Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98879
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dc.contributor.authorDu, Jianien
dc.contributor.authorLiu, Zhitaoen
dc.contributor.authorChen, Canen
dc.contributor.authorWang, Youyien
dc.date.accessioned2013-08-02T02:59:46Zen
dc.date.accessioned2019-12-06T20:00:45Z-
dc.date.available2013-08-02T02:59:46Zen
dc.date.available2019-12-06T20:00:45Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.urihttps://hdl.handle.net/10356/98879-
dc.identifier.urihttp://hdl.handle.net/10220/12835en
dc.description.abstractIn this paper, a method for modeling and estimation of Li-ion battery state of charge (SOC) using extreme learning machine (ELM) and extended Kalman filter (EKF) is proposed. The Li-ion battery model from ELM, which is established by training the data from the battery block in MATLAB/Simulation, could describe the dynamics of Li-ion battery very well. And it has higher accuracy and needs less calculation than using the traditional neural networks. Moreover, the battery model and discrete SOC definition equation constitute state-space equations, and EKF is used to estimate the SOC of Li-ion battery. Comparing the actual SOC with the estimated SOC by simulation, it reveals that the method proposed in this paper has good performance on Li-ion battery SOC estimation.en
dc.language.isoenen
dc.titleLi-ion battery SOC estimation using EKF based on a model proposed by extreme learning machineen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceIEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore)en
dc.identifier.doihttp://dx.doi.org/10.1109/ICIEA.2012.6360990en
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:EEE Conference Papers

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