User-centric Charging Service Recommendation for Electric Vehicles
Bibliographic record
Abstract
The rapid expansion of the Internal Combustion Engine (ICE) vehicles in the transportation system has caused serious concerns about air pollution, prompting the introduction of low-carbon vehicles as an alternative solution. However, establishing a transition to Electric Vehicles (EVs) demands appropriate infrastructure before large-scale manufacturing can be feasibly sustained. Unfortunately, the steep installation costs and the improper placement of Charging Stations (CS) have prevented the development of charging infrastructure, impeding its ability to satisfy the escalating number of EVs. EV manufacturers are trying to elevate user satisfaction and alleviate range anxiety by accurately forecasting State-of-Charge (SoC) amounts and offering user-centric suitable recommendations among all types of available charging services (stationary/fixed or mobile). Nevertheless, the scarcity of historical data for Artificial Intelligence (AI)-based predictions poses a significant difficulty. In the paper we propose an optimized recommendation system within the Internet of Vehicles (IoV) framework, considering both stationary and mobile charging services to optimize the overall experience for EV consumers and owners. The theoretical analysis and evaluations demonstrate a more efficient optimization of recommendations for EVs in designated areas, affirming the effectiveness of the recommended system.
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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 | 0.001 |
| 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".