An Offsetting-Based Correction to Improve the Accuracy of Low-Rate OCV Curves for Lithium-Ion Batteries
Bibliographic record
Abstract
Accurate modeling of the open-circuit voltage (OCV) as a function of state of charge (SOC) is critical for battery management systems (BMS) in lithium-ion battery applications, particularly under varying temperature conditions. Traditional low-rate OCV testing methods, while simple and widely adopted, suffer from limitations at low temperatures due to increased internal resistance. This leads to premature termination of charging and discharging processes, causing inaccuracies in the resulting OCV-SOC curve. In this paper, we propose a novel offsettingbased correction method that extends the charge and discharge voltage curves by extrapolating terminal voltage differences at the SOC limits. This approach restores the full intended OCV range (3.0V to 4.2V) across all tested temperatures without requiring modifications to standard test protocols. The results highlight its potential to improve BMS performance by enabling more accurate OCV characterization, especially for temperatureaware battery modeling.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".