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A New Configuration and Bypassing Strategy for Dynamic Wireless EV Charging

2020· article· en· W3048145997 on OpenAlex
Ali Ramezani, Mehdi Narimani

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
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTransmitterTopology (electrical circuits)Electromagnetic coilElectrical engineeringWirelessInverterNetwork topologyComputer scienceInductive chargingWireless sensor networkVoltageElectronic engineeringWireless networkEngineeringTelecommunicationsComputer networkChannel (broadcasting)

Abstract

fetched live from OpenAlex

This paper presents a new dynamic wireless Electric Vehicle (EV) charging topology with a lower number of switches, resonant components, and implementation cost compared to conventional dynamic wireless charger topologies. Conventional dynamic wireless EV chargers require a full-bridge inverter and a resonant network for each transmitter coil. The implementation cost of the conventional dynamic wireless chargers is one of the main barriers to the development of dynamic chargers. In the proposed topology, multiple transmitter coils are supplied by a single inverter and LCC network. Each transmitter side coil will turn on only when the load is detected and other coils are bypassed. Moreover, a new load detection method is proposed in this paper to control the proposed topology. In this method, only the amplitude of the transmitter side coils voltages is used to detect the load which can easily be implemented. Therefore, the implementation cost can be reduced significantly. The proposed circuit is analyzed in detail and simulation 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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.523

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 head0.019
GPT teacher head0.230
Teacher spread0.210 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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