EP3833860A1

EP3833860A1 - Method for the model-based control and regulation of an internal combustion engine

Info

Publication number: EP3833860A1
Application number: EP19749301.8A
Authority: EP
Inventors: Knut GRAICHEN; Michael Buchholz; Daniel Bergmann; Jens Niemeyer; Jörg REMELE
Original assignee: MTU Friedrichshafen GmbH
Current assignee: Rolls Royce Solutions GmbH
Priority date: 2018-08-10
Filing date: 2019-07-30
Publication date: 2021-06-16
Also published as: WO2020030481A1; US20210180535A1; DE102018006312A1; DE102018006312B4; CN112513447A

Abstract

The invention relates to a method for the model-based control and regulation of an internal combustion engine (1), in which, as a function of a set torque, injection system set values for controlling the injection system actuators are calculated via a combustion model (20), and gas path set values for controlling the gas path actuators are calculated via a gas path model (22), the combustion model (20) being adapted during operation of the internal combustion engine (1) into the form of a complete data-based model. A measure of quality is minimised by an optimiser (23) by changing the injection system set values and gas path set values within a prediction horizon, and the injection system set values and gas path set values are set by the optimiser (23) as critical for adjusting the operating point of the internal combustion engine (1) by using the minimised measure of quality.

Description

Process for model-based control and regulation of an internal combustion engine

The invention relates to a method for model-based control and regulation of an internal combustion engine, in which, depending on a target torque, a

Combustion model injection system setpoints for controlling the injection system actuators and via a gas path model gas path setpoints for controlling the gas path actuators are calculated.

The behavior of an internal combustion engine is largely determined by an engine control unit as a function of a desired performance. For this purpose, corresponding characteristic curves and characteristic maps are applied in the software of the engine control unit. These are used to calculate the manipulated variables of the internal combustion engine, for example the start of injection and a required rail pressure, from the desired output, for example a target torque. These characteristics / maps are populated by the manufacturer of the internal combustion engine during a test bench run. However, the large number of these characteristic curves / characteristic diagrams and the interaction of the characteristic curves / characteristic diagrams with one another cause a great deal of coordination.

In practice, attempts are therefore made to reduce the coordination effort by using mathematical models. A model-based control and regulating method for an internal combustion engine is known from the unpublished German patent application with the official file number DE 10 2017 005 783.4, in which injection system setpoints are used for a combustion model

Control of the injection system actuators and gas path setpoints for controlling the gas path actuators can be calculated via a gas path model. These setpoints are then changed by an optimizer with the aim of minimizing a measure of quality within a prediction horizon. The minimized quality measure in turn then defines the best possible operating point of the internal combustion engine. From the unpublished German patent application with the official

File number DE 10 2018 001 727.4 is a procedure for adapting the

Combustion model known in addition to the control and regulation process described above. The combustion model is adapted using a first Gauss process model to represent a basic grid and a second Gauss process model to represent adaptation data points. The data for the first Gaussian process model are determined from measured values which are based on a

Single cylinder test bench were obtained. Via a subsequent physical

Modeling, all input variables are varied to cover the entire working range of the internal combustion engine. The data for the second Gauss process model are determined from measured values of a full engine, which were generated during a DoE test bench run (DoE: Design of Experiments) of the internal combustion engine in the stationary mobile area. The physical modeling from the

Single cylinder data is very time-consuming and cost-intensive, since appropriate software development tools and a high level of expert knowledge are required.

The invention is therefore based on the object, the previously described

Optimizing the adaptation process with regard to the time required.

This object is achieved by the features of claim 1. The configurations are shown in the subclaims.

