Utilization of DEM Simulations to Quantify Cell Level Thickness and Volume Changes in Large Format Pouch Cells Article Swipe
Hunter Teel
,
Taylor R. Garrick
,
Brian J. Koch
,
Miguel Fernández
,
Srikant Srinivasan
,
Fengkun Wang
,
Yangbing Zeng
,
Sirivatch Shimpalee
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1149/1945-7111/ad749e
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1149/1945-7111/ad749e
In this work, a 3D representation of a lithium ion electric vehicle battery cell was created and modeled through the discrete element method (DEM) to capture the porous electrode volume change during cell operation and its effects on electrode strain, porosity changes, and pressure generation for each electrode. This was coupled with a representative volume element approach and the multi species reaction model to quantify the impact of these changes at an electrode level have on the cell level operation. Results on both the electrode level and cell level response were discussed to give insights on how the volume changes contribute to both strain and porosity changes and the potential effects these changes have on the electrochemical response of the generated representative cells. Predictions on the cell level response, particularly for porosity changes which can be difficult to capture experimentally, are essential for the further development of high energy density cells that utilize unique chemistries prone to high levels of volume change such as silicon and silicon oxides. The ability to predict the active material volume change and its nuances will be informative and essential to rapidly develop and design cells for both automotive and grid storage applications.
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- Language
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- OA Status
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All OpenAlex metadata
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https://openalex.org/W4402292416Canonical identifier for this work in OpenAlex
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https://doi.org/10.1149/1945-7111/ad749eDigital Object Identifier
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Utilization of DEM Simulations to Quantify Cell Level Thickness and Volume Changes in Large Format Pouch CellsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-09-02Full publication date if available
- Authors
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Hunter Teel, Taylor R. Garrick, Brian J. Koch, Miguel Fernández, Srikant Srinivasan, Fengkun Wang, Yangbing Zeng, Sirivatch ShimpaleeList of authors in order
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https://doi.org/10.1149/1945-7111/ad749ePublisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1149/1945-7111/ad749eDirect OA link when available
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Volume (thermodynamics), Pouch, Materials science, Environmental science, Geology, Physics, Thermodynamics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.lithium | 8 |
| abstract_inverted_index.modeled | 17 |
| abstract_inverted_index.nuances | 179 |
| abstract_inverted_index.oxides. | 167 |
| abstract_inverted_index.predict | 171 |
| abstract_inverted_index.rapidly | 186 |
| abstract_inverted_index.silicon | 164, 166 |
| abstract_inverted_index.species | 60 |
| abstract_inverted_index.storage | 196 |
| abstract_inverted_index.strain, | 39 |
| abstract_inverted_index.through | 18 |
| abstract_inverted_index.utilize | 152 |
| abstract_inverted_index.vehicle | 11 |
| abstract_inverted_index.approach | 56 |
| abstract_inverted_index.changes, | 41 |
| abstract_inverted_index.discrete | 20 |
| abstract_inverted_index.electric | 10 |
| abstract_inverted_index.insights | 94 |
| abstract_inverted_index.material | 174 |
| abstract_inverted_index.porosity | 40, 105, 131 |
| abstract_inverted_index.pressure | 43 |
| abstract_inverted_index.quantify | 64 |
| abstract_inverted_index.reaction | 61 |
| abstract_inverted_index.response | 89, 117 |
| abstract_inverted_index.difficult | 136 |
| abstract_inverted_index.discussed | 91 |
| abstract_inverted_index.electrode | 28, 38, 72, 84 |
| abstract_inverted_index.essential | 141, 184 |
| abstract_inverted_index.generated | 120 |
| abstract_inverted_index.operation | 33 |
| abstract_inverted_index.potential | 109 |
| abstract_inverted_index.response, | 128 |
| abstract_inverted_index.automotive | 193 |
| abstract_inverted_index.contribute | 100 |
| abstract_inverted_index.electrode. | 47 |
| abstract_inverted_index.generation | 44 |
| abstract_inverted_index.operation. | 79 |
| abstract_inverted_index.Predictions | 123 |
| abstract_inverted_index.chemistries | 154 |
| abstract_inverted_index.development | 145 |
| abstract_inverted_index.informative | 182 |
| abstract_inverted_index.particularly | 129 |
| abstract_inverted_index.applications. | 197 |
| abstract_inverted_index.representation | 5 |
| abstract_inverted_index.representative | 53, 121 |
| abstract_inverted_index.electrochemical | 116 |
| abstract_inverted_index.experimentally, | 139 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.19132397 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |