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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.020
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

Quick stats

Citations13
Published2018
Admission routes1
Has abstractyes

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