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Converter Topology Comparison for a Two-Stage Level-2 Onboard Charger in 800-V EV Powertrains

2022· article· en· W4310970459 on OpenAlexaff
Rachit Pradhan, Mehdi Narimani, Ali Emadi

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPowertrainTopology (electrical circuits)Battery chargerAutomotive engineeringElectrical engineeringComputer sciencePhysicsEngineeringTorqueBattery (electricity)Power (physics)

Abstract

fetched live from OpenAlex

The operating voltage of powertrains in Battery Electric Vehicles (BEVs) has witnessed an upward trend due to advantages in ultra-fast charging and reduction in run-time losses due to lower operating currents in the powertrain. Original Equipment Manufacturers (OEMs) such as Porsche, Audi, and Lucid Motors have introduced vehicles with powertrains operating from 800 V to 924 V. Due to increased switching losses at these voltage levels, the operation of DC-DC converters requires cascading of existing topologies or a multi-level operation. With increasing battery sizes, traditional Level-2 3.3 kW on-board chargers (OBCs) are deemed insufficient to fully complete a charge overnight. Thus, due to increasing voltage and power levels in commercial BEVs, this paper investigates the Grid to Vehicle (G2V) mode’s priority operating regions for a 11.5 kW Level-2 on-board charger in context of an 800 V powertrain. A design procedure for three potential DC-DC converter configurations is performed, and an optimization process is established. A multi-domain comparison highlighting the cost, advantages, and disadvantages of each configuration has been presented.

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), Research integrity
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 score0.146
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 integrity0.0000.003
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 head0.084
GPT teacher head0.314
Teacher spread0.230 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics SocietySame topicAdvanced Battery Technologies ResearchFrench-language works237,207