On real-time optimal control of a series Hybrid Electric Vehicle with an ultra-capacitor
Bibliographic record
Abstract
To design a supervisory plan for a Hybrid Electric Vehicle (HEV), different methods are presented in the literature. In many of these controllers, there are a few parameters that must be tuned according to future driving conditions. To address this issue, a novel feedback controller is introduced in this paper that serves as the supervisory plan of a series HEV, and does not require the exact knowledge of the drive cycle. To find this controller, first a mathematical model of the hybrid drivetrain is developed, then Pontryagin's Minimum Principle is applied assuming that the drive cycle is known in advance. Based on the mechanism of the optimal control, a set of mathematical rules is extracted, and an optimal feedback controller is designed. It is also shown that a priori knowledge of the future drive cycle is not required; it is possible to tune the controller parameters, knowing only the cruise time and the available negative energy during braking.
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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".