Vehicle-to-everything mode of operation technologies: A state-of-art systematic review
Bibliographic record
Abstract
Electric Vehicles (EVs) are among the counterpart components in sustainable transportation and its economy by reducing carbon emissions, improving air quality, and decreasing reliance on fossil fuels. To this end, Vehicle-to-Everything (V2X) technologies are considered the main part of EV development. Consequently, V2X technologies, including Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B), Vehicle-to-Load (V2L), Vehicle-to-Vehicle (V2V), and Vehicle-to-Ship (V2S) enable energy and data transfer between EVs and their environment, enhancing power grid stability, supporting building energy needs, and facilitating inter-vehicle communication for more efficient and sustainable transportation systems. Based on the importance of V2X, many reviews have been conducted in recent years, highlighting their different aspects and components. However, no one has anticipated a complete overview of this technology. Therefore, this overview paper presents a complete review of EVs force/energy modeling, V2X (V2G, V2B, V2V, V2L, and V2S) technologies, human intention and machine interface, electricity market, as well as the recent Artificial Intelligence (AI)-based strategies integrated with them. Furthermore, the official standards, datasets, and financed projects related to V2X technologies are investigated. Finally, the challenges of each technology are analyzed, and future works are presented. The future of EVs promises widespread adoption driven by advancements in battery technology, enhanced charging infrastructure, and supportive V2X technologies, leading to cleaner and more efficient transportation systems that contribute to global decarbonization and net-zero emission goals.
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.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".