MétaCan
Menu
Back to cohort
Record W2465611247 · doi:10.1109/ipemc.2016.7512724

New single-stage EV charger for V2H applications

2016· article· en· W2465611247 on OpenAlexaff
Huaibao Wang, Xiaoyu Jia, J. Li, Xiaoqiang Guo, Bo Wang, Xiaoyu Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsCarleton University
FundersYanshan UniversityChina Postdoctoral Science Foundation
KeywordsBattery chargerBackupElectrical engineeringVoltageBattery (electricity)Computer sciencePower (physics)Electronic engineeringEngineeringTopology (electrical circuits)Physics

Abstract

fetched live from OpenAlex

Recently, electric vehicle (EV) chargers with their batteries have been developed for vehicle-to-home (V2H) applications, acting as a backup generation to supply emergency power directly to a home. Traditional EV charger in V2H applications mainly consists of DC/DC and DC/AC stages, which complicate the control algorithm and result in low conversion efficiency. In order to solve the problem, a novel EV charger is proposed for V2H applications. It can boost the battery voltage and output AC voltage with only one-stage power conversion. Also, the DC, 1-phase and 3-phase loads can be fed with the proposed single-stage EV charger. The system control strategy is also provided to deal with versatile load variations. Finally, the performance evaluation results verify the effectiveness of the proposed solution.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.034
GPT teacher head0.285
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Battery Technologies ResearchFrench-language works237,207