Indicated pressure-based data-driven diesel engine NOx modeling

Benatzky, Christian; Stadlbauer, Stephan; Formentin, Simone; Schilling, Alexander; Alberer, Daniel

doi:10.1177/1468087414529420

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Generally, the strategies of optimization and control in the diesel engines are usually built upon the basis of physics models, however, the complicated combustion and gas flowfield become obstacles to the development of modeling. 78 To this end, many researchers intend to utilize data-driven techniques to deal with the problems in diesel engines. In this section, we would give a brief review of data-driven approaches applied to diesel engines.…”

Section: Applications Of Data-driven Approaches In Diesel Enginesmentioning

confidence: 99%

Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges

Jiang

Yan

Zhang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Model-based approaches have played a significant role in automotive electronics for diesel engines and aftertreatment systems, while they also have some drawbacks that cannot be neglected, such as difficult model construction and time-consuming model calibration. To this end, data-driven techniques have been put forward and become a topical issue in diesel engines and aftertreatment systems. This article provides an overview of the recent progress of the last decade in data-driven techniques and relevant applications for diesel engines and aftertreatment systems. Moreover, brief future trends, challenges, and opportunities of data-driven strategies and potential applications in this field are further discussed.

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Section: Applications Of Data-driven Approaches In Diesel Enginesmentioning

confidence: 99%

Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges

Jiang

Yan

Zhang

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Therefore, purely data-driven approaches are widely employed in such cases. Benatzky et al [12] presented the design and evaluation of NOx models for heavy-duty off-road engines by using static polynomial black-box modeling. They investigate three approaches, differing in the chosen sets of regressors such as the input signals available on a standard electronic control unit (ECU approach), engine speed and the features extracted from the in-cylinder pressure trace by singular value decomposition (SVD approach), and finally the geometric values from the pressure trace and heat release curves (HRL approach).…”

Section: Introductionmentioning

confidence: 99%

Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification

et al. 2019

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Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements.A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steadystate experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-tointerpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively.

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Thermal dynamics aspect identification of loop heat pipe with capillary tube wick using nonlinear autoregressive exogenous neural network

Setyo Pambudi,

Giarno,

Hatmoko

et al. 2024

Nuclear Engineering and Technology

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Indicated pressure-based data-driven diesel engine NOx modeling

Cited by 6 publications

References 17 publications

Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges

Data-driven control of automotive diesel engines and after-treatment systems: State of the art and future challenges

Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification

Thermal dynamics aspect identification of loop heat pipe with capillary tube wick using nonlinear autoregressive exogenous neural network

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