The present invention relates generally to a closed loop air-fuel mixture control system for an internal combustion engine based on the exhaust emission of the engine, and in particular to such a mixture control system employed in combination with an electronic fuel injection system of the speed density type wherein base fuel injection is derived from data representing intake manifold vacuum and engine speed.
Conventional speed density fuel injection systems employ two sensors, one for sensing the absolute pressure of the engine's intake manifold and the other for detecting the rotational speed of the engine's output shift to derive a control variable as a basic quantity of fuel to be injected to the engine.
In a combined speed-density fuel injection and closed-loop air-fuel mixture system, the detected vacuum is used as an input variable to determine the optimum value of air-fuel ratio. For the closed-loop air-fuel mixture control to operate efficiently, the detected vacuum parameter must be correlated with the quantity of air inducted to the engine as precisely as possible. Such correlation is made on the basis of a fluid model from which the following Equation is derived: ##EQU1## where, Q is the quantity of the inducted air, A represents the cross-sectional area of an airflow conduit, m is the ratio of the cross-sectional area of the conduit to that of a restriction or orifice, P.sub.1 and P.sub.2 are the pressures on the upstream and downstream sides of the orifice respectively, .alpha. represents the flow coefficient of the fluid which is determined by the nature of the fluid, the configuration of the orifice and the pressures P.sub.1 and P.sub.2, .epsilon. is the coefficient of the fluid's compressibility, g is the acceleration of fluid, and .gamma. is the specific gravity of the fluid. Since the above Equation shows that the quantity Q is not exclusively determinable as a function of the pressure P.sub.1 which represents the detected manifold vacuum, difficulties have been encountered to derive the quantity of the air actually supplied to the engine from the detected manifold vacuum and hence the optimum value for the basic quantity of fuel injected.
The usual practice is to use the oxygen content of the exhaust gases as detected by an oxygen sensor to correct the basic fuel injection quantity so that the air-fuel ratio of the mixture is maintained at the stoichiometric point.
However, the closed loop operation is disabled when the oxygen sensor remains inactive during engine cold start or when the environment is going to change rapidly such as during engine acceleration. During such disablements, however, the fuel injection control is deprived of the information with which its basic fuel quantity is controlled.
To meet this problem, the applicants' firm has proposed a learning closed-loop mixture control system in which a nonvolatile memory is used for storing engine status trimming values in a matrix of cell locations which are addressable as a function of different engine operating conditions. The stored trimming values are constantly updated according to varying engine operating conditions and retrieved as a function of intake air pressure and engine speed to correct the quantity fuel to be injected so that the air-fuel ratio is optimized for the varying engine conditions. During closed loop disablement periods, the stored data are retrieved to correct the fuel quantity.
However, the proposed system requires a large memory capacity and a complicated algorithm for controlling the memory.