Improving the Lifetime and Cycle Life of NMC/Graphite Li-Ion Cells Charged to 4.4 or 4.5V
Bibliographic record
Abstract
Significant extra capacity and energy density can be obtained from NMC/graphite cells by increasing the upper voltage cutoff from 4.2V to 4.5V. However, when this is done, parasitic reactions between the charged NMC electrodes and the electrolytes accelerate and cell lifetime is compromised. In this lecture, I will review recent work in our laboratory that has focussed on increasing the lifetime of NMC/graphite Li-ion cells charged to 4.4 or 4.4V. This work has centered around: 1) Quantifying parasitic reactions using Ultra High Precision Coulometry, Isothermal Battery Microcalorimetry and Automated Cycling/Impedance Spectroscopy measurements; 2) Reducing the rate of parasitic reactions through the use of electrolyte additives; and 3) Using surface science techniques, e.g. XPS, to understand how these electrolyte additives function. The results of this work are NMC622/graphite, NMC532/graphite, NMC422/graphite and NMC111/graphite cells which have improved properties for high voltage use. This work has been carried out by a large number of graduate students and post doctoral fellows including Dr. Mengyun Nie, Dr. Jian Xia, Dr. Lenaic Madec, Dr. David Hall, Dr. David Yaohui Wang, Lin Ma, Kathlyne Nelson, Laura Downie, Remi Petibon, Julian Self, Leah Ellis, Deijun Xiong and Chris Burns. Collaborations with researchers at 3M Company, including Dr. Ang Xiao, Dr. Bill Lamanna and Dr. Kiah Smith have been vital for this work. The authors acknowledge Dr. Jing Li of BASF for supply of many of the electrolyte solvents and additives used in these studies. Dr. Yong-Shou Lin and Dr. David Wang of ATL Battery Co. are acknowledged for the supply of some of the dry pouch cells used in these studies. The authors thank the Natural Sciences and Engineering Research Council of Canada and 3M Canada Company for funding of this work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".