Use of Asymmetric Average Charge- and Average Discharge- Voltages as an Indicator of the Onset of Unwanted Lithium Deposition in Lithium-Ion Cells
Bibliographic record
Abstract
Unwanted lithium-metal deposition on the negative electrode of a lithium-ion cell causes capacity loss due to poor lithium deposition and stripping efficiency and the possibility for internal short circuits. Internal short circuits may cause thermal runaway, which is especially dangerous in applications requiring many individual cells. This article proposes a method capable of identifying the onset of unwanted lithium deposition in-situ using cycles to full depth of discharge in a cell of any form-factor. The two most important factors affecting the average voltage of a Li-ion cell under load are the internal resistance increase and loss of lithium inventory. Increasing internal resistance increases average charge voltage and decreases average discharge voltage. Loss of lithium inventory, which occurs rapidly during unwanted lithium deposition and stripping, increases both average charge and average discharge voltage. Increasing internal resistance and loss of lithium inventory have a linearly additive effect on average voltage; therefore, tracking the average of average charge- and average discharge-voltages versus cycle count allows one to determine where rapid changes to lithium inventory onset, indicative of the onset of unwanted lithium deposition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".