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Titel:

Influence of Experimental Designs on Semi-Empirical and Deep Learning-based Modeling of Li-Ion Battery Aging

Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
18.11.2024
Verantwortlich:
Kröger, Thomas
Autorinnen / Autoren:
Kröger, Thomas; Maisel, Sven; Jank, Georg; Abo Gamra, Kareem; Brehler, Tobias; Lienkamp, Markus
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Identifikator:
doi:10.14459/2024mp1745519
Enddatum der Datenerzeugung:
26.10.2022
Fachgebiet:
ELT Elektrotechnik; MAS Maschinenbau; VER Technik der Verkehrsmittel
Quellen der Daten:
Experimente und Beobachtungen / experiments and observations
Datentyp:
Tabellen / tables
Methode der Datenerhebung:
In total, 42 lithium-ion cells (LGChem 18650 MJ1) were cycled under different operation conditions, with different temperatures, DoDs, and charging rates. All cells were cycled for 6 months or until they reached 60% SoH and every 5 days a check-up was conducted. All measurements within this study are conducted using ACT0550 cell test systems from PEC™, MK 240 climate chambers from Binder™, and Pt100 temperature probes for temperature measurements.
Beschreibung:
This dataset contains the measurements of 42 lithium-ion cells (LGChem 18650 MJ1) during a 6 months cycle life testing study, in which the influence of different operation conditions (0.3C-1C charging, 10-30°C ambient temperature, 5-100% DoD) on the aging behavior has been investigated. The tested conditions contain a full-factorial design with 3 factors and 3 levels per factor, 9 additional validation test conditions, and 3 test conditions that have been tested with 3 cells to quantify the cell...     »
Schlagworte:
Lithium-Ion Battery; Battery Aging Modeling; Design of Experiment; Semi-empirical modeling; Deep Learning
Technische Hinweise:
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Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
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