An Optimal Battery Charging Algorithm in Electric Vehicle-Assisted Battery Swapping Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In battery swapping environments, electric vehicles (EVs) can play roles as battery providers as well as consumers. In this paper, we propose an optimal battery charging algorithm (OBCA) where a battery swapping station (BSS) charges batteries in its storage with the consideration of the profile of the electricity price and the arrival rates of EVs. To maximize the net profit of BSS while maintaining the battery changing probability above a certain level (i.e., maintaining high quality of service (QoS) of BSS), we formulate a constraint Markov decision process (CMDP) problem and the optimal charging schedule for batteries in BSS is obtained by a linear programming (LP). Evaluation results demonstrate that OBCA with the optimal policy can improve the net profit of BSS up to 418% compared to an electric price-aware scheme while maintaining high QoS of BSS.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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