A Novel Pattern- driven Stochastic Process for End-of-Life Forecasting
Bibliographic record
Abstract
In recent decades, there has been significant growth in the development of rechargeable batterypowered devices, such as electric vehicles, leading to a huge demand for batteries with high reliability and quality.End of life (EoL) is a critical indicator of battery health and can be estimated by either adaptive stochastic processes or advanced machine learning techniques.However, these approaches follow the degradation path that can be modelled as simple mathematical form such as linear or exponential function and lack interpretability due to its black-box nature.To address these shortfalls, an GRU-driven degradation process is proposed to learn complex battery degradation patterns, in which degradation progression is controlled by a recursive Gaussian distribution with its mean learnt from an GRU-driven degradation pattern.Due to the non-Markovian state transitions, a joint-learning sampling-based expectation maximization algorithm is developed to estimate model parameters based on historical observations.To validate the superiority of the proposed methods, a case study of battery data was implemented.The results show a better performance with respect to EoL accuracy than that achieved with traditional methods.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".