Patent Application
20120279202
To comply with NOx emission limits for current and future emission levels, the Method for Exhaust-Gas Recirculation (EGR) known, inter alia, from the encyclopedia Motortechnik (Engine Technology), 1st edition, April 2004, p. 9 ff., is used.
This means that the exhaust gas from the engine is cooled by a system provided for that purpose and is returned in defined quantities to the induction tract of the engine. The NOx emissions can be thereby significantly lowered, this tending to result, at the same time, in an increase in particulate matter emissions. The low limits for NOx and particulate matter emissions compel the engine manufacturers to adapt their engines to operate only very slightly below the mandatory limits. At the same time, it is necessary to ensure that the aging-induced change in the system components and the production variance thereof does not result in exceedance of the limiting values. A practical procedure derived therefrom is to use the EGR to control the NOx emissions to the required value. To permit adjustment of the NOx emissions, it is necessary to ascertain the emitted NOx as accurately as possible. However, all of the commercially available NOx sensors for use in production engines are only suited for measuring, respectively controlling NOx emissions in steady-state engine operation, and not in transient operations. The reason for this is the relatively substantial response time, respectively delay time inherent in these sensors relative to the speed and load changes in transient engine operation. In control engineering, response time (also referred to as propagation delay or transport time) is described as the period of time between the change at the system input and the response at the system output of a controlled system. The delay time is described, for example, as the filter constant of a PT1 filter. To deal with this problem, the NOx emission is estimated in transient operation using a mathematical relationship among different variables. Since all of the components and measuring devices of this system are subject to fault tolerances and aging effects, the NOx estimator must be individually corrected for the propagation delay for each engine. The correction is performed under steady-state operating conditions using the NOx values measured correctly under steady-state conditions by the NOx sensor, which are compared to those of the estimator. In transient engine operation, steady-state deviations between the sensor value and the estimate likewise result in a deviation of the emissions in the same direction. The NOx estimator or also the physical model contain(s) characteristics maps and algorithms that make it possible for the actual NOx emissions to be inferred from current measured values (various sensors on the engine) and from the reference values mapped in the characteristic maps, even during transient engine operation. Thus, the NOx control is possible even during transient operations.
If the operating state of the engine is virtually steady-state, the value of the estimator is compared to that of the NOx sensor. In the case of a deviation, the reference values of the estimator are corrected to allow the desired NOx emissions to remain exactly within the bounds of the measuring accuracy of the NOx sensor. This correction value must then be stored for this operating point in order to have a corrective effect even in transient engine operation, during which the NOx sensor again does not yield any reliable values.
It is an object of the present invention to provide a method to implement operating point-dependent storage of the correction value as a characteristics map. This type of correction effectively counters the aging-induced change in NOx emissions and an excessive production variance.
In technical applications, operating point-dependent states, respectively values are often determined as a function of one or a plurality of variables. A method for mathematically describing and storing such a relationship is constituted of a characteristics map having one axis each for the dependence of the input variables and a matrix for the output variable. In processor-based real-time applications, the axes have a predefined number of data points for the particular input variable. The values of the data points must be strictly monotonic. Dimension n*m of the output matrix is defined by number n of the data points of axis i and number m of the data points of axis y. Thus, an element from the output matrix may be uniquely assigned to each value pair of the x data point value and y data point value. The pairings of the x and y input values are described as the operating point. Such characteristic maps are typically parameterized by measurements at the test stand, in the laboratory or also using calculated values. The operating points are selected to allow the individual data point values to be precisely reached by the input variables and, thus, only one element of the output matrix to be value-determining. In the example shown in mapping 1, input variable x has value “90,” and input variable y value “400.” As shown in mapping 4, the thereby value-determining element of the output matrix has value “5.”
If, at this point, the input variable values do not coincide exactly with the values of the data points, but are therebetween (for example, x=80.2 and y=1787), the corresponding output value (z=7.103) must be determined by linear interpolation. The value sought is calculated from the four values of the output matrix that include the operating point as shown in mapping 5. This means that the weighting of the four individual values is dependent on the particular distance from the operating point.
On the other hand, the intersections of the lines among themselves and the intersections of the lines with the axes indicate the individual elements of the output matrix.
The problem entailed in storing the correction value for the reference value used by the estimator is that the ascertained value of the error is not directly assignable to an element of the output matrix, since, typically, the input variables do not reside precisely on a data point value. Thus, useful values are sought for those elements of the output matrix that include the current operating point. For an operating point having the corresponding output value, a theoretically infinite number of value combinations may be found for the individual elements which represent the output value in the case of a linear interpolation. Thus, the problem formulation is initially focused on finding the value combination that also represents an optimal approximation of the particular correction value for the other operating points within the same elements of the output matrix.
Only in exceptional cases are the values of the elements in question able to be determined directly in the first step without requiring further correction. In the case of an operating point displacement, the previously determined values must typically be corrected once more to permit precise mapping of the new value as well.
