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Record W3034833110 · doi:10.1109/tte.2020.3001024

A Simple Three-Level Switching Architecture to Enhance the Power Delivery Duration of Supercapacitor Banks in Electrified Transportation

2020· article· en· W3034833110 on OpenAlexafffund
Yashwanth Dasari, Deepak Ronanki, Sheldon S. Williamson

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercapacitorBattery (electricity)VoltageEnergy storagePower (physics)Electrical engineeringLow voltageElectric vehicleComputer scienceTopology (electrical circuits)EngineeringAutomotive engineeringCapacitancePhysics

Abstract

fetched live from OpenAlex

Hybridization of supercapacitors (SCs) with batteries in electric vehicular applications improves battery life, acceleration, and driving range. However, the efficiency of the power electronic interface that unites batteries and SCs is affected due to wide voltage variations at the SC bank terminals. Moreover, the SCs in a bank offer low storage capacity that restricts them from serving a series of transients on a single charge. This article aims at improving the power delivery duration and energy utilization of SCs with controlled terminal voltage variations using a new bank switching configuration. A simple three-level transition control scheme is implemented for charge and discharge of the SC bank. The effectiveness of the proposed topology is validated using PLECS simulations and experimental studies on a laboratory-developed prototype. Furthermore, the suitability of the proposed SC bank in a hybrid energy storage system (HESS) for an electric vehicle is verified in accordance with varying load demands. The superiority of the proposed architecture is shown in terms of the SC bank voltage variations and depth of discharge under standard urban and highway drive cycles.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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.

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

Citations16
Published2020
Admission routes2
Has abstractyes

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