Comprehensive modeling of electric vehicles to analyze their performance based on different propulsion profiles
Bibliographic record
Abstract
Battery electric vehicle (BEV) technology is the most often used type of EV due to its reliable energy storage system. According to the different driving cycles of EVs, electric motors undergo degradation as a result of sudden variations in the dynamic state of torque. Therefore, reliability and performance assessment of the electric motor are important. This paper presents transient and harmonic analysis techniques that were engaged to observe the motor conditions in different operating modes. In order to study the system's behavior, in addition to the harmonic analysis on torque and stator currents, the transient response of stator current for different electric motors is also touched upon in this paper. In an ideal BEV, the motor voltage supplied by the battery is considered to be constant, whereas in real applications, the voltage drops due to the SOC characteristics of the batteries. This paper will also discuss the motor frequency response used to investigate the effects of motor input voltage on the performance of the system. Complete sets of the experimental test is discussed and reported in this study to verify the numerical investigation results.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".