Predictor Observer Based Adaptive Air-Fuel Ratio Control For SI/HCCI Gasoline Engines

There exists great potential to reduce the fuel consumption and emissions of gasoline engines by simultaneously combining spark ignition(SI)with HCCI combustion,but the combustion process is very sensitive to the in-cylinder air-fuel ratio.The traditional feedback control method is prone to fluctuations in the air-fuel ratio during transience,due to the complicated influence of the intake and exhaust valve’s double variable valve timing(VVT)structure and the obvious delay of air-fuel ratio in the exhaust pipe.For reasons above,this paper presents a control algorithm of air-fuel ratios based on a self-learning predictive observer,trying to solve the control problem of the transient air-fuel ratio in a multi-mode gasoline engine.First of all,a self-learning air-fuel ratio prediction model of a SI / HCCI multimode gasoline engine was established for a 2.0L four-cylinder dual VVT directinjection gasoline engine,and a prediction model of intake air volume was established according to the laws of conservation of mass and energy and the basic flow equation.A hybrid dynamic model of the air-fuel ratio in the exhaust pipe and transport delay model were created based on the estimation of mass conservation and exhaust flow rate.The model can be constantly revised with engine operation to continuously improve accuracy by introducing two self-learning factors into the intake air volume model.Experimental results show that the model estimation error is less than 10mg/cycle..Secondly,an air-fuel ratio control algorithm based on a self-learning disturbance observer is proposed.According to the air-fuel ratio model,feedforward control can be realized through online estimates and compensated by the influence of the fuel injection quantity on the transient change of air-fuel ratio under different working conditions and operating modes.The deviation of the model and the disturbance of the external engine operating environment are regarded as the only disturbances.The predictive disturbance observer is used to make online estimation to compensate the delay effect of the air-fuel ratio and improve the anti-interference ability of the controller.According to the estimation error of the air-fuel ratio model,the self-learning factor of the model is constantly debugged to continuously improve the accuracy of the air-fuel ratio model using the recursive least square method with adopted forgetting factor,so as to improve feedforward accuracy and reduce the burden of disturbance observation.Finally,the control algorithm was formulated using Matlab / Simulink and written to the rapid prototyping controller MicroAutoBox for dynamic control of the engine.Compared with the traditional ADRC algorithm,results show that this algorithm only needs one set of control parameters for the rotating speed from 1500 rpm to 3000 rpm and torque from 10 Nm to 40 Nm.It can also compensate for the delay of 0.2 to 0.7 seconds,can minimize reductions in air-fuel ratio oscillations over 0.04 while simultaneously ensuring response speed,and can track target value changes without overshooting.For throttle or valve load adjustment,the use of the self-learning function gradually optimized air-fuel ratio control,keeping fluctuations in the range of 0.08 or less,an improvement of 16% or more compared to non-self-learning control.In summary,the proposed air-fuel ratio control algorithm effectively improved the transient air-fuel ratio control effect of the SI / HCCI gasoline engine,laying a foundation for the engineering application of SI/HCCI combustion.