Reinforcement Learning for Smart Charging of Electric Buses in Smart Grid
Bibliographic record
Abstract
In recent years, the environmental issues caused by using conventional energy resources, such as gasoline and diesel, become more and more serious. One promising solution to these issues is the electrification of public transit by replacing the internal combustion engine buses with electric buses (EBs). However, due to the degradation of EB batteries, the optimization of EB charging schedules during operating time is still challenging for the public transit service providers. This challenge is further complicated by the randomnesses of traffic and road conditions. In this paper, the problem of optimizing EB charging schedules is formulated as a Markov decision process, based on the battery degradation model of EBs and the information available via vehicular communication networks in smart grid. A double Q-leaning algorithm is used to optimize the charging schedules by minimizing the battery degradation cost of EBs. The performance of the proposed algorithm is evaluated by comparing with existing algorithms based on the real data of EB mobility and energy consumption collected from St. Albert Transit, AB, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".