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Real-Time Simulation of a Fast Charger Using a Low-Cost FPGA Platform

2022· article· en· W4310971904 on OpenAlexaff
Karim Meddah, Hossein Chalangar, Tarek Ould‐Bachir

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceLatency (audio)ConvertersSolverEmbedded systemState (computer science)Electronic engineeringElectrical engineeringEngineeringVoltageTelecommunications

Abstract

fetched live from OpenAlex

Advances in semiconductor technologies bring to the market power electronic converters (PECs) switched at high frequencies, and make their real-time simulation and testing using a hardware-in-loop configuration difficult. Various approaches have been reported in the literature that demonstrate the feasibility of simulating high switching frequency (HSF) PECs in real-time, but require high-end devices. This paper demonstrates that low-cost FPGA platforms can be leveraged for the accurate real-time simulation of HSF of PEC. The proposed platforms make use of a solver that uses state variables from the previous time-point to determine the status of uncontrolled switches. The performance of the proposed approach is assessed using a battery charger test case. The paper demonstrates that the proposed method is latency- and resource-efficient, and achieves a time-step of 32 ns.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.247
Teacher spread0.213 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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