Design and simulation of a fast-charging station for plug-in hybrid electric vehicle (PHEV) batteries
Bibliographic record
Abstract
With the increasing interest in green technologies in transportation, plug-in hybrid electric vehicles (PHEV) have proven to be the best short-term solution to minimize greenhouse gas emissions. Despite such interest, conventional vehicle drivers are still reluctant in using such a new technology, mainly because of the long duration (4-8 hours) required to charge PHEV batteries with the currently existing Level I and II chargers. For this reason, Level III fast-charging stations capable of reducing the charging duration to 10-15 minutes are being considered. The present thesis focuses on the design of a fast-charging station that uses, in addition to the electrical grid, two stationary energy storage devices: a flywheel energy storage and a supercapacitor. The power electronic converters used for the interface of the energy sources with the charging station are designed. The design also focuses on the energy management that will minimize the PHEV battery charging duration as well as the duration required to recharge the energy storage devices. For this reason, an algorithm that minimizes durations along with its mathematical formulation is proposed, and its application in fast charging environment will be illustrated by means of two scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".