A Pattern-driven Stochastic Process for Degradation Forecasting with Applications to Rechargeable Batteries
Bibliographic record
Abstract
In recent decades, there has been significant growth in the development of rechargeable battery-powered devices such as electric vehicles, leading to an urgent demand for batteries with high reliability and quality. The end-of-life (EoL), a critical indicator of battery health, can be estimated by adaptive stochastic processes or advanced machine learning techniques. However, unless such approaches assume that the degradation path has a specific form, they operate as black boxes and are unable to provide stochastic interpretation. To address these challenges, a pattern-driven degradation process (PdDP) was executed that can model battery degradation, controlling degradation fluctuation by a GRU-driven degradation pattern. Further, a joint-learning sampling-based expectation maximization (JSEM) algorithm was developed to handle non-Markovian state transitions in estimating model parameters. Finally, a case study showed that the proposed methods outperform traditional methods with respect to both one-step and multistep-ahead prediction accuracies.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".