Research On Modeling And Air/Fuel Ratio Control Of Spark-Ignition Engine

The fast developing electronic techniques and computer techniques are increasingly applied to the control system of automobile engine. These techniques lead the engine along the direction of increasing driveability and reducing energy consumption. With the daily tightening standard of exhaust emission, higher requirements are raised for the engine control system, while increasing the control accuracy of air-fuel ratio is crucial to enhancing the engine performance.In view of the physical characters of spark-ignition engine, this thesis presents a mean value engine model(MVEM), which mainly consists of three subsystems - the air-flow dynamics, the fuel-flow dynamics and the crankshaft dynamics. Under the environment of Matlab/Simulink, a simulation model of MVEM is built up, which has reduced the research cost and saved the developing time, with which some inside characters and control strategies can be analyzed and testified.As for the nonlinear part of intake manifold subsystem, such as effective throttle area and volume efficiency, modified BP neural network and RBF neural network are used for identification respectively. Estimation of intake air mass flow is made by use of the model based on neural network, and simulation results indicate that the method proposed in this thesis has increased the corresponding identification precision.Automotive engine is a highly nonlinear and multi-disturb system. Air-fuel ratio has a great influence on the vehicle driveability, fuel economy and emission levels. The comprehensive analysis of air-fuel ratio control strategy is given in the paper. Several methods applied to the air-fuel ratio control are presented respectively, such as the PID control based on classical theory and its combination with nonlinear control methods, modern control technology, intelligent control theory and so on, with which the application and developing trend of air-fuel ratio is also analyzed.Considering the nonlinear characters and time delay of the plant, as far as the transient dynamics of engine is concerned, in this thesis a predictive air-fuel ratio control method is given based on neural network. In this paper a discrete nonlinear engine model is also presented, using forward neural network of three layers to predict and control air-fuel ratio. Simulation results illustrate that the control method proposed in the paper has increased the air-fuel ratio control accuracy under the transient conditions, which is better than that of automotive ECU.