Design and Experimental Demonstration of a Seamless Automated Manual Transmission using an Eddy Current Torque Bypass Clutch for Electric and Hybrid Vehicles
Bibliographic record
Abstract
Electric Vehicles (EVs) with single-ratio gearbox provide high levels of smoothness, but using multi-speed gearbox can provide significant benefits in terms of vehicle acceleration, top speed, powertrain cost, mass, and energy consumption. In particular, Automated Manual Transmissions (AMTs) have characteristics of smooth shifts without torque interruption when coupled to a torque bypass clutch. However, conventional friction clutches are not best suited as torque bypass clutches because of their limited controllability and because large amount of heat must be dissipated to slow down the motor during gearshifts. This paper studies the feasibility of a seamless AMT architecture for EVs and Hybrid Electric Vehicles (HEVs) using an eddy current torque bypass clutch that is highly controllable, robust, low cost, and has no wearable parts. A system-level study using a backward-facing model is used to assess the advantages of multi-speed gearboxes for EVs in terms of energy consumption, powertrain cost, and mass. A full-scale clutch prototype is designed for a custom two-speed AMT of an 80 kW EV. Numerical simulations show that a two-speed gearbox can improve energy consumption by 7.2 % for a compact class EV such as the Nissan Leaf. Moreover, using two gear ratios allows a downsizing of the electric motor, which could reduce powertrain cost and mass by 23.1 % and 15.6 %, respectively. The clutch is characterized experimentally and its maximum torque agrees well with analytical results (within 6 %). The prototype performed a seamless shift on a test bench demonstrating the viability of using an eddy current clutch for torque bypass clutches in AMTs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".