A Comprehensive Review About Machine Learning For Battery Packs Remaining Useful Life Prediction

Han, Y; Lei, G; Li, L; Zheng, L

A Comprehensive Review About Machine Learning For Battery Packs Remaining Useful Life Prediction

Han, Y Lei, G Li, L

Zheng, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2024, 00, pp. 276-279
Issue Date:: 2024-03-19

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Field	Value	Language
dc.contributor.author	Han, Y
dc.contributor.author	Lei, G
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Zheng, L
dc.date	2023-11-20
dc.date.accessioned	2024-05-14T04:13:14Z
dc.date.available	2024-05-14T04:13:14Z
dc.date.issued	2024-03-19
dc.identifier.citation	2023 IEEE International Future Energy Electronics Conference (IFEEC), 2024, 00, pp. 276-279
dc.identifier.isbn	979-8-3503-3989-5
dc.identifier.uri	http://hdl.handle.net/10453/178960
dc.description.abstract	Battery pack Remaining Useful Life RUL prediction stands at the crossroads of technology and sustain ability in electrified transportation and energy storage This review journeys through the landscape of RUL prediction from the traditional empirical models to the cutting edge machine learning techniques It is a technical analysis and a narrative of evolution challenges and possibilities The paper delves into the complexities of data quality algorithm intricacy and real world applicability casting a critical eye on the road ahead It calls for collaboration innovation and a shared vision for a future where battery systems are efficient and resonate with our broader sustainability goals
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Future Energy Electronics Conference (IFEEC)
dc.relation.ispartof	2023 IEEE International Future Energy Electronics Conference
dc.relation.isbasedon	10.1109/ifeec58486.2023.10458568
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Comprehensive Review About Machine Learning For Battery Packs Remaining Useful Life Prediction
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Sydney, Australia
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-14T04:13:12Z
pubs.finish-date	2023-11-23
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-11-20
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Battery pack Remaining Useful Life RUL prediction stands at the crossroads of technology and sustain ability in electrified transportation and energy storage This review journeys through the landscape of RUL prediction from the traditional empirical models to the cutting edge machine learning techniques It is a technical analysis and a narrative of evolution challenges and possibilities The paper delves into the complexities of data quality algorithm intricacy and real world applicability casting a critical eye on the road ahead It calls for collaboration innovation and a shared vision for a future where battery systems are efficient and resonate with our broader sustainability goals

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178960