Estimation of Ethanol Content and Control of Air-to-Fuel Ratio in Flex Fuel Vehicles

Currently available flexible fuel vehicles (FFVs) can operate on a blend of gasoline and ethanol in any concentration of up to 85% ethanol (93% in Brazil). Accurate estimation of ethanol content is important to cope with potential problems caused by fuel variability. This thesis provides the first-ever comprehensive collection of models, model-based analysis and control design for ethanol estimation in FFVs. The common practice in ethanol content estimation exploits the differences in stoichiometric air-to-fuel ratios (SAFR) between gasoline and ethanol. In this approach, the online identification of SAFR depends on air and fuel metering during the closed-loop regulation of air-to-fuel ratio (AFR) to the stoichiometric value via the feedback of the exhaust gas oxygen sensor (EGO) measurement. In this thesis, first, we develop a simple phenomenological model of the AFR control process and a simple ethanol estimation law which represents the currently practiced system in FFVs. We then show that the SAFR-based ethanol estimation is sensitive to mass air flow (MAF) sensor drifts and/or fuel injector drifts. A physics-based control-oriented model for fuel puddle dynamics in port fuel injection (PFI) FFVs is then proposed. The transient fuel compensation (TFC) derived from this model allows faster ethanol estimation and improved AFR control. Since the conventional SAFR-based ethanol content estimation is sensitive to MAF sensor error, a method to correct MAF sensor error is next proposed. The correction is realized by using additional measurements of the intake manifold pressure to prevent mis-estimation of ethanol content during MAF sensor drifts. Finally, an integrated estimation scheme in direct injection (DI) FFVs is formulated. This process is able to estimate not only ethanol content but also fuel injector drift. Further, it exploits the difference in latent heat of vaporization (LHV) between gasoline and ethanol by using in-cylinder pressure measurements in addition to conventional SAFR-based estimation. The proposed algorithm and the associated parameter tuning method take the data-driven model errors into account. Feasibility of the integrated estimation scheme is validated by simulations and engine dynamometer tests.