Comprehensive chassis control strategy of FWIC‐EV based on sliding mode control
Bibliographic record
Abstract
Four‐wheel independent control electric vehicle has possessed tremendous potentials because the enhancement of driving performance and energy savings can be simultaneously carried out by independent and precise driving/braking/steering control. The study has proposed a comprehensive control strategy aiming at all normal conditions, which employs hierarchical architecture to reach the above‐mentioned control. In the high‐level controller, sliding mode control scheme is developed to figure out total force and yaw moment. In the low‐level controller, energy‐efficiency optimisation allocation is presented to reduce motor power losses and obtain energy recovery based on motor efficiency map, and then steering angle allocation is conducted to decrease the lateral force so as to reduce power losses caused by the tyre sideslip. Considering insufficient motor braking torque during large deceleration or even larger, the blended brake control strategy with the motor brake and electric hydraulic brake and further anti‐skid brake system control via adopting fuzzy logic method are carried out. The torque and pressure are gained to deliver the corresponding actuators model established according to their physical characteristics. Through CarSim‐MATLAB/Simulink‐AMEsim co‐simulation, results suggest that the developed strategy can boost the vehicle manoeuvrability and reduce energy consumption generated by motors and tyre sideslip under all the conventional occasions.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".