A Unified Piecewise Modeling Framework for Battery Knee Point Detection and State of Health Estimation
Bibliographic record
Abstract
The knee point in battery capacity curves signifies the onset of accelerated capacity degradation. This phenomenon leads to markedly different degradation rates and patterns before and after the knee point, affecting the accurate estimation of the battery state of health (SOH). To address this challenge, we propose a unified piecewise modeling framework for simultaneous knee point detection and SOH estimation. First, knee point detection is formulated as a binary classification problem for each cycle. A convolutional neural network (CNN) is employed to construct a classification model for knee point detection, utilizing informative features extracted from capacity-voltage(QV) curves. Subsequently, two Elastic Net-based models are respectively trained to estimate the SOH for cycles occurring before and after the knee point. Finally, the SOH for a given cycle is estimated using a weighted sum of the predictions from these two Elastic Net models, where the weights are determined by the probability of class membership output by the CNN classification model. A case study demonstrates that the proposed piecewise strategy and models achieve superior results with an average accuracy of 94.5% in identifying knee points and estimating SOH with an average root mean squared error (RMSE) of 0.014.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".