EP3642467A1 - Method for the model-based open-loop and closed-loop control of an internal combustion engine - Google Patents

Abstract

The invention relates to a method for the model-based management and control of an internal combustion engine (1). In function of a theoretical torque, theoretical values of the injection system for the activation of the injection system control element are calculated by means of a combustion model (19), and theoretical values of gas path for activation of the gas path adjusting element are calculated by means of a gas path model (20), a quality measurement is calculated by an optimizer (21) as a function of the theoretical values of the injection system and the theoretical values of the gas path, the quality measurement is minimized within a prediction horizon by the optimizer (21) by modifying the theoretical values of the injection system and the values Theoretical gas path, and the theoretical values of the injection system and the theoretical gas path values are defined by the optimizer (21) by means of the minimized quality measurement as applicable to adjust the operating point of the engine at internal combustion (1).

Description

 DESCRIPTION Method for model-based control of an internal combustion engine

The invention relates to a method for model-based control and regulation of a

Internal combustion engine, in which, depending on a desired torque on a

Combustion model Injection system setpoint values for controlling the injection system actuators and via a gas path model Gas path setpoint values for controlling the gas path actuators are calculated.

The behavior of an internal combustion engine is governed by an engine control unit in

Dependence of a desired performance determined. These are in the software of the

Engine ECU applied corresponding characteristics and maps. From these, the desired values, for example a setpoint torque, become the manipulated variables of the

Calculated internal combustion engine, for example, the start of injection and a required rail pressure. These characteristics / maps are fitted with data at the manufacturer of the internal combustion engine on a test bench. The multiplicity of these characteristic curves / maps and the correlation of the

However, characteristic curves / maps with each other cause a high coordination effort.

In practice, it is therefore attempted to reduce the coordination effort by using mathematical models. For example, DE 10 2006 004 516 B3 describes a Bayes network with probability tables for determining an injection quantity, and US 2011/0172897 A1 describes a method for adapting the start of injection and injection quantity via combustion models by means of neural networks. Critical here is that only trained data are mapped, which must be learned only at a test bench run.

From US 2016/0025020 Al a model-based control method for the gas path of an internal combustion engine is known. The gas path includes both the air side and the exhaust side together with an exhaust gas recirculation. In a first step of the process is from the

Measured variables of the gas path, for example, the charge air temperature or the NOx concentration, the current operating situation of the internal combustion engine detected. In a second step, a quality measure within a prediction horizon is then likewise calculated from the measured variables via a physical model of the gas path. From the quality measure and the operating situation turn then in a third step the

Control signals for the actuators of the gas path set. The given procedure refers exclusively to the gas path and is based on a linearized gas path model. Due to the linearization a loss of information is unavoidable.

The invention is therefore based on the object to develop a method for model-based control and regulation of the entire engine at high quality.

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

The method is that depending on a desired torque on a

Combustion model Injection system setpoint values for controlling the injection system actuators and via a gas path model Gaspath setpoints for controlling the gas path actuators are calculated and that an optimizer calculates a quality measure as a function of the injection system setpoints and the gas path setpoints. Furthermore, the method consists in the optimizer minimizing the quality measure by changing the injection system setpoint values and gas path setpoint values within a prediction horizon and setting the injection system setpoint values and gas path setpoint values as relevant for setting the operating point of the internal combustion engine by the optimizer on the basis of the minimized quality measure become. The minimized quality measure is calculated by the optimizer calculating a first quality measure at a first point in time, forecasting a second quality measure within the prediction horizon at a second time, and then a deviation of the two

Quality measures is determined. If the deviation is smaller than a limit value, then the optimizer sets the second quality measure as a minimized quality measure. The limit value analysis is a termination criterion insofar as further minimization would not lead to any more precise adaptation. Instead of the limit value analysis, a predefinable number of

Recalculations are set as abort criterion. On the basis of the minimum quality measure, a setpoint rail pressure value for a subordinate rail pressure control loop and immediately an injection start and an injection end for controlling an injector are then specified by the optimizer as an injection system setpoint. In addition, the optimizer indirectly then the gas path setpoints, for example, a lambda setpoint for a subordinate lambda control loop and an EGR setpoint for a

subordinate EGR control loop, specified.

