Distributed Control Design for V2G in DC Fast Charging Stations
Bibliographic record
Abstract
In this paper, an SoC-based droop control design is proposed for the vehicle-to-grid (V2G) operating mode of electric vehicles (EVs) in a DC fast charging station (DCFCS). The proposed primary control design aggregates EVs in the V2G mode while balancing EV batteries SoCs during charge and discharge modes to protect EVs from overcharge/overdischarge and increase the time duration at which all connected EVs are able to operate in the V2G mode. The proposed controller employs the output of a sigmoid function as the droop coefficient to allocate power among EV batteries. EV batteries’ energy capacities and SoCs are the variables in the sigmoid function. Compared to the conventional methods, the proposed controller balances SoCs without using the average SoCs to provide complete independence among chargers, considers the effect of different EV batteries energy capacities on the SoC balancing, and balances SoCs faster. The effectiveness of the proposed controller is evaluated through steady-state and transient case studies in the DCFCS with three EVs connected to chargers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".