Secure Optimal Itinerary Planning for Electric Vehicles in the Smart Grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although the number of electric vehicles (EVs) on the road has been steadily increasing in the last few years, the problems of autonomy and limited driving range of EVs still represent a big challenge for automotive industry. In this paper, we first propose a secure architecture where EVs and the smart grid exchange information for itinerary planning and charging time-slots' reservations at charging stations. The architecture ensures privacy, and includes authentication and authorization in order to secure EVs sensitive information. Second, we introduce a new scheme for EV itinerary planning, which takes into account the state-of-charge of the EV, its destination, and available charging stations on the road. The scheme minimizes the waiting time of the EV and its overall energy consumption to attain destination. MATLAB and CPLEX simulations were performed to show the performance of our proposed scheme. Simulation proved that our model is able to optimize paths in terms of energy consumption and waiting time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it