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Article: Design and implementation of neural network based capacity indicator for lithium-ion battery
Title | Design and implementation of neural network based capacity indicator for lithium-ion battery |
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Authors | |
Keywords | Battery capacity indicator Neural network Lithium-ion battery Hardware implementation |
Issue Date | 2004 |
Publisher | Asian 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? |
Abstract | Usable 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 Identifier | http://hdl.handle.net/10722/73982 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Chan, MSW | en_HK |
dc.contributor.author | Chau, KT | en_HK |
dc.contributor.author | Chan, CC | en_HK |
dc.date.accessioned | 2010-09-06T06:56:39Z | - |
dc.date.available | 2010-09-06T06:56:39Z | - |
dc.date.issued | 2004 | en_HK |
dc.identifier.citation | Journal of Asian Electric Vehicles, 2004, v. 2 n. 2, p. 627-632 | en_HK |
dc.identifier.issn | 1348-3927 | - |
dc.identifier.uri | http://hdl.handle.net/10722/73982 | - |
dc.description.abstract | Usable 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.language | eng | en_HK |
dc.publisher | Asian Electric Vehicle Society. The Journal's web site is located at http://www.elec.eng.osaka-cu.ac.jp/aevc/ | - |
dc.relation.ispartof | Journal of Asian Electric Vehicles | en_HK |
dc.subject | Battery capacity indicator | - |
dc.subject | Neural network | - |
dc.subject | Lithium-ion battery | - |
dc.subject | Hardware implementation | - |
dc.title | Design and implementation of neural network based capacity indicator for lithium-ion battery | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Chan, MSW: swchan@eee.hku.hk | en_HK |
dc.identifier.email | Chau, KT: ktchau@eee.hku.hk | en_HK |
dc.identifier.email | Chan, CC: ccchan@eee.hku.hk | - |
dc.identifier.authority | Chau, KT=rp00096 | en_HK |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.doi | 10.4130/jaev.2.627 | - |
dc.identifier.hkuros | 101765 | en_HK |
dc.identifier.volume | 2 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 627 | - |
dc.identifier.epage | 632 | - |
dc.identifier.issnl | 1348-3927 | - |