Lithium-ion Differential Thermal Analysis Studies of the Effects of Long-Term Li-ion Cell Storage on Electrolyte Composition and Implications for Cell State of Health
Bibliographic record
Abstract
Li-ion cells being developed for long lifetime applications are often subjected to storage tests at various states-of-charge and various temperatures. Storage is interrupted from time to time for reference performance tests so that cell capacity and impedance can be checked. These reference performance tests give no information about any compositional changes that may have occurred in the electrolyte. Lithium-ion differential thermal analysis applied to cells after years of storage can be used to determine if the electrolyte has changed significantly due to unwanted reactions with the electrode materials or if little to no change has occurred. Here, Li-ion differential thermal analysis is used to study electrolyte changes in a more-or-less “yes/no” manner for single crystal NMC532/graphite cells stored between 3.67 and 4.3 V at 20, 40 and 55 °C for up to five years. Such measurements can be used to give confidence about lifetime predictions. Several such cells are detailed here, with correlation between degree of cell degradation and degree of change in electrolyte composition. Relationships are shown between degradation and evolution of state of electrolyte in elevated temperature and voltage storage experiments.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".