Online Determination of Lithium-ion Battery State of Health based on Normalized Change of State of Temperature for e-Mobility Applications
Bibliographic record
Abstract
Accurate estimation of lithium-ion battery state of health (SOH) is essential for safe and reliable operation, especially for e-mobility applications due to dynamic variation of operating parameters. Existing SOH estimation methods primarily depend on changes in battery terminal voltage and capacity fade. Experimental and practical data demonstrated that the battery capacity fade is significantly influenced by battery operating temperature and discharge current profiles. Therefore, existing methods fail to provide accurate SOH information in practical scenarios despite excellent performance in a laboratory environment. A solution to that, an adaptive and robust SOH estimation method based on normalized change of battery state of temperature is proposed in this paper for practical applications. The proposed SOH estimation method accommodates the influence of the rate of change of battery temperature due to battery aging, making the method highly adaptive to the change in operating parameters and the rise in battery internal resistances due to aging. The proposed method is then validated using experimental data which are collected on one 21700 NMC lithium-ion battery cell under a wide range of operating conditions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".