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Aftertreatment control and adaptation for automotive lean burn engines with HEGO sensors
Author(s) -
Sun Jing,
Kim Yong Wha,
Wang Leyi
Publication year - 2004
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.786
Subject(s) - adaptation (eye) , lean burn , parametric statistics , automotive industry , automotive engineering , sensitivity (control systems) , adaptive control , computer science , control (management) , engineering , electronic engineering , chemistry , aerospace engineering , physics , artificial intelligence , organic chemistry , combustion , nox , statistics , mathematics , optics
Control of aftertreatment systems for lean burn technology engines represents a big challenge, due to the lack of on‐board emission measurements and the sensitivity of the hardware components to ageing and sulphur poisoning. In this paper, we consider the control and adaptation of aftertreatment systems involving lean NO x trap (LNT). A phenomenological LNT model is presented to facilitate the model‐based control and adaptation. A control strategy based on the LNT model and HEGO (heated exhaust gas oxygen) sensor feedback is discussed. A linear parametric model, which is derived by exploiting the physical properties of the LNT is used for adaptation of trap capacity and feedgas NO x emission models. The conditions under which parameter convergence will be achieved are derived for the proposed adaptive scheme. Simulation results for different scenarios are included to demonstrate the effectiveness of control and adaptation. Copyright © 2004 John Wiley & Sons, Ltd.