Lean nitrogen oxide trap modeling for lean burn engine control and improved fuel economy
Abstract
Presented is a nonlinear model of a, lean NOx trap with applications to diagnostics and to a lean burn engine control strategy for purging NO x from a trap. A lean NOx trap purging strategy is proposed based on a fundamental understanding of trap behavior and a model of the propagating transient reductant front through the trap during purge. Purging a lean NOx trap of NOx emissions is challenging for several reasons. First, the engine air/fuel ratio must be controlled such that lean and rich operation are possible. Secondly, spark ignition engine torque changes with air/fuel ratio. Therefore, engine torque control is necessary for lean burn applications. Thirdly, the engine a.ir/fuel ratio purge profile, which determines the rich exhaust gas profile used to purge the lean NO x trap, must accomplish two goals. The profile must be designed such that the fuel economy penalty, during purge, is minimized to ensure the overall fuel economy during both lean and rich operation is maximized. In addition, the purge profile must send reductant to reduce NOx and oxygen stored in the trap without excessive tailpipe emissions. The focus of this work is the development of a lean NOx trap model to be used in conjunction with a model based purge profile which meets the aforementioned goals. The purge profile proposed in this work is based on the development of a nonlinear lean NOx trap purge model. The model includes critical purging phenomena such as NOx storage and release dynamics, NO x and oxygen reduction dynamics, and includes an on-line stored oxygen estimate. A first principle based chemical model describing the reduction of NOx during purge forms the fundamental lean NOx trap purge model. Carbon monoxide and hydrogen are considered the most effective reductants and a method of estimating exhaust gas hydrogen content downstream of a catalyst is presented. Validation of the NOx purge model is achieved through engine dynamometer experiments.
Degree
Ph.D.
Advisors
Franchek, Purdue University.
Subject Area
Automotive engineering|Chemical engineering
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.