Multi-Fidelity Near-Optimal on-Line Control of a Parallel Hybrid Electric Vehicle Powertrain
Bibliographic record
Abstract
On-line optimal control is a crucial issue in the development of hybrid electric vehicles (HEVs). In this paper, optimal on-line control policies for a parallel HEV are firstly derived from off-line optimization. Then, two plant models with different grades of fidelity are considered for the on-line forward HEV simulation. An optimal calibration methodology is proposed to adapt the control policies previously extracted to the specific plant model. A multi-fidelity procedure can thus be established in adopting a low-fidelity plant model to extract a first estimation of optimal on-line control policies, while subsequently refining them through an high-fidelity plant model. Obtained results demonstrate the effectiveness of the proposed approach and measure the impact of the considered model fidelity level on the fuel consumption estimation, the computational time required and the specifically extracted control rules.
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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 | 0.000 |
| 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".