UEGO Based Measurement of EGR Rate and Residual Gas Fraction

Welling, Orian; Collings, Nick

doi:10.4271/2011-01-1289

Cited by 18 publications

(7 citation statements)

References 9 publications

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“…There are several articles where the orifice equation is validated and tuned for this aim [19][20][21]. The composition change estimation is much rare [22][23][24].…”

Section: Exhaust Gas Recirculationmentioning

confidence: 99%

Verification and Comparison of Nine Exhaust Gas Recirculation Mass Flow Rate Estimation Methods

Nyerges

Zöldy

2020

Sensors

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Modern Diesel engines have complex exhaust gas recirculation (EGR) systems. Due to the high temperatures, it is a typical issue to measure EGR mass flow rates in these complex control systems. Therefore, it is expedient to estimate it. Several sensed values can help the estimation: the fresh air mass flow rate, the fuel consumption, pressures, temperatures and mass fractions in the air path system. In most of the articles, the EGR mass flow rate estimation is done by the pressures. However, gas composition based models usually would be better for control aims. In this paper, nine EGR estimation methods will be presented: an important outcome is to present the required sensor architectures and estimation challenges. The comparison will be made by measurement results both in stationary operation points and transient cycles. The estimated EGR mass flow rates will be evaluated by verification conditions. The results will prove that the intake and exhaust side oxygen sensors can give verifiable signals for EGR mass flow rate estimation. In contrast, the applied fresh air mass flow rate and the nitrogen-oxide signals are not accurate enough to provide verifiable EGR mass flow rates in every operating condition. The effects of sensor inaccuracies will also be considered.

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“…There are several articles where the orifice equation is validated and tuned for this aim [19][20][21]. The composition change estimation is much rare [22][23][24].…”

Section: Exhaust Gas Recirculationmentioning

confidence: 99%

Verification and Comparison of Nine Exhaust Gas Recirculation Mass Flow Rate Estimation Methods

Nyerges

Zöldy

2020

Sensors

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“…Since the conventional UEGO sensor becomes inaccurate depending on the pressure and the air-to-fuel ratio (AFR) of its working point, the O 2 level was measured through an ETAS ES636 module that could compensate the O 2 level against the boosted pressure and the AFR. According to Welling and Collings 16 and Yoshimura et al, 17 the EGR rate from each of these devices was defined using…”

Section: Experimental Environmentmentioning

confidence: 99%

“…One approach for EGR rate estimation is based on comparisons of the gas composition in the intake and the gas composition in the exhaust manifold. [14][15][16][17] The result of EGR rate estimation based on the carbon dioxide (CO 2 ) level is commonly used as a reference value for the EGR rate. However, the CO 2 -level-based estimation has a limitation which must be applied in mass-produced engines where estimation relies on the use of a complex and expensive gas analyser.…”

Section: Introductionmentioning

confidence: 99%

Observer design for exhaust gas recirculation rate estimation in a variable-geometry turbocharger diesel engine using a model reference identification scheme

Lee

Park

Sunwoo

2014

Proceedings of the Institution of Mechanical Engineers, Part D:

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Exhaust gas recirculation systems are used in diesel engines to reduce nitrogen oxide emissions. Since excessive recirculation in the cylinders may lead to an increase in generation of particulate matter and to unstable combustion, the exhaust gas recirculation rate should be measured correctly and should be controlled precisely. Unfortunately, the harsh conditions of the exhaust gas recirculation path make it difficult to measure the exhaust gas recirculation mass flow rate directly by using the relevant sensors. Therefore, precise control of the exhaust gas recirculation system depends on accurate estimation of the exhaust gas recirculation rate. To estimate accurately the exhaust gas recirculation rate in a turbocharged diesel engine, we propose an observer based on a model reference identification scheme. A linear parameter-varying model of the intake manifold pressure was derived to serve as the observer’s reference model. An update rule of the observer was designed with the model reference identification scheme. The intake and exhaust temperature models were developed through an empirical approach. Convergence of the proposed observer was proven in terms of the Lyapunov stability criterion. The proposed observer was implemented on a real-time embedded system and validated successfully through experiments on the engine.

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“…This notable dynamic capability is also well known to overcome the limited transient performance of the conventional physical sensors for EGR measurements, such as universal exhaust gas oxygen (UEGO) sensors. 24,25…”

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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UEGO Based Measurement of EGR Rate and Residual Gas Fraction

Cited by 18 publications

References 9 publications

Verification and Comparison of Nine Exhaust Gas Recirculation Mass Flow Rate Estimation Methods

Verification and Comparison of Nine Exhaust Gas Recirculation Mass Flow Rate Estimation Methods

Observer design for exhaust gas recirculation rate estimation in a variable-geometry turbocharger diesel engine using a model reference identification scheme

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

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