Research on Control Strategies for Improving the Minimum Turning Diameter in Pure Electric Vehicles
Bibliographic record
Abstract
The minimum turning diameter is a direct reflection of a vehicle's agility. In research aimed at optimizing control to minimize the turning diameter, the key lies in understanding and adjusting various factors that impact vehicular steering performance. This paper focuses on front-wheel drive electric vehicles, with the primary research emphasis on identifying the optimal hydraulic brake distribution strategy under cornering conditions, targeting enhanced maneuverability. By adopting a control scheme that involves coordinated braking of non-driven wheels, particularly focusing on the outer wheel, simulation analysis reveals that implementing this control strategy can reduce the minimum turning diameter from 10.82 meters to 9.89 meters. Through real-vehicle functional testing, integrating this control strategy into an onebox braking system further demonstrates its effectiveness, decreasing the minimum turning diameter from 10.92 meters to 9.94 meters. The similarity between simulation and real-vehicle test results indicates that this control strategy significantly improves the vehicle's minimum turning diameter, thereby enhancing its maneuverability during turns while ensuring driving safety and handling stability.This finding highlights the potential of advanced braking coordination techniques, specifically targeting non-driven wheels during cornering maneuvers, to achieve tighter turning radii in electric vehicles without compromising safety or dynamic handling. This development holds significant promise for improving overall driving experience and efficiency in urban environments where tight maneuverability is often required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".