Noise and Vibration in Switched Reluctance Motors: A Review on Structural Materials, Vibration Dampers, Acoustic Impedance, and Noise Masking Methods
Bibliographic record
Abstract
Today, the majority of the commercial electrified vehicles use Interior Permanent Magnet Motors (IPMSMs) for propulsion. An IPMSM can deliver high starting torque and high efficiency in the low- and medium-speed operation, which makes it attractive for propulsion applications. However, IPMSMs generally utilize rare-earth permanent magnets and there are growing concerns about the price volatility and supply chain of these materials. Switched Reluctance Motors (SRMs) can potentially replace IPMSMs in various applications including propulsion. An SRM has a simple and low-cost construction, and it can provide reliable operation at high-speed and high-temperature conditions. Compared to IPMSMs, SRMs radiate considerably higher acoustic noise, which have historically hindered their widespread acceptance. Various approaches to mitigate acoustic noise and vibration at the source level have already been explored in the literarture by improving the electromagnetic design and current control. This paper explores multiple noise and vibration mitigation methods that can be applied at the transmission stage. First, the noise comparison between the Internal Combustion Engine (ICE) and SRMs is discussed, and then the methods used for ICEs are presented for their applicability to SRMs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".