Low Pressure Cooled EGR Transient Estimation and Measurement for an Turbocharged SI Engine

Liu, Feilong; Pfeiffer, Jeffrey; Caudle, Ron; Marshall, Peter; Olin, Peter M.

doi:10.4271/2016-01-0618

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…Moreover the strategy of burned gas mass fraction will reduce the performance of the engine too much (Darlington, Glover, & Collings, 2006). Apart from diesel engines, there are some methods that has been developed on gasoline engines, regarding estimation of LPEGR rate considering transportation delays from the EGR valve to intake valve of the cylinder which can be helpful to design the LPEGR control during transients (Liu, Pfeiffer, Caudle, Marshall, & Olin, 2016). This study acts as a continuation of above studies in order to find out strategies for emission reduction with existing production engine structure without increasing cost related to high-tech after treatment system or whole other new technology.…”

Section: Abstract−mentioning

confidence: 99%

EGR Transient Operations in Highly Dynamic Driving Cycles

Galindo

Climent

Plá

et al. 2020

Int.J Automot. Technol.

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Section: Abstract−mentioning

confidence: 99%

EGR Transient Operations in Highly Dynamic Driving Cycles

Galindo

Climent

Plá

et al. 2020

Int.J Automot. Technol.

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“…14,15 However, practical realization of LP-EGR faces several challenges due to the highly nonlinear behavior and time delay from a long passage of the LP-EGR system. 16–19 These are especially critical issues for precise estimation of the LP-EGR rate with respect to sophisticated LP-EGR control. 20…”

Section: Introductionmentioning

confidence: 99%

In-cylinder pressure-based convolutional neural network for real-time estimation of low-pressure cooled exhaust gas recirculation in turbocharged gasoline direct injection engines

Jung

Hwang

et al. 2019

International Journal of Engine Research

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Low-pressure cooled exhaust gas recirculation is one of the most promising technologies for improving fuel efficiency of turbocharged gasoline direct injection engines. To realize the beneficial effects of the low-pressure cooled exhaust gas recirculation, the accurate estimation of the low-pressure cooled exhaust gas recirculation rate is essential for precise low-pressure cooled exhaust gas recirculation control. In this respect, previous studies have suggested in-cylinder pressure-based low-pressure cooled exhaust gas recirculation models to obtain the low-pressure cooled exhaust gas recirculation rate into the cylinders with fast response. However, these methods require considerable manual process of feature engineering to extract and analyze the combustion characteristics from the cylinder pressure traces. Furthermore, the performance of the entire model is limited solely to certain hand-crafted characteristics and their mathematical formulations. To resolve these limitations, we propose an in-cylinder pressure-based convolutional neural network for low-pressure cooled exhaust gas recirculation estimation. Because the convolutional neural network model automatically learns the complex function between the raw input of the high-dimensional cylinder pressure traces and the low-pressure cooled exhaust gas recirculation rate through an end-to-end deep learning framework, this convolutional neural network model provides a more effective and precise modeling process compared to the conventional combustion characteristics-based regression models. The proposed convolutional neural network model consists of the input layer with the previous consecutive cycles of the pressure traces to resolve the model uncertainty from cycle-to-cycle variations. This input layer is connected to one convolutional layer, two fully connected layers, and the final output layer that is the target low-pressure cooled exhaust gas recirculation rate. The proposed model was trained, validated, and tested using a total of 50,000 cycles of engine experimental data under various transient driving conditions. The remarkable accuracy of the proposed model was evaluated with R2 values over 0.99 and root mean square error values of less than 1.5% under the transient conditions. Moreover, the real-time performance and low memory requirement were also verified on the target embedded platform.

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“…The cooled external exhaust gas recirculation (EGR) has been widely recognized as an effective way to reduce fuel consumption of spark-ignition engines. 1–3 Mainly, EGR can reduce pumping loss under low-medium load-operating conditions, while it suppresses knock tendency and potentially improves the engine efficiency at higher loads by allowing more optimal combustion phasing and less fuel enrichment. 2 In addition, compared with other fuel saving technologies, it is particularly attractive for downsized turbocharged engines and hybrid applications.…”

Section: Introductionmentioning

confidence: 99%

“…2 In addition, compared with other fuel saving technologies, it is particularly attractive for downsized turbocharged engines and hybrid applications. 3…”

Section: Introductionmentioning

confidence: 99%

Nonlinear observer-based exhaust manifold pressure estimation and fault detection for gasoline engines with exhaust gas recirculation

Jiang

Shen

2019

International Journal of Engine Research

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This article presents a nonlinear observer-based method to estimate the exhaust manifold pressure for the gasoline engines equipped with an exhaust gas recirculation system. A dynamic model is designed to estimate the exhaust manifold pressure, which includes both the intake manifold and exhaust manifold dynamics focusing on gas mass flows. Based on the developed model, a nonlinear exhaust manifold pressure observer is proposed to replace the exhaust manifold pressure sensor, and the global convergence is analyzed by a constructed Lyapunov function and the physical meaning of the time-varying parameters. The experimental validations show that the observer-based exhaust manifold pressure estimator is able to converge to the real value at arbitrary initial value and estimates the exhaust manifold pressure accurately during both the steady-state and transient conditions. Finally, the proposed exhaust manifold pressure observer is applied into the fault detection problem for the exhaust gas recirculation system. The experimental validations show that the observer is able to be used to estimate the exhaust gas recirculation ratio and as an extra signal to assist to detect the faults of the exhaust gas recirculation system accurately.

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Low Pressure Cooled EGR Transient Estimation and Measurement for an Turbocharged SI Engine

Cited by 15 publications

References 6 publications

EGR Transient Operations in Highly Dynamic Driving Cycles

EGR Transient Operations in Highly Dynamic Driving Cycles

In-cylinder pressure-based convolutional neural network for real-time estimation of low-pressure cooled exhaust gas recirculation in turbocharged gasoline direct injection engines

Nonlinear observer-based exhaust manifold pressure estimation and fault detection for gasoline engines with exhaust gas recirculation

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