Residual gas fraction measurement and computation

Giansetti, P.; Colin, Guillaume; Higelin, Pascal; Chamaillard, Yann

doi:10.1243/14680874jer00407

Cited by 18 publications

(8 citation statements)

References 23 publications

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“…The available data are provided, on one hand, by the modeling and simulation environment Amesim [7], which uses a high frequency zero-dimensional thermodynamic model [4] and, on the other hand, by off line measurements, which are accurate, but complex and costly to obtain, by direct in-cylinder sampling [4]. The problem is thus as follows.…”

Section: Estimation Of the In-cylinder Residual Gas Fractionmentioning

confidence: 99%

Support vector regression from simulation data and few experimental samples

Bloch

Lauer

Colin

et al. 2008

Information Sciences

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a b s t r a c tThis paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data provided by the simulator, possibly biased, into the learning of the model is addressed. This problem, although particular, is very representative of numerous situations met in engine control, and more generally in engineering, where complex models, more or less accurate, exist and where the experimental data which can be used for calibration are difficult or expensive to obtain. The first proposed method constrains the function to fit to the values given by the simulator with a certain accuracy, allowing to take the bias of the simulator into account. The second method constrains the derivatives of the model to fit to the derivatives of a prior model previously estimated on the simulation data. The combination of these two forms of prior knowledge is also possible and considered. These approaches are implemented in the linear programming support vector regression (LP-SVR) framework by the addition, to the optimization problem, of constraints, which are linear with respect to the parameters. Tests are then performed on an engine control application, namely, the estimation of the in-cylinder residual gas fraction in Spark Ignition (SI) engine with Variable Camshaft Timing (VCT). Promising results are obtained on this application. The experiments have also shown the importance of adding potential support vectors in the model when using Gaussian RBF kernels with very few training samples.

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Section: Estimation Of the In-cylinder Residual Gas Fractionmentioning

confidence: 99%

Support vector regression from simulation data and few experimental samples

Bloch

Lauer

Colin

et al. 2008

Information Sciences

View full text Add to dashboard Cite

show abstract

“…The available data are provided, on one hand, from the modeling and simulation environment Amesin (Imagine 2006), which uses a high frequency zero-dimensional thermodynamic model and, on the other hand, from off line measurements, which are accurate, but complex and costly to obtain, by direct in-cylinder sampling (Giansetti et al 2007). The problem is thus as follows.…”

Section: Estimation Of In-cylinder Residual Gas Fractionmentioning

confidence: 99%

Incorporating prior knowledge in support vector regression

Lauer¹,

Bloch²

2007

Mach Learn

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This paper explores the incorporation of prior knowledge in support vector regresion by the addition of constraints. Equality and inequality constraints are studied with the corresponding types of prior knowledge that can be considered for the method. These include particular points with known values, prior knowledge on any derivative of the function either provided by a prior model or available only at some specific points and bounds on the function or any derivative in a given domain. Moreover, a new method for the simultaneous approximation of multiple outputs linked by some prior knowledge is proposed. This method also allows consideration of different types of prior knowledge on single outputs while training on multiple outputs. Synthetic examples show that incorporating a wide variety of prior knowledge becomes easy, as it leads to linear programs, and helps to improve the approximation in difficult cases. The benefits of the method are finally shown on a reallife application, the estimation of in-cylinder residual gas fraction in spark ignition engines, which is representative of numerous situations met in engineering.

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“…The supervisor gives an air mass set point m air sp from the torque set point (figure 4). The intake manifold pressure set point, computed by model (20), is corrected by the error ∆m air (18) to yield the final set point p man sp as …”

Section: Computing the Manifold Pressure Set Pointsmentioning

confidence: 99%

“…The available data are provided, on one hand, from the modeling and simulation environment Amesim [21], which uses a high frequency zero-dimensional thermodynamic model and, on the other hand, from off line measurements, which are accurate, but complex and costly to obtain, by direct in-cylinder sampling [18]. The problem is this.…”

Section: Estimation Of In-cylinder Residual Gas Fractionmentioning

confidence: 99%

On Learning Machines for Engine Control

Bloch

Lauer

Colin

2008

Studies in Computational Intelligence

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Summary. The chapter deals with neural networks and learning machines for engine control applications, particularly in modeling for control. In the first section, some basics on the common features of engine control are recalled, based on a layered engine management structure. Then the use of neural networks for engine modeling, control and diagnosis is briefly described. The need for descriptive models for model-based control and the link between physical models and black box models are emphasized at the end of this section by exposing the grey box approach taken in this chapter. The second section introduces the neural models most used in engine control, namely, MultiLayer Perceptrons (MLP) and Radial Basis Function (RBF) networks. A more recent approach, known as Support Vector Regression (SVR), to build models in kernel expansion form is then presented. The third section is devoted to examples of application of these models in the context of turbocharged Spark Ignition (SI) engines with Variable Camshaft Timing (VCT). This specific context is representative of modern engine control problems. In the first example, the airpath control is studied, where open loop neural estimators are combined with a dynamical polytopic observer. The second example considers modeling the in-cylinder residual gas fraction by Linear Programming SVR (LP-SVR), based on a limited amount of experimental data and a simulator built from prior knowledge. Each example tries to show that models based on first principles and neural models must be joined together in a grey box approach to obtain efficient and acceptable results.

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Residual gas fraction measurement and computation

Cited by 18 publications

References 23 publications

Support vector regression from simulation data and few experimental samples

Support vector regression from simulation data and few experimental samples

Incorporating prior knowledge in support vector regression

On Learning Machines for Engine Control

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