${\rm NO}_{\rm x}$ Estimation in Diesel Engines via In-Cylinder Pressure Measurement

Formentin, Simone; Corno, Matteo; Waschl, Harald; Alberer, Daniel; Savaresi, Sergio M.

doi:10.1109/tcst.2013.2252012

Cited by 17 publications

(8 citation statements)

References 20 publications

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“…This solution has shown promising results when dealing with control and estimation issues of internal combustion engines (see [23], [6], [31]). PCA aims at dimensionality reduction or feature extraction.…”

Section: B Grey-box Approachmentioning

confidence: 99%

Engine Knock Margin Estimation Using In-Cylinder Pressure Measurements

Panzani

Östman

Onder

2017

IEEE/ASME Trans. Mechatron.

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Abstract-Engine knock is among the most relevant limiting factors in the improvement of the operation of spark ignited engines. Due to an abnormal combustion inside the cylinder chamber, it can cause performance worsening or even serious mechanical damage. Being the result of complex local chemical phenomena, knock turns out to have a significant random behaviour but the increasing availability of new on-board sensors permits a deeper understanding of its mechanism. The aim of this paper is to exploit in-cylinder pressure sensors to derive a knock estimator, based on the logistic regression technique. Thanks to the proposed approach it is possible to explicitly deal with knock random variability and to define the so-called margin (or distance) from the knocking condition, which has been recently proven to be an effective concept for innovative knock control strategies. In a model-based estimation fashion, two modelling approaches are compared: one relies on well-known physical mechanisms while the second exploits a principal component analysis to extract relevant pressure information, thus reducing the identification effort and improving the estimation performance.

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Section: B Grey-box Approachmentioning

confidence: 99%

Engine Knock Margin Estimation Using In-Cylinder Pressure Measurements

Panzani

Östman

Onder

2017

IEEE/ASME Trans. Mechatron.

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“…This can be done using methods of the PCA, which is essentially a coordinate transformation. 20 Assume n measurements with N samples each, and then we can build up a matrix P of size n × N and use the singular value decomposition 24 to obtain…”

Section: Combustion Information Extractionmentioning

confidence: 99%

“…In fact, there are many potential benefits of using cylinder pressure data for diagnostics and control. 18 For instance, Youssef et al estimated the mass trapped in-cylinder in 2011 based on part of the in-cylinder pressure, 19 and Formentin et al built a polynomial model for NO x 20 using both pressure and other state variables of the engine.…”

Section: Introductionmentioning

confidence: 99%

Real time soot models for production CI engines based on pressure trace information

Zhang

Waschl

Fuerhapter

et al. 2016

International Journal of Engine Research

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Better knowledge of the instantaneous soot and nitrogen oxides (NOx) emissions in Diesel engines would allow better choices of injection parameters and thus lead to better raw emissions than, for instance, with classical averaged measures, like smoke maps. Unfortunately, while for most regulated emissions fast measurements are possible and reasonably fast sensors are available, the same is not true for soot. Against this background, this paper proposes a real time, delay-free soot model based on the pressure trace information. The model essentially maps changes of the pressure trace into changes of the raw soot emissions. The key elements of the approach are the design of experiments, segmentation of the pressure trace and the use of the principal component analysis technique to extract the essential information. The model is based only on data; that is, it needs an initial calibration with working points measured with a standard device, such as an AVL Microsoot or Opacimeter. This paper describes the method and then shows validation results with an additional amount of pilot injection in one cylinder which confirms the delay-free soot estimation.

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“…Proposed method predicts the NOx mass flow with an average relative error of 5.1% under steady-state engine operation, but the prediction performance during transient cycles is still needed to be verified. Formentin et al [15] utilized engine speed and indicated pressure measurements to estimate NOx emissions of a heavy-duty diesel engine. They used principal component analysis and L2 regularization technique to derive a reliable and straightforward estimator.…”

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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${\rm NO}_{\rm x}$ Estimation in Diesel Engines via In-Cylinder Pressure Measurement

Cited by 17 publications

References 20 publications

Engine Knock Margin Estimation Using In-Cylinder Pressure Measurements

Engine Knock Margin Estimation Using In-Cylinder Pressure Measurements

Real time soot models for production CI engines based on pressure trace information

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

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