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Mean Value Engine Models (MVEMs) are dynamic models which describe dynamic engine variable (or state) responses as mean rather than instantaneous values on time scales slightly longer than an engine event. Such engine variables are the independent variables in nonlinear differential (or state) equations which can be quite compact but nevertheless quite accurate. One of the most important of the differential equations for a spark ignition (SI) engine is the intake manifold filling (often manifold pressure) state equation. This equation is commonly used to estimate the air mass flow to an SI engine during fast throttle angle transients to insure proper engine fueling. The purpose of this paper is to derive a modified manifold pressure state equation which is simpler and more physical than those currently found in the literature. This new formulation makes it easier to calibrate a MVEM for different engines and provides new insights into dynamic SI engine operation.

An important element in nearly all engine control systems is the lambda control feedback system and its associated switching exhaust gas oxygen sensor (EGO). This feedback loop is necessary to keep the mean value of the normalized air/fuel ratio close to one. This is a necessary condition for proper operation of the three-way catalyst systems which are a part of nearly all production emissions control systems. Currently many systems are based on using classical proportional-integral (PI) controllers in lambda control feedback loops which are self-oscillating. Proper design of such systems is dependent on knowing the time delay between the injection time and the time when a corresponding signal appears at the engine exhaust EGO sensor. Recently a new method of designing the vital larnbda control loop has emerged which is claimed to be very robust with respect to the injection/exhaust time delay.