Optimizing Electric Vehicle Charging Through an Artificial Intelligence Mechanism for Smart Transportation
Bibliographic record
Abstract
Artificial intelligence of vehicles (AIVs) is poised to revolutionize transportation by promoting low-carbon alternatives, such as electric vehicles (EVs). However, the deployment of fixed charging stations (FCSs) lags behind the growing demand, particularly in rural areas, causing range anxiety among potential EV owners. This article proposes a smart transportation solution within the Artificial Intelligence of Things (AIoT) framework to establish a sustainable, low-carbon system. AIoT systems enable real-time data acquisition and analysis through extensive embedded IoT EV sensors and communication networks for pattern recognition and decision making on the cloud. The proposed solution integrates sensor information from vehicle-to-vehicle (V2V) charging, smart home charging stations (HCSs), and mobile charging services (MCSs), coordinated by the cloud-fog nodes in geographically distributed zones. This article employs the Hungarian matching algorithm for optimal decision making of matching EVs with charging services. Our approach incorporates AIV and AIoT technologies to enhance decision making by using an ensemble-based machine learning (ML) model for precise EV range estimation. The comprehensive details and specifications of these proposed models are elaborated in this article.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".