Concept Analysis and Initial Results of Engine-Out NOx Estimator Suitable for on ECM Implementation

Kihas, Dejan; Pachner, Daniel; Baramov, L.; Uchanski, Michael; Naik, Priya; Khaled, Nassim

doi:10.4271/2016-01-0611

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…Основные методы исследования возникающих неисправностей -визуальный осмотр и применение технологического оборудования [2][3][4].…”

Section: материалы и методыunclassified

Determination of Dependence of Emerging Malfunctions in the Electronic Engine Control Unit on the Operating Conditions

Krush,

Galin

2023

VAGAU

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Section: материалы и методыunclassified

Determination of Dependence of Emerging Malfunctions in the Electronic Engine Control Unit on the Operating Conditions

Krush,

Galin

2023

VAGAU

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“…Several phenomenological and physical controloriented models (COMs) of diesel engine performance and emission have been developed for feedback control of emissions. [62][63][64] Physical and chemical submodels such as a spray model, mixture formation, ignition delay, combustion characteristics, and emission formation can be included in a physics-based model providing insight into the combustion phenomena that affect gaseous and particulate emissions. However, despite the ongoing advancements in the physics-based combustion and emission models, they cannot capture all the complex phenomena that take place inside the combustion chamber to exactly predict the emission levels.…”

Section: Motivation and Objectivesmentioning

confidence: 99%

A correlation-based model order reduction approach for a diesel engine NO_xand brake mean effective pressure dynamic model using machine learning

Norouzi

Aliramezani

Koch

2020

International Journal of Engine Research

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A correlation-based model order reduction algorithm is developed using support vector machine to model [Formula: see text] emission and break mean effective pressure of a medium-duty diesel engine. The support vector machine–based model order reduction algorithm is used to reduce the number of features of a 34-feature full-order model by evaluating the regression performance of the support vector machine–based model. Then, the support vector machine–based model order reduction algorithm is used to reduce the number of features of the full-order model. Two models for [Formula: see text] emission and break mean effective pressure are developed via model order reduction, one complex model with high accuracy, called high-order model, and the other with an acceptable accuracy and a simple structure, called low-order model. The high-order model has 29 features for [Formula: see text] and 20 features for break mean effective pressure, while the low-order model has nine features for [Formula: see text] and six features for break mean effective pressure. Then, the steady-state low-order model and high-order model are implemented in a nonlinear control-oriented model. To verify the accuracy of nonlinear control-oriented model, a fast response electrochemical [Formula: see text] sensor is used to experimentally study the engine transient [Formula: see text] emissions. The high-order model and low-order model support vector machine models of [Formula: see text] and break mean effective pressure are compared to a conventional artificial neural network with one hidden layer. The results illustrate that the developed support vector machine model has shorter training times (5–14 times faster) and higher accuracy especially for test data compared to the artificial neural network model. A control-oriented model is then developed to predict the dynamic behavior of the system. Finally, the performance of the low-order model and high-order model is evaluated for different rising and falling input transients at four different engine speeds. The transient test results validate the high accuracy of the high-order model and the acceptable accuracy of low-order model for both [Formula: see text] and break mean effective pressure. The high-order model is proposed as an accurate virtual plant while the low-order model is suitable for model-based controller design.

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Towards ECU-ready nonlinear model predictive control: Tip-in maneuver case study

Šantin

Mikuláš

Pachner

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Concept Analysis and Initial Results of Engine-Out NOx Estimator Suitable for on ECM Implementation

Cited by 5 publications

References 48 publications

Determination of Dependence of Emerging Malfunctions in the Electronic Engine Control Unit on the Operating Conditions

Determination of Dependence of Emerging Malfunctions in the Electronic Engine Control Unit on the Operating Conditions

A correlation-based model order reduction approach for a diesel engine NO_xand brake mean effective pressure dynamic model using machine learning

Towards ECU-ready nonlinear model predictive control: Tip-in maneuver case study

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Concept Analysis and Initial Results of Engine-Out NOx Estimator Suitable for on ECM Implementation

Cited by 5 publications

References 48 publications

Determination of Dependence of Emerging Malfunctions in the Electronic Engine Control Unit on the Operating Conditions

Determination of Dependence of Emerging Malfunctions in the Electronic Engine Control Unit on the Operating Conditions

A correlation-based model order reduction approach for a diesel engine NOxand brake mean effective pressure dynamic model using machine learning

Towards ECU-ready nonlinear model predictive control: Tip-in maneuver case study

Contact Info

Product

Resources

About

A correlation-based model order reduction approach for a diesel engine NO_xand brake mean effective pressure dynamic model using machine learning