Battery digital twin with physics-based modeling, battery data and machine learning
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
In: Aachen : ISEA, 2021 (Aachener Beiträge des ISEA; 164)
RWTH Aachen University, Dissertation, 2021
- Klassifikation
-
Elektrotechnik, Elektronik
- Schlagwort
-
Physics
Machine learning
- Ereignis
-
Veröffentlichung
- (wo)
-
Aachen
- (wer)
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Universitätsbibliothek der RWTH Aachen
- (wann)
-
2021
- Urheber
- Beteiligte Personen und Organisationen
- DOI
-
10.18154/RWTH-2022-02292
- URN
-
urn:nbn:de:101:1-2022042907365378209293
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:35 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
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Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Li, Weihan
- Sauer, Dirk Uwe
- Viswanathan, Venkat
- Universitätsbibliothek der RWTH Aachen
Entstanden
- 2021
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