A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries
Bibliographic record
Abstract
Recently, there has been a significant growth in the development of rechargeable battery-powered devices such as electric vehicles, leading to an urgent need for reliable and safe batteries. The remaining useful life (RUL) is a critical health indicator of battery, which is defined as the remaining number of charge and recharge cycles before the state-of-health falls below a user-specified threshold under certain operating settings. Substantially, the RUL can be estimated by adaptive stochastic processes or advanced machine learning techniques. However, the existing approaches either assume over-simplified degradation pattern in accordance with physics laws leading to poor generalizability or act as a black box offering no interpretation. To address these limitations, in this article, we develop a pattern-driven degradation process by integrating a recursive Gaussian distribution with its mean learnt from a gated recurrent unit (GRU) driven degradation pattern to capture degradation fluctuation into the model. Due to the non-Markovian state transitions, a joint-learning sampling-based expectation maximization algorithm was developed to estimate model parameters based on historical observations. Finally, numerical studies using real battery data showed that the proposed method achieves over 3% and 40% higher accuracy in RUL prediction than the GRU and adaptive Wiener process, respectively.
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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 | 0.001 |
| 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".