Battery Aging Dataset for 15 Minute Fast Charging of Samsung 30T Cells
Bibliographic record
Abstract
This aging dataset was designed to be used for training/parameterization and testing of machine learning and conventional filter based state of charge and state of health estimation models. A number of characterization tests (HPPC, C/20 charge discharge, etc) are applied to each cell and are followed by repeating series of drive cycle discharges and a fifteen minute fast charge. The characterization and drive cycle tests are repeated until the battery cells reach 70% SOH (around 1500 to 2000 cycles). The rate of aging for each cell is different because each cell is fast charged using a different method (standard CC/CV - same profile on two cells, boost charge - higher current at low SOC, and three pulsed charge methods). The six cells tested are brand new 3Ah Samsung INR21700-30T lithium ion battery cells. The testing was performed in a thermal chamber at 25 degrees Celsius using an Arbin battery cycler.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".