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Record W2587853843 · doi:10.1109/ecce.2016.7854759

A primary full-integrated active filter auxiliary power module in electrified vehicle applications with single-phase onboard chargers

2016· article· en· W2587853843 on OpenAlexafffund
Ruoyu Hou, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
FundersCanada Research Chairs
KeywordsCapacitorBattery (electricity)Electrical engineeringInductorBattery chargerActive filterVoltageAC powerBuck converterEngineeringPower (physics)HarmonicBoost converterComputer scienceElectronic engineeringPhysics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.243
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2016
Admission routes2
Has abstractyes

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