Fast Offline Battery Capacity Estimation Approach With Performance Bounds
Bibliographic record
Abstract
Capacity of a battery is a measure of the amount of charge, in Ampere hours (Ah), stored in the battery. With the battery ageing processes, the capacity of the battery reduces. Consequently, it is essential to have an accurate knowledge of battery capacity in order to perform crucial battery management tasks. For example, in the field of battery reuse, the estimated residual capacity of batteries will help in determining a suitable secondary application for the retired battery pack. Existing literature on offline capacity estimation are often based on low-discharge or 1C rate discharge of the battery. This means that a minimum of one hour wait is required before an estimate of capacity is made. In this paper, a novel approach is proposed for fast offline estimation of battery capacity using the knowledge of the open-circuit voltage (OCV) - state of charge (SOC) curve of the battery. The proposed approach consists of a novel method to estimate the battery OCV by applying a current profile that is optimized to reduce the uncertainty in OCV estimation. For capacity estimation, a constant current pulse is applied for a short duration. The capacity estimation accuracy is theoretically derived as a function of this current duration. The proposed fast capacity estimation approach is shown to be able to estimate the battery capacity within as short as 1 minute duration with an estimation error standard deviation of 0.02 Ah.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".