Performance Comparison of Open-Circuit Voltage Modelling of Li-ion Batteries at Different C-Rates
Bibliographic record
Abstract
The characterization of a battery to estimate its capacity is a crucial step in open-circuit voltage modelling. The battery capacity estimation is essential to determine diagnostic details on the battery and in determining several other battery parameters. Past research has shown that a normalized open-circuit voltage characterization independent of temperature is also dependent on accurate capacity estimation. In most works, the normalized OCV characterization approaches were done at C/30 rates where the entire data collection took approximately 60 hours. The undesirably long data collection process motivated the need to determine the expected accuracy at lower C-rates in realistic conditions. However, little attention was paid in the literature to investigate capacity estimation error at various C-rates. Thus, in this paper, the battery capacity estimation is repeated at seven C-rates: C/2, C/4, C/8, C/16, C/32, C/64 and C/128, to compare their accuracy using data collected from a laboratory-based battery cycler. It was found that with a lower current rate, a maximum of 0.3 Ah error is observed in the charge capacity. An error of 0.16 Ah was observed for the discharge capacity at the lowest C-rate of C/2 A.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".