Driver-in-the-Loop Drivability and Energy Efficiency Analysis of Regenerative Braking Strategies for Electric Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper investigates different regenerative braking strategies applied to Battery Electric Vehicles, such as series and parallel brake blends. The comparison includes energy efficiency assessment using homologation and real-world drive cycle and objective and subjective drivability evaluation. Multiple simulations are performed using a one-dimensional (1D) vehicle model developed in Simulink and a static driving simulator. The driving simulator provides a fair comparison of real-world driving since it creates repeatable highway and urban traffic conditions. These simulations compare the system energy efficiency by looking at the battery's state of charge (SOC). The drivability is assessed on top of consumption by using the static driving simulator. It is objectively measured by calculating the longitudinal acceleration change ratio over time, which occurs during the regeneration ramp-in and ramp-out, for different pedal positions and pedal gradients. The drivability is also subjectively evaluated by assessing the system's smoothness and absence of shakes during braking maneuvers and the deceleration feels while “freely” coasting at high speeds. This study clarifies the utilization of a driving simulator integrated with a model-based design apporoach to develop regenertive braking controls and braking system architectures for electrified vehicles. In the study case presented in this paper, the Series regenerative braking shows better efficiency and better drivability, especially for conditions of low accelerations lower than 0.3g.</div></div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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