A Predictable and Real-Time Electric Vehicle Charging Framework with a Dynamic Protection System
Bibliographic record
Abstract
The increasing adoption of electric vehicles demands advanced charging frameworks that ensure real-time safety, operational efficiency, and cost-effectiveness. This paper presents a dual-system architecture combining real-time safety monitoring with predictive scheduling for enhanced performance. The proposed system utilizes an adaptive protection mechanism that dynamically adjusts thresholds and implements a sophisticated tiered fault response strategy to safeguard charging operations for Level 1 and Level 2 chargers under varying conditions. Complementing this, a predictive model based on advanced Long Short-Term Memory networks leverages historical and real-time grid data to forecast optimal charging windows, significantly reducing electricity costs and grid stress. Secure data communication between the on-site controller and the cloud is facilitated through robust protocols, enabling seamless real-time monitoring and intelligent decision-making. Experimental results highlight the system's effectiveness, achieving over 95% fault detection accuracy, substantial cost savings of up to $ 0.05 per session, and ensuring scalability for diverse applications in residential and commercial environments. By integrating adaptive protection mechanisms with predictive scheduling models, the proposed framework addresses the inherent limitations of conventional static systems, offering a highly scalable, reliable, and economically efficient solution for modern electric vehicle charging infrastructure. This innovative approach sets a new standard by advancing safety, optimization, and sustainability, meeting the critical needs of current and future charging networks.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".