A Wide Range of Testing Results on an Excellent Lithium-Ion Cell Chemistry to be used as Benchmarks for New Battery Technologies
Bibliographic record
Abstract
We present a wide range of testing results on an excellent moderate-energy-density lithium-ion pouch cell chemistry to serve as benchmarks for academics and companies developing advanced lithium-ion and other "beyond lithium-ion" cell chemistries to (hopefully) exceed. These results are far superior to those that have been used by researchers modelling cell failure mechanisms and as such, these results are more representative of modern Li-ion cells and should be adopted by modellers. Up to three years of testing has been completed for some of the tests. Tests include long-term charge-discharge cycling at 20, 40 and 55°C, long-term storage at 20, 40 and 55°C, and high precision coulometry at 40°C. Several different electrolytes are considered in this LiNi 0.5 Mn 0.3 Co 0.2 O 2 /graphite chemistry, including those that can promote fast charging. The reasons for cell performance degradation and impedance growth are examined using several methods. We conclude that cells of this type should be able to power an electric vehicle for over 1.6 million kilometers (1 million miles) and last at least two decades in grid energy storage. The authors acknowledge that other cell format-dependent loss, if any, (e.g. cylindrical vs. pouch) may not be captured in these experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".