An Adaptive Regenerative Braking Strategy Design Based on Naturalistic Regeneration Performance for Intelligent Vehicles
Bibliographic record
Abstract
The effectiveness of regenerative braking strategies plays an important role in extending the driving range of electric vehicles. Since the driver is still an essential factor in levels 3 and 4 of intelligent electric vehicles, improving user acceptance and adoption of the braking control strategy is crucial. This paper puts forward a new regenerative braking strategy to find a compromise between optimal braking control performance and naturalistic regeneration performance while satisfying the maximum speed preference when driving between two-stop events. Unlike other similar works that only maximize regenerative braking energy while satisfying the physical limits of an electrified powertrain, this paper considers naturalistic regeneration performance. To achieve this, firstly, the power regenerated by three drivers is predicted with a long-horizon (30 seconds), using long-short-term memory networks (LSTM) and non-linear autoregressive exogenous model (NARX). Subsequently, an estimation of the energy recovery maximization rate is performed to give a perception of the naturalistic regeneration performance. As this performance varies, the deceleration planning employs three horizon scales of long, medium, and short, determined by the energy recovery maximization rate. Finally, dynamic programming (DP) is utilized to optimize a deceleration profile. The study utilizes real data of inverter efficiency, transmission efficiency, and motor-to-battery efficiency map. The outcome of this study shows that the proposed regeneration braking strategy is adaptive, improving regeneration efficiency by 39,6% for driver 1, 16% for driver 2, and 26% for driver 3, and forecasting the optimality of some deceleration behaviors.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".