摘要：In order to monitor the health of an internal combustion engine, there is a need to check the engine parameters from time to time. These parameters are further used in control and calibration. In this work, mean value engine dynamics of spark ignition model are taken and parameter estimation is done using Recursive Least Squares with forgetting factor. To get the information of unknown states, Unscented Kalman Filter is used for state estimation. Nonlinearity of the system is transferred by distributions. The parameters are estimated as time varying signals without having to define their dynamic behavior or as a function of other variables. This work explores a new combination of Unscented Kalman Filter and Recursive Least Squares with forgetting factor to estimate the physics based model parameters of spark ignition engine. This methodology outperforms many other methodologies by the fact that it does not require huge training data, parameter equations and restores the system nonlinearity. Simulation results are provided for Spark Ignition engine model with constant and time varying parameters. Comparison with Unscented Kalman Filter for the joint engine state and parameter estimation is done.
关键词：Spark Ignition engine;Recursive Least Squares method;Unscented Kalman Filter;coefficient of discharge in throttle body;volumetric efficiency;thermal efficiency
