Non-Linear Model Predictive Anti-Jerk Cruise Control for Electric Vehicles with Slip-Based Constraints
Bibliographic record
Abstract
Electric vehicles (EVs) are usually fitted with Cruise Control (CC) systems, an Advanced Driver Assistance System (ADAS), which regulates the speed of the vehicle in response to an acceleration input. The rate of acceleration is usually regulated by the traction controller. However, most traction controllers are on-off controllers and are only activated when slip exceeds the desired limits resulting in deterioration in the performance of the cruise controller. EVs have a much faster torque response as compared to conventional vehicles, resulting in jerk arising as a result of wheel slip or flexibility in the half-shaft. In this research, we develop a non-linear model predictive low-jerk cruise controller for an electric vehicle, which reduces jerk occurring due to halfshaft flexibility and wheel slip concurrently. A high-fidelity longitudinal dynamics model has been developed for the test vehicle for our research, a Toyota Rav4EV. A powertrain model based on Pacejka relaxation length tire model has been used to study the slip response characteristics. The jerk performance of the controller has been assessed using the high-fidelity vehicle model while following a US06 driving cycle. The real-time capability of the MPC controller has been demonstrated through Hardware-in-the-loop (HIL) experiments.
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".