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Machine Learning Based Lifetime Prediction of Lithium-Ion Cells

Schofer, Kai and Laufer, Florian and Stadler, Jochen and Hahn, Severin and Gaiselmann, Gerd and Latz, Arnulf and Birke, Kai Peter (2022) Machine Learning Based Lifetime Prediction of Lithium-Ion Cells. Advanced Science. Wiley. doi: 10.1002/advs.202200630. ISSN 2198-3844.

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Item URL in elib:https://elib.dlr.de/192726/
Document Type:Article
Title:Machine Learning Based Lifetime Prediction of Lithium-Ion Cells
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Schofer, KaiMercedes Benz AGUNSPECIFIEDUNSPECIFIED
Laufer, FlorianMercedes Benz AGUNSPECIFIEDUNSPECIFIED
Stadler, JochenMercedes Benz AGUNSPECIFIEDUNSPECIFIED
Hahn, SeverinMercedes Benz AGUNSPECIFIEDUNSPECIFIED
Gaiselmann, GerdMercedes Benz AGUNSPECIFIEDUNSPECIFIED
Latz, ArnulfDLRhttps://orcid.org/0000-0003-1449-8172UNSPECIFIED
Birke, Kai PeterUniversität StuttgartUNSPECIFIEDUNSPECIFIED
Date:26 August 2022
Journal or Publication Title:Advanced Science
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1002/advs.202200630
Publisher:Wiley
ISSN:2198-3844
Status:Published
Keywords:Batteriesimulation, Lebensdauervorhersage, Machine Learning
HGF - Research field:Energy
HGF - Program:Materials and Technologies for the Energy Transition
HGF - Program Themes:Electrochemical Energy Storage
DLR - Research area:Energy
DLR - Program:E SP - Energy Storage
DLR - Research theme (Project):E - Electrochemical Storage
Location: Ulm
Institutes and Institutions:Institute of Engineering Thermodynamics > Computational Electrochemistry
Deposited By: Latz, Arnulf
Deposited On:05 Jan 2023 15:18
Last Modified:01 Dec 2023 08:50

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