A new control strategy for hybrid electric vehicles equipped with a continuously variable transmission
Bibliographic record
Abstract
The electric assist control strategy is one of the well-known methods for managing the power sources of hybrid electric vehicles. Because of the dependence of the electric assist control strategy approach on its parameters, optimization of this strategy can improve its performance, which results in lower fuel consumption and lower emission levels. However, one of the main concerns about optimization of this strategy is its dependence on the driving behaviour. This paper aims to propose a new control strategy based on the electric assist control strategy with a smaller number of control parameters and less dependence on the driving behaviour. This strategy is usable only for vehicles equipped with a continuously variable transmission. In this strategy, the engine’s operating point is determined by considering its best point in terms of the fuel consumption and the emissions, and also the high-efficiency region of the powertrain. After optimization of the proposed strategy and the electric assist control strategy approach, the methods are compared. It is shown that the proposed strategy provides a better performance in terms of the fuel consumption and the emissions during all the considered driving cycles. Moreover, it is demonstrated that the performance of the proposed strategy has less dependence on the driving-cycle pattern because fewer parameters are used than in the electric assist control strategy. This feature makes the devised control strategy desirable for real-world conditions where the vehicle undergoes various driving patterns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".