Braking Strategy Characterization for a Dual-Motor Battery Electric Vehicle and Regenerative Torque Limit Derivation
Bibliographic record
Abstract
Recent research on braking control strategies for battery electric vehicles (BEVs) commonly limits the front-to-rear braking force distribution. However, no public evidence shows that production vehicles observe these limits. The present study evaluates this assumption through road tests and high-fidelity simulations for dual- and single-motor powertrains. On-road measurements acquired from a mass-produced BEV indicate that, under typical driving conditions, the force split can diverge safely from conventional design curves—including the ECE and the ideal curves. Building on these data, a comparative analysis quantifies the efficiency penalties associated with constrained braking (fixed ratios or ideal curve tracking) relative to an unconstrained, efficiency-oriented approach. In dual-motor configurations, removing the constraint recovers up to 6.8% more kinetic energy over a real-world cycle. A power loss-based method is proposed to establish a torque curve limiting regenerative braking at low-speed operation. Applied to a single-motor delivery-van model, the regenerative torque limitation increases energy recuperation by as much as 4.8% during moderate to intense decelerations. These findings provide guidance for future brake control designs, demonstrating that efficiency-focused regeneration can coexist with anti-lock braking systems to extend BEVs’ driving range without compromising vehicle stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".