Editors' Choice—Hindering Rollover Failure of Li[Ni<sub>0.5</sub>Mn<sub>0.3</sub>Co<sub>0.2</sub>]O<sub>2</sub>/Graphite Pouch Cells during Long-Term Cycling
Bibliographic record
Abstract
Eventual rapid capacity loss or "rollover" failure of lithium-ion cells during long-term cycling (>3000 cycles in many cases) at room temperature was studied with Li[Ni0.5Mn0.3Co0.2]O2/graphite pouch cells. The effects of positive electrode material coating, electrolyte additives, upper cutoff voltage, LiPF6 concentration, cell rest periods, electrode thickness, graphite type and electrolyte solvent formulation were probed. Cells were tested under 1C charge and discharge with "rate maps" (discharges at C/20, C/2, 1C, 2C, 3C) applied every 100 cycles. The loss of high rate capability (3C) is shown to be an early warning of impending rollover failure. Electrochemical impedance spectroscopy (EIS) and studies of symmetric cells made using electrodes from disassembled cells demonstrate that impedance growth at the positive electrode and associated DC resistance growth is responsible for rollover failure in these cells. Ultra-high precision coulometry (UHPC) shows that cells that were charged to higher voltages, which increase the rate of electrolyte oxidation, or show higher rates of electrolyte oxidation at the same cutoff potential due to changes in electrolyte formulation, normally are more prone to eventual rollover failure. In order to avoid rollover and extend the cycling life of Li-ion cells, it is important to choose optimal cell chemistries, some of which are enumerated in this report.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".