Optimization of Powertrain Parameters of a Battery Electric Vehicle for Shuttle Service Usage
Bibliographic record
Abstract
Although Electric Vehicles (EVs) had been used for transportation since the end of 19 th century, they were superseded by Internal Combustion Engine (ICE) propelled vehicles due to limited performance and low driving range problems.In the last 2 decades considering the performance advancements and price drop in the battery technology, EVs started to gain significant attention and usage.Furthermore, they have zero in use emissions, reducing the effects of fossil fuels in terms of air pollution and Global Warming (GW).However, compared to ICE propelled vehicles main drawbacks like low driving range and long charging durations limit the favourability of EVs.Considering special use cases such as shuttle services in specified areas, these drawbacks lose their importance.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 selected configuration.The results of this study will serve as a valuable input for the autonomous driving car development project planned at AUM. .
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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 | 0.001 |
| 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 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".