Analysis and Design of an Optimum Powertrain for an Electric Battery Vehicle for AUM Campus Shuttle Service
Bibliographic record
Abstract
In order to achieve both high performance and a longer driving range, conventional automobiles rely on fossil fuels.However, there are certain drawbacks, such as low engine efficiency and emissions of exhaust gases that contribute to the pollution of the environment and Global Warming (GW).To this end phenomenon, there has been a recent rise in the interest of Electric Vehicles (EVs).On the other hand, EVs have certain drawbacks like low range and long charging duration.Considering special use cases such as shuttle services in specified areas, these drawbacks lose their significance.The proposed study involves, the selection design and optimization of an EV to be used in the American University of the Middle East (AUM) campus considering the main objective as to complete the daily tasks with a single charge during the night.A longitudinal vehicle model is generated for the EVs in MATLAB/Simulink, a benchmarking vehicle is selected using vehicle model outputs and parameter optimization for battery capacity and final drive ratio (FDR) is performed.The final design has 32.47 % less battery capacity, 1.94 % less vehicle weight and 7.901 seconds 0 -25 kph vehicle acceleration duration, 17.86 % less than the original configuration.The results of this study will be used for the autonomous driving car project to be developed in AUM in the near future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".