Hierarchical coordinated control strategy for regenerative braking energy recuperation with an electrobooster
Bibliographic record
Abstract
To guarantee the braking performance and safety of electric vehicles (EVs) and to improve their regenerative energy performance, a regenerative braking recuperation control strategy based on hierarchical coordinated control of an electrohydraulic brake system (eBooster) is proposed in this paper. First, considering the EV braking system characteristics, the eBooster model, vehicle dynamics model and energy recuperation model are established. Then, a hierarchical coordinated control strategy (HCCS) is proposed, and the top-level controller based on model predictive control (MPC) reasonably coordinates the braking torque according to the control objectives and constraints. Additionally, as per the eBooster characteristics, the bottom-level controller adopts a double closed-loop controller to improve the control accuracy and response speed. Finally, verification in the Simulink/AMESim joint simulation environment shows that the eBooster and the HCCS enable combined regenerative and hydraulic braking, which improves brake safety and performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".