Intelligent power management of plug-in hybrid electric vehicles, part I: real-time optimum SOC trajectory builder
Bibliographic record
Abstract
Offering better fuel economy and lower emissions than conventional vehicles, plug–in hybrid electric vehicles (PHEVs) are promising near–term options for high efficiency, 'sustainable' transportation. It has recently been found that these efficiency benefits can be further improved with access to upcoming trip and driving conditions. This study is organised into two parts: in part I, upcoming trip data is used to find the optimal SOC trajectory of our PHEV model that will help minimise the total cost of electricity and fossil fuel. In part II, the optimum SOC trajectory is applied within the real–time controller to optimally distribute propulsion power between two energy sources. Autonomie was used to develop and implement a high fidelity PHEV model. The optimal SOC trajectory which has been found by real–time optimisation technique is in close agreement with the global optimum solution of Dynamic Programming. Moreover, the real–time technique is much less computationally expensive.
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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 | 0.001 | 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".