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.
New 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.
The 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.
This 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.
This 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.
Two 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.
The 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.