Mechanism of the entire overdischarge process and overdischarge-induced internal short circuit in lithium-ion batteries
Bibliographic record
Abstract
Lithium-ion batteries connected in series are prone to be overdischarged. Overdischarge results in various side effects, such as capacity degradation and internal short circuit (ISCr). However, most of previous research on the overdischarge of a cell was terminated when the cell voltage dropped to 0 V, leaving the further impacts of overdischarge unclear. This paper investigates the entire overdischarge process of large-format lithium-ion batteries by discharging the cell to -100% state of charge (SOC). A significant voltage platform is observed at approximately -12% SOC, and ISCr is detected after the cell is overdischarged when passing the platform. The scanning electron microscopy (SEM) and X-ray diffraction (XRD) results indicate that the overdischarge-induced ISCr is caused by Cu deposition on electrodes, suggesting possible Cu collector dissolution at the voltage platform near -12% SOC. A prognostic/mechanistic model considering ISCr is used to evaluate the resistance of ISCr (RISCr), the value of which decreases sharply at the beginning of ISCr formation. Inducing the ISCr by overdischarge is effective and well controlled without any mechanical deformation or the use of a foreign substance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".