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Article: Design and implementation of neural network based capacity indicator for lithium-ion battery

TitleDesign and implementation of neural network based capacity indicator for lithium-ion battery
Authors
KeywordsBattery capacity indicator
Neural network
Lithium-ion battery
Hardware implementation
Issue Date2004
PublisherAsian Electric Vehicle Society. The Journal's web site is located at http://www.elec.eng.osaka-cu.ac.jp/aevc/
Citation
Journal of Asian Electric Vehicles, 2004, v. 2 n. 2, p. 627-632 How to Cite?
AbstractUsable remnant energy of a rechargeable battery is proportional to its state of charge, but the values of these two parameters are not exactly the same. A circuit based on neural network is developed for available capacity estimation of lithium-ion battery. To ensure the network consisting of optimal numbers of hidden layers and neurons, intensive experiments have been performed and various training algorithms have been evaluated. The experimental results reveal that the circuit is able to estimate the available capacity of a battery with arbitrary discharging profiles as well as continuous constant current discharging profiles. This paper presents the roadmap for the design and the practical constraints for hardware implementation of a neural network based lithium-ion battery capacity indicator.
Persistent Identifierhttp://hdl.handle.net/10722/73982
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChan, MSWen_HK
dc.contributor.authorChau, KTen_HK
dc.contributor.authorChan, CCen_HK
dc.date.accessioned2010-09-06T06:56:39Z-
dc.date.available2010-09-06T06:56:39Z-
dc.date.issued2004en_HK
dc.identifier.citationJournal of Asian Electric Vehicles, 2004, v. 2 n. 2, p. 627-632en_HK
dc.identifier.issn1348-3927-
dc.identifier.urihttp://hdl.handle.net/10722/73982-
dc.description.abstractUsable remnant energy of a rechargeable battery is proportional to its state of charge, but the values of these two parameters are not exactly the same. A circuit based on neural network is developed for available capacity estimation of lithium-ion battery. To ensure the network consisting of optimal numbers of hidden layers and neurons, intensive experiments have been performed and various training algorithms have been evaluated. The experimental results reveal that the circuit is able to estimate the available capacity of a battery with arbitrary discharging profiles as well as continuous constant current discharging profiles. This paper presents the roadmap for the design and the practical constraints for hardware implementation of a neural network based lithium-ion battery capacity indicator.-
dc.languageengen_HK
dc.publisherAsian Electric Vehicle Society. The Journal's web site is located at http://www.elec.eng.osaka-cu.ac.jp/aevc/-
dc.relation.ispartofJournal of Asian Electric Vehiclesen_HK
dc.subjectBattery capacity indicator-
dc.subjectNeural network-
dc.subjectLithium-ion battery-
dc.subjectHardware implementation-
dc.titleDesign and implementation of neural network based capacity indicator for lithium-ion batteryen_HK
dc.typeArticleen_HK
dc.identifier.emailChan, MSW: swchan@eee.hku.hken_HK
dc.identifier.emailChau, KT: ktchau@eee.hku.hken_HK
dc.identifier.emailChan, CC: ccchan@eee.hku.hk-
dc.identifier.authorityChau, KT=rp00096en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.4130/jaev.2.627-
dc.identifier.hkuros101765en_HK
dc.identifier.volume2-
dc.identifier.issue2-
dc.identifier.spage627-
dc.identifier.epage632-

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