Optimal drivetrain component sizing for a Plug-in Hybrid Electric transit bus using Multi-Objective Genetic Algorithm
Bibliographic record
Abstract
Plug-in Hybrid Electric Vehicles (PHEVs) can significantly reduce petroleum consumption and the only difference from hybrid electric vehicles (HEVs) is the ability of PHEVs to use off-board electricity generation to recharge their energy storage system. The fuel economy of PHEV is highly dependent on All-Electric-Range (AER), drivetrain component size and control strategy parameter. In this study we consider PHEV version of parallel hybrid NOVA transit bus model developed with the Powertrain System Analysis Toolkit (PSAT). A genetic based derivative free algorithm called Multi-Objective Genetic Algorithm (MOGA) is used to optimize conflicting drivetrain and control strategy parameters. The AER, fuel economy, emissions and main performance constraints of the PHEVs will be compared for the initial design and final optimal design.
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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 | 0.001 |
| 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".