INTEGRATION OF PLUG-IN HYBRID ELECTRIC VEHICLE WITH THE GRID USING VEHICLE-TO-HOME AND HOME-TO-VEHICLE CAPABILITIES
Bibliographic record
Abstract
The challenge for the next few years is to reduce CO2 emissions, which are the cause of global climate warming. CO2 emissions are mainly due to thermal engines used in transportation. To decrease this emission, a viable solution lies in using non-polluting electric vehicles recharged by low CO2 emission energy sources. \nNew transportation penetration has effected on energy production. Energy production has already reached peaks. At the same time, load demand has drastically increased. Hence, it has become imperative to increase daily energy production. It is well-known that world energy production is mainly produced thermal pollutant power plants, except in Québec, where energy is produced by hydro power plants. \nThe more recent electricity utility trend is that electric, and plug-in hybrid electric vehicles (EV, PHEV) could allow storage and/or production of energy. EV/PHEV batteries can supply the electric motor of the vehicle, and act as an energy storage that assists the grid to supply household loads. This power flow is called vehicle-to-grid, V2G. In this dissertation, the V2G power flow is specifically called vehicle-to-home, V2H. That is battery is used during peak. Moreover, the EV battery is charged during the night, when energy production is low and cheap. This important aspect of Vehicle-to-Home (V2H) is that the vehicle battery is not connected to the grid, but is a part of a house micro-grid. \nThis dissertation presents an offline optimization technique, which includes different energy flows, between the home, EV/PHEV, and a renewable energy source (such as photovoltaic - PV and/or wind) which forms the micro-grid. \nThis optimization has been realized through the dynamic programming algorithm. The optimization objective is to minimize energy cost, including fuel cost, electricity cost, and renewable energy cost. \nTwo fuzzy logic controllers, one located in the vehicle and the second one in the house, have been designed, tested by simulation (online simulation) and validated by experiments. \nThe research analyses two seasonal case studies: one in winter and the other one in summer. In the winter case, a cost reduction of 40 % for the offline simulation, 27 % for the online simulation and 29 % for the experiment is realized whereas in the summer case a cost reduction of 62 % for the offline simulation, 60 % for the online simulation and 64 % for the experiment is presented.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".