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One-shot battery degradation trajectory prediction with deep learning

; ; ; ; ;

In
Journal of power sources Seiten/Artikel-Nr.:230024

ImpressumNew York, NY [u.a.] : Elsevier

Umfang[1]-11

ISSN0378-7753

Available online 10 June 2021

Online
DOI: 10.18154/RWTH-2021-05565
DOI: 10.1016/j.jpowsour.2021.230024

URL: http://publications.rwth-aachen.de/record/820366/files/820366.pdf

Einrichtungen

  1. Lehrstuhl für Elektrochemische Energiewandlung und Speichersystemtechnik (618310)
  2. JARA-ENERGY (080011)
  3. Institut für Stromrichtertechnik und Elektrische Antriebe (614500)


Thematische Einordnung (Klassifikation)
DDC: 620

OpenAccess:
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Dokumenttyp
Journal Article

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85107537124
WOS Core Collection: WOS:000685081000002

Interne Identnummern
RWTH-2021-05565
Datensatz-ID: 820366

Beteiligte Länder
Germany, UK, USA

 GO

Related:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Dataset
Time-series cyclic aging data on 48 commercial NMC/graphite Sanyo/Panasonic UR18650E cylindrical cells
[10.18154/RWTH-2021-04545]  GO OpenAccess  Download fulltext Files  Download fulltextFulltext BibTeX | EndNote: XML, Text | RIS

Medline ; Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 ; OpenAccess ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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614500
080011
618310
 Record created 2021-06-10, last modified 2022-07-29
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