Traffic Information-Based Hierarchical Control Strategies for Eco-Driving of Plug-In Hybrid Electric Vehicles
Bibliographic record
Abstract
The development of intelligent transportation technology provides a great opportunity for energy efficiency improvement of electrified vehicles. However, for plug-in hybrid vehicles, eco-driving control usually involves three problems, including speed planning, SOC planning, and energy management. Solving the above three problems requires considering not only the fuel economy but also the computational efficiency. To this end, this paper proposes a hierarchical control strategy to improve driving comfort and fuel economy simultaneously for a PHEV. Specifically, three main contributions are presented to distinguish our efforts from the existing research. First, in the control framework, the traffic light information is utilized to calculate optimal driving speed by minimizing a multi-objective function. Then, the SOC planning problem is solved by convex optimization, while the fuel consumption is minimized by a predictive equivalent consumption minimization strategy. Second, the speed trajectories and fuel consumptions in the other two traffic scenarios with different traffic light SPaT (Signal Phasing and Timing) are presented to validate the effectiveness of the proposed method. Finally, the robustness with respect to prediction horizon length, initial co-state value, and gain coefficient value are analyzed and discussed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".