How do Depth of Discharge, C-rate and Calendar Age Affect Capacity Retention, Impedance Growth, the Electrodes, and the Electrolyte in Li-Ion Cells?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lithium-ion cells testing under different state of charge ranges, C-rates and cycling temperature have different degrees of lithium inventory loss, impedance growth and active mass loss. Here, a large matrix of polycrystalline NMC622/natural graphite Li-ion pouch cells were tested with seven different state of charge ranges (0-25, 0-50, 0-75, 0-100, 75-100, 50-100 and 25-100%), three different C-rates and at two temperatures. First, capacity fade was compared to a model developed by Deshpande and Bernardi. Second, after 2.5 years of cycling, detailed analysis by dV/dQ analysis, lithium-ion differential thermal analysis, volume expansion by Archimedes’ principle, electrode stack growth, ultrasonic transmissivity and x-ray computed tomography were undertaken. These measurements enabled us to develop a complete picture of cell aging for these cells. This then led to an empirical predictive model for cell capacity loss versus SOC range and calendar age. Although these particular cells exhibited substantial positive electrode active mass loss, this did not play a role in capacity retention because the cells were anode limited during full discharge under all the tests carried out here. However, the positive electrode mass loss was strongly coupled to positive electrode swelling and electrolyte “unwetting” that would eventually cause dramatic failure.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it