A Robust Model Order Reduction Scheme for Lithium-Ion Batteries in Control-Oriented Vehicle Models
Why this work is in the frame
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Bibliographic record
Abstract
The role of batteries in electrification of vehicles is eminent; thus, a dynamic model that represents the physics-based phenomena of the battery system at a minimum computational cost is essential in the model-based design of electrified vehicle control systems. Furthermore, robustness of the reduced-order battery model when maintaining the dominant physics-based phenomena governing the dynamic behavior of the battery system is crucial. Characterization of the power signal applied to the lithium-ion battery in the energy management controller of a plug-in hybrid electric vehicle shows that there is a dominant frequency range in the input signal to the battery. This key feature can be considered as a basis to construct a reduced-order model in which the training input is different from the original power signal. The original idea in this paper is to generate the training input by applying a low-pass filter to the white-noise random signal to maintain the same dominant frequency range observed in the original power signal. Response of the reduced-order model, constructed using the proper orthogonal decomposition, compared to the high-fidelity battery model shows promising results; a maximum relative error of 1% was obtained for the battery state of charge while simulation time was reduced by 42.9%.
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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 | 0.000 | 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 | 0.000 |
| 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