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Internal Combustion Engines

Real-Time Transient Soot and NOx Virtual Sensors for Diesel Engine Using Neuro-Fuzzy Model Tree and Orthogonal Least Squares

[+] Author and Article Information
Rajit Johri

Mechanical Engineering,  University of Michigan, Ann Arbor, MI 48109rajit@umich.edu

Ashwin Salvi

Mechanical Engineering,  University of Michigan, Ann Arbor, MI 48109asalvi@umich.edu

Zoran Filipi

Mechanical Engineering,  University of Michigan, Ann Arbor, MI 48109filipi@umich.edu

J. Eng. Gas Turbines Power 134(9), 092806 (Jul 23, 2012) (9 pages) doi:10.1115/1.4006942 History: Received December 02, 2011; Revised May 25, 2012; Published July 23, 2012; Online July 23, 2012

Diesel engine combustion and emission formation is highly nonlinear and thus creates a challenge related to engine diagnostics and engine control with emission feedback. This paper presents a novel methodology to address the challenge and develop virtual sensing models for engine exhaust emission. These models are capable of predicting transient emissions accurately and are computationally efficient for control and optimization studies. The emission models developed in this paper belong to the family of hierarchical models, namely the “neuro-fuzzy model tree.” The approach is based on divide-and-conquer strategy, i.e., to divide a complex problem into multiple simpler subproblems, which can then be identified using a simpler class of models. Advanced experimental setup incorporating a medium duty diesel engine is used to generate training data. Fast emission analyzers for soot and NOx provide instantaneous engine-out emissions. Finally, the engine-in-the-loop is used to validate the models for predicting transient particulate mass and NOx .

Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Quasi-steady state model prediction compared with measured soot emissions

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Figure 2

Engine-in-the-loop test cell configuration

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Figure 3

Algorithm for creating virtual emission sensors

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Figure 4

Schematic for generating m-level pseudo random signal

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Figure 5

Staircase test at 2000 rpm

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Figure 6

Throttle signal to engine for SYSID

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Figure 7

Speed signal to engine for SYSID

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Figure 8

Engine visitation points during SYSID test

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Figure 9

Cross correlation of fast NOx and boost

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Figure 10

Network structure of a dynamic neuro-fuzzy model with M local models and n inputs with k tapped-delay output feedback for transient systems

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Figure 11

Construction of a neuro-fuzzy model

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Figure 12

Hierarchical model structure of neuro-fuzzy model tree based emission sensors

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Figure 13

Soft model partition based on engine speed with Gaussian validity function

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Figure 14

Predicted versus measured transient soot emission for a series hydraulic hybrid over FTP75

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Figure 15

Predicted versus measured transient NOx emission for a series hydraulic hybrid over FTP75

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