Interpretable Data-Driven Capacity Estimation of Lithium-ion Batteries
Bibliographic record
Abstract
Battery degradation poses a significant challenge for the usage of Lithium-ion batteries, making accurate capacity estimation crucial for efficient operation. Data-driven approaches hold promise for addressing this task, yet their complex structures often lead to overfitting and obscure the decision-making process. The objective of this work is to build a robust and interpretable model for capacity estimation. We propose the utilization of a robust decision tree-based ensemble model, extremely randomized trees (ERT), to accurately estimate battery capacity based on the features extracted from the partial charging curve. The random splits in the tree construction process enhance the model’s generalization ability. Given that the combination of multiple decision trees reduces interpretability, we further employ SHAP to interpret the contributions of each feature to the ERT model’s predictions. The effectiveness of the proposed method is validated on a large cycling dataset of Lithium-ion batteries.
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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 | 0.001 |
| 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".