A Structure and Calibration Method for Data-Driven Modeling of NO<sub>X</sub> and Soot Emissions from a Diesel Engine

Grahn, Markus; Johansson, Krister; Vartia, Christian; McKelvey, Tomas

doi:10.4271/2012-01-0355

Cited by 18 publications

(11 citation statements)

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“…The performance of the models for NO X and soot emissions when derived using the B-spline method presented in this paper is better than the performance of the models when they are derived according to the method described in [13]. This is achieved even though fewer nodes are used for the interpolation maps.…”

Section: Discussionmentioning

confidence: 82%

Data-driven emission model structures for diesel engine management system development

Grahn

Johansson

McKelvey

2014

International Journal of Engine Research

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This paper discusses some specific data-driven model structures suitable for prediction of NOX and soot emissions from a diesel engine. The model structures can be described as local linear regression models where the regression parameters are defined by two-dimensional look-up tables. It is highlighted that this structure can be interpreted as a B-spline function. Using the model structure, models are derived from measured engine data. The smoothness of the derived models is controlled by using an additional regularization term and the globally optimal model parameters can be found by solving a linear least-squares problem. Experimental data from a 5-cylinder Volvo passenger car diesel engine is used to derive NOX and soot models, using a leave-one-out cross validation strategy to determine the optimal degree of regularization. The model for NOX emissions predicts the NOX mass flow with an average relative error of 5.1% and the model for soot emissions predicts the soot mass flow with an average relative error of 29% for the measurement data used in this study. The behavior of the models for different engine management system settings regarding boost pressure, amount of exhaust gas recirculation, and injection timing has been studied. The models react to the different engine management system settings in an expected way, making them suitable for optimization of engine management system settings. Finally, the model performance dependence on the selected model complexity, and on the number of measurement data points used to derive the models has been studied.

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Section: Discussionmentioning

confidence: 82%

Data-driven emission model structures for diesel engine management system development

Grahn

Johansson

McKelvey

2014

International Journal of Engine Research

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“…Some also include points from parametric variation studies. [35][36][37][38] Some studies use only very few points. 39,40 It is often the case of studies concerning marine or industrial Diesel engines for which the authors have limited access to appropriate experimental facilities.…”

Section: State Of the Art In Nox And Soot Models Calibrationmentioning

confidence: 99%

Semi-physical NOx and soot model for CI engines: Study of its calibration procedure and portability

Karaky

Marty

Tauzia

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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A series of papers has previously presented a semi-physical model for NOx and soot emissions prediction for diesel engines. In this paper, the work is continued with an original analysis of the model’s capacity to be ported to a new engine and a sensitivity analysis of the number of training points required to obtain the desired accuracy. These two aspects are rarely developed in similar studies.

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“…measurements, the ICE was operated at 2596 input combinations, found according to the D-optimal design methodology [49]. The collected data was fitted to models describing boost pressure, oxygen fraction in the intake manifold, engine torque and NO X emissions, as functions of engine speed, injected fuel and z (see [50], [51], [52] for details). The maps were then enlarged to 10 × 17 × 30 × 30 × 30 grid points using linear interpolation, and the torque map was inverted to get torque as input, rather than fuel flow.…”

Section: A Ice Data Gathering and Calibrationmentioning

confidence: 99%

Automated Engine Calibration of Hybrid Electric Vehicles

Murgovski

Grahn

Johannesson

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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We present a method for automated engine calibration, by optimizing engine management settings and power-split control of a hybrid electric vehicle. The problem, which concerns minimization of fuel consumption under a NOX constraint, is formulated as an optimal control problem. By applying Pontryagin's maximum principle, this study shows that the problem is separable in space. In the case where the limits of battery state of charge are not activated, we show that the optimization problem is also separable in time. The optimal solution is obtained by iteratively solving the power-split control problem using dynamic programming or the Equivalent Consumption Minimization Strategy. In addition, we present a computationally efficient suboptimal solution, which aims at reducing the number of power-split optimizations required. An example is provided concerning optimization of engine management settings and power-split control of a parallel hybrid electric vehicle.

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A Structure and Calibration Method for Data-Driven Modeling of NO_X and Soot Emissions from a Diesel Engine

Cited by 18 publications

References 11 publications

Data-driven emission model structures for diesel engine management system development

Data-driven emission model structures for diesel engine management system development

Semi-physical NOx and soot model for CI engines: Study of its calibration procedure and portability

Automated Engine Calibration of Hybrid Electric Vehicles

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A Structure and Calibration Method for Data-Driven Modeling of NOX and Soot Emissions from a Diesel Engine

Cited by 18 publications

References 11 publications

Data-driven emission model structures for diesel engine management system development

Data-driven emission model structures for diesel engine management system development

Semi-physical NOx and soot model for CI engines: Study of its calibration procedure and portability

Automated Engine Calibration of Hybrid Electric Vehicles

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Product

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A Structure and Calibration Method for Data-Driven Modeling of NO_X and Soot Emissions from a Diesel Engine