Influence of Open Circuit Voltage Measurement on Battery Voltage Modeling
Bibliographic record
Abstract
Accurate State of Charge (SOC) - Open Circuit Voltage (OCV) relationship plays a significant role in battery modeling. This paper compares two methods to obtain the SOCOCV relationship: low-rate constant current ($\mathrm{C} / 20$) testing and Galvanostatic Intermittent Titration Technique (GITT). While GITT tests typically use rest periods of 1-2 hours, the optimal duration remains unclear, especially at low SOC levels and lower temperatures. This research investigates the optimal rest period required for GITT tests at 25° C and 40° C by analyzing voltage relaxation rates$d V / d t$at various intervals after the current pulse. Experiments were conducted with relaxation periods ranging from 5 minutes to 4 hours across the full SOC range. The resulting SOC-OCV relationships were then used with parameters obtained from Hybrid Pulse Power Characterization (HPPC) test data to develop second-order Thevenin equivalent circuit model (ECM). Model performance was evaluated using constant current profiles and dynamic drive cycles, demonstrating the impact of different methods of OCV measurement on the accuracy of the prediction. Results indicate that SOC-dependent relaxation periods optimize the balance between test duration and measurement accuracy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".