An Analytical Approach to Improve Vehicle Maneuverability via Torque Vectoring Control: Theoretical Study and Experimental Validation
Bibliographic record
Abstract
To improve the maneuverability of a vehicle and fully leverage the advantages of torque vectoring control (TVC) in improving vehicle dynamics, a method to analytically improve the cornering response based on TVC is proposed in this paper. A feedforward and feedback control architecture based on a two-degree-of-freedom vehicle model is first introduced. An analytical expression of the yaw moment feedforward model is derived under the condition that the transfer function of the ideal yaw rate with respect to the real one is equal to 1. Then, the key influencing factors of the additional yaw moment are investigated in detail. More importantly, the real experimental results under steady and transit state are analyzed to demonstrate how the proposed controller can improve vehicle maneuverability. Experimental results show that the bandwidth of vehicle transient response could be improved by 29.6% in the tests. The controller can be easily extended to any type of TVC even though it is applied to a rear-wheel driven electric vehicle in this paper.
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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 | 0.001 |
| 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 | 0.000 | 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".