Both the combustion model and the gas path model map the system behavior of the internal combustion engine as mathematical equations. These are determined once based on a reference internal combustion engine at a test bench run, the so-called DoE test bench run (DoE: Design of Experiments) or from simulation experiments. Since, for example, different emission targets for one and the same type of engine can be displayed, the coordination effort is significantly reduced. A distinction between a stationary and a transient operation, for example, in a load application in the

Generator operation, is no longer required. In addition, the target torque is set precisely while maintaining the emission limit value. The models are individually tunable, the models in the sum of the internal combustion engine. The previously required characteristics and maps can thus be omitted.

In the figures, a preferred embodiment is shown. Show it:

FIG. 1 shows a system diagram,

 FIG. 2 shows a model-based system diagram,

 Figure 3 is a program flowchart and

 Figure 4 timing diagrams

FIG. 1 shows a system diagram of an electronically controlled internal combustion engine 1 with a common rail system. The common rail system includes the following mechanical

Components: a low-pressure pump 3 for conveying fuel from a fuel tank 2, a variable intake throttle 4 for influencing the flowing through

Fuel volume flow, a high-pressure pump 5 for conveying the fuel under pressure increase, 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 Railsystem be executed with individual memories, in which case, for example, in Injector 7 a single memory 8 is integrated as an additional buffer volume. The others

Functionality of the common rail system is assumed to be known.

The illustrated gas path includes both the air supply and the exhaust gas removal.

Arranged in the air supply to the compressor of a turbocharger loader 11, a

 Intercooler 12, a throttle valve 13, a junction 14 for merging the charge air with the recirculated exhaust gas and the inlet valve 15. In the exhaust system are arranged next to the exhaust valve 16, an EGR actuator 17, the turbine of the

Exhaust gas turbocharger 11 and a turbine bypass valve 18th

The operation of the internal combustion engine 1 is determined by an electronic control unit 10 (ECU). The electronic control unit 10 includes the usual components of a

Microcomputer system, such as a microprocessor, I / O devices, buffers and memory devices (EEPROM, RAM). In the memory modules relevant for the operation of the internal combustion engine 1 operating data are applied as models. About this calculates the electronic control unit 10 from the input variables, the output variables. The following input variables are shown by way of example in FIG. 1: A setpoint torque M (SET), which is predetermined via an operator, the rail pressure pCR, which is measured by means of a rail pressure sensor 9, the engine speed nIST, the charge air pressure pLL, the charge air temperature TLL, the humidity phi of the charge air, the exhaust gas temperature TAbgas, the air-fuel ratio lambda, the NOx actual value, optionally the pressure pES of the individual memory 8 and a

Input size ON. Under the input ON, the others are not shown

Sensor signals summarized, for example, the coolant temperatures. In FIG. 1, the output variables of the electronic control unit 10 are shown as a signal PWM for

Control of the intake throttle 4, a signal ve to control the injectors 7 (start of injection / injection end), a control signal DK to control the throttle valve 13, a control signal AGR to control the EGR actuator 17, a control signal TBP to control the turbine bypass valve 18 and an output size OFF. The output variable OFF is representative of the further control signals for controlling and regulating the internal combustion engine 1,

For example, for a control signal for activating a second exhaust gas turbocharger in a register charging.

FIG. 2 shows a model-based system diagram. In this illustration, within the electronic control unit 10, a combustion model 19, a gas path model 20 and a Optimizer 21 is listed. Both the combustion model 19 and the gas path model 20 map the system behavior of the internal combustion engine as mathematical equations. The combustion model 19 statically maps the combustion processes. In contrast, the gas path model 20 forms the dynamic behavior of the air duct and the

Exhaust system from. Combustion model 19 includes single models, for example, for NOx and soot formation, exhaust gas temperature, exhaust gas mass flow, and peak pressure. These individual models in turn depend on the boundary conditions in the cylinder and the parameters of the injection. The combustion model 19 is determined in a reference internal combustion engine in a test bench run, the so-called DoE test bench run (DoE: Design of Experiments). The DoE test bed run becomes systematic

Operating parameters and manipulated variable with the goal varies, the overall behavior of the

