Bibliographic record
Abstract
The use of PHEVs (Plug-in Hybrid Electric Vehicles) is fast expanding due to their low energy cost and low environmental pollution. However, the biggest hurdle is that PHEVs have short driving range and long battery charging time even when using supercharging stations. Therefore, better queuing models are necessary to improve the quality of services using public charging stations. In this paper, first the PHEV queue is simulated based on the M/M/1 queuing model, where M denotes a Markov process for both inter-arrival time and service times. Then, the electrical consumption of PHEVs is simulated for urban driving standard (FTP-72) to better understand of the behavior the battery state of charge (SOC). In addition, discharging characteristics of the PHEVs' batteries in urban city are analyzed using different cycles. Moreover, a computer model is implemented to study waiting time of PHEV at public charging stations. Simulation results suggest that, assigning charging stations by considering the queue length at each charging nodes (station), as well as the distance, considerably reduces the queue length at each station.
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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".