Integration of emerging technologies in next-generation electric vehicles: Evolution, advancements, and regulatory prospects
Bibliographic record
Abstract
In the era of shifting toward greener and zero-emission energy production and transportation, Electrical Vehicles (EVs) gained substantial attention worldwide owing to their potentiality of the least carbon footprints on the environment. Nowadays, climate change is considered as the principal side effects of using fossil fuels and using conventional transportation systems. Considering the replacement of conventional plants with Renewable Energy (RE), the electrification of energy consumption is one of the key elements of the energy transition, due to the variability of Renewable Energy Sources (RESs), and EVs are one of the main ways to increase it. Meanwhile, limited infrastructure for charging and maintenance has made us step forward in battery management, Battery Thermal Management Systems (BTMSs), and predictive maintenance for EVs to optimize energy efficiency, to have a range prediction over the distance these smart vehicles will commute. In this study, we had a comprehensive review of integrating Artificial Intelligence (AI), Digital Twins (DTs), and Metaverse into the EVs sector to anticipate the energy consumption behavior of electric machines and vital factors that affect their distance navigation. Having energy-related insights and also developing a road map for project owners to commence replacing traditional methods with cutting-edge optimizing technologies distinguishes this paper from other studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".