UTILIZING COMPUTATIONAL TECHNIQUES TO DESIGN A HYBRID VEHICLE FOR THE ECOCAR MOBILITY CHALLENGE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis documents computational techniques and results used in designing a shared-mobility hybrid electric vehicle developed for the Georgia Tech EcoCAR Team, a collegiate engineering team participating in the EcoCAR Mobility Challenge. The competition challenges 12 university teams, 10 from the United States and 2 from Canada, to hybridize a 2019 Chevrolet Blazer and upfit it to SAE Level 2 autonomous operation, primarily for the Mobility-as-a-Service market. The formation and use of dynamic programming for selecting a hybrid architecture is first detailed. The architecture chosen for the competition is then introduced and a selection of custom components engineered for the vehicle is documented. These include a P4 motor mount using CNC machining and topology optimized weldments, a custom 6061-T6 aluminum fuel tank with topology optimized tabs and multiple revisions, and a high voltage A/C compressor mount made with topology optimized weldments and rubber bushings. These efforts help the Georgia Tech team to quickly make improved design decisions that increase vehicle fuel economy
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.
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 | 0.001 |
| 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 it