State Estimation for Spark-Ignition Engines Using New Noise Adaptive Laws In Unscented Kalman Filter

Abstract

To ensure maximum efficiency, low emissions, and lower fuel consumption in the vehicles, advanced control schemes are required. Due to the engine operation, the sensors cannot be installed to measure all the variables that are needed for an effective control. While addressing this issue, a new adaptive Unscented Kalman filter (UKF) algorithm is proposed in this paper to estimate the intake manifold pressure, engine speed, and fuel flow rate. New adaptive laws are designed to update the process noise and measurement noise covariance matrices within the constrained augmented state-based UKF (CASUKF). Another contribution lies in the new combination of the novel adaptive laws, and CASUKF, unlike other variants of the UKF that either adapt the process noise and measurement noise covariance matrices on the standard UKF or implement CASUKF with constant values of the process noise and measurement noise matrices. Simulation results are provided for the nonlinear mean value spark-ignition engine model, and the effectiveness of the algorithm is also compared with other variants of the UKF.