A primary full-integrated active filter auxiliary power module in electrified vehicle applications with single-phase onboard chargers
Bibliographic record
Abstract
The active filter auxiliary power module (AFAPM) has been proposed for electrified vehicle applications with single-phase onboard chargers. It has two modes: 1) the high-voltage (HV) active filtering mode, in which the vehicle is connected to the grid and the converter assimilates the significant second-order harmonic current introduced by the single-phase power; 2) the low-voltage (LV) battery charging mode, in which the vehicle is running and the converter charges the LV battery from HV battery. This yields a significant capacitance reduction on the DC-link of HV battery charger without an additional active filter (AF) circuit. However, extra relay and inductors are needed. This paper proposes a primary full-integrated AFAPM, which is composed of a two-phase buck converter to work as an AF and a dual-active-bridge (DAB) to operate as a LV battery charger auxiliary power module (APM). With the proposed converter, only an active energy storage capacitor is needed to achieve the active filtering and store the second-order harmonic energy. All the switches and inductors on the primary stage are shared between the AF and APM. Therefore, the cost and size of the dual-voltage charging system in the vehicle applications can be reduced further. To confirm the effectiveness of the proposed converter, a 720 W prototype has been built and experimental results are presented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".