Method For Analyzing The Behavior Of Complex Systems, Especially Internal Combustion Engines

• US 20070288213A1
• Filed: 07/21/2005
• Published: 12/13/2007
• Est. Priority Date: 07/22/2004
• Status: Active Grant

• Current Assignee: AVL List GmbH (List Privatstiftung)
• Original Assignee: AVL List GmbH (List Privatstiftung)
• Inventors: Ebner, Thomas, Schantl, Rainer, Vogels, Marie-Sophie
• Granted Patent: US 7,848,910 B2
• Time in Patent Office: Days
• US Class Current: 703/8
• CPC Class Codes: G05B 17/02 electric

Abstract

The invention relates to a method for analyzing the behavior of complex systems, particularly internal combustion engines, by forming a model that represents different test variables in accordance with input variables. Said method comprises the following steps:—different test points corresponding to different constellations of test variables are selected, and measurements are taken to determine test variables on a real system;—a model is established which shows the dependence of the test variables on the input variables, and said model is calibrated based on the test values of the real system obtained at the test points;—the model is subdivided into at least two partial models;—at least one first partial model is established which shows a first subset of the test variables;—at least one first principal influential parameter is identified for the first partial model;—an optimal value of the first principal influential parameter is determined at each test point;—the first principal influential parameter is interpolated for all plausible constellations of input variables to calibrate the first partial model;—another partial model is established to show another subset of test variables in accordance with the input variables and the previously determined first subset of test variables;—at least one additional principal influential parameter is identified for said other partial model;—an optimal value of the additional principal influential parameter is determined at each test point.

Claims

1. A method for analyzing the behavior of complex systems, particularly internal combustion engines, by calculating a model which represents various measured variables of the function of input variables, having the following basic steps:

selecting various measured points which correspond to different constellations of measured variables and performing measurements to ascertain measured variables on a real system;

preparing a model which simulates the dependence of the measured variables on the input variables and calibrating the model on the basis of the measured values of the real system obtained at the measured points;

characterized in that the following steps are specifically performed;

subdividing the model into at least two partial models;

preparing at least one first partial model which simulates a first partial set of the measured variables;

identifying at least one first main influence parameter for the first partial model;

determining an optimum value of the first main influence parameter in each measured point;

interpolating the first main influence parameter for all meaningful constellations of input variables to calibrate the first partial model;

preparing a further partial model to simulate a further partial set of the measured variables as a function of input variables and the previously ascertained first partial set of the measured variables;

identifying at least one further main influence parameter for the further partial model;

determining an optimum value of the further main influence parameter in each measured point;

interpolating the further main influence parameter for all meaningful constellations of input variables to calibrate the further partial model.

2. The method according to claim 1, characterized in that multiple partial models are calibrated in sequence, the measured variables ascertained in each partial model representing input variables for subsequent partial models.

3. The method according to claim 1, characterized in that precisely one measured variable is ascertained in a partial model.

4. The method according to claim 1,

characterized in that a single main influence parameter is assigned in a measured variable and the calibration of the associated partial model is performed by full factorial variation.

5. The method according to claim 1, characterized in that the interpolation of the main influence parameter is performed as a linear interpolation.

6. The method according to claim 1, characterized in that an internal combustion engine is analyzed and the following variables are used as the measured variables:

inflowing air mass,

indexed mean pressure,

maximum cylinder pressure, and

exhaust gas temperature before catalytic converter and/or turbine.

7. The method according to claim 1, characterized in that the optimum value of the main influence parameter in the measured points is determined in that a setpoint value for the particular measured variable is calculated for various discrete values of the main influence parameter, i.e., at predetermined support points, an interpolation model is prepared from the values thus calculated, which indicates setpoint values for the particular measured variable for arbitrary values of the main influence parameter, a difference between the setpoint values and the measured actual value of the measured variable is then minimized, and the optimum value of the main influence parameter is concluded therefrom.

9. The method according to claim 1, characterized in that a measured variable of at least one partial model is obtained by the following steps:

selecting multiple first vectors, each of which represents a specific constellation of input variables and which cover the meaningful operating range of the system;

obtaining computational values of a measured variable by using the base model in order to calculate simulation values of the measured variable which are assigned to the first vectors;

selecting multiple second vectors, each of which represents a further constellation of input variables;

performing measurements to obtain experimental values of the measured value which are assigned to the second vectors;

expanding each vector by one dimension by incorporating a block variable, which is fixed at a first value for the first vectors and a second value for the second vectors;

preparing a multivariate regression model, which represents the measured variable as a polynomial function of the expanded vectors of the input variables, on the basis of the previously determined computational values of the measured variable and the experimental values of the measured variable;

determining at least one third vector, which represents a constellation of the input variables for which the system is to be analyzed.

10. The method according to claim 9, characterized in that the regression model is nonlinear.

11. The method according to claim 10, characterized in that the regression model is quadratic.

12. The method according to claim 9, characterized in that the number of the first vectors is greater than the number of the second vectors.

13. The method according to claim 9, characterized in that the system is an internal combustion engine, and speed, load, and possibly further variables are used as the input variables.

8. A method for analyzing the behavior of complex systems, particularly internal combustion engines, by calculating a model which represents various measured variables of the function of input variables, having the following basic steps:

selecting various measured points which correspond to different constellations of measured variables and performing measurements to ascertain measured variables on a real system;

preparing a model which simulates the dependence of the measured variables on the input variables and calibrating the model on the basis of the measured values of the real system obtained at the measured points;

characterized in that the following steps are specifically performed;

selecting multiple first vectors, each of which represents a specific constellation of input variables and which cover the meaningful operating range of the system;

obtaining computational values of a measured variable by using the base model in order to calculate simulation values of the measured variable which are assigned to the first vectors;

selecting multiple second vectors, each of which represents a further constellation of input variables;

performing measurements to obtain experimental values of the measured value which are assigned to the second vectors;

expanding each vector by one dimension by incorporating a block variable, which is fixed at a first value for the first vectors and a second value for the second vectors;

preparing a multivariate regression model, which represents the measured variable as a polynomial function of the expanded vectors of the input variables, on the basis of the previously determined computational values of the measured variable and the experimental values of the measured variable;

determining at least one third vector, which represents a constellation of the input variables for which the system is to be analyzed;

expanding the third vector by a block variable which is fixed to the second value;

calculating the measured variable using the regression model having the expanded third vector as the input variable.

14. The method according to claim 8, characterized in that the regression model is nonlinear.

15. The method according to claim 14, characterized in that the regression model is quadratic.

16. The method according to claim 8, characterized in that the number of the first vectors is greater than the number of the second vectors.

17. The method according to claim 8, characterized in that the system is an internal combustion engine, and speed, load, and possibly further variables are used as the input variables.