Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In: 10.1016/j.ensm.2021.10.023
- Classification
-
Elektrotechnik, Elektronik
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2021
- Creator
-
Li, Weihan
Demir, Iskender
Cao, Decheng
Jöst, Dominik
Ringbeck, Florian
Junker, Mark
Sauer, Dirk Uwe
- DOI
-
10.18154/RWTH-2021-11456
- URN
-
urn:nbn:de:101:1-2021121502523554437995
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.03.2025, 12:48 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Li, Weihan
- Demir, Iskender
- Cao, Decheng
- Jöst, Dominik
- Ringbeck, Florian
- Junker, Mark
- Sauer, Dirk Uwe
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2021
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Optimized charging of lithium-ion batteries with physico-chemical models
Multiperspective Optimization of Cell and Module Dimensioning for Different Lithium‐Ion Cell Formats on Geometric and Generic Assumptions
The Influence of Electrolyte Volume on Calendaric Aging of Lithium‐Ion Batteries
Analysis of predominant processes in electrochemical impedance spectra and investigation of aging processes of lithium-ion batteries with layered oxide cathodes and graphitic anodes
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Understanding the Energy Potential of Lithium‐Ion Batteries: Definition and Estimation of the State of Energy
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