A Framework for Normalizing Physical Features of Li-Ion Batteries to Form a Generic Health Estimation Model
Bibliographic record
Abstract
This paper presents a framework for health estimation of a Li-ion battery that is conceptually formed based on dimensionless and common physical characteristics of a Li-ion battery. The proposed normalized features and framework enable the formation of a data-driven model to be trained using an available dataset and applied to estimate the state of health of another Li-ion battery. It is established based on introducing the new concepts of participation factors and normalized dimensionless features which are independent of a battery capacity and its dimensions. Effective training of a model using a conventional data-driven approach needs an enriched dataset that is not readily available, particularly in high-power and custom-made battery energy storage applications. The reason is that data generation needs time-consuming and expensive lifespan experiments using several samples of Li-ion batteries. To evaluate the performance of the proposed framework, four widely used Li-ion battery datasets with different capacities and dimensions are selected and compared with conventional z-score and min-max normalization methods. Test results show that the average error using the proposed method in the early stage of battery lifespan is 3.5% that is 10% less than the error in existing methods.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".