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Record W3216748084 · doi:10.1109/tte.2021.3131917

Electrified Automotive Propulsion Systems: State-of-the-Art Review

2021· article· en· W3216748084 on OpenAlexaff
Daniel Barroso, Yinye Yang, Fabricio Machado, Ali Emadi

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectrificationPropulsionAutomotive industryAutomotive engineeringElectrically powered spacecraft propulsionProcess (computing)EngineeringCarbon footprintElectricityComputer scienceGreenhouse gasElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The increase in pollution and carbon dioxide in the transportation sector motivates many countries to establish rules to reduce the emission and carbon footprint. The partial or total electrification of the automotive propulsion system is mandatory to minimize the impact of transportation on the environment. There are multiple possible architectures to electrify the vehicle propulsion system, with different degrees of electrification, and each of the architectures can provide a different balance between energy efficiency, vehicle performance, comfort, drivability, and safety. This article aims to present the definition of the automotive propulsion system electrification, the various degrees of electrification and operation modes, and the possible configurations of an electrified propulsion system by the electric machine position. This article also introduces the definition of the subsystems of the propulsion systems and their components, with an explanation of the subsystem and each of their parts. Based on the state-of-the-art technology development, it intends to present the industry’s common sense and knowledge on automotive propulsion system electrification and proposes a process to follow in the electrified propulsion system architecture selection.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 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

Citations31
Published2021
Admission routes1
Has abstractyes

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