In-Cylinder Oxygen Concentration Estimation Based on Virtual Measurement and Data Fusion Algorithm for Turbocharged Diesel Engines
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Combustion Engine Technologies · Combustion and flame dynamics · Advanced Sensor Technologies Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Intelligent Network R&D Institute, China FAW Group Co., Ltd., Changchun 130011 , China
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