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Research Papers: Internal Combustion Engines

A Real-Time Combustion Control With Reconstructed In-Cylinder Pressure by Principal Component Analysis for a CRDI Diesel Engine

[+] Author and Article Information
Jaesung Chung

Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro, Seongdong-gu,
Seoul 04763, Korea
e-mail: jaesung6@hanyang.ac.kr

Junhyeong Oh

Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro, Seongdong-gu,
Seoul 04763, Korea
e-mail: qwerty993@hanyang.ac.kr

Myoungho Sunwoo

Professor
Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro, Seongdong-gu,
Seoul 04763, Korea
e-mail: msunwoo@hanyang.ac.kr

1Corresponding author.

Contributed by the IC Engine Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 8, 2016; final manuscript received October 29, 2016; published online February 1, 2017. Assoc. Editor: Stani Bohac.

J. Eng. Gas Turbines Power 139(6), 062802 (Feb 01, 2017) (8 pages) Paper No: GTP-16-1206; doi: 10.1115/1.4035395 History: Received June 08, 2016; Revised October 29, 2016

This paper proposes a real-time combustion control algorithm using reconstructed in-cylinder pressure traces by principal component analysis (PCA). The PCA method reconstructs the in-cylinder pressure traces using the principal components of the in-cylinder pressure traces. It was shown that using only five principal components, we were able to reconstruct the in-cylinder pressure traces within 1% root mean squared percent error. Furthermore, the reconstructed in-cylinder pressure traces were validated to effectively reduce the cycle-to-cycle variations caused by the noise signals. As a result, the standard deviation of MFB50 which was calculated from the reconstructed in-cylinder pressure was reduced by 45%. Furthermore, this combustion parameter was applied to a real-time combustion control. Since variations of the control variables for the real-time combustion control were reduced, the control performances were enhanced.

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References

Figures

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Fig. 1

Operating conditions for in-cylinder pressure reconstruction experiments

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Fig. 2

First ten normalized eigenvalues for the in-cylinder pressure traces

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Fig. 3

Selected eigenvectors for the in-cylinder pressure reconstruction

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Fig. 4

Results of the in-cylinder pressure reconstruction (engine speed: 1500 rpm): (a) BMEP 140 kPa, (b) BMEP 460 kPa, and (c) BMEP 750 kPa

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Fig. 5

Results of in-cylinder pressure reconstruction (engine speed: 2000 rpm): (a) BMEP 140 kPa, (b) BMEP 560 kPa, and (c) BMEP 920 kPa

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Fig. 6

Results of heat release analysis (engine speed: 1500 rpm and BMEP: 750 kPa)

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Fig. 7

Results of heat release analysis (engine speed: 2000 rpm and BMEP: 920 kPa)

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Fig. 8

Comparison of ten consecutive ROHR curves: (a) ROHR curves calculated from in-cylinder pressure traces filtered by Savitzky–Golay filter and (b) ROHR curves calculated by PCA method (engine speed: 1500 rpm and BMEP: 460 kPa)

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Fig. 15

Results of in-cylinder pressure reconstruction (engine speed: 2375 rpm and BMEP: 960 kPa)

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Fig. 14

Results of in-cylinder pressure reconstruction (engine speed: 1625 rpm and BMEP: 140 kPa)

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Fig. 13

Results of in-cylinder pressure reconstruction (engine speed: 1125 rpm and BMEP: 580 kPa)

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Fig. 12

Execution time for CyPAS algorithm including the in-cylinder pressure reconstruction algorithm (NEDC test conditions)

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Fig. 11

Comparison of IMEP calculation results for NEDC test case (Savitzky–Golay filter and PCA method)

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Fig. 10

Comparison of SOC calculation results for NEDC test case (Savitzky–Golay filter and PCA method)

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Fig. 9

Comparison of MFB50 calculation results for NEDC test case (Savitzky–Golay filter and PCA method)

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Fig. 17

Comparison of MFB50 calculation results when main injection timing varies (engine speed: 2000 rpm and BMEP: 600 kPa)

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Fig. 16

Comparison of MFB50 calculation results when main injection timing varies (engine speed: 1500 rpm and BMEP: 300 kPa)

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Fig. 18

Comparison of MFB50 control results between Savitzky–Golay filter and PCA methods

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Fig. 19

Comparison of NOx emission results between Savitzky–Golay filter and PCA method

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Fig. 20

Comparison of integrated NOx emission results between Savitzky–Golay filter and PCA method

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