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Record W2167734803 · doi:10.1109/vppc.2008.4677707

Recent status and future prospects of integrated starter-generator based hybrid electric vehicles

2008· article· en· W2167734803 on OpenAlexaff
Peng Zhang, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsAutomotive engineeringTrainElectric machineGenerator (circuit theory)Computer scienceDual (grammatical number)EngineeringNetwork topologyElectric generatorPermanent magnet synchronous generatorControl engineeringVoltageElectrical engineeringPower (physics)Stator

Abstract

fetched live from OpenAlex

The integrated starter generator (ISG) technique has been widely used in the past in hybrid electric vehicle (HEV) drive trains. Instead of 2 independent electric machines, as in conventional vehicles, the ISG functions both as a starting engine as well as a generator, which facilitates the generation of on-board electric power. In this paper, the usage of the ISG in various advanced HEV topologies, including mild HEVs, and different dual-voltage vehicular electrical systems, will be briefly reviewed and compared. In addition, the prime ISG options, including induction machine (IM), permanent magnet synchronous machine (PMSM), as well as the switched reluctance machine (SRM) will be investigated from the point of view of performance characteristics and efficiency requirements. The current status and major improvements in terms of future action plans, for popularly proposed ISG control strategies, will also be presented in this paper.

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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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

Citations20
Published2008
Admission routes1
Has abstractyes

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