Adaptive Fuzzy Sliding Mode Observer for Cylinder Mass Flow Estimation in SI Engines

In this paper, a novel algorithm for the cylinder mass flow estimation in four-stroke spark ignition (SI) gasoline engines is developed to improve the estimation precision under transient conditions. The error of the cylinder mass flow is compensated by the error variable of the volumetric efficiency caused by the calibration errors and ambient changes. Since the volumetric efficiency error in SI gasoline engines is dependent on the intake manifold pressure, engine speed, and ambient temperature, a fuzzy logic system (FLS) with three inputs is adopted to parameterize the volumetric efficiency error. With the combination of the FLS and the gasoline engine air path system, an adaptive fuzzy sliding mode observer is presented to estimate the states and parameters jointly and suppress the disturbance from the FLS approximation error. With the conditions of persistent excitation and the given inequality, the convergence of the proposed method is proven. The performance of the proposed method is validated in the environment of a R4-cylinder SI gasoline engine from enDYNA during different driving-cycle conditions, demonstrating that the estimation precision of the cylinder air inflow can be obviously improved by the proposed algorithm under transient conditions.