Show simple item record

dc.contributor.authorRen, Lei
dc.contributor.authorZhao, Li
dc.contributor.authorHong, Sheng
dc.contributor.authorZhao, Shiqiang
dc.contributor.authorWang, Hao
dc.contributor.authorZhang, Lin
dc.date.accessioned2019-09-16T10:42:25Z
dc.date.available2019-09-16T10:42:25Z
dc.date.created2019-01-13T19:31:10Z
dc.date.issued2018
dc.identifier.citationIEEE Access. 2018, 6 50587-50598.nb_NO
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/11250/2616945
dc.description.abstractAccurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in deep learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with deep neural network (DNN). First, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model-based DNN is trained for multi-battery remaining cycle life estimation. The proposed approach is applied to the real data set of lithium-ion battery cycle life from NASA, and the experiment results show that the proposed approach can improve the accuracy of RUL prediction.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleRemaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approachnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber50587-50598nb_NO
dc.source.volume6nb_NO
dc.source.journalIEEE Accessnb_NO
dc.identifier.doi10.1109/ACCESS.2018.2858856
dc.identifier.cristin1655709
dc.description.localcode(C) 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permissionnb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record