In the method according to the invention, the combustion model is adapted in the form of a completely data-based model while the internal combustion engine is in operation. The data-based model is generated by varying the manipulated variables of the internal combustion engine on a single-cylinder test bench in a first step, by generating trend information from the measured variables of the single-cylinder test bench in a second step and by deviating the measured variables of the single cylinder in a third step -Test bench for a first Gauss process model is minimized while observing the trend information. The data-based model allows extrapolation to generate new, reliable data values. These data values then apply in the unmeasured operating ranges of the internal combustion engine. The physical modeling known from the prior art is replaced by the data-based model. An advantage is the significantly reduced development effort, since the determination of the trend information from the single-cylinder measurement data and the adjustment to which DoE data can be automated using mathematical algorithms. This also results in a high degree of reliability of the data-based model, so it is robust. By extrapolating new data values for the unmeasured ones

The model behaves good-naturedly in operating ranges, which means that there are no extremes or sudden reactions in the non-measured operating ranges of the internal combustion engine.

In general, the procedure according to the invention can be used to describe the behavior of technical processes in which measurement data of a device are present in defined operating areas and system behavior of the device is mapped on the basis of the trend information in non-measured operating areas. A device is to be understood, for example, as an exhaust gas aftertreatment system or a battery management system.

A preferred exemplary embodiment is shown in the figures. Show it:

FIG. 1 shows a system diagram,

FIG. 2 shows a model-based system diagram,

FIG. 3 shows a flow chart,

FIG. 4A, B a diagram,

Figure 5 is a diagram of the first Gaussian process model and

Figure 6 is a table.

Figure 1 shows a system diagram of an electronically controlled

Internal combustion engine 1 with a common rail system. The common rail system comprises the following mechanical components: a low pressure pump 3

Delivery of fuel from a fuel tank 2, a variable suction throttle 4 to influence the fuel volume flow flowing through, a

High-pressure pump 5 for conveying the fuel while increasing the pressure, a rail 6 for storing the fuel and injectors 7 for injecting the fuel into the combustion chambers of the internal combustion engine 1. Optionally, the common rail system can also be designed with individual stores, in which case, for example, an injector 7 Individual memory 8 is integrated as an additional buffer volume. The further one

Functionality of the common rail system is assumed to be known. The gas path shown includes both the air supply and the exhaust gas discharge. The compressors are arranged in the air supply

Exhaust gas turbocharger 1 1, an intercooler 12, a throttle valve 13, one

Junction point 14 for merging the charge air with the recirculated exhaust gas and the inlet valve 15. A are arranged in the exhaust gas discharge

Exhaust valve 16, the turbine of the exhaust gas turbocharger 11 and a turbine bypass valve 19. An exhaust gas recirculation path branches off from the exhaust gas discharge, in which an EGR actuator 17 for setting the EGR rate and the EGR cooler 18 are arranged.

The operating mode of the internal combustion engine 1 is determined by an electronic control unit 10 (ECU). The electronic control unit 10 contains the usual components of a microcomputer system, for example a microprocessor, I / O modules, buffers and memory modules (EEPROM, RAM). The operating data relevant for the operation of the internal combustion engine 1 are applied as models in the memory modules. The electronic control unit 10 uses this to calculate the input variables

Output variables. The relevant input variable is a target torque M (TARGET), which is specified by an operator as a desired performance. The input variables of the control device 10 related to the common rail system are the rail pressure pCR, which is measured by means of a rail pressure sensor 9, and optionally the individual storage pressure pES. The input variables of the electronic control device 10 related to the air path are an opening angle W1 of the throttle valve 13, the engine speed nIST, the charge air pressure pLL, the charge air temperature TLL and the humidity phi of the charge air. The input variables of the electronic control device 10 related to the exhaust gas path are an opening angle W2 of the EGR actuator 17, the exhaust gas temperature TAabgas, the air / fuel ratio lambda and the actual NOx value downstream of the turbine of the

Exhaust gas turbocharger 1 1. The other input variables of the, not shown

Electronic control unit 10 are summarized with reference symbol EIN, for example the coolant temperatures.

1 shows the output variables of the electronic control unit 10: a signal PWM for controlling the suction throttle 4, a signal ve for controlling the injector 7 (start of injection / end of spray), an actuating signal DK for controlling the

Throttle valve 13, an actuating signal EGR for actuating the EGR actuator 17, an actuating signal TBP for actuating the turbine bypass valve 19 and an output variable OUT. The output variable AUS represents the other actuating signals for controlling and regulating the internal combustion engine 1, for example an actuating signal for activating a second exhaust gas turbocharger in the case of register charging or a variable valve train.

