Design of an Eddy Current Torque Bypass Clutch for Seamless Automated Manual Transmissions of Electric and Hybrid Vehicles
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper presents the design and experimental validation of an eddy current torque transfer clutch for use inside Automated Manual Transmissions (AMTs) to perform seamless gear upshifts. Electric vehicles (EVs) with a single-ratio gearbox may provide high levels of smoothness, but using a multi-speed gearbox provides significant benefits in terms of vehicle acceleration, top speed, powertrain cost, mass, and energy consumption. AMTs can provide smooth shifts without torque interruption when coupled to a normally-open torque bypass clutch. However, conventional dry friction clutches are not best suited for such torque bypass due to wear and controllability concerns, while wet clutches would decrease powertrain efficiency due to viscous losses. An eddy current clutch would be highly controllable, simple to manufacture, low-cost, robust, and do not wear compared to friction clutches. The potential of eddy current clutches is assessed from a representative case study consisting of a ∼190 Nm clutch for a seamless two-speed AMT. The clutch design is based upon Wouterse's model, which is further refined using a Finite Element Analysis (FEA) on the clutch magnetic circuit. A prototype clutch is built and installed inside an existing AMT. The measured clutch coupling torque agrees well with Wouterse's model results, with a maximum error of 6%. The AMT performed seamless gear upshifts on a test bench thanks to the eddy current clutch, which confirms their viability for torque bypass.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".