Research Papers: Internal Combustion Engines

In-Cylinder Pressure-Based Low-Pressure-Cooled Exhaust Gas Recirculation Estimation Methods for Turbocharged Gasoline Direct Injection Engines

[+] Author and Article Information
Donghyuk Jung

Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro,
Seoul 04763, Korea
e-mail: dh1776@naver.com

Haksu Kim

Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro,
Seoul 04763, Korea
e-mail: yomovs@naver.com

Seungwoo Hong

Research & Development Division,
Hyundai Motor Company,
150 Hyundaiyeonguso-ro Namyang-eup,
Hwaseong 18280, Korea
e-mail: swhong@hyundai.com

Yeongseop Park

Research & Development Division,
Hyundai Motor Company,
150 Hyundaiyeonguso-ro Namyang-eup,
Hwaseong 18280, Korea
e-mail: ypark@hyundai.com

Hyungbok Lee

Research & Development Division,
Hyundai Motor Company,
150 Hyundaiyeonguso-ro Namyang-eup,
Hwaseong 18280, Korea
e-mail: HyungBok.Lee@hyundai.com

Donghee Han

Research & Development Division,
Hyundai Motor Company,
150 Hyundaiyeonguso-ro Namyang-eup,
Hwaseong 18280, Korea
e-mail: dh@hyundai.com

Manbae Han

Department of Mechanical and Automotive
Keimyung University,
1095 Dalgubeol-daero,
Daegu 42601, Korea
e-mail: Mbhan2002@kmu.ac.kr

Myoungho Sunwoo

Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro,
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 May 18, 2018; final manuscript received May 28, 2018; published online December 4, 2018. Assoc. Editor: David Wisler.

J. Eng. Gas Turbines Power 141(4), 042801 (Dec 04, 2018) (13 pages) Paper No: GTP-18-1219; doi: 10.1115/1.4040578 History: Received May 18, 2018; Revised May 28, 2018

This paper proposes three different methods to estimate the low-pressure cooled exhaust gas recirculation (LP-EGR) mass flow rate based on in-cylinder pressure measurements. The proposed LP-EGR models are designed with various combustion parameters (CP), which are derived from (1) heat release analysis, (2) central moment calculation, and (3) principal component analysis (PCA). The heat release provides valuable insights into the combustion process, such as flame speed and energy release. The central moment calculation enables quantitative representations of the shape characteristics in the cylinder pressure. The PCA also allows the extraction of the influential features through simple mathematical calculations. In this paper, these approaches focus on extracting the CP that are highly correlated to the diluent effects of the LP-EGR, and the parameters are used as the input states of the polynomial regression models. Moreover, in order to resolve the effects of cycle-to-cycle variations on the estimation results, a static model-based Kalman filter is applied to the CP for the practically usable estimation. The fast and precise performance of the proposed models was validated in real-time engine experiments under steady and transient conditions. The proposed LP-EGR mass flow model was demonstrated under a wide range of steady-states with an R2 value over 0.98. The instantaneous response of the cycle-basis LP-EGR estimation was validated under transient operations.

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Fig. 1

Schematic diagram of the target engine

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Fig. 2

Software architecture of the CyPAS

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Fig. 3

Engine operating conditions for modeling and validation

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Fig. 4

Overview of the modeling process

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Fig. 5

Comparison of the heat release calculation methods

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Fig. 6

Representative combustion characteristics and trend analysis of the heat release analysis method (engine speed = 2500 rpm)

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Fig. 7

Selected CP for input candidates of the LP-EGR estimation model in the heat release analysis method

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Fig. 8

In-cylinder pressure traces of the spark advance experiments in accordance with different LP-EGR rates (engine speed = 2000 rpm, MAF = 650 mg/str)

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Fig. 9

Clear correlation between the LP-EGR rate and central moments

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Fig. 10

Singular values and eigenvectors of the cylinder pressure set

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Fig. 11

Trend between the LP-EGR and analyzed features

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Fig. 12

Modeling results of the proposed models

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Fig. 13

Comparison of the Kalman filter and moving average filter

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Fig. 14

Steady-state validation results of the proposed models (LP-EGR mass flow)

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Fig. 15

Steady-state validation results of the proposed models (LP-EGR rate)

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Fig. 16

Transient validation results of the proposed models (engine speed = 2250 rpm, MAF = 425 mg/str)

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Fig. 17

Enlargement view of the transient validation results in case of the composite model



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