FIG. 2 shows a model-based system diagram. In this illustration, the input variables of the electronic control device 10 are a first library Bibliol, a second library Biblio 2, measurement variables MESS and the collective reference symbol EIN, which represents the input variables shown in FIG. 1. The first library Biblio 1 identifies the operation of the internal combustion engine according to the MARPOL (Marine Pollution) emission class of the IMO or according to the emission class EU IV / Tier 4 final. The second library Biblio 2 identifies the type of internal combustion engine and a maximum mechanical component load to

Example the combustion peak pressure or the maximum speed of the

Exhaust gas turbocharger. The input variable MESS identifies both the directly measured physical variables and the auxiliary variables calculated from them. The output variables of the electronic control unit are the setpoints for the

subordinate control loops, the start of spraying SB and the end of spraying SE. The

Subordinate control loops are a rail pressure control loop 24, a lambda control loop 25 and an EGR control loop 26. Within the electronic control unit are a combustion model 20, an adaptation 21, a gas path model 22 and a

Optimizer 23 arranged.

Both the combustion model 20 and the gas path model 22 form this

System behavior of the internal combustion engine as mathematical equations. The combustion model 20 statically maps the processes during the combustion. in the

The difference to this is the gas path model 22, the dynamic behavior of the

Air duct and the exhaust duct. Combustion model 20 includes

Individual models, for example for the formation of NOx and soot, for the

Exhaust gas temperature, for the exhaust gas mass flow and for the peak pressure. This

Individual models in turn depend on the boundary conditions in the cylinder and the

Parameters of the injection. The combustion model 20 is determined for a reference internal combustion engine in a test bench run, the so-called DoE test bench run (DoE: Design of Experiments) for the mobile area. During the DoE test bench run, operating parameters and manipulated variables are systematically aimed at varies, the overall behavior of the internal combustion engine depending on

map motor sizes and environmental conditions. Also in

Combustion model 20 processes the measured values determined on a single-cylinder test bench. The combustion model 20 is supplemented by the adaptation 21. The aim of the adaptation is to reduce the production spread of an internal combustion engine.

After activation of the internal combustion engine 1, the optimizer 23 first reads the emission class from the first library Biblio 1 and the maximum mechanical component loads from the second library Biblio 2. Then the

Optimizer 23 from the combustion model 20 with regard to the

Target torque M (TARGET), the emission limit values, the environmental conditions, for example the humidity phi of the charge air, the operating situation of the internal combustion engine and the adaptation data points. The operating situation is defined in particular by the engine speed nIST, the charge air temperature TLL and the charge air pressure pLL. The function of the optimizer 23 now consists in evaluating the injection system setpoints for controlling the injection system actuators and the gas path setpoints for controlling the gas path actuators. Here, the optimizer 23 selects the solution in which a quality measure is minimized. The quality measure is calculated as an integral of the quadratic target-actual deviations within the prediction horizon. For example in the form:

(1) J = J [w1 (NOx (TARGET) -NOx (ACTUAL)] ² + [w2 (M (TARGET) -M (ACTUAL)] ² + [w3 (....)] + ...

Weighting factors are represented with w1, w2 and w3. As is known, the nitrogen oxide emissions result from the moisture phi of the charge air, the charge air temperature, the start of spraying SB and the rail pressure pCR. In the actual values at

For example, the actual NOx value or the actual exhaust gas temperature value, the adaptation 21 intervenes.

The quality measure is minimized by the optimizer 23 calculating a first quality measure at a first point in time, the injection system setpoints and the gas path setpoints are varied and a second quality measure within the prediction horizon is predicted on the basis of these. On the basis of the deviation of the two quality measures from one another, the optimizer 23 then defines a minimum quality measure and sets this as decisive for the internal combustion engine. How to proceed regarding the Prediction is made to the unpublished German patent application with the official file number DE 10 2017 005 783.4.

