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Design of a Low-latency Power Electronics-based Power-HIL System for an EV Motor Controller

2021· article· en· W3217131625 on OpenAlexaff
Troy Eskilson, Carl Ngai Man Ho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsField-programmable gate arrayMotor controllerPower electronicsAmplifierController (irrigation)Transient (computer programming)Computer scienceGate arrayElectrical engineeringEngineeringPower (physics)Electronic engineeringEmbedded systemBandwidth (computing)VoltageTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a platform for conducting Power-hardware-in-the-loop (PHIL) testing of an electric vehicle (EV) motor controller implementing field oriented control (FOC). A fast-switching power-electronics-based amplifier is used to achieve the required transient response speeds necessary for emulating a three phase AC motor with a high fundamental frequency. A field programmable gate array (FPGA) is used to implement the bidirectional high-speed fibre-optic interface, communicating signals between the physical system and a real time digital simulator (RTDS) platform. This allows for a 1 microsecond simulation timestep and a total delay less than 4 microseconds. Using RTDS allows for the simulation to be developed through a traditional circuit simulator interface, rather than requiring a specialized simulation developed to run on an FPGA. Separately from the power amplifier, a resolver emulator system has been developed, allowing for all sensing systems of an EV motor controller to be validated before connecting a physical motor.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.735

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 head0.218
Teacher spread0.208 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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