No abstract

An accurate estimation of the intake oxygen concentration (IOC) is a prerequisite to develop the optimal control strategy because it directly affects the combustion and emissions. Since the IOC is determined based on the mass conservation law in the intake manifold, estimating the mass flow rate of the exhaust gas recirculation (EGR) is most critical. However, to estimate the EGR mass flow rate, the conventional orifice valve model causes extrapolation error or inaccurate estimation results under transient operating conditions. In order to improve the estimation performance, this study proposes a correction algorithm for estimating IOC. A dynamic correction state is determined for the orifice valve model. In addition, the intake pressure dynamics is also derived based on the energy conservation law in the intake manifold. Using these dynamic models, a nonlinear parameter varying model is determined, and an extended Kalman filter (EKF) is applied to derive the value of correction state. Furthermore, unmeasurable physical states of the nonlinear parameter varying model are estimated from an air system model that only requires the engine-equipped sensors of mass production engines. The correction algorithm is validated through the engine experiments that clearly demonstrate high accuracy of the IOC estimation during transient conditions, which may apply for the vehicle application.

This paper proposes three different methods to estimate the low-pressure cooled exhaust gas recirculation (LP-EGR) mass flow rate based on in-cylinder pressure measurements. The proposed LP-EGR models are designed with various combustion parameters (CP), which are derived from (1) heat release analysis, (2) central moment calculation, and (3) principal component analysis (PCA). The heat release provides valuable insights into the combustion process, such as flame speed and energy release. The central moment calculation enables quantitative representations of the shape characteristics in the cylinder pressure. The PCA also allows the extraction of the influential features through simple mathematical calculations. In this paper, these approaches focus on extracting the CP that are highly correlated to the diluent effects of the LP-EGR, and the parameters are used as the input states of the polynomial regression models. Moreover, in order to resolve the effects of cycle-to-cycle variations on the estimation results, a static model-based Kalman filter is applied to the CP for the practically usable estimation. The fast and precise performance of the proposed models was validated in real-time engine experiments under steady and transient conditions. The proposed LP-EGR mass flow model was demonstrated under a wide range of steady-states with an R2 value over 0.98. The instantaneous response of the cycle-basis LP-EGR estimation was validated under transient operations.