Design and Simulation of Vehicle-to-Load System with Nissan Leaf
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".