Applied Integrated Active Filter Auxiliary Power Module for Electrified Vehicles With Single-Phase Onboard Chargers
Bibliographic record
Abstract
In single-phase onboard chargers for electrified vehicles, second-order harmonic currents and corresponding ripple voltages exist on the dc bus. The low-frequency harmonic current is normally filtered using a bulk film capacitor or an additional active filter (AF) circuit. This presents an obstacle for improving the power density as well as reducing the cost. This paper proposes a simple and effective method that reduces the bulk capacitor in single-phase chargers and alleviates the low-frequency sinusoidal harmonic current in automotive applications. It applies the low-voltage (LV) battery charger auxiliary power module as an AF to filter the low-frequency harmonic currents in the high-voltage (HV) battery charger when the HV battery is charging. Hence, the integrated active filter auxiliary power module (AFAPM)-based dual-voltage charging system can achieve the AF function without extra power switches, heat sinks, and corresponding gate-drive circuits. In addition, the proposed AFAPM converter can obtain an almost unchanged switch rating to achieve 2.4-kW LV battery charging and 6.6-kW HV active filtering functions. Therefore, the proposed method can reduce the cost for the dual-voltage charging system in electrified vehicles. A 1.2-kW proof-of-concept prototype has also been built and experiments show promising results confirming the effectiveness of the proposed concept.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".