Intelligent control system for improving the efficiency of a series hybrid for the EcoCAR 2 challenge
Bibliographic record
Abstract
Embry-Riddle Aeronautical University (ERAU) is participating in EcoCAR 2: Plugging in to the Future [1], an advanced vehicle competition run by Argonne National Labs and sponsored by General Motors and the United States Department of Energy. The competition challenges 15 schools from the United States and Canada to design and implement the most efficient hybrid vehicle architecture. As part of the EcoCar Challenge, ERAU has been developing the Intelligent Driver Efficiency Assistant (IDEA), an advanced predictive supplementary control system for the university's EcoCAR 2 competition vehicle. The goal of the IDEA system is to minimize the emissions and energy consumption of ERAU's hybrid-electric vehicle through the application of artificially intelligent, and adaptive and predictive control system theory. These predictions allow IDEA to place its control system in the best possible mode of operation to minimize overall vehicle emissions and energy consumption. This paper discusses the EcoCAR 2 competition, IDEA's architecture and role within the competition, and ERAU's future work to complete, validate, and demonstrate the IDEA system.
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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 | 0.001 |
| 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 | 0.000 | 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".