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Record W1945726897 · doi:10.1109/ecce.2015.7310545

Evaluation of integrated active filter auxiliary power modules in electrified vehicle applications

2015· article· en· W1945726897 on OpenAlexafffund
Ruoyu Hou, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsMcMaster University
FundersCanada Research Chairs
KeywordsCapacitorElectrical engineeringRippleVoltageHarmonicPower electronicsActive filterInverterBattery (electricity)AC powerPower (physics)EngineeringElectronic engineeringTraction (geology)Computer sciencePhysics

Abstract

fetched live from OpenAlex

In electrified vehicles, distinct harmonic currents and corresponding ripple voltages exist mainly on the DC-links of the traction inverter and single-phase high voltage (HV) battery charger. Those harmonic currents are normally filtered by a bulk 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 explores and evaluates a new method for reducing the bulk film capacitors in electrified vehicles. It applies a proposed integrated active filter auxiliary power module (AFAPM) to fulfill both the low voltage (LV) battery charging mode and the active filtering mode functions. Hence, the integrated AFAPM can achieve the AF function without extra power switches, heat sinks, and corresponding gate drivers. Therefore, the proposed method can reduce the cost and volume of the power electronics system in electrified vehicle applications. The evaluation results show that using the AFAPM to mitigate the single-phase HV battery charger's second-order harmonic current is promising and easy to achieve.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.261
Teacher spread0.229 · 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 designOther design
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

Citations13
Published2015
Admission routes2
Has abstractyes

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