Abstract
In-cylinder oxygen concentration (ICOC) is critical for advanced combustion control of internal combustion engines, and is hard to be accessed in commercial measurements. In existing research, ICOC is predicted by conventional dynamical model based on mass/energy conservation, which suffers from uncertainties such as inaccuracy of volumetric efficiency or the error of orifice geometry. In this paper, we enhance the ICOC estimation by implementing two vital strategies. Firstly, we introduce a method called virtual measurement to resist the conventional model uncertainties, in this method we modeling the ICOC as a function of ignition delay which can be obtained by measuring the in-cylinder pressure. Secondly, we apply Kalman filter to fuse the ICOC results from the conventional dynamical model and the virtual measurement. The data fusion algorithm turns the estimation to a predictor-corrector fashion, which further improves the overall accuracy and robustness. The proposed approach is validated through a calibrated GT-Power engine model. The results show that the estimation error can be achieved form at worst 0.03 to at best 0.01 on steady state.
Highlights
Advanced combustion concepts, including homogeneous charge compression ignition (HCCI), low temperature diffusion combustion (LTDC), premixed charge compression ignition (PCCI), and reactivity controlled compression ignition (RCCI), offer a promising solution for emission-reducing [1] and engine efficiency-promoting [2] for commercial vehicles
An incylinder oxygen concentration (ICOC) model based on ignition delay is proposed; Kalman filter is applied as a data fusion algorithm to improve ICOC estimation results; The influence of gas mass uncertainty to ICOC estimation is studied quantitative; A calibrated GT-power model is designed to validate the proposed method, results show that the ICOC estimation errors improved form at worst 0.03 to at best 0.01 on steady state
The commonly happened uncertainties of gas mass can contaminate the accuracy of the ICOC estimated by mass flow based method
Summary
Advanced combustion concepts, including homogeneous charge compression ignition (HCCI), low temperature diffusion combustion (LTDC), premixed charge compression ignition (PCCI), and reactivity controlled compression ignition (RCCI), offer a promising solution for emission-reducing [1] and engine efficiency-promoting [2] for commercial vehicles These technologies operate in a limited range of engine conditions closing to the combustion stability boundaries [2,3,4]. The virtual measurement is related to the in-cylinder pressure which is independent with the existing dynamical model-based strategy (on the basis of mass/energy conservation). An ICOC model based on ignition delay is proposed; Kalman filter is applied as a data fusion algorithm to improve ICOC estimation results; The influence of gas mass uncertainty to ICOC estimation is studied quantitative;.
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