INTEGRATION OF PLUG-IN HYBRID ELECTRIC VEHICLE WITH THE GRID USING VEHICLE-TO-HOME AND HOME-TO-VEHICLE CAPABILITIES
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».