Development of a Fuzzy Slip Control System for Electric Vehicles with In-wheel Motors
Bibliographic record
Abstract
A two-passenger all-wheel drive urban electric vehicle (AUTO21EV) with four direct-drive in-wheel motors and an active steering system has been designed and developed at the University of Waterloo. A novel fuzzy slip control system is developed for this vehicle using the advantage of four in-wheel motors. A conventional slip control system uses the hydraulic brake system in order to control the tire slip ratio, which is the difference between the wheel center velocity and the velocity of the tire contact patch along the wheel plane, thereby influencing the longitudinal dynamics of a vehicle. The advantage of the proposed fuzzy slip controller is that it acts as an ABS system by preventing the tires from locking up when braking, as a TCS by preventing the tires from spinning out when accelerating. More importantly, the proposed slip controller is also capable of replacing the entire hydraulic brake system of the vehicle by automatically distributing the braking force between the wheels using the available braking torque of the in-wheel motors. In this regard, the proposed fuzzy slip controller guarantees the highest traction or braking force on each wheel on every road condition by individually controlling the slip ratio of each tire with a much faster response time. The performance of the proposed fuzzy slip controller is confirmed by driving the AUTO21EV through several test maneuvers using a driver model in the simulation environment. As the final step, the fuzzy slip controller is implemented in a hardware- and operator-in-the-loop driving simulator and its performance and effectiveness is confirmed.
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.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".