Fuel-Efficient Control System Design for Cooperative Truck Platooning at Large Separation Distances
Bibliographic record
Abstract
A platoon of multiple trucks in close proximity has the potential to improve fuel efficiency due to the reduced aerodynamic drag, but can the truck platoon also achieve a benefit under large separation distances? To investigate the performance of truck platooning under large separation distances, a model predictive controller (MPC) is implemented to optimize the fuel efficiency. The control objectives are not only to maintain the spacing at a desired range, but also to smooth the speed profiling of follower trucks by coordinating with each other to avoid unnecessary accelerations and decelerations. A two-truck platoon is simulated using the designed controller in Matlab/Simulink, and the results show that the proposed MPC can still help the follower truck achieve a fuel-saving ratio of 5.4 % under 5 sec time gap in the driving cycle with large speed variations. Furthermore, aerodynamic drag reduction plays a dominant role in saving fuel with time gaps of less than 4 sec at highway driving speeds. The follower truck benefits more fuel saving by cooperative platooning under transient driving speeds when time gaps are larger than 3 sec.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".