Theoretical Performance Bound and Its Experimental Validation of Battery Capacity Estimates in Rechargeable Batteries
Bibliographic record
Abstract
Capacity of a battery is a salient indicator of aging. Accurate estimation of capacity will help in evaluating the state of health (SOH), predicting its remaining useful life (RUL), and in determining a suitable second use application for the retired battery. The existing methods in the literature employ slow discharge, standard C-rate discharge, or curve-based approaches to estimate the battery capacity. A major drawback of these methods is that the quality of the estimate is not known. The Cramer–Rao lower bound (CRLB) defines the theoretical minimum error variance of an unbiased estimator. This article discusses several real-world uncertainties that influence the capacity estimation in batteries. Such uncertainties are taken into consideration in deriving the CRLB for the estimated capacity. Based on the performance analysis of the obtained estimates, approaches are proposed to improve the accuracy of capacity estimation. The derived performance limits help one to choose the method that best fits the quality of capacity estimation in a given application. Experimental data obtained from cylindrical Li-ion battery cells are used to demonstrate the theoretically derived quality of performance reported in this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".