Data-driven prognostic techniques for estimation of the remaining useful life of lithium-ion batteries
Bibliographic record
Abstract
This paper aims to study the use of various data-driven techniques for estimating the remaining useful life (RUL) of the Li-ion batteries. These data-driven techniques include neural networks, group method of data handling, neuro-fuzzy networks, and random forests as an ensemble-based system. These prognostic techniques make use of the past and current data to predict the upcoming values of the capacity to estimate the remaining useful life of the battery. This work presents a comparative study of these data-driven prognostic techniques on constant load experimental data collected from Li-ion batteries. Experimental results show that these data-driven prognostic techniques can effectively estimate the remaining useful life of the Li-ion batteries. However, the random forests and neuro-fuzzy techniques outperform other competitors in terms of the RUL prediction error and root mean square error (RMSE), 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 | 0.001 |
| 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 | 0.001 | 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".