Systematic Identification of Key Prognostic Features for Early-Stage Lithium-Ion Battery Degradation Prediction
Bibliographic record
Abstract
Accurate prognostics of lithium-ion batteries (LIBs) are essential for ensuring safe, reliable, and cost-effective operation in electric vehicles and grid-scale storage systems.While sequence models such as Long Short-Term Memory (LSTM) networks have shown strong predictive capability, their performance depends critically on the quality and structure of input features.Although prior studies have demonstrated the utility of statistical descriptors extracted from early-cycle data, no systematic investigation has established a reproducible and standardized feature subset suitable for benchmarking prognostic models across multiple degradation markers.This study addresses this gap by extracting 70 statistical descriptors from the first 100 cycles of voltage and current data, transforming them using multicycle statistics, and ranking their prognostic value using Pearson correlation with respect to three targets: knee onset, knee point, and end-of-life (EoL).Based on this ranking, two reduced subsets were evaluated using a stacked LSTM model under cross-validation.The results show that a Top-40 subset consistently outperforms both the full 70-feature set and a smaller Top-20 set, particularly in EoL forecasting.Because this subset is systematically derived and validated across three prognostic markers, it provides a reproducible benchmark for researchers and a practical, computationally efficient feature basis for deployment in battery management systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".