Fuzzy logic-based charging strategy for Electric Vehicles plugged into a smart grid
Bibliographic record
Abstract
The smart grid allows its consumers to participate in producing cost effective, sustainable, and environmentally friendly electricity. The consumers in a smart grid, for example, can plug their Electric Vehicles (EVs) into the grid to charge and discharge their vehicles' batteries. However, charging of the electric vehicles, especially during the peak periods, can adversely impact the grid performance. Thus, in this paper, the coordinated charging of the electric vehicles problem is tackled. A fuzzy logic-based approach is developed to coordinate the electric vehicle charging such that the system minimum voltage is within the allowable limits. The inputs to the Fuzzy Charging Controller (FCC) include the States of Charge (SOC) of the electric vehicles, the grid parameters represented in the system minimum voltage, and the hourly energy price. The output of the FCC is the charging levels of the electric vehicles' batteries. The developed fuzzy logic-based charging strategy was validated on the 69-bus test system. The Fuzzy Charging (FC) was compared with three modes of uncoordinated charging, namely Slow Charging (SC), Medium Charging (MC), and Fast Charging (FC). The results of the comparative study prove the superiority of the developed fuzzy charging approach over uncoordinated charging schemes.
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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".