Cell Chemistry Considerations for Long-Lived Li-Ion Cells at High Temperatures
Bibliographic record
Abstract
This study aimed to improve the high-temperature performance of Li-ion cells with liquid electrolyte to enable long lifetimes with wide operable temperature ranges. Three different positive electrode chemistries (Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 , Li[Ni 0.83 Mn 0.06 Co 0.11 ]O 2 , Li[Ni 0.6 Mn 0.4 Co 0.0 ]O 2 ), six different electrolyte solvent blends (combining ethylene carbonate (EC), dimethyl carbonate, ethyl methyl carbonate (EMC), diethyl carbonate (DEC), and dimethyl-2,5-dioxahexane carboxylate), and 10 different electrolyte additive combinations (combining vinylene carbonate (VC), ethylene sulfate (DTD), prop-1-ene-1,3-sultone, and tris(trimethylsilyl) phosphite) were cycled at C/3 and 85 °C. Selected electrode and electrolyte combinations were cycled at C/3 and 20 °C. The charge transfer resistance of all cells was measured at 10 °C. It was determined that gas production in the pouch cells increases with the Ni content in the positive electrode. The most optimal electrolyte solvent combination is EC:DEC. EC:EMC possibly enhances low-temperature performance without significant lifetime cost at 85 °C. Lowering the EC content in Ni83 cells reduces gas production and extends lifetime. The best-performing electrolyte additive combination was 2 wt% VC 1 wt% DTD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".