A Generalizable Method for Capacity Estimation and RUL Prediction in Lithium-Ion Batteries
Bibliographic record
Abstract
Data-driven methods have attracted much attention in capacity estimation and remaining useful life (RUL) prediction of lithium-ion batteries. However, existing studies rely on complex machine learning models (e.g., Gaussian process regression, neural networks, and so on.) that are applicable to specific observed operating conditions, and the prediction accuracy can be affected by different usage scenarios. This paper proposes to adopt a linear and robust machine learning technique, partial least-squares regression, for battery capacity estimation, and RUL prediction based on the partial incremental capacity curve. The features can be easily obtained by interpolation of the measured charging profiles without data smoothing, and the bootstrapping technique is used to give confidence intervals of the predictions, which helps to evaluate the robustness and reliability of the model. The proposed method is validated on three battery data sets with different operating conditions provided by NASA. We train the model on one battery and test its performance on the other two batteries without changing the model weights. Experimental results show that the suggested classical method exhibits greater generalizability compared to complex and sophisticated methods proposed in the literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".