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Energy Management Systems for Electric Vehicle Charging Stations: A Review

2022· review· en· W4310972373 on OpenAlexaff
Anindita Golder, Sheldon S. Williamson

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typereview
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsElectric vehicleEnergy managementAutomotive engineeringEnergy (signal processing)Computer scienceElectrical engineeringAerospace engineeringEnergy management systemEnvironmental scienceEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

Looking at how electric vehicle charging stations are using renewable and clean energy resources such as fuel cells, solar photovoltaic and energy storage systems to reduce the impact on the grid, it is important that these resources are managed optimally. Therefore, the energy management systems (EMS) play a significant role for charging stations. In this paper, we analyze the different strategies and types of energy management systems used for electric vehicle charging stations.

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 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.841
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.045
GPT teacher head0.266
Teacher spread0.221 · 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 teacher head, not a consensus.

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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics SocietySame topicElectric Vehicles and InfrastructureFrench-language works237,207