Guest Editorial: Special issue on transportation electrification and vehicle systems
Bibliographic record
Abstract
Electrified vehicles include more electric vehicles (MEVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and electric vehicles (EVs). This Special Issue is focused on transportation electrification and its enabling technologies and related vehicle and grid systems, components, and controllers. It includes state-of-the-art research and development contributions in the following areas: vehicular power electronics and electric motor drives; electric and hybrid electric powertrains; powertrain components and control; propulsion systems; energy storage systems; battery electronics; on-board and off-board chargers, fast chargers, and opportunity chargers; vehicle-to-grid (V2G) interface and grid interface technologies; EVs, HEVs, and PHEVs; electrification of trains and rail vehicles; applications of fuel cells in transportation; and electrical systems and components for various vehicles. For this Special Issue, we received 113 paper submissions. We have conducted a rigorous review process and have accepted 48 high-quality papers published in this Special Issue.We hope that this Special Issue serves as a reference for initiating and continuing state-of-the-art research in the critical areas of transportation electrification and vehicle systems.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".