Components sizing optimisation of hybrid electric heavy duty truck using multi-objective genetic algorithm
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
Components sizing optimisation of a novel architecture of hybrid drivetrain for line-haul truck has been considered. This drivetrain architecture employs a self-propelled trailer and the traction is shared between the tractor and trailer. The comprehensive model of the vehicle, including the hybrid electric drivetrain is developed. The drivetrain components have been optimised using multi-objective genetic algorithm to minimise three objective functions, namely, the acceleration time, fuel consumption and the drivetrain price. The overall efficiency of the optimised hybrid drivetrain has been evaluated using computer model simulations. Engineering economic analysis is performed to demonstrate the ownership cost of the proposed drivetrain when compared with the non-hybrid and the non-optimised hybrid drivetrain for heavy duty vehicles. The results show that the proposed drivetrain has a superior capability in reducing the fuel consumption and the ownership cost.
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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 it