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Record W6981008972

Design and Simulation of Vehicle-to-Load System with Nissan Leaf

2023· dissertation· en· W6981008972 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyPower (physics)Energy (signal processing)Greenhouse gasEfficient energy useMATLABElectric power systemElectric powerCo-simulationElectricity generationGlobal warming
DOInot available

Abstract

fetched live from OpenAlex

The fact that the Global Warming problem poses a more significant threat to our society every day has pushed us to use energy more efficiently and cleanly. Using power more efficiently in every aspect of our lives can pave the way for achieving the goal of reducing global gas emissions and saving the planet. This can be done by applying various approaches. One of the most effective ways is to use electric car technology, which is one of the best ways not to cause more carbon emissions and to increase energy efficiency and savings.
\nThis study aims to design and simulate a vehicle-to-load system using an electric vehicle, Nissan Leaf, to power emergency independent loads of the Future Building Laboratory (FBL). The FBL is a solar research house at the Loyola Campus of Concordia University, Montréal, Canada. This research facility is built to investigate numerous renewable energy systems that can help achieve the net-zero energy goal for a typical detached single-family dwelling in Québec. It has integrated renewable energy sources such as solar, solar-thermal, and wind, allowing the opportunity to test different power management scenarios.
\nIn this research, the vehicle-to-load system of the FBL and Nissan Leaf is designed and simulated in MATLAB software, considering the house's rated load and the real-life system's exact ratings. The design reflects the actual characteristics of the load, EV battery, and power electronic elements in interaction. The simulation is a straightforward model of the actual system.
\nThe last step is to validate the simulation results. The simulation model was tested experimentally at the PEER group laboratory at Concordia University, using the available converters, devices, and a real-time DSP microcontroller. Various experiments are conducted to observe the system's performance in real conditions. All time-domain and frequency-domain results match the ones obtained via simulation.
\nMethods for enabling the discharging feature of EVs that utilize CHAdeMO are studied and explored. The structure of the CHAdeMO connector and charging sequence are explained. Possible integration methods for Vehicle-to-Home are also explored.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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