EcoCAR Mobility Challenge Electrified Powertrain System, Design, and Integration
Bibliographic record
Abstract
This paper investigates the methodology for the specification, design, simulation, and testing of a hybrid electrified parallel-through-the-road drivetrain for the EcoCAR Mobility Challenge. The performance of the prototype electric rear powertrain in terms of space optimization, mechanical robustness and vehicle fuel economy is proposed in this paper. Overview of the powertrain component design and performance simulation data is presented to validate the proposed design methodology. Finite Element Analysis (FEA) is conducted to assess the reliability and robustness of the proposed electric vehicle powertrain architecture. The addition of the rear electric powertrain shows a simulated 20% reduction in vehicle combined fuel consumption as compared to the stock vehicle. Two in-vehicle drive tests were carried out over two driving scenarios, a combined city-biased drive test, and a combined highway-biased drive test. These road tests showed the latter scenario to have a 3 miles per gallon (MPG) greater fuel economy than the former.
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 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 | 0.000 | 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".