Global modeling and control strategy simulation
Bibliographic record
Abstract
To optimize the operation of internal combustion engine (ICE), maximize fuel economy, and minimize emissions, many novel traction schemes have been developed. Among those, an electromechanical converter known as electric variable transmission (EVT) was presented in, which enables a continuously variable transmission (CVT), starter, and generator. It is especially suitable for hybrid electric vehicles (HEVs) as a series- and parallel-hybrid or a split-power hybrid transmission system. Similar designs could be found in with emphasis on either the design of machine structure and cooling or the analysis of electromagnetic field coupling. However, to successfully use EVT in HEVs, it is necessary to study the vehicle power flows and EVT control method to satisfy vehicle performance and optimize operation of subsystems. Besides, the EVT design specifications, such as rated power, rated torque and rated speed, are also closely related to the vehicle control target and control strategy.The objective of this article is to provide a control strategy for an HEV using an EVT. A global modeling for an EVT equipped HEV is needed to develop control strategy. Energetic macroscopic representation (EMR) is used to model such a complex system. It is a graphical tool (see "Synoptic of EMR") suitable for modeling and control of complex electromechanical systems. Using EMR, the interconnection of subsystems is organized according to the physical causality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".