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Record W2017773969 · doi:10.1109/itec.2012.6243500

Development of a bi-directional off-board level-3 quick charging station for electric bus

2012· article· en· W2017773969 on OpenAlexaff
Xiaomin Lu, K. Lakshmi Varaha Iyer, Kaushik Mukherjee, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBattery (electricity)Charging stationElectric vehicleGridElectrical engineeringComputer scienceAutomotive engineeringPower (physics)Engineering

Abstract

fetched live from OpenAlex

Quick charging of an electric bus calls for advanced research and development on quick and safe battery charging technologies with vehicle-to-grid and grid-to-vehicle integration capability. The integration of many buses with high power transfer requirement in a short span of time, would leave a huge stress on the power utility if the power quality is poor. This paper proposes an improved high-performance bi-directional off-board level-3 quick charging station based on vector control technique adopted for electrical machines, for fast and efficient charging of electric bus. The proposed off-board battery charging station can be scaled down for small electric vehicles also. This paper aims to provide a comprehensive understanding of the developed charger at the design stage prior to its implementation and prototyping stages. A detailed description on the design of the developed charger is provided and the performance of the bi-directional charger is elicited through numerical investigations.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.048
GPT teacher head0.299
Teacher spread0.251 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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