Intelligent power management of plug-in hybrid electric vehicles, part II: real-time route based power management
Bibliographic record
Abstract
PHEVs are considered as a viable solution to address environmental concerns facing the global automotive industry. In this paper, we design a new real–time route–based power management system for PHEVs based on the adaptive equivalent consumption minimisation strategy (A–ECMS). The designed controller takes advantage of preview trip information to achieve even higher efficiencies. To evaluate the designed system, this controller was assigned to a high fidelity model of Toyota Prius Plug–in Hybrid built in Autonomie. The results are compared against those of several existing power management systems, including: charge depleting charge sustaining (CDCS), manual CDCS, and rule–based strategies as well as Pontryagin's minimum principle (PMP). While PMP efficiency is slightly higher than the new controller, the real–time power management system proved more reliable in terms of the computational time. It is shown that route based power management strategy improved the fuel economy up to 11% compared to the rule–based controller within Autonomie.
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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) | 0.001 | 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".