Energy based graphical user interface modeling for PHEV energy management system
Bibliographic record
Abstract
Plug-in hybrid electric vehicles (PHEVs) are one of the solutions to the increasing environmental concerns and emission standards across the globe. Unlike conventional vehicles, they have charge sustaining capability, thus enabling them to use their stored electrical energy during the charge depleting mode, in turn decreasing the amount of fuel consumption. PHEVs have the provision of an off-board recharging facility in addition to the on-board chargers. Since the amount of electrical energy stored in PHEV is limited, it is important to have an optimal energy distribution among the internal combustion engine (ICE) and the motor in order to improve the fuel efficiency. In this paper, models for each component of a series-parallel HEV are developed and simulated in the case of Toyota hybrids during different driving conditions such as braking, acceleration, and deceleration for a given driving schedule. The power consumption is estimated for every instant. An algorithm can be developed in order to ensure that vehicle is driven mostly in the electric mode and/or in the most efficient operating range of the ICE. The developed vehicle model has been designed in order to validate real-time information of the driving conditions accessible by GPS. This would lead to the optimization of the available energy sources, thus reducing fuel consumptions and the consequent emissions.
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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 | 0.000 |
| 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) | 0.000 | 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".