The change in the values of the elements of the output matrix is converted in a way that allows the element most heavily weighted for the particular operating point to also undergo the greatest change. Analogously thereto, the least heavily weighted element also undergoes the smallest change. The new values for the output matrix elements are not only determined as a function of the new value, but also as a function of historical values. Thus, the characteristic map learns the required correction of the estimator, while taking all previous situations into account.
The following equations describe the learning relationship mathematically.
Mathematical Description of the Adaptation
Identification of the Output Matrix Elements
In the following executions, a, b, c and d, respectively a′, b′, c′ and d′ are assumed to be those output matrix elements that include the current operating point. Each element of the output matrix may influence the output value. The same element may either participate not at all, since it does not directly include the operating point in question, or may also participate in the interpolation as a, b, c or d. Thus, it is absolutely essential that the element be uniquely identified as a, b, c or d, depending on the operating point, to enable the correct value to be stored in the corresponding element. The elements may be identified in this manner by comparing the row and column index of the elements. For purposes of the interpolation, the corresponding elements are already identified by the weighting factor thereof. Thus, it is known which elements participate in the interpolation. For the subsequently described method, it is still to be determined whether an element is participating as a, b, c or d.
Element aj,k is to be considered:
Case 1: Besides aj,k, elements aj,k+1, aj+1,k and aj+1,k+1 also participate in the interpolation→aj,k=a in mapping 4.
Mapping 4: Schematic Representation Case 1
Case 2: Besides aj,k, elements aj,k−1, aj,k+1, k and aj+1,k−1 also participate in the interpolation→aj,k=b in mapping 5.
Mapping 5: Schematic Representation Case 2
Case 3: Besides aj,k, elements aj−1, aj−1,k and aj,k+1 also participate in the interpolation→aj,k=c in mapping 6.
Mapping 6: Schematic Representation Case 3
Case 4: Besides aj,k, elements aj,k−1, aj−1,k and aj−1,k−1 also participate in the interpolation→aj,k=d in mapping 7.
Mapping 7: Schematic Representation Case 4
If none of these cases applies, then aj,k is neither a, b, c nor d, and does not participate in the current learning process.
Equations for Adapting the Output Matrix
The assumption is that the equations for determining weighting factors ga, gb, gc and gd are generally known, so they are not mentioned separately.
The “old” output value is determined from the individual “old” output matrix elements a, b, c and d using weighting factors ga, gb, gc and gd as follows (linear interpolation):
Z=a×ga+b×gb+c×gc+d×dg (6.1)
The “new” output value Z′ is determined from the individual “new” output matrix elements a′, b′, c′ and d′ using weighting factors ga, gb, gc and gd as follows (linear interpolation):
{grave over (Z)}=à×ga+{grave over (b)}×gb+{grave over (c)}×gc+{grave over (d)}×gd (6.2)
The “new” values of the output matrix elements are ascertained as follows:
à=a+Δa (6.3)
{grave over (b)}=b+Δb (6.4)
{grave over (c)}=c+Δc (6.5)
{grave over (d)}=d+Δd (6.6)
if the sum of the implemented change to the old values is defined as:
ΔG=Δa+Δb+Δc+Δd (6.7)
The following equations describe the intention to implement the magnitude of the change in the values in the output matrix elements as a function of the level of the weighting for the interpolation:
Δa=gaΔ{grave over (G)} (6.8)
Δb=gbΔG (6.9)
Δc=gcΔG (6.10)
Δd=gdΔG (6.11)
The difference in output value ΔZ=Z′−Z may also be described by the following equation.
ΔZ=ga×Δa+gb×Δb+gc×Δc+gd×Δd (6.12)
Inserting equations (6.8) through (6.11) into equation (6.12), one ultimately obtains:
This makes it possible for Δa, Δb, Δc and Δd, ultimately also for a′, b′, c′ and d′ to then be determined.
Located in the exhaust tract of combustion engine 1 is an exhaust system, respectively particulate filter and/or a catalytic converter. Situated in the exhaust tract, particularly in the region of the particulate filter, is an NOx sensor 5. A control device 6 communicates with EGR valve 2, throttle valve 3, NOx sensor 5 and physical model 7, as well as with correction module 10 and data interface 11.
Located in the exhaust tract of combustion engine 1 is an exhaust system, respectively particulate filter and/or a catalytic converter. Situated in the exhaust tract, particularly in the region of the particulate filter, is an NOx sensor 5. A control device 6 communicates with EGR valve 2, throttle valve 3, NOx sensor 5 and physical model 7, as well as with correction module 10 and data interface 11.
| Number | Date | Country | Kind |
|---|---|---|---|
| 102009058713.6 | Dec 2009 | DE | national |
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/EP2010/007735 | 12/17/2010 | WO | 00 | 5/23/2012 |
