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Record W2896810009 · doi:10.1109/sege.2018.8499462

An Assessment of Batteries form Battery Electric Vehicle Perspectives

2018· article· en· W2896810009 on OpenAlexaff
Devang Kirtikumar Bhatt, Mohamed El Darieby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsElectric vehicleBattery (electricity)Automotive engineeringAutomotive batteryLead–acid batteryElectric-vehicle batteryElectrical engineeringCapacitorBattery electric vehicleDriving rangeElectric motorTraction motorComputer scienceEngineeringVoltagePower (physics)

Abstract

fetched live from OpenAlex

A battery is the key component in Battery Electric Vehicles. Main challenge for the Battery Electric Vehicle is low range and need frequent charging. Another issue is the longer charging time required by the batteries. In this review paper different types of batteries and their development is discussed. Lithium Ion, Zebra and Lead acid battery are discussed from Battery Electric Vehicle point of view. Electric motors another key component in electric vehicles is discussed with application of batteries point. The technological development in the batteries to overcome barriers for the electrical vehicle can be a solution, but use of super capacitor or small internal combustion engines as range extender in conjunction with the batteries sounds to be an smart alternative for the market penetration of electric vehicles. This paper will provide current trends in this field.

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)0.002
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 score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.005

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 head0.014
GPT teacher head0.327
Teacher spread0.314 · 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

Citations25
Published2018
Admission routes1
Has abstractyes

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