Simulative Comparison between Electric and Thermal Powertrains on Different Real Road Missions
Bibliographic record
Abstract
Pure electric mobility is still struggling to emerge in the present road vehicles scenario. This is mostly due to costs, nowadays still very high, and to battery range, which is intrinsically very limited with respect to the fuel tank of a traditional vehicle.To be effectively competitive, e-mobility should not be thought as the mere substitution of the thermal powertrain with the electric one on the same vehicle; instead, a holistic approach comprehensive of the integration of a charging network within the territory should be adopted. The vehicles should be tailored on the missions to be accomplished promoting the lightness, simplicity and low cost, and should be integrated within a charging infrastructure and a car-sharing system implemented in the reference territory.In this paper the authors aim at exposing their idea of e-mobility, justifying it by simulations carried out on three different vehicles (a Diesel-fuelled Renault Kangoo, an electric driven Renault Kangoo and an electric micro-vehicle Renault Twizy) and experimental data. The simulations were carried out with the help of a validated road vehicle model in different real road missions, namely a urban, an extra-urban and a mountain mission.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".