Search Results

Mean Value Engine Models, commonly used for model based fault diagnosis of SI engines, fail to capture the within-cycle dynamics of engines, often resulting in reduced fault sensitivity. This paper presents a new Hybrid Automata based modeling approach for characterizing the within-cycle dynamics of the thermo-fluidic processes in a Spark Ignition Gasoline Engine, targeted for use in model based fault diagnosis. Further, using a hybrid version of the Extended Kalman Filter (EKF), the states from the nonlinear hybrid automata based dynamic model are estimated and their results validated w.r.t standard industrial simulation software, AMESim. It is observed that due to the switching of within cycle engine dynamics, causing mode change, there is a corresponding change in model's structure which in turn can cause change in system's observability.

Mean Value Engine Models (MVEM) represent average behaviour of an engine over one or more thermodynamic cycles and have been designed for automotive control and diagnosis applications. However, most MVEMs are limited to the description of the dynamics of few engine sub-systems. The diagnostic capabilities of a vehicular engine health management (VEHM) system that uses such MVEMs are limited. In this paper, the process of deriving an MVEM for an entire engine system from an instantaneous within-cycle crank-angle model (WCCM) is described. This is expected to be more beneficial for fault diagnosis in VEHMs since such MVEMs in the context of state observers, can be used to detect a broader range of faults and also generate a larger number of fault signatures for better fault detection and isolation (FDI). Extended Kalman Filter (EKF) based estimators are developed that use this MVEM for state estimation.