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Record W2979770435 · doi:10.1109/ccece.2019.8861908

Design of a Ferrite-Less Power Pad for Wireless Charging Systems of Electric Vehicles

2019· article· en· W2979770435 on OpenAlexaff
Muhammad Sifatul Alam Chowdhury, Xiaodong Liang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Power Transfer Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFerrite (magnet)Electrical engineeringWirelessFerrite coreAutomotive engineeringElectric vehicleInductive chargingComputer scienceEngineeringMaterials sciencePower (physics)TelecommunicationsPhysicsElectromagnetic coil

Abstract

fetched live from OpenAlex

Inductive power transfer (IPT) systems unleashed a new era for wireless charging systems of electric vehicles (EVs). EVs can be charged stationary or in motion through IPT systems. Different types of power pads are introduced to increase the efficiency of IPT systems, among them, the most commonly used power pads is circular structure, another type with good potential is double D (DD) structure as recommended in Society of Automotive Engineers (SAE) recommended practice J2954. In this paper, a new power pad structure is proposed by combining circular and DD power pads to further improve the performance. Physical models of circular and DD power pads are first built by using specifications in SAE J2954, these models are then combined to form the proposed power pad. The design and simulation of the proposed power pad are carried out using the Finite Element Analysis (FEA) simulation software, ANSYS Maxwell 3D. Simulation results indicate that the proposed power pad shows improved performance compared to circular and DD power pads.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.015
GPT teacher head0.205
Teacher spread0.190 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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