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Record W3113110972 · doi:10.1109/tte.2020.3044938

800-V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends

2020· article· en· W3113110972 on OpenAlexaff
Iman Aghabali, Jennifer Bauman, Phillip J. Kollmeyer, Yawei Wang, Berker Bilgin, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPowertrainAutomotive engineeringBattery (electricity)PropulsionElectric vehicleRange (aeronautics)Power (physics)Battery capacityComputer scienceEngineeringElectrical engineeringTorqueAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

Two of the main challenges for electric vehicle (EV) adoption include limited range and long recharge times. Ultrafast charging can help to mitigate both these concerns. However, for typical 400-V battery EVs (BEVs), the charging rate is limited by the practical cable size required to carry the charging current. To reach ultrahigh charge rates of 350 or 400 kW, 800-V BEVs are a promising alternative. However, the design of an 800-V EV requires careful new considerations for all electrical systems. This article reviews the current state of 800-V vehicle powertrain electrical design and performs an analysis of benefits, challenges, and future trends regarding multiple vehicle powertrain components. Specifically, detailed benefits and challenges related to the battery, propulsion motor, inverter, auxiliary power unit, and on- and off-board chargers are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations395
Published2020
Admission routes1
Has abstractyes

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