Numerical and Experimental Investigations of Noise and Vibration Characteristics for a Dual-Motor Hybrid Electric Vehicle
Bibliographic record
Abstract
This paper investigates the abnormal noise and vibration of a hybrid electric vehicle during the electric-only driving mode. The sources of the noise and vibration are first identified through the frequency analysis, and then several measures are implemented to reduce the noise and vibration level. The experimental results have identified the gear meshing in the compound planetary gear set is the main source of the noise and vibration. The theoretical analysis demonstrates that gear pairs of the short planet-small sun gear and short planet-long planet in the compound planetary gear set contribute to the main noise and vibration of the hybrid system in the electric-only driving mode. This research also shows that the gear-meshing noise level in the compound planetary gear set can be decreased significantly by matching the engagement parameters, such as meshing stiffness, tooth error, and pitch error. The observation results in this paper are able to provide a practical reference for improving the ride comfort of hybrid electric vehicles in the future study.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".