MétaCan
Menu
Back to cohort
Record W2218817006 · doi:10.1109/vppc.2015.7353001

Pulsed Injection Braking for EV Power Train: Fault Tolerant Application for Hybrid and Electric Vehicle (HEV - EV)

2015· article· en· W2218817006 on OpenAlexaff
Cynthia Moussa, Cédric Somers, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsElectric vehicleAutomotive engineeringTraction motorAutomotive industryComputer scienceBattery (electricity)Dynamic brakingTraction (geology)Regenerative brakePower (physics)Topology (electrical circuits)Braking systemEngineeringElectrical engineeringRetarderBrake

Abstract

fetched live from OpenAlex

This article is subject to the improvements made over an emergency DC injection braking system for AC motors. The modifications put in place a bi-directional braking circuit capable of flowing the power into the traction system in one direction or recharging the battery in the other. We applied the proposed topology over a specific need: automotive application like HEV, EV and electric motorcycle. With the use of the new semiconductors and computing power, we were able to design a compact and reliable braking system with a low part count. The reduce time and enhance current control make this topology a perfect candidate for a redundant braking unit. A simulation of the system with a well evaluated load will be presented. Our paper will discuss also the advantages and drawbacks of this method.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.231
Teacher spread0.215 · 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 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicElectric and Hybrid Vehicle TechnologiesFrench-language works237,207