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 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 | 0.001 | 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 | 0.000 |
| 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".