Increasingly stringent government regulations and the rising price of oil are causing automotive manufactures to develop vehicles capable of obtaining higher fuel economies and lower emissions. To achieve these goals, automotive manufactures have been developing hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV) that use both electricity and petroleum based fuels as their power sources. The additional power the vehicle receives from the high voltage batteries and the electric machines allow automotive manufacturers to downsize the engine inside of the vehicle. Vehicles with smaller engines are able to obtain a higher overall fuel economy because the smaller engine is able to operate at its more efficient high load operating points more frequently.
The addition of a high voltage battery pack and at least one electric machine to a vehicle’s conventional powertrain significantly increases the complexity of optimizing the operation of the vehicle’s powertrain components. In a hybrid vehicle, the driver’s power demand from the accelerator pedal can be met by the engine, the electric machines or a combination of the two. Therefore the vehicle needs a sophisticated control strategy that can divide the driver’s power demand between the different torque producing powertrain components as efficiently as possible. The process of designing an optimal control strategy for a vehicle can require a significant amount of time, money and in-vehicle testing. Therefore many automotive manufacturers use Software-in-the-Loop (SIL) simulation to both speed up and reduce the cost of developing a vehicle’s control strategy. Software-in-the-Loop simulation allows multiple versions of a control strategy to be tested in a virtual environment, in order to find the control strategy version most likely to increase the vehicle’s fuel economy. The best version of the control strategy from the SIL simulations can then be tested later on the vehicle.
The work described in this thesis details the simulation work done with the Ohio State University EcoCAR 2 team to select the team’s hybrid vehicle architecture and develop a control strategy for their vehicle. The EcoCAR 2 competition challenges 15 teams of university students to take a production vehicle and completely redesign it so it gets better fuel economy and emissions, while still maintaining the vehicle’s original performance and consumer acceptability. The Ohio State team had a limited amount of time to both select the hybrid vehicle architecture and design the initial structure of their vehicle’s control strategy. Therefore the team utilized SIL simulations to determine what type of hybrid vehicle architecture best met their goals. Once their hybrid vehicle architecture was selected, a custom model of the architecture was created for the SIL environment. This custom hybrid vehicle model was used to test out different hybrid vehicle control strategies to figure out which strategy could most effectively split up the driver’s power demand between the vehicle’s different torque producing components.