Thermal-electric modelling and multi-state joint parameter identification of lithium-ion batteries
Bibliographic record
Abstract
Abstract This paper proposes a model for the characterization of thermal-electric properties of lithium-ion batteries. The model achieves high accuracy estimation of the temperature and voltage-current characteristics of the battery considering thermal electric coupling. A second-order equivalent circuit model and a lumped-parameters thermal model are developed to capture the electric and temperature characteristics of the cell separately. A polynomial product equation is proposed to capture the relationship between battery temperature and state of charge for the purpose of describing the interactions of thermal and electric parameters during the charging/discharging processes. Experimental data of an NCR18650B lithium-ion battery cell are gathered in laboratory tests and used for parameter estimation of the model built in the paper. Compared with the existing thermal-electric coupling models that rely on a look-up table, the model proposed achieves better accuracy in terms of temperature and voltage-current characteristics estimation.
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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 | 0.000 | 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".