Integration of emerging technologies in next-generation electric vehicles: Evolution, advancements, and regulatory prospects
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
• Focus on electric vehicles with considering the economic and technical restrictions. • Energy transition to obtain the most usage of RESs. • Challenges in infrastructure in the development of battery technologies are considered. • Technological integration and practical contributions are investigated. 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.
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 | 0.001 | 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 it