Noise and Vibration Phenomena of On-Line Electric Vehicle<sup>®</sup>
Bibliographic record
Abstract
It is a global research and development trend to introduce electric vehicle into the market in a prompt manner; however, there have been technological issues with batteries, or in general, an energy storage technology in moving vehicles. KAIST, a globally leading university majoring in science and technology in Korea, has been developing a break-through wireless power transfer technology by applying inductive power transfer technology, as demonstrated in a public park in March, 2010, which is referred to as “OLEV- On-line Electric Vehicle.” With the technology, it is possible to drive the electric powertrain and charge its battery simultaneously while the vehicle is in operation on the road. In this paper, a couple of specific noise and vibration phenomena are introduced which have been observed during the development phase of the proto-type of test vehicle. This noise issue became noticeable because the customers' expectations on noise and vibration levels of the electric vehicles are much higher than those on conventional Diesel or CNG vehicles. In addition, the noises from other sources, such as power electronic components or auxiliary equipment, became more audible because these noises were masked by the IC-engine operation in conventional vehicles. There were two noise phenomena identified, ‘high pitch whine’ and ‘back buzz.’ In order to understand the root-causes of the noise and vibration, a series of test plans was prepared and performed applying the fundamental ‘source-path-radiator’ model. With the understanding on the issues, it was possible to develop a series of recommended actions, which was very helpful in developing noncontact charging vehicle, especially in deciding how to design and implement the noise path isolation from the motor room of the vehicle or from the power electronics compartments. Thus we present the noise and vibration phenomena, root-cause analysis and possible remedies of those two noises and vibration issues in this paper. For the high pitch whine, the reduction was noticeable by changing the switching frequency of the buck converter as identified from the test results.
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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) | 0.000 | 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) | 0.002 | 0.001 |
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".