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Research Papers: Internal Combustion Engines

Set-Point Adaptation Strategy of Air Systems of Light-Duty Diesel Engines for NOx Emission Reduction Under Acceleration Conditions

[+] Author and Article Information
Kyunghan Min

Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro,
Seongdong-gu,
Seoul 04763, South Korea
e-mail: kyunghah.min@gmail.com

Haksu Kim

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

Manbae Han

Professor
Department of Mechanical and
Automotive Engineering,
Keimyung University,
1095 Dalgubeol-daero,
Daegu 42601, South Korea
e-mail: mbhan2002@kmu.ac.kr

Myoungho Sunwoo

Professor
Department of Automotive Engineering,
Hanyang University,
222 Wangsimni-ro,
Seongdong-gu,
Seoul 04763, South 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 January 21, 2017; final manuscript received October 6, 2017; published online April 10, 2018. Assoc. Editor: David L. S. Hung.

J. Eng. Gas Turbines Power 140(7), 072801 (Apr 10, 2018) (12 pages) Paper No: GTP-17-1027; doi: 10.1115/1.4038543 History: Received January 21, 2017; Revised October 06, 2017

Modern diesel engines equip the exhaust gas recirculation (EGR) system because it can suppress NOx emissions effectively. However, since a large amount of exhaust gas might cause the degradation of drivability, the control strategy of EGR system is crucial. The conventional control structure of the EGR system uses the mass air flow (MAF) as a control indicator, and its set-point is determined from the well-calibrated look-up table (LUT). However, this control structure cannot guarantee the optimal engine performance during acceleration operating conditions because the MAF set-point is calibrated at steady operating conditions. In order to optimize the engine performance with regard to NOx emission and drivability, an optimization algorithm in a function of the intake oxygen fraction (IOF) is proposed because the IOF directly affects the combustion and engine emissions. Using the NOx and drivability models, the cost function for the performance optimization is designed and the optimal value of the IOF is determined. Then, the MAF set-point is adjusted to trace the optimal IOF under engine acceleration conditions. The proposed algorithm is validated through scheduled engine speeds and loads to simulate the extra-urban driving cycle of the European driving cycle. As validation results, the MAF is controlled to trace the optimal IOF from the optimization method. Consequently, the NOx emission is substantially reduced during acceleration operating conditions without the degradation of drivability.

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Figures

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Fig. 3

Extended Kalman filter for oxygen fraction estimation

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Fig. 5

Modeling results about IOF, NOx, and Torque index models: (a) IOF modeling result, (b) NOx modeling result, and (c) torque index modeling result

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Fig. 2

Overall mode structure of air system model

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Fig. 1

Engine experimental environment

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Fig. 4

Correlations between EGR rate and combustion phase under actuator varying conditions: (a) Engine speed: 1250 rpm, (b) engine speed: 1500 rpm, and (c) engine speed: 1750 rpm

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Fig. 7

Cost function curves about intake oxygen fraction: (a) cost function curve and searching optimal point and (b) normalized cost function curve according to weight parameter w1

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Fig. 8

Optimization process

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Fig. 11

Overall structure of the MAF control

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Fig. 12

Estimation results under extended-urban driving cycle (EUDC) engine experiment

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Fig. 13

Engine operating condition under EUDC engine experiment

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Fig. 14

Adaptation results under EUDC engine experiment (a, b)

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Fig. 6

Sensitivities between input variables and the NOx emission: (a) sensitivity of the NOx emission to the change of IOF, (b) sensitivity of the MFB50 to the change of IOF, and (c) sensitivity of the NOx emission to the chnge of MFB50

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Fig. 15

Adaptation results under EUDC engine experiment (c)

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Fig. 9

Overall structure of set-point adaptation algorithm

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Fig. 10

Adaptation process under the acceleration conditions

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