Design of Half Shafts for McMaster EcoCAR EV Considering Driveline Articulation Simulations
Bibliographic record
Abstract
Integrating an electrified powertrain into a production vehicle is a complex task accompanied by an increasing number of electronic drive units being installed into vehicles. One significant aspect of the integration phase is the need for half shafts to transmit torque from the powertrain or driveline to the wheels. These shafts have a fixed set of requirements, including shaft length and angularity that must be incorporated into their design. This paper addresses the identified gap by providing a mathematical model used to find half shaft parameters. The proposed solution leverages driveline articulation simulations and returns measurements for use in manufacturing that are within the margin of error defined for the project. A 2023 Cadillac LYRIQ was integrated with an electrified powertrain, and the presented methodology was used for the design of the vehicle's half shafts. The results indicated that the mathematical model for the shaft parameters was within 98% accuracy of the required values obtained via vehicle measurement. Future work includes creating a generalized calculator that will be useful for other powertrain development projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".