Cyber-Physical Predictive Energy Management for Through-the-Road Hybrid Vehicles
Bibliographic record
Abstract
In this study, a Cyber-Physical System approach is proposed for the optimization of Through-The-Road (TTR) hybrid vehicles, in view of energy saving with a prior knowledge of road elevation. First, a hybrid powertrain for TTR vehicle is proposed for energy saving purposes and then the modeling of TTR hybrid vehicle is presented. Second, the proposed average power based model predictive controller (AP-MPC) is utilized to address the energy management problem for this TTR hybrid vehicle with only road elevation known as a priori obtained from the cyber word in Cyber-Physical System. A comparison study is proceeded to demonstrate the benefit of the hybrid powertrain compared to the traditional heavy commercial vehicle and the AP-MPC strategy compared with the prescient MPC and the rule-based energy management. Simulation results demonstrate that the proposed AP-MPC without any priori velocity information can achieve an impressive improvement of 15.6% in three typical case studies even only with the road elevation information enabled by the cyber world in Cyber-Physical System.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".