Review on State-of-the-Art Unidirectional Non-Isolated Power Factor Correction Converters for Short-/Long-Distance Electric Vehicles
Bibliographic record
Abstract
Electrification of the transportation sector has originated a worldwide demand towards green-based refueling infrastructure modernization. Global researches and efforts have been pondered to promote optimal Electric Vehicle (EV) charging stations. The EV power electronic systems can be classified into three main divisions: power charging station configuration (e.g., Level 1 (i.e., slow-speed charger), Level 2 (i.e., fast-speed charger), and Level 3 (i.e., ultra-fast speed charger)), the electric drive system, and the auxiliary EV loads. This paper emphasizes the recent development in Power Factor Correction (PFC) converters in the on-board charger system for short-distance EVs (e.g., e-bikes, e-trikes, e-rickshaw, and golf carts) and long-distance EVs (passenger e-cars, e-trucks, and e-buses). The EV battery voltage mainly ranges between 36 V and 900 V based on the EV application. The on-board battery charger consists of either a single-stage converter (a PFC converter that meets the demands of both the supply-side and the battery-side) or a two-stage converter (a PFC converter that meets the supply-side requirements and a DC-DC converter that meets the battery-side requirements). This paper focuses on the single-phase unidirectional non-isolated PFC converters for on-board battery chargers (i.e., Level 1 and Level 2 charging infrastructure). A comprehensive classification is provided for the PFC converters with two main categories: (1) the fundamental PFC topologies (i.e., Buck, Boost, Buck-Boost, SEPIC, Ćuk, and Zeta converters) and (2) the modified PFC topologies (i.e., improved power quality PFC converters derived from the fundamental topologies). This paper provides a review of up-to-date publications for PFC converters in short-/long-distance EV applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".