Software Defined Networking Assisted Electric Vehicle Charging: Towards Smart Charge Scheduling and Management
Bibliographic record
Abstract
Recent advancements in plug-in Electric Vehicles (EV) have opened up Intelligent Transportation Systems (ITS) services to a greater extent. However, charging rechargeable batteries for EV remains a major concern among researchers. Moreover, EV charge management systems require optimal and personalized charging schedules that opt for a centralized controller. This article proposes a Software-Defined Network (SDN)-assisted EV Charge (SEVC) scheduling and management strategy for effective charge scheduling and providing personalized charging services. The SEVC framework has SDN as a centralized controller that receives the charging requests from Vehicular Edge Computing (VEC) servers. The proposed Federated Support Vector Machine (FS) algorithm in SEVC trains the local model available in VEC nodes and updates the SDN global model. The FS algorithm estimates EV charging demand and schedules EV to charge from optimal Recharging Terminals (RT). Moreover, based on the historical charging requirements of EV, the SEVC framework predicts future charging demands of EV, which helps in scheduling and managing EV charging. Since model parameters alone are transmitted to SDN via VEC nodes, the overhead of the SEVC framework is reduced drastically. Our experimental analysis shows that the proposed SEVC framework is 17.32% more efficient than existing algorithms in terms of accuracy, latency in processing charging requests, waiting time of EV, and total running time of algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".