Model adaptive torque control and distribution with error reconstruction strategy for RWID EVs
Bibliographic record
Abstract
The performance of the torque control and distribution is a critical problem that can affect the stability, safety and energy efficiency of a rear‐wheel independent drive (RWID) electric vehicle (EV). This study proposes a model adaptive torque control and distribution method for RWID EVs. First, the torque control and distribution problem is analysed in detail. Then an RWID EVs’ longitudinal model is built and a torque control and distribution scheme is proposed. To avoid the over‐actuation and the under‐actuation of the powertrain system, a controller is designed based on the longitudinal model to adaptively control the driving torque. To comprehensively consider the stability and safety, an error reconstruction strategy based on the fuzzy logic theory is proposed to evaluate the errors in the side slip angle and in the yaw rate. In order to accurately distribute the driving torque to the rear wheels, a torque distribution controller is designed. Finally, the proposed method is validated on a co‐simulation platform, and the simulation results demonstrate the excellent performance of the proposed method for RWID EVs’ torque control and distribution compared with the counterparts of fuzzy logic direct yaw‐moment control and two‐loop torque distribution and control.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".