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Record W2560341745 · doi:10.1109/wow.2016.7772091

Quasi-wireless capacitive energy transfer for the dynamic charging of personal mobility vehicles

2016· article· en· W2560341745 on OpenAlexafffund
C. W. Van Neste, Arindam Phani, Richard W. Hull, J. E. Hawk, Thomas Thundat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Alberta
FundersCanada Research ChairsUniversity of Alberta
KeywordsWireless power transferWirelessBattery (electricity)Inductive chargingPersonal mobilityMaximum power transfer theoremComputer scienceTransfer (computing)Capacitive sensingPower (physics)Electrical engineeringAutomotive engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Electrified personal mobility vehicles are extremely important tools for elderly and disabled individuals. These devices offer freedom to partake in daily activities that would otherwise be difficult or perhaps impossible. Nearly all electrified mobility vehicles are battery powered, making the act of charging a necessity. Loss of power from an uncharged battery could place users in frustrating or even dangerous situations. Dynamic wireless charging is a promising solution to mitigate this problem. However, high installation costs to sidewalks and roadways could potentially reduce wide-scale use of these wireless solutions. Here we present a method of dynamic wireless power transfer that operates over inexpensive surfaces, greatly reducing the implementation cost. Our system utilizes a unipolar capacitive coupling to transfer energy to a special standing wave receiver which in turn delivers power to the vehicle. We demonstrate charging of a 200 W personal mobility scooter over a 3 m long aluminum foil surface and discuss system efficiency, safety, and optimization parameters.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.418

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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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 designSimulation or modeling
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

Citations22
Published2016
Admission routes2
Has abstractyes

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