Real-time Battery Capacity Estimation Based on Opportunistic Measurements
Bibliographic record
Abstract
This paper presents an approach to real-time battery capacity estimation by combining the advantages of the opportunistic zero-current states in the dynamic current profile of the battery and the knowledge of the open circuit voltage (OCV)-state of charge (SOC) curve of the battery. With the knowledge of OCV parameters, the SOC can be estimated through OCV lookup using the OCV-SOC curve. The difference in SOC between two different points is the change in Coulombs normalized by the battery capacity - this relationship is exploited to estimate the battery capacity. In the capacity estimation using the OCV-SOC curve, there are two existing approaches to OCV estimation. In the first approach, the battery is completely rested and the terminal voltage is measured; in a rested battery, the terminal voltage is treated as the OCV. In the second approach, the voltage drop is computed by estimating the equivalent circuit model (ECM) parameters of the battery; the OCV is then computed by subtracting the voltage drop from the measured terminal voltage. Both of these approaches have limitations: it takes a long time to fully rest a battery and ECM parameter estimation problem suffers form non-linearities and sub-optimal solutions as a result of that. In this paper, we propose an approach to estimate the battery capacity without the wait for complete rest of the battery or for the estimation of ECM parameters. Rather than waiting for battery rests, it is proposed to make OCV measurements whenever the current through the battery is zero. It is hypothesized in this paper that, the resulting OCV error, due to both the hysteresis and relaxation effect, can be considered zero-mean when sufficient number of measurements are taken. The proposed approach, when tested using real world battery data, show significantly accurate estimation of battery capacity. Further, it is observed that the amount of rest time before taking the OCV measurement positively correlated with capacity estimation accuracy. The standard deviation of the computed capacities immediately after zero current and after one hour of rest, relative to true capacity is 0. 3Ah and 0. 2Ah respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".