Model‐based optimal parameter identification incorporating C‐rate, state of charge and temperature effect for advance battery management system in electric vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The battery management system in electrified transportation requires an accurate battery model for online state estimation of the battery. The parameters of the battery model depend upon state of charge, C‐rate, and temperature. A detailed battery model defined by 31 polynomial coefficients is used for determination of battery parameters. The parameter estimation is formulated as an optimisation problem and six different meta‐heuristic optimisation techniques are utilised for solving it. The efficiency of optimisation techniques is compared in terms of solution quality, computation efficiency, and convergence characteristics. Further, their performance is analysed statistically using parametric ( t ‐test) and non‐parametric tests (Wilcoxon test). The parameters values estimated by applying optimisation techniques are cross‐validated with value of parameters extracted using standard constant‐current pulse charge–discharge test to establish the effectiveness of the proposed approach.
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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 | 0.001 | 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 it