Probabilistic Modeling of Plug-in Electric Vehicles Charging from Fast Charging Stations
Bibliographic record
Abstract
In this paper, a probabilistic charging demand profile of plug-in battery electric vehicles charging from a fast charging station is presented. Different vehicles' models are utilized, such as the Chevrolet Bolt, the Tesla Model S, and the Nissan Leaf. Two different parameters are considered, the daily distance travelled, and the battery state-of-charge. The Markov Chain Monte Carlo, relying on Metropolis-Hastings sampler, is utilized to estimate the aforementioned parameters based on exponential and Weibull distributions. The convergence of the algorithm is assessed based on the Gelman-Rubin approach. Distributions for the parameter of daily distance travelled were selected by comparing the data of daily mileage driven, corresponding to three types of electric vehicles collected in a mixed and marine climate zone, with theoretical empirical cumulative distributions calculated from seven standard distribution functions. The battery state-of-charge depends on the parameter of daily distance travelled and estimated by taking the difference between the battery total range and the daily distance traveled. The results have shown that the best distribution is exponential for Chevrolet Bolt and Nissan Leaf whereas Weibull represents the best distribution for Tesla Model S. The best distribution has been determined by calculating the Sum of Squares Error.
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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".