A Fast OCV Characterization Approach for Battery Reuse Applications
Bibliographic record
Abstract
This paper considers the problem of open circuit voltage (OCV) to state of charge(SOC) characterization in rechargeable batteries for battery reuse applications. The traditional approach to OCV-SOC characterization is done by collecting voltage and current data through a slow discharge and charge process; this process usually takes about 60 hours. Such OCV-SOC characterization is performed on a few sample batteries because the OCV-SOC characterization is the same for new batteries coming out of the same manufacturing process. However, the characteristics of a battery may change as it is used for years in different environmental and usage conditions. Hence, they may need to be re-characterized before secondary use. Unlike primary characterization, secondary characterization may have to be done faster in order to save time and cost. This paper presents a new and faster approach for OCV-SOC characterization. The proposed approach in this paper consists of constant current profiles that halve in magnitude after a specified time. Such reducing current magnitude allows for fully depleting the battery; similarly, the battery is charged back with a reducing current profile in order to make sure the battery is fully charged back. The resulting current profile reduces the total characterization time by 1/5. It was hypothesized that the changing current magnitude may result in hysteresis voltage bias. For this, a new OCV modelling approach consisting of separate resistance estimation at each pulse was developed. The proposed approach was tested using data collected from four cylindrical Li-ion batteries. Compared to the traditional OCV modelling approach, the proposed approach results in 3% of SOC error and takes 20% of the time.
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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 | 0.002 |
| 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 | 0.001 | 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".