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Research Papers

Trapped Unburned Fuel Estimation and Robustness Analysis for a Turbocharged Diesel Engine With Negative Valve Overlap Strategy

[+] Author and Article Information
Song Chen

Department of Mechanical Engineering,
McMaster University,
Hamilton, ON L8S 4L7, Canada
e-mail: chens78@mcmaster.ca

Fengjun Yan

Department of Mechanical Engineering,
McMaster University,
Hamilton, ON L8S 4L7, Canada
e-mail: yanfeng@mcmaster.ca

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 15, 2014; final manuscript received October 18, 2014; published online January 27, 2015. Assoc. Editor: Gregory Shaver.

J. Dyn. Sys., Meas., Control 137(6), 061004 (Jun 01, 2015) (13 pages) Paper No: DS-14-1120; doi: 10.1115/1.4028873 History: Received March 15, 2014; Revised October 18, 2014; Online January 27, 2015

Turbocharger and negative valve overlap (NVO) strategy are widely used among advanced combustion modes for internal combustion engines. In order to achieve well emission performance, the NVO can be as large as 100 crank angle (CA) degrees, such that the residual gas fraction can be up to 40%. With such amount of residual gas in the cylinder, the trapped unburned fuel is not trivial. It has a significant impact on the combustion process. However, the trapped unburned fuel mass is hard to be measured directly. In this paper, a novel method based on the signals of oxygen fraction is proposed to estimate it. By analyzing the combustion process, dynamic equations for the intake/exhaust manifolds and in-cylinder oxygen fractions, as well as actual fuel mass in the cylinder are constructed. A smooth variable structure filter (SVSF) was designed to estimate oxygen fractions and further the trapped unburned fuel. As a comparison, Kalman filter (KF) and linear matrix inequality (LMI) based linear parameter-varying (LPV) filter were also applied. Robustness properties of the three observers are analyzed based on the theory of input-to-state (ISS) stability. The proposed models and methods and theoretical analysis are validated and compared through a set of simulations in high-fidelity GT-Power environment. The simulation results match well with theoretical analysis that the SVSF has good properties of strong robustness (with a root mean square error (RMSE) of 0.24, comparing with 0.4 of LPV filter and 0.49 of KF, for the unburned fuel estimation).

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Figures

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Fig. 1

Engine architecture with VGT and EGR loop

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Fig. 2

EGR valve opening and models validation: (a) EGR opening, (b) mr model validation, (c) mcyl model validation, (d) Fcyl model validation, (e) Fint model validation, and (f) Fexh model validation

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Fig. 4

In-cylinder oxygen concentration and combustion efficiency estimated by SVSF: (a) in-cylinder oxygen concentration and (b) combustion efficiency

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Fig. 5

In-cylinder oxygen concentration and combustion efficiency estimated by KF

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Fig. 3

Estimation flow chart

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Fig. 10

Unburned fuel mass estimated by KF with parameter uncertainty: (a) in-cylinder oxygen concentration and (b) combustion efficiency

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Fig. 11

In-cylinder oxygen fraction and combustion efficiency estimated by SVSF with parameter uncertainty

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Fig. 12

Unburned fuel mass estimated by SVSF with parameter uncertainty: (a) in-cylinder oxygen concentration and (b) combustion efficiency

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Fig. 6

Unburned fuel mass estimated by KF: (a) in-cylinder oxygen concentration and (b) combustion efficiency

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Fig. 7

In-cylinder oxygen concentration and combustion efficiency estimated by LPV

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Fig. 8

Unburned fuel mass estimated by LPV: (a) in-cylinder oxygen concentration and (b) combustion efficiency

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Fig. 9

In-cylinder oxygen fraction and combustion efficiency estimated by KF with parameter uncertainty

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Fig. 13

In-cylinder oxygen fraction and combustion efficiency estimated by LPV with parameter uncertainty

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Fig. 14

Unburned fuel mass estimated by LPV with parameter uncertainty: (a) in-cylinder oxygen concentration and (b) combustion efficiency

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Fig. 15

Unburned fuel mass estimated by LPV with parameter uncertainty

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