Model adaptive torque control and distribution with error reconstruction strategy for RWID EVs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it