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Record W2903188547 · doi:10.1109/ias.2018.8544484

Emerging Wireless Charging Systems for Electric Vehicles - Achieving High Power Transfer Efficiency: A Review

2018· review· en· W2903188547 on OpenAlexaff
Xiaodong Liang, Muhammad Sifatul Alam Chowdhury

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsUnavailabilityWireless power transferWirelessBattery (electricity)Computer scienceElectric vehicleElectrical engineeringEngineeringTelecommunicationsAutomotive engineeringPower (physics)Reliability engineering

Abstract

fetched live from OpenAlex

Electric vehicles (EVs) are increasingly purchased worldwide, however, several issues, such as the limited driving range, battery deterioration, unavailability of charging stations etc, present barriers for advancement of EVs. To overcome the range anxiety of EVs, it's crucial to develop its advanced charging infrastructure. In this paper, a literature review is conducted on EV wireless charging systems with the focus on achieving high power transfer efficiency. A general classification of wireless charging techniques is firstly presented; various state-of-the-art wireless charging techniques reported in the literature are summarized and compared.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.020
GPT teacher head0.264
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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