Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects

Hossain Lipu, MS; Ansari, S; Miah, MS; Meraj, ST; Hasan, Kamrul; Shihavuddin, ASM; Hannan, MA; Muttaqi, KM; Hussain, A

Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects

journal contribution

posted on 2024-08-06, 01:01 authored by MS Hossain Lipu, S Ansari, MS Miah, ST Meraj, Kamrul Hasan, ASM Shihavuddin, MA Hannan, KM Muttaqi, A Hussain

Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects

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Journal

Journal of Energy Storage

Volume

55

Article number

105752

Pagination

1-27

Location

Amsterdam, The Netherlands

ISSN

2352-152X

eISSN

2352-152X

Language

eng

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https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000865358000004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a045e4b2bb1f2b747c68c720ec8913b7

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C1 Refereed article in a scholarly journal

Publisher

Elsevier

Publication URL

http://dx.doi.org/10.1016/j.est.2022.105752

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Keywords

And electric vehicle Battery management system CHALLENGES Deep learning ELECTRIC VEHICLES Energy & Fuels GATED RECURRENT UNIT HYBRID LITHIUM-ION BATTERIES MODEL NEURAL-NETWORK OF-CHARGE PREDICTION Remaining useful life Science & Technology SHORT-TERM-MEMORY State of charge State of health Technology

Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects

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