Range Extension Control of a Three-Wheel Electric Vehicle Prototype Based on Aggregation and Distribution
Bibliographic record
Abstract
This paper presents an approach for the range extension control of a three-wheel electric vehicle prototype.By using the torque distribution vector to aggregate motor speeds, the physical model of the vehicle is mapped to an aggregation-and-distribution model (AaDM), which possesses the passivity property.Based on the AaDM, motion control and energy optimization can be designed separately.In particular, a speed controller was designed for the system to operate in the automatic cruise mode.A disturbance observer was designed to operate in the human driving mode.In this study, the conditions for the controllers were obtained to sufficiently ensure the L 2 stability of the control system.The conditions can be checked conveniently without establishing the dynamical equation of the overall system.Under the practically reasonable assumption on motor parameters, the analytical solutions of the optimal torque distribution ratios and d-axis currents were approximately derived in this study.Various test scenarios were considered to validate the proposed control systems.The test results show that in either operation modes, the system can prevent wheel slip, thereby simultaneously improving motion control and energy minimization.
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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) | 0.000 | 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 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".