Experimental Platform for Evaluation of On-Board Real-Time Motion Controllers for Electric Vehicles
Bibliographic record
Abstract
Electric vehicles are considered to be a greener and safer means of transport thanks to the distinguished advantages of electric motors. Studies on this object require experimental platforms for control validation purpose. Under the pressure of research, the development of these platforms must be reliable, safe, fast, and cost effective. To practically validate the control system, the controllers should be implemented in an on-board micro-controller platform; whereas, the vehicle model should be realized in a real-time emulator that behaves like the real vehicle. In this paper, we propose a signal hardware-in-the-loop simulation system for electric vehicles that are driven by four independent electric motors installed in wheels (in-wheel motor). The system is elaborately built on the basis of longitudinal, lateral, and yaw dynamics, as well as kinematic and position models, of which the characteristics are complete and comprehensive. The performance of the signal hardware-in-the-loop system is evaluated by various open-loop testing scenarios and by validation of a representative closed-loop optimal force distribution control. The proposed system can be applied for researches on active safety system of electric vehicles, including traction, braking control, force/torque distribution strategy, and electronic stability program.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.006 | 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".