RLC: A Reinforcement Learning Based Charging Scheme for Battery Swap Stations
Bibliographic record
Abstract
Over the past decade, the Electric Vehicle (EV) market has witnessed remarkable expansion. Nevertheless, apprehensions regarding prolonged charging durations frequently contribute to range anxiety. Battery Swap Station (BSS) is a promising solution to the range anxiety problem. Typically, when an EV arrives at a BSS, the depleted battery in the EV is replaced with a fully charged one, then the depleted battery is charged at the maximum speed. In this paper, we propose a novel battery swapping/charging scheme for BSS, Reinforcement Learning based Charging (RLC), to serve as many EVs as possible and minimize the total electricity cost. Specifically, with RLC, for the EVs that do not need full batteries, partially charged ones will be provided. To reduce the electricity cost whenever possible, RLC tries to shift the charging time of a battery to a low-electricity-price period. Technically, RLC uses Deep Deterministic Policy Gradient (DDPG), a Deep Reinforcement Learning (DRL) algorithm, to optimize the charging strategy for the batteries in BSS. Our experimental results indicate that RLC outperforms the existing charging/swapping schemes in terms of battery service rate and total electricity cost.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".