Automotive engine diagnosis and control via nonlinear estimation

The aim of this article is to explore a possible approach to the problem of designing control and diagnostic strategies for future generations of automotive engines. The work described focuses on the use of physical models to estimate unmeasured or unmeasurable variables and parameters, to be used for control and diagnostic purposes. We introduce the mean value engine model used, present a conceptual strategy for combined control and diagnosis focusing mainly on the problem of air fuel ratio control, review basic concepts related to estimation in nonlinear systems, and propose various forms of the estimators that serve different objectives. We illustrate estimation problems in the context of a simplified engine model, assuming both linear and nonlinear measurement of oxygen concentration in the exhaust. Some simulation and experimental results related to estimation-based control and diagnosis are shown.