FIG. 3 shows a flow diagram which shows the program steps of an executable program. The interaction of the two Gaussian process models to create the combustion model is shown (Fig. 2:20). Gaussian process models are known to the person skilled in the art, for example from

DE 10 2014 225 039 A1 or DE 10 2013 220 432 A1. In general, a Gaussian process is defined by an average function and a covariance function. The mean function is often assumed to be zero or a linear / polynomial curve is introduced. The covariance function specifies the relationship between any points and describes the statistical reliability of the model at a considered operating point of the internal combustion engine. Due to the covariance a

Confidence range defines in which the value of the real system lies with a probability of 95%. A function block 27 contains the DoE data of the full engine. These data are determined for a reference internal combustion engine during a test bench run by using the in the stationary mobile area

Internal combustion engine all variations of the input variables are determined over their entire setting range. This data characterizes the behavior of the internal combustion engine in the stationary mobile area with high accuracy. A function block 28 contains data which are obtained on a single-cylinder test bench. On the single-cylinder test bench, those operating ranges can be set, for example high geodetic heights or extreme temperatures, which cannot be checked during a DoE test bench run. From these measurement data, the system properties are automatically calculated in function block 29 in the form of trend information in function block 29. The further explanation is given in connection with FIGS. 4A and 4B.

FIG. 4A shows the individual storage pressure pES on the abscissa, normalized to the maximum pressure pMAX of the individual storage pressure. The actual NOx value is shown on the ordinate as a measured value. The measured values entered with a cross were determined by a VVT actuator (VVT: variable valve control)

Spray start SB, the engine speed nIST, the charge air temperature TLL and the humidity phi of the charge air were kept constant. The injected

The amount of fuel was set to a first value. After that the Individual storage pressure pES varies by changing the delivered fuel volume. The measured values marked with a circle were determined by setting the fuel quantity to a second value, varying the individual storage pressure pES and the previously constant parameters, i.e. the VVT actuator, the start of spraying SB, the engine speed nIST, the charge air temperature TLL and the humidity phi of the charge air were left unchanged. The measured values entered with a triangle were determined by setting the engine speed nIST to a new value, changing the individual accumulator pressure pES and adopting the other parameters unchanged. From FIG. 4A it can be derived as the first statement that the actual NOx value increases with increasing individual storage pressure pES and as the second statement it can be derived that the increase is continuously increasing. For the example shown, the trend information is therefore: monotonous (increasing) and linear. In FIG. 4B the is on the abscissa

Start of spraying SB, normalized to a maximum value SB (MAX) of the start of spraying, is plotted. The actual NOx value is shown on the ordinate as a measured value. The data values shown in FIG. 4B result in an analogous procedure to FIG. 4A, the individual storage pressure pES being kept constant and the start of injection SB being changed instead. For the examples shown in FIG. 4B, the trend information is: only monotonous (increasing).

The extrapolation-capable model is identified in FIG. 3 by the reference symbol 30, in which the deviation of the data from the single-cylinder test bench from the DoE data 27 is minimized while observing the trend information. Reference number 31 denotes a first Gaussian process model 31 (GP1) for representing a basic grid. Merging the two sets of

The second Gauss process model 32 forms data points

Operating ranges of the internal combustion engine, which are described by the DoE data, are also determined by these values, and operating ranges for which no DoE data are available are reproduced by data of the model 30. Since the second Gaussian process model is adapted during operation, it serves to represent the adaptation points. In general, the following applies to data-based model 33:

(2) E [x] = GP1 + GP2 Here GP1 correspond to the first Gaussian process model to represent the basic grid, GP2 to the second Gaussian process model to represent the

Adaptation data points. The data-based model E [x] is the

Input variable for the optimizer, for example an actual NOx value or an actual exhaust gas temperature value. The double arrow in the figure shows two

Information paths presented. The first information path marks the

Provision of data of the basic grid from the first Gaussian process model 31 to the data-based model 33. The second information path characterizes the

Readjustment of the first Gauss process model 31 via the second Gauss process model 32. For further procedure regarding the adaptation, reference is made to the unpublished German patent application DE 10 2018 001 727.4.

