Characterisation of the electric drive of EV: on‐road versus off‐road method
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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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".