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Operando and In Situ Study on the Lithium Redistribution within Silicon-Graphite Composite Electrodes in Li-Ion Full Cells

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Blended electrodes are known for buffering effects due to the different kinetic and thermodynamic properties of the combined active materials, causing an inhomogeneous distribution of Li between the different active materials during operation.[1] In this study, different operando, in situ, and ex situ methods, as well as 3D microstructure-resolved simulations were used to investigate the charge redistribution during relaxation periods in silicon-graphite (SiG) composite electrodes at different state-of-charges (25%, 50%, 75%). Therefore, two different cell chemistries, graphite/NMC622 and SiG (~21 wt.-% Si)/NMC622, at two different C-rates (0.1C and 0.5C) were analyzed. Operando X-ray diffraction (XRD) in bilayer pouch cells and cross-sectional in situ optical microscopy (CS-IOM) were used to investigate the differences in the lithiation behavior of the two cell chemistries by tracking the graphite component during operation. Relaxed cells at 0%, 25%, 50%, 75%, and 100% SOC were analyzed with in situ XRD in bilayer pouch cells. By comparing operando and in situ XRD, significant differences in the lithiation state of graphite in the SiG cells were found. The relaxation process at 75% SOC after a 0.1C charge was directly recorded in CS-IOM measurements and the observed lithiation states were confirmed by ex situ optical microscopy. All these measurements indicate a redistribution of charge from the graphite component into silicon at 0.1C. For validation, simulations using digital twins of the two cell chemistries was performed and allowed analyzing the Li concentration in both the graphite and Si components of the blended electrode. This enabled validation of the indirect assumptions on the Li concentration changes in Si from XRD and microscopy as well as linking the results of the surface sensitive microscopy measurements and averaged bulk values from XRD. The gained insights not only highlight what is accomplishable by applying several different experimental and simulative methods but also can support a better understanding of aging of blended SiG anodes during operation.
[1] C. Heubner, T. Liebmann, O. Lohrberg, S. Cangaz, S. Maletti and A. Michaelis, Batteries & Supercaps, 5(1) (2022).