5 shows the first Gaussian process model for the

Individual storage pressure pES, which is standardized to maximum pressure pMAX, is shown. The measured NOx value is plotted on the ordinate. Within the

In the diagram, the DoE data values determined on the full engine are marked with a cross and the course of the first Gauss process model from the data values recorded on the single cylinder are marked with a circle. For example, these are the three data values of points A, B and C. In a first step, the location of the

Data values, ie the trend information (Fig. 3: 29) determined with respect to one another. Since the data value of point B results in a higher actual NOx value than at point A, the function in this area is monotonous. This applies analogously to the data value at point C, that is to say the actual NOx value at point C is higher than at point B. For data values A to C, the following therefore results as trend information:

monotone. In a second step, the deviation (model error) of these data values from the DoE data is minimized. In other words, a mathematical function is determined which best represents the DoE data values taking into account the trend information. For data values A, B and C this is the monotonous, linear and increasing function F1. A function F2 is characterized by the data values A, D and E only as monotonous. A function F3 is represented by the data values A, F and G. With reference to FIG. 6, the measured variables individual storage pressure pES shown as examples behave,

Fuel mass mKrSt, start of injection SB, rail pressure pCR and the charge air temperature TLL in accordance with function F1, that is to say increasing monotonically and linearly. The measured variable engine speed nIST behaves according to function F3, ie unlimited. Unlimited means that there is none for this measurement

Trend information is available. As can also be derived from FIG. 5,

Intermediate values, for example the data value H, are extrapolated. The model is therefore capable of extrapolation (Fig. 3:30). The first Gauss process model is determined automatically, which means that expert knowledge is not required. The model's automated extrapolation capability in turn guarantees a high degree of robustness and good-naturedness, since in unknown areas the model does not allow extremes or sudden reactions based on the trend information.

reference numeral

1 internal combustion engine 33 data-based model

2 fuel tank

3 low pressure pump

4 suction throttle

5 high pressure pump

6 rails

7 injector

8 individual memories

9 rail pressure sensor

10 Electronic control unit

1 1 exhaust gas turbocharger

12 intercooler

13 throttle valve

14 confluence point

15 inlet valve

16 exhaust valve

17 EGR actuator (EGR: exhaust gas recirculation)

18 EGR coolers

19 turbine bypass valve

20 combustion model

21 adaptation

22 Gas path model

23 optimizers

24 Rail pressure control loop

25 Lambda control loop

26 EGR control loop

27 Function block, DoE data

28 Function block, single cylinder data

29 Function block, generating trend information

30 model

31 First Gauss process model (GP1)

32 Second Gaussian process model (GP2)

Claims

claims

1. Method for model-based control and regulation of an internal combustion engine (1), in which depending on a target torque (M (TARGET)) via a

Combustion model (20) injection system setpoints for controlling the

Injection system actuators and a gas path model (22) gas path setpoints for controlling the gas path actuators are calculated, in which the

Combustion model (20) in the form of a completely data-based model (33) is adjusted during operation of the internal combustion engine (1), in which an optimizer (23) measures a quality measure (J) about changing the injection system setpoints and gas path setpoints within a prediction horizon is minimized and in which the optimizer (23) uses the minimized quality measure to set the injection system setpoints and gas path setpoints as decisive for setting the

Operating point of the internal combustion engine (1) are set.

2. The method according to claim 1, characterized in that the data-based model (33) is generated in that in a first step the manipulated variables of the internal combustion engine (1) are varied on a single-cylinder test bench, in a second step by trend information (29) the measurands of the

Single-cylinder test bench are generated and in a third step a deviation of the measured variables of the single-cylinder test bench from a first Gaussian process model (31) is minimized while observing the trend information (29).

3. The method according to claim 2, characterized in that via the data-based model (33) by means of extrapolation new data values for unmeasured

Operating areas of the internal combustion engine (1) are generated.

4. The method according to claim 2, characterized in that the trend information (29) is stored in the sense of a linear, monotonous or unlimited function.