Lithium-ion Battery Open-Circuit Voltage Analysis for Extreme Temperature Applications<sup> †</sup>
Bibliographic record
Abstract
Accurate estimation of the open-circuit voltage (OCV) as a function of state of charge (SOC) is fundamental for reliable battery management system (BMS) design in lithium-ion battery applications. However, at low temperatures, traditional low-rate OCV testing methods suffer from polarization-induced voltage drops that truncate the measured voltage range. This results in capacity underestimation and distorted OCV–SOC profiles, directly impacting SOC estimation accuracy. In this paper, we demonstrate how low-temperature conditions can severely distort the OCV-SOC models due to elevated polarization leading to premature voltage cutoffs. This paper presents a novel offsetting based correction method that extrapolates the charge/discharge curves beyond polarization-induced cutoff points to recover the full OCV span that otherwise would be lost at low temperatures. The approach is demonstrated using experimental low-rate OCV characterization data collected from Samsung EB575152 Li-ion cells from negative −25°C to 50°C. Results show that the proposed method significantly restores the usable OCV-SOC profile without requiring any modifications to the standard low-rate test protocol. By preserving complete voltage curves across a wide temperature range, this technique significantly improves the SOC estimation accuracy for battery management system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".