Improving Precision and Accuracy in Coulombic Efficiency Measurements of Li-Ion Batteries
Bibliographic record
Abstract
In order to develop Li-ion batteries with improved lifetimes, a means of quickly and accurately estimating battery life is required. The use of coulombic efficiency (CE) is an important tool which provides a way to quantify parasitic reactions occurring within cells. As more stable battery chemistries are developed, the rates of parasitic reactions become smaller and differences in CE among cells with different electrolyte additives become increasingly smaller. In order to resolve these differences, charger systems must be developed which can measure CE with increased precision and accuracy. This paper investigates various ways to improve the precision and accuracy of CE measurements. Using the high-precision charger (HPC) at Dalhousie University (built in 2009) as a starting point, a new prototype charger was built with several modifications to the design of the existing HPC. The effect of each of these modifications is investigated in detail to provide a blueprint for the development of next-generation charger systems. This prototype charger shows greatly improved precision and accuracy, with CE results that are approximately four times more precise than those of the existing HPC and over an order of magnitude more precise than high-end commercially available charger systems.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".