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.
Estimation of Intake Oxygen Concentration Using a Dynamic Correction State With Extended Kalman Filter for Light-Duty Diesel Engines
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 22, 2016; final manuscript received July 21, 2017; published online September 8, 2017. Assoc. Editor: Ryozo Nagamune.
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Min, K., Shin, J., Jung, D., Han, M., and Sunwoo, M. (September 8, 2017). "Estimation of Intake Oxygen Concentration Using a Dynamic Correction State With Extended Kalman Filter for Light-Duty Diesel Engines." ASME. J. Dyn. Sys., Meas., Control. January 2018; 140(1): 011013. https://doi.org/10.1115/1.4037390
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