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In this paper, a nonlinear dynamic engine model is introduced, which is developed to represent an SI engine over a wide range of operating conditions. The model includes intake manifold dynamics, fuel film dynamics, and engine rotational dynamics with transport delays inherent in the four-stroke engine cycles, and can be used for designing engine controllers. The model is validated with engine-dynamometer experimental data. The accuracy of the model is evaluated by the comparison of the simulated and the measured data obtained from a 2.0 L inline four-cylinder engine over wide operating ranges. The test data are obtained from 42 operating conditions of the engine. The speed range is from 1500 (rpm) to 4000 (rpm), and the load range is from 0.4 (bar) to WOT. The results show that the simulation data from the model and the measured data during the engine test are in good agreement.

The introduction of inexpensive cylinder pressure sensors provides new opportunities for precise engine control. This paper presents a control strategy of spark advance and air-fuel ratio based upon cylinder pressure for spark ignition engines. In order to extend the cylinder pressure based engine control to a wide range of engine speeds, the appropriate choice of control parameters is important as well as essential. For this control scheme, peak pressure and its location for each cylinder during every engine cycle are the major parameters for controlling the air-fuel ratio and spark timing. However, the conventional method requires the measurement of cylinder pressure at every crank angle degree to determine the peak pressure and its location. In this study, the peak pressure and its location were estimated, using a multi-layer feedforward neural network, which needs only five cylinder pressure samples at -40°, -20°, 0°, 20°, and 40° after TDC.