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Efficiency Evaluation of 2L and 3L SiC-Based Traction Inverters for 400V and 800V Electric Vehicle Powertrains

2021· article· en· W3193007623 on OpenAlexaff
Wesam Taha, Babak Nahid‐Mobarakeh, Jennifer Bauman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInverterPowertrainSilicon carbideElectric vehicleAutomotive engineeringVoltageTraction motorTorqueEfficient energy useMOSFETElectrical engineeringEnergy consumptionTopology (electrical circuits)Computer scienceEngineeringMaterials scienceTransistorPower (physics)Physics

Abstract

fetched live from OpenAlex

This paper presents an efficiency evaluation of three silicon carbide (SiC) inverter topologies: two-level (2L) voltage source inverter (VSI), 3L neutral-point clamped (NPC), and 3L T-type inverter. Their efficiency is evaluated for powertrains rated at 400 V and 800 V, and using SiC MOSFET devices rated at 650 V and 1200 V operating at a switching frequency of 30 kHz. Firstly, the efficiency is evaluated at different operating load currents, on a per-unit scale. Secondly, the efficiency curves are mapped into torque-speed 2D maps of 120 kW interior permanent magnet (IPM) motors. Thirdly, the resulting efficiency maps are employed in an electric vehicle (EV) model, in order to study the performance of the three inverters on standard drive cycles. At the vehicle level, the energy consumption of the vehicle using the studied inverters is analyzed. It is found that 3L SiC-based inverters are most competitive in the 800 V powertrain. When compared to VSI, NPC and T-type offer 0.6% and 1.2% energy consumption savings. In 400 V, only T-type enjoys 0.9% energy savings over VSI.

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: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.410

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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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
Published2021
Admission routes1
Has abstractyes

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