Application of a combined physical and data-based model for improved numerical simulation of a medium-duty diesel engine

Lang, Marcel; Bloch, Peter; Koch, Thomas; Eggert, Torsten; Schifferdecker, Robin

doi:10.1007/s41104-019-00054-w

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Hybrid emission modeling was used to predict diesel engine emission in past studies. [3][4][5][6] In our previous works, 3,4 GT Power software was used for physical combustion model simulation. Artificial neural network (ANN), Gaussian process models (GPM), polynomial equations and support vector machines (SVM) were used as data-driven parts of the model and trained with physically modeled and measured inputs which were selected partly based on physical considerations related to the formations of NO x , CO, HC, and soot emissions.…”

Section: State Of the Art Emission Modeling And Dimensionality Reductionmentioning

confidence: 99%

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

Mohammad

Rezaei

Hayduk

et al. 2022

International Journal of Engine Research

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The development of internal combustion engines is affected by the exhaust gas emissions legislation and the striving to increase performance. This demands for engine-out emission models that can be used for engine optimization for real driving emission controls. The prediction capability of physically and data-driven engine-out emission models is influenced by the system inputs, which are specified by the user and can lead to an improved accuracy with increasing number of inputs. Thereby the occurrence of irrelevant inputs becomes more probable, which have a low functional relation to the emissions and can lead to overfitting. Alternatively, data-driven methods can be used to detect irrelevant and redundant inputs. In this work, thermodynamic states are modeled based on 772 stationary measured test bench data from a commercial vehicle diesel engine. Afterward, 37 measured and modeled variables are led into a data-driven dimensionality reduction. For this purpose, approaches of supervised learning, such as lasso regression and linear support vector machine, and unsupervised learning methods like principal component analysis and factor analysis are applied to select and extract the relevant features. The selected and extracted features are used for regression by the support vector machine and the feedforward neural network to model the NOx, CO, HC, and soot emissions. This enables an evaluation of the modeling accuracy as a result of the dimensionality reduction. Using the methods in this work, the 37 variables are reduced to 25, 22, 11, and 16 inputs for NOx, CO, HC, and soot emission modeling while maintaining the accuracy. The features selected using the lasso algorithm provide more accurate learning of the regression models than the extracted features through principal component analysis and factor analysis. This results in test errors RMSETe for modeling NOx, CO, HC, and soot emissions 19.22 ppm, 6.46 ppm, 1.29 ppm, and 0.06 FSN, respectively.

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Section: State Of the Art Emission Modeling And Dimensionality Reductionmentioning

confidence: 99%

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

Mohammad

Rezaei

Hayduk

et al. 2022

International Journal of Engine Research

View full text Add to dashboard Cite

show abstract

“…More recent emission standards restrict specific particle sizes and particulate Gray-box models were used to predict NOx, CO, HC, and soot emissions in [24]. A combination of a 1D-CFD model and a GPR ML method with a fixed input feature set were used in [25] for emission modeling including NOx and soot emissions. Using only GPR method as the data driven part of the gray-box model is the limitation of this study.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

et al. 2021

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The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and physical knowledge are examined to develop computationally efficient soot models with good precision. The physical model is a virtual engine modeled in GT-Power software that is parameterized using a portion of experimental data. Different machine learning methods, including Regression Tree (RT), Ensemble of Regression Trees (ERT), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN) are used to develop the black-box models. The gray-box models include a combination of the physical and black-box models. A total of five feature sets and eight different machine learning methods are tested. An analysis of the accuracy, training time and test time of the models is performed using the K-means clustering algorithm. It provides a systematic way for categorizing the feature sets and methods based on their performance and selecting the best method for a specific application. According to the analysis, the black-box model consisting of GPR and feature selection by LASSO shows the best performance with test R2 of 0.96. The best gray-box model consists of SVM-based method with physical insight feature set along with LASSO for feature selection with test R2 of 0.97.

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Kombination von datenbasiertem Verbrennungsmodell und erweiterter Turbolader–Simulationsmethodik

Lang

Koch

Eggert

et al. 2021

Experten-Forum Powertrain: Ladungswechsel Und Emissionierung 2020

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Application of a combined physical and data-based model for improved numerical simulation of a medium-duty diesel engine

Cited by 5 publications

References 17 publications

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

Physical-oriented and machine learning-based emission modeling in a diesel compression ignition engine: Dimensionality reduction and regression

Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

Kombination von datenbasiertem Verbrennungsmodell und erweiterter Turbolader–Simulationsmethodik

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