Investigating the effect of engine noise on power management strategy of a hybrid electric vehicle
Bibliographic record
Abstract
Hybrid electric vehicles (HEVs) have been developed as a promising way to decrease the fuel consumption and emissions of conventional vehicles. Although the noise emission of HEVs is generally lower than that of conventional vehicles, it is still an issue, especially in urban transportation. In this paper, a power management strategy is developed to minimize the annoying noise of the engine for an HEV. This is a modified version of the strategy that was originally established based on the speed ratio of continuously variable transmission (CVT) as the control parameter (CVT-based strategy). The engine combustion noise is assessed using the experimental data of the in-cylinder pressure. Also, the engine brake specific fuel consumption (bsfc) is defined from the experiments. The bsfc and noise data are implemented in the power management strategy. The proposed strategy offers a better performance in terms of reducing engine noise and fuel consumption in comparison with an electric assist control strategy (EACS). On the other hand, the proposed strategy results in a lower level of engine noise than the original CVT-based strategy at the expense of slightly increasing the fuel consumption. For instance, the noise level (dB) in an urban dynamometer driving cycle using the proposed strategy is 49% lower than the case of CVT-based strategy, while the vehicle FC is about 1.1% more than the CVT-based case.
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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) | 0.000 | 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) | 0.001 | 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".