Multi-step-ahead prediction techniques for Lithium-ion batteries condition prognosis
Bibliographic record
Abstract
This paper focuses on the use of different multi-step prediction techniques for long-term prognosis of the Lithium-ion batteries condition. Various inductive algorithms including adaptive neuro-fuzzy inference systems, random forests, and group method of data handling are used along with three strategies for multi-step prediction and prognosis. These prediction strategies including iterative, direct, and DirRec schemes make use of the historical and current data in different manners to forecast the future values of the capacity over a long horizon for estimation of the remaining useful life (RUL) of the Li-ion batteries. These multi-step predictors are trained by means of constant current Li-ion battery datasets. The attained results present the effectiveness of these techniques for the long-term prognosis of the RUL of the batteries. Besides, a statistical analysis of the attained results indicates that the RF predictor outperforms other techniques.
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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".