Characterisation of the electric drive of EV: on‐road versus off‐road method
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
For system design, analysis of global performance and energy management of electric vehicles (EVs), it is common to use the efficiency map of electric traction drive. The characterisation of the efficiency map with high accuracy is then an important issue. In this study, an on‐road method and an off‐road method are compared experimentally to determine the efficiency map of electric drive of EVs. The off‐road method requires a dedicated experimental test bed, which is expensive and time consuming. The on‐road method is achieved directly in‐vehicle. Experimental data, recorded during an on‐road driving cycle, are used to determine the efficiency map using non‐intrusive measurements from global positioning system antenna, voltage and current sensors. A versatile experimental setup is used to compare both methods on the same platform. A maximal efficiency difference of 6% is achieved in most of the torque–speed plane. It is shown that, in an energetic point of view, both methods yield similar results.
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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