Improving Precision and Accuracy in Coulombic Efficiency Measurements of Lithium Ion Batteries
Bibliographic record
Abstract
Lithium-ion batteries have been used extensively over the past two decades in the\nportable consumer electronics industry. More recently, Li-ion batteries have become\ncandidates for much larger-scale applications such as electric vehicles and energy grid\nstorage, which impose much more stringent requirements on batteries, especially in terms\nof cell lifetime. In order to develop batteries with improved lifetimes, a means of quickly\nand accurately evaluating battery life is required. The use of coulombic efficiency (CE)\nis an important tool in this regard, which provides a way to quantify parasitic reactions\noccurring within the cell. As more stable battery chemistries are developed, the rates of\nparasitic reactions occurring in the cell become reduced, and differences in CE among\ncells become increasingly smaller. In order to resolve these differences, charger systems\nmust be developed which can measure CE with increased precision and accuracy.\nThis thesis investigates various ways to improve the precision and accuracy of CE\nmeasurements. Using the high-precision charger (HPC) at Dalhousie University (built in\n2009) as a starting point, a new prototype charger was built with several modifications to\nthe design of the existing HPC. The effect of each of these modifications is investigated\nin detail to provide a blueprint for the development of next-generation charger systems.\nThis prototype charger shows greatly improved precision and accuracy, with CE results\nthat are approximately four times more precise than those of the existing HPC and over\nan order of magnitude more precise than high-end commercially available charger\nsystems
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".