Two-Dimensional Lithium-Ion Battery Modeling with Electrolyte and Cathode Extensions
Bibliographic record
Abstract
A two-dimensional model for transport and the coupled electric field is applied to simulate a charging lithium-ion cell and investigate the effects of lithium concentration gradients within electrodes on cell performance. The lithium concentration gradients within electrodes are affected by the cell geometry. Two different geometries are investigated: extending the length of the electrolyte past the edges of the electrodes and extending the length of the cathode past the edge of the anode. It is found that the electrolyte extension has little impact on the behavior of the electrodes, although it does increase the effective conductivity of the electrolyte in the edge region. However, the extension of the cathode past the edge of the anode, and the possibility for electrochemical reactions on the flooded electrode edges, are both found to impact the concentration gradients of lithium in electrodes and the current distribution within the electrolyte during charging. It is found that concentration gradients of lithium within electrodes may have stronger impacts on electrolytic current distributions, depending on the level of completeness of cell charge. This is because very different gradients of electric potential are expected from similar electrode gradients of lithium concentrations at different levels of cell charge, especially for the LixC6 cathode investigated in this study. This leads to the prediction of significant electric potential gradients along the electrolyte length during early cell charging, and a reduced risk of lithium deposition on the cathode edge during later cell charging, as seen experimentally by others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".