On Optimization of Plug-in Hybrid Electric Vehicles
The rising concerns about the global warming and emissions on one hand, and the limited sources of fossil fuels on the other hand, has made electrification
of vehicles an interesting topic among researchers and companies. Hybrid electric vehicles (HEV) have been on market or several years. These vehicles proved to decrease the fuel consumption due to downsized engine,
regeneration of the braking energy, and the higher efficiency gained from the extra freedom in choosing the engine operating points. Plug-in HEVs have the additional ability to store energy from the electricity grid using
large capacity batteries. The extra source of energy in these systems opens new questions concerning both the energy management (the strategy that decides the power split between power sources) and sizing of the components.
The first part of this thesis is on energy management strategies for a PHEV. A trivial strategy is to run the vehicle on battery energy until the battery state of charge reaches a lower level and it is kept around that level. This strategy requires no information about the trip; however, it does not result in the best fuel economy. An energy management strategy is proposed for PHEVs which is based on minimizing an equivalent fuel consumption. To implement this strategy, some a priori information about the trip is required. The proposed strategy can improve the fuel economy considerably, even when using only information about the trip length, compared to the trivial discharge strategy. Increasing the information details about the trip results in fuel consumption close to the optimal, calculated by using dynamic programming, when full information about the trip is available.
The second part of the thesis focuses on design of PHEVs. The goal here is to design a vehicle that has low cost and low fuel consumption. An approach based on convex optimization is used for simultaneous optimization of component sizes and energy management for passenger PHEVs. The optimal sizes of key components, i.e. battery, electric motor, and engine/engine generator unit are obtained by minimizing a cost function, including operational and components costs. The effects of different performance requirement levels, change in prices of batteries and energy, and also driving pattern of different drivers, on the optimal design are studied. Since the result of the optimization depends highly on the driving cycle, a systematic way to generate driving cycles that reflect driving patterns of different drivers is given.
Plug-in Hybrid Electric Vehicles