determine the BAC, this paper presents a new neural network (NN) model of the lead-acid battery, based on the battery discharge current and temperature. Comparisons between the calculated BAC from the NN model and the measured BAC from experiments show good agreement. Furthermore, this new approach can readily be extended to the calculation of the BAC for other types of batteries. © 2002 Elsevier Science 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.subjectBattery available capacityen_HK
dc.subjectElectric vehiclesen_HK
dc.subjectNeural network modelen_HK
dc.titleA new battery available capacity indicator for electric vehicles using neural networken_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0196-8904&volume=43&issue=6&spage=817&epage=826&date=2002&atitle=A+new+battery+available+capacity+indicator+for+electric+vehicles+using+neural+networken_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/S0196-8904(01)00078-4en_HK
dc.identifier.scopuseid_2-s2.0-0036532847en_HK
dc.identifier.hkuros70864en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036532847&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume43en_HK
dc.identifier.issue6en_HK
dc.identifier.spage817en_HK
dc.identifier.epage826en_HK
dc.identifier.isiWOS:000174569500006-
dc.publisher.placeUnited Kingdomen_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

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