Specification of a P3 Parallel Hybrid Electric Vehicle Architecture for the EcoCAR 3 Competition
Bibliographic record
Abstract
The Georgia Tech EcoCAR 3 team’s selection of a parallel hybrid electric vehicle (HEV) architecture for the EcoCAR 3 competition is presented in detail, with a focus on the team’s modeling and simulation efforts and how they informed the team’s architecture selection and subsequent component decisions. EcoCAR 3, sponsored by the United States Department of Energy and General Motors, is the latest in a series of Advanced Vehicle Technology Competitions (AVTCs) and features 16 universities from the United States and Canada competing to transform the 2016 Chevrolet Camaro into a hybrid electric American performance vehicle. Team vehicles will be scored on performance, emissions, fuel economy, consumer acceptability, and more over the course of the four-year competition. During the first year, the Georgia Tech team considered numerous component combinations and HEV architectures, including series RWD and AWD, parallel, and power-split. Using model-based design to predict performance, fuel economy, and emissions metrics, as discussed in detail, the team iteratively decided upon a post-transmission parallel architecture with packaging, cost, and complexity in mind. Due to its modularity and packaging freedom, the architecture chosen has potential for applications in cost-effective hybridization of RWD vehicles, though best performance from the electric machine is limited by maximum speed considerations due to its post-transmission location and single-speed coupling to the drivetrain. The proposed vehicle is predicted to have a fuel economy of 36 MPGge, 18 miles of Charge Depletion Mode driving (in which the internal combustion engine only provides propulsion for high acceleration demands), and an Initial Vehicle Movement-to-60 mph acceleration time of 7.5 seconds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".