Can One Project Cell Lifetime Using Wide Temperature Range Ultra-High Precision Coulometry?
Bibliographic record
Abstract
Using low charge voltage with Li-ion cells significantly extends their lifetime. This makes them advantageous in enabling the implementation of Li-ion batteries for widespread renewable energy storage1. Li[Ni0.83Mn0.06Co0.11]O2/graphite (Ni83/graphite) cells combined with dimethyl-2,5-dioxahexane carboxylate (DMOHC) as electrolyte solvent and lithium bis(fluorosulfonyl)imide (LiFSI) as salt, operating to upper cutoff voltages of 3.8 or 3.9 V, can achieve incredible capacity retention and reduce gassing while cycling at 85°C2. Since these cells have exceptional lifetime projected to be decades when cycling at room temperature, there is keen interest in using high temperatures to screen cell performance in an accelerated manner3. However, how to project a high-temperature lifetime to room temperature remains to be determined. Ultra-high precision coulometry (UHPC) can give valuable insight into cell degradation within weeks of cycling4. This has been applied to Ni83/graphite cells using a blend of DMOHC and diethyl carbonate (DEC) as electrolyte solvent, LiFSI as salt, and vinylene carbonate (VC) and ethylene sulfate (DTD) as additives. Figure 1 shows the results of UHPC measurements on a series of cells over a wide temperature range between 20-100°C to help determine pathways to project cell lifetime under normal use conditions. The results present exceptional performance at high temperatures and indicate a clear relation between degradation mechanisms and temperature. Remarkably, they also demonstrate similar capacity fade rates when the cycling temperature is increased from 55°C to 100°C. The potential causes of this are further elucidated by post-mortem electrolyte analysis of the measured cells. REFERENCES C. P. Aiken, T. Taskovic, and J. R. Dahn, J. Electrochem. Soc., 169, 090523 (2022). T. Taskovic, A. Eldesoky, C. P. Aiken, and J. R. Dahn, J. Electrochem. Soc., 169, 100547 (2022). T. Taskovic, A. Eldesoky, W. Song, M. Bauer, and J. R. Dahn, J. Electrochem. Soc., 169, 040538 (2022). A. J. Smith, J. C. Burns, D. Xiong, and J. R. Dahn, J. Electrochem. Soc., 158, A1136–A1142 (2011). Figure 1 . Fractional capacity fade per hour (a), fractional charge endpoint slippage per hour (b), and coulombic inefficiency (CIE) per hour (c) of Ni83/graphite cells with 1 M LiFSI in DMOHC:DEC (20:80 w/w) with 2 wt% VC and 1 wt% DTD calculated at cycle 19 of UHPC cycling at different temperatures. Notice that the y-axis is logarithmic. Figure 1
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".