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Record W3127612696 · doi:10.1109/tte.2021.3056432

Powertrain Design and Control in Electrified Vehicles: A Critical Review

2021· review· en· W3127612696 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2021
Typereview
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsOntario Tech University
FundersState Key Laboratory of Advanced Design and Manufacturing for Vehicle BodyNational Natural Science Foundation of China
KeywordsPowertrainEnergy managementSizingAutomotive engineeringComponent (thermodynamics)MechatronicsComputer scienceEngineeringSystems engineeringControl engineeringEnergy (signal processing)Torque

Abstract

fetched live from OpenAlex

Electrified vehicles are considered a promising technology for energy savings and emission reductions. Both powertrain design (configuration design and component sizing) and energy management strategies (EMSs) for electrified vehicles have been studied extensively. However, powertrain design and energy management need to be examined holistically and optimized simultaneously, from a mechatronic viewpoint, for maximizing the potential of electrified powertrains. This article provides a comprehensive, critical review of the current state, and prospects of electrified powertrain design and energy management. The research status in both powertrain design and energy management development is reviewed and discussed. First, the modeling techniques for rapid configuration design are thoroughly reviewed and summarized. Then, the optimization methods for component sizing are elucidated. Next, the classical EMSs are categorized, and several near-optimal strategies used for powertrain design are elaborated. Finally, the current challenges and future trends of electrified powertrain design and control are discussed, which provides a useful reference to researchers in this area.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.037
GPT teacher head0.292
Teacher spread0.255 · 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