Potential and Current Distributions in Planar Electrodes of Lithium-Ion Batteries
Bibliographic record
Abstract
Two-dimensional distribution of potential and current density in planar electrodes of pouch-type lithium-ion batteries are investigated numerically and analytically. A concentration-independent polarization expression, obtained experimentally, is used to mimic the electrochemical performance of the battery. By numerically solving the charge balance equation on each electrode in conjugation with the polarization expression, the battery behavior during constant-current discharge processes is simulated. The numerical analysis shows that reaction current density between the electrodes remains approximately uniform during most of the discharge process, i.e., when depth-of-discharge varies from 5% to 85%. This observation suggested simplifying the electrochemical performance of the battery such that the charge balance equation on each electrode can be solved analytically to obtain closed-form solutions for potential and current density distributions. The analytical model shows fair agreement with experimental and numerical data at modest computational cost. The model is applicable for both charge and discharge processes, and its application is demonstrated for 20 Ah and 75 Ah prismatic lithium-ion batteries. The analytical model is used to describe electrical conduction in the electrodes, and to investigate the effects of tab design on voltage drop. It is demonstrated that constriction/spreading resistance in current collectors of the considered battery is fairly small; about 10% of the total cell resistance but it is larger than the contribution of bulk resistance which is about 3%. The model confirms that constriction/spreading increase with: decrease in the aspect ratio of the current collector, decrease in the tab width, and increase in the tab eccentricity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".