Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143507
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLiu, Xiaoyuen_US
dc.date.accessioned2020-09-07T02:45:25Z-
dc.date.available2020-09-07T02:45:25Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/143507-
dc.description.abstractWith the development of machine learning technology, data-driven methods are widely applied in researching complex systerms. The extreme learning machine (ELM) is one of the most advanced data-driven methods nowadays because of its high accuracy and efficiency. Besides, as the key factors in electric vehicles, the battery degradation is hard to model and estimate in real application because the battery is a complicated system. Thus, this paper uses ELM to solve the battery health monitoring problem.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineering::Electric poweren_US
dc.titleData-driven battery health monitoringen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorXu Yanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Power Engineering)en_US
dc.contributor.supervisoremailxuyan@ntu.edu.sgen_US
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
Amended Dissertation LiuXY.pdf
  Restricted Access
1.26 MBAdobe PDFView/Open

Page view(s)

341
Updated on Jul 13, 2024

Download(s)

16
Updated on Jul 13, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.