Real Time Evaluation of the Driver Ability to Recover Energy during Braking, by Observing the Slip Ratio of the Powered Wheels, on an Electric Vehicle
Bibliographic record
Abstract
This article is about a new method to evaluate the ability of the pilot to recover energy during the regenerative braking period. In order to optimize the amount of energy recovered from electrical braking, most of the existing literatures present optimal methods which consist in defining the optimal braking torque as a function of vehicle speed. The originality of the present study is to propose a new strategy based on the observation of the powered wheel slip. It has been proved that regenerative braking based on a slip controller was less affected by the majority of the parametric changes [1], including the type of road. This method proposes to evaluate the pilot by observing the slip ratio instead of the applied torque. Thus, the optimal engine torque is reduced when the pilot is on a slippery road. Thereby the pilot evaluation is possible in all road conditions.
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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 | 0.001 | 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 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".