Electrified Automotive Propulsion Systems: State-of-the-Art Review
Bibliographic record
Abstract
The increase in pollution and carbon dioxide in the transportation sector motivates many countries to establish rules to reduce the emission and carbon footprint. The partial or total electrification of the automotive propulsion system is mandatory to minimize the impact of transportation on the environment. There are multiple possible architectures to electrify the vehicle propulsion system, with different degrees of electrification, and each of the architectures can provide a different balance between energy efficiency, vehicle performance, comfort, drivability, and safety. This article aims to present the definition of the automotive propulsion system electrification, the various degrees of electrification and operation modes, and the possible configurations of an electrified propulsion system by the electric machine position. This article also introduces the definition of the subsystems of the propulsion systems and their components, with an explanation of the subsystem and each of their parts. Based on the state-of-the-art technology development, it intends to present the industry’s common sense and knowledge on automotive propulsion system electrification and proposes a process to follow in the electrified propulsion system architecture selection.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".