Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes
Abstract A stochastic 3D modeling approach for the nanoporous binder-conductive additive phase in hierarchically structured cathodes of lithium-ion batteries is presented. The binder-conductive additive phase of these electrodes consists of carbon black, polyvinylidene difluoride binder and graphite...
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SpringerOpen
2025-05-01
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| Series: | Journal of Mathematics in Industry |
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| Online Access: | https://doi.org/10.1186/s13362-025-00174-z |
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| author | Phillip Gräfensteiner Markus Osenberg André Hilger Nicole Bohn Joachim R. Binder Ingo Manke Volker Schmidt Matthias Neumann |
| author_facet | Phillip Gräfensteiner Markus Osenberg André Hilger Nicole Bohn Joachim R. Binder Ingo Manke Volker Schmidt Matthias Neumann |
| author_sort | Phillip Gräfensteiner |
| collection | DOAJ |
| description | Abstract A stochastic 3D modeling approach for the nanoporous binder-conductive additive phase in hierarchically structured cathodes of lithium-ion batteries is presented. The binder-conductive additive phase of these electrodes consists of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. The model parameters are calibrated to 3D image data of cathodes in lithium-ion batteries acquired by focused ion beam scanning electron microscopy. Subsequently, model validation is performed by comparing model realizations with measured image data in terms of various morphological descriptors that are not used for model fitting. Finally, we use the stochastic 3D model for predictive simulations, where we generate virtual, yet realistic, image data of nanoporous binder-conductive additives with varying amounts of graphite particles. Based on these virtual nanostructures, we can investigate structure-property relationships. In particular, we quantitatively study the influence of graphite particles on effective transport properties in the nanoporous binder-conductive additive phase, which have a crucial impact on electrochemical processes in the cathode and thus on the performance of battery cells. |
| format | Article |
| id | doaj-art-df82a87319a24bd099eb2562977359f0 |
| institution | OA Journals |
| issn | 2190-5983 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
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| series | Journal of Mathematics in Industry |
| spelling | doaj-art-df82a87319a24bd099eb2562977359f02025-08-20T02:29:26ZengSpringerOpenJournal of Mathematics in Industry2190-59832025-05-0115112510.1186/s13362-025-00174-zData-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodesPhillip Gräfensteiner0Markus Osenberg1André Hilger2Nicole Bohn3Joachim R. Binder4Ingo Manke5Volker Schmidt6Matthias Neumann7Institute of Stochastics, Ulm UniversityInstitute of Applied Materials, Helmholtz-Zentrum Berlin für Materialien und EnergieInstitute of Applied Materials, Helmholtz-Zentrum Berlin für Materialien und EnergieInstitute for Applied Materials, Karlsruhe Institute of TechnologyInstitute for Applied Materials, Karlsruhe Institute of TechnologyInstitute of Applied Materials, Helmholtz-Zentrum Berlin für Materialien und EnergieInstitute of Stochastics, Ulm UniversityInstitute of Statistics, Graz University of TechnologyAbstract A stochastic 3D modeling approach for the nanoporous binder-conductive additive phase in hierarchically structured cathodes of lithium-ion batteries is presented. The binder-conductive additive phase of these electrodes consists of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. The model parameters are calibrated to 3D image data of cathodes in lithium-ion batteries acquired by focused ion beam scanning electron microscopy. Subsequently, model validation is performed by comparing model realizations with measured image data in terms of various morphological descriptors that are not used for model fitting. Finally, we use the stochastic 3D model for predictive simulations, where we generate virtual, yet realistic, image data of nanoporous binder-conductive additives with varying amounts of graphite particles. Based on these virtual nanostructures, we can investigate structure-property relationships. In particular, we quantitatively study the influence of graphite particles on effective transport properties in the nanoporous binder-conductive additive phase, which have a crucial impact on electrochemical processes in the cathode and thus on the performance of battery cells.https://doi.org/10.1186/s13362-025-00174-zStochastic 3D modelingStochastic geometryStructure-property relationshipStatistical image analysisNanoporous cathodeLithium-ion battery |
| spellingShingle | Phillip Gräfensteiner Markus Osenberg André Hilger Nicole Bohn Joachim R. Binder Ingo Manke Volker Schmidt Matthias Neumann Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes Journal of Mathematics in Industry Stochastic 3D modeling Stochastic geometry Structure-property relationship Statistical image analysis Nanoporous cathode Lithium-ion battery |
| title | Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes |
| title_full | Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes |
| title_fullStr | Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes |
| title_full_unstemmed | Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes |
| title_short | Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes |
| title_sort | data driven stochastic 3d modeling of the nanoporous binder conductive additive phase in battery cathodes |
| topic | Stochastic 3D modeling Stochastic geometry Structure-property relationship Statistical image analysis Nanoporous cathode Lithium-ion battery |
| url | https://doi.org/10.1186/s13362-025-00174-z |
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