Collaborative control of a dual electro-hydraulic regenerative brake system for a rear-wheel-drive electric vehicle
Bibliographic record
Abstract
Within the field of electric vehicles, the cooperative control of a dual electro-hydraulic regenerative brake system using the foot brake pedal as the sole input of driver brake requests is a challenging control problem, especially when the electro-hydraulic brake system features on/off solenoid valves which are widely used in the automotive industry. This type of hydraulic actuator is hard to use to perform a fine brake pressure regulation. Thus, this paper focuses on the implementation of a novel controller design for a dual electro-hydraulic regenerative brake system featuring on/off solenoid valves which track an “ideal” brake force distribution. As an improvement to a standard brake force distribution, it can provide the reach of the maximum braking adherence and can improve the energy recovery of a rear-wheel-drive electric vehicle. This improvement in energy recovery is possible with the complete substitution of the rear hydraulic brake force with a regenerative brake force until the reach of the electric powertrain constraints. It is done by performing a proper brake pressure fine regulation through the proposed variable structure control of the on/off solenoid valves provided by the hydraulic platform of the vehicle stability system. Through road tests, the tracking feasibility of the proposed brake force distribution with the mechatronic system developed is validated.
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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 | 0.001 | 0.001 |
| 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".