Optimization of Novel Corner Module for Urban Electric Vehicle
Bibliographic record
Abstract
With urban populations on the rise, sustainable design of cities will be necessary to maintain reasonable quality of life for its inhabitants. Space to accommodate citizens in these densely populated cities will be in short supply and high demand. Strategic shifts in the transportation industry can alleviate the lack of space for residential and commercial facilities in densely populated areas. One opportunity to mitigate this growing problem is to reduce the size of personally owned, commuter vehicles. Smaller vehicles will reduce the storage space and increase the density of vehicles on roads. Another solution gaining traction in the automotive industry today are autonomous vehicles. Autonomous technology can allow cars to travels closer to one another without increasing the likelihood of a crash. Lastly, changing the market from personally owned vehicles to fleets owned by the company to be used as public transportation would reduce the traffic density. These changes to the automotive industry will facilitate a change in the layout and packaging of commercial vehicles to meet new objectives. This paper proposes a novel corner module design that meets the market’s needs for mass production of X-by-wire systems integrated into a compact space while maintaining current levels of vehicle stability, handling and ride comfort. The proposed corner module features an in-wheel motor with electronic steering and braking. To increase the handling of the vehicle, the corner module has active camber control and can be modified for active ride height adjustment. Furthermore, the simplicity and minimal quantity of the components makes the corner module design ready for mass production. The geometry of the purposed corner module was optimized using a genetic algorithm. The objectives were to target a wheel lateral displacement of 10 cm at the −15° of camber angle and to minimize the longitudinal displacement of the wheel in a steer range of −20° to 20° at 0° of camber angle. The optimization had three types of constraints: packaging space limits, component interference and cylinder size. The optimization successfully found a solution that met both objectives while remaining within the constraints. The workspace of the wheel was limited by the rear cylinder size and the fixed length of the linkage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".