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Poster



TESCAN GmbH
Zum Lonnenhohl 46
44319 Dortmund

https://info.tescan.com/batteries

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Datum_UhrzeitPosterTitleAutorCompanyCategory
10/4/2024/TESCAN GmbH
Zum Lonnenhohl 46
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https://info.tescan.com/batteries
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Co-Autoren:
Sebastian Schuhmann, Jens Tübke, Hermann Nirschl
Karlsruher Institut für TechnologiePack Level 2: Pack Safety
11.04.2024/13:553CComparison of Internal Short-Circuit diagnosis methods with synthetic and experimental dataMSc Jon Perez
Co-Autoren:
Mikel Arrinda, Mikel Oyarbide, Haritz Macicior, Erik Garayalde, Unai Iraola
Fundación CIDETECPack Level 2: Pack Safety
11.04.2024/14:153CSafety Assessment of Lithium Plating in Lithium-Ion Batteries for Use in Second-Life ApplicationsMax Feinauer
Co-Autoren:
Dr. Gabriela Gerosa, Michael Wörz, Margret Wohlfahrt-Mehrens, Olaf Böse, Thomas Waldmann
Zentrum für Sonnenenergie- und Wasserstoff-Forschung Baden-Württemberg (ZSW)Pack Level 2: Pack Safety
11.04.2024/15:354AOperando and In Situ Study on the Lithium Redistribution within Silicon-Graphite Composite Electrodes in Li-Ion Full CellsChristin Hogrefe
Co-Autoren:
Neelima Paul (MLZ), Lioba Boveleth (DLR, HIU), Marius Bolsinger (HSAA), Marius Flügel, Timo Danner (DLR, HIU), Arnulf Latz (DLR, HIU), Ralph Gilles (MLZ), Volker Knoblauch (HSAA), Margret Wohlfahrt-Mehrens, Markus Hölzle, Thomas Waldmann
Zentrum für Sonnenenergie- und Wasserstoff-Forschung (ZSW)Material Ledvel 3: Li-Ion Batteries
11.04.2024/15:554AAn investigation of the cathode electrolyte interphase (CEI) formation of Ni-rich layered materials by Ni ion catalyzation: monolayer CEI formation from an oligomerPhD Fu-Ming WangNational Taiwan University of Science and TechnologyMaterial Ledvel 3: Li-Ion Batteries
11.04.2024/16:154AUnderstanding and Developing High Voltage Li-Ion Batteries via Commercial ElectrolytesDr. Johannes KasnatscheewUniversität MünsterMaterial Ledvel 3: Li-Ion Batteries
11.04.2024/16:354AMachine Learning-guided Optimization of the Ionic Conductivity with the Liquid Electrolyte Composition Analysis (LECA) packageMSc Mirko Fischer
Co-Autoren:
Harrison Martin, Moumita Maiti, Anand Narayanan Krishnamoorthy, Diddo Diddens, Andreas Heuer, Peng Yan, Christian Wölke, Isidora Cekic-Laskovic
Universität MünsterMaterial Ledvel 3: Li-Ion Batteries
11.04.2024/15:354BInfluence of Process Parameters in Direct Aqueous Recycling of NMC811 CathodesFelix Nagler
Co-Autoren:
Nino Christian, Leonhard Kolb, Philip Daubinger, Andreas Flegler, Michael Hofmann, Guinevere A. Giffin
Fraunhofer ISCLife Cycle 2: Recycling
11.04.2024/15:554BInfluences on the properties of black mass from mechanical lithium-ion battery recyclingChristian Wilke
Co-Autoren:
Christian Wilke, Alexandra Kaas, Denis Werner, Jannik Born, Harald Zetzener, Arno Kwade, Urs A. Peuker
TU Bergakademie FreibergLife Cycle 2: Recycling
11.04.2024/16:154BDirect Recycling of Ni-rich Layered Oxide-based Cathodes from Spent Lithium Ion BatteriesMaike Michelle Gnutzmann
Co-Autoren:
Ardavan Makvandi, Julius Buchmann, Bianca Helm, Aurora Gomez-Martin, Martin Winter, Johannes Kasnatscheew
Universität MünsterLife Cycle 2: Recycling
11.04.2024/16:354BEffects of early lithium removal on the hydro- and pyrometallurgical treatment of battery black mass – A general process description from a technical point of viewAlexander Nickol
Co-Autoren:
Hans-Jürgen Friedrich, Prof. Alexander Michaelis
Fraunhofer IKTSLife Cycle 2: Recycling
11.04.2024/15:354CMachine learning-based fast charging of lithium-ion batteries by monitoring and controlling internal physical statesDr.-Ing. Weihan Li
Co-Autoren:
Dirk Uwe Sauer
RWTH Aachen University | ISEAPack Level 3 - Modelling & Machine learning
11.04.2024/15:554CModeling the change of the open-circuit voltage curve of commercial lithium-ion batteries during agingM.Sc. Alexander Karger
Co-Autoren:
Julius Schmitt, Cedric Kirst, Jan P. Singer, Leo Wildfeuer, Andreas Jossen
Technische Universität MünchenPack Level 3 - Modelling & Machine learning
11.04.2024/16:154CSegregating the Degradation Mechanisms in Lithium-Ion Cells during Strongly Accelerated Cyclic Ageing with Critically Extended Voltage RangesM.Sc. Philippa Scharpmann
Co-Autoren:
Robert Leonhardt, Tim Tichter, Anita Schmidt, Julia Kowal, Jonas Krug von Nidda
Bundesanstalt für Materialforschung und -prüfungPack Level 3 - Modelling & Machine learning
11.04.2024/16:354CEnhancing Battery State-of-Health Estimation with Transfer Learning: Leveraging Voltage Window Features and Combining Synthetic and Real Cell Data in FCNN ModelsMarkel Azkue
Co-Autoren:
I. Gandiaga, S. Bockrath, E. Miguel, L. Oca, U. Iraola
IkerlanPack Level 3 - Modelling & Machine learning