A Q-Learning Based Charging Scheduling Scheme for Electric Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a Q-learning algorithm based dynamic charging scheduling scheme which intent to optimize the operation benefit for electric vehicles. The method imitates the charging station operator's illation and decision procedure which similar to solving a reinforcement learning problem. The scheduling problem involved is focusing on the bidirectional interaction between the vehicle and the grid, including the grid-to-vehicle charging and the vehicle-to-grid (V2G) electricity returning. Regarding the dynamic characteristics of the electricity market, the scheme has included the time-of-use electricity rates as a core parameter to establish the reward tables which is necessary for learning. Furthermore, several simulations were conducted which demonstrates the day-long optimal vehicle charging decisions under the proposed scheme. Favorable expansibility and maintainability can be achieved in this Q-Iearning framework.
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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 it