摘要：AbstractAutomotive engine is a sophisticated dynamical control system involving both continuous-time dynamic behavior and event-based cyclic state transition. To grasp the engine dynamics accurately, this paper proposes a hybrid model structure for automotive gasoline engines that not only consists of the continuous air path model, actuator response model, but also includes the cyclic residual gas mass model and combustion process model. The proposed model adopts an extended Kalman filter to on-line estimate the cyclic state and it has the potential to be applied for the real-time optimal control design to improve the transient control performance. The precision of the model has been evaluated by comparing with the measurement data and the validation results demonstrate the satisfactory model matching behavior.
关键词：Keywordscycle-by-cycle modelingspark ignition engineExtended Kalman FilterGauss Process Regression
