ECMS Controller Robustness in Flex-Fuel Hybrid Vehicles

Abstract

Control algorithms for hybrid vehicles have undergone extensive research and development leading to near-optimal techniques being employed and demonstrated in prototype vehicles over the previous decade. The use of different implementations of optimal controllers is inevitably linked through the assumed knowledge of the system being controlled. With the growing interest in alternative fuels, such as ethanol, liquified petroleum gas (LPG), and compressed natural gas (CNG) due to enhanced emissions and fuel security considerations, a natural extension is to hybridize these engines to improve fuel economy and CO2 emissions. This step is complicated by the potential variation in fuel composition seen with many gasoline and diesel alternatives, leading to uncertainty in the models used by the hybrid powertrain controller. This work investigates the robustness of one hybrid powertrain optimal control approach, the equivalent consumption minimization strategy (ECMS). Two case studies are performed involving experimentally obtained engine maps from two significantly different prototypes flex-fuel vehicles to quantify the potential impact of map error caused by incorrect fuel assumptions.