Smart electric vehicle charging management for smart cities
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
In recent years, attraction to alternative urban mobility paradigms such as electric vehicles (EVs) is increasing since EVs can significantly minimise fossil fuel dependency and reduce carbon emission in urban areas. Nonetheless, there are several barriers toward widespread adoption of EVs. Moreover, as EV penetration increases in urban areas, uncoordinated charging may cause power outage. Deployment of EV charging network can allow EVs to communicate with the service provider to coordinate charging activities. Taking into account, increased growth of EVs, number of charging facilities will be inadequate in urban areas, so efficient EV charging management is required for managing and allocating scarce charging station (CS) resources. In this study, the authors have designed and implemented a smart EV charging management system utilizing charging strategy that includes effective reservation management and efficient slot allocation of CSs. Considering composite cost that includes waiting time, estimated charging time, estimated charging cost, user discontent factor and CS congestion impact in such a method, their scheduling scheme shall furnish a set of optimal solutions. Viewing user discontent factor and average waiting time, they have evaluated performance of proposed strategy. The proposed charging strategy is effective than the existing one in terms of average 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 | 0.000 |
| 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 it