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@ARTICLE{Zou:999121,
author = {Zou, Jiayu and Gao, Yingbo and Frieges, Moritz Holger and
Börner, Martin Florian and Kampker, Achim and Li, Weihan},
title = {{M}achine learning for battery quality classification and
lifetime prediction using formation data},
journal = {Energy and AI},
volume = {18},
issn = {2666-5468},
address = {Amsterdam},
publisher = {Elsevier ScienceDirect},
reportid = {RWTH-2024-11881},
pages = {100451},
year = {2024},
cin = {618310 / 080011 / 420910 / 080070 / 122010 / 120000},
ddc = {624},
cid = {$I:(DE-82)618310_20140620$ / $I:(DE-82)080011_20140620$ /
$I:(DE-82)420910_20140620$ / $I:(DE-82)080070_20210623$ /
$I:(DE-82)122010_20140620$ / $I:(DE-82)120000_20140620$},
pnm = {OAPKF - Open-Access-Publikation mit Unterstützung der RWTH
Aachen University (021000-OAPKF) / BMBF 03XP0585 - SPEED -
Schnelle Charakterisierung der Leistungsfähigkeit von
Lithium-Ionen-Batterien aus der Produktionslinie mit
maschinellem Lernen (03XP0585) / BMBF 03XP0334 - Model2Life-
Modellbasierte Systemauslegung für
2nd-Life-Nutzungsszenarien von mobilen Batteriesystemen
(03XP0334)},
pid = {G:(DE-82)021000-OAPKF / G:(BMBF)03XP0585 /
G:(BMBF)03XP0334},
typ = {PUB:(DE-HGF)16},
UT = {WOS:001371742100001},
doi = {10.1016/j.egyai.2024.100451},
url = {https://publications.rwth-aachen.de/record/999121},
}
Gast ::