Development of a predictive model for Regenerative Braking System
Bibliographic record
Abstract
The basic problem that this project addresses is the recovery of the kinetic energy lost during braking in a conventional vehicle. With Regenerative Braking Systems (RBS) it is possible to slow a vehicle down by converting its kinetic energy into electric energy, which can be either used immediately or stored until needed. This contrasts with conventional braking systems, where the excess kinetic energy is converted into heat by friction and wasted into the environment. In hybrid electric vehicles, the regenerative braking action is performed using the electric motor as a generator. In this way the energy from the wheels is converted from kinetic into electric and the magnetic friction between the rotor and the stator windings provides the braking effect. The aim of this study is to design a model of an electric system that allows converting the kinetic energy and storing it into a high voltage battery. The powertrain configurations investigated in this project are the mild and full hybrids, in which the internal combustion engine is coupled with an electric motor, able to provide a start/stop and a power assist functions in addition to the regenerative braking actions. The final result of the study is represented by a tool that has been implemented into Matlab <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> in order to predict the time variation of the electric quantities in a vehicle performing the New European Driving Cycle. This model also offers a predictive tool for dimensioning the main components of the system, according to the target electric parameters. Finally, the improvements that such a system could give in terms of efficiency, fuel consumption and emissions reduction have been analyzed. With respect to the existing models, this approach requires few main input parameters to characterize the RBS, resulting in a higher flexibility and a wider range of application.
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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 | 0.000 |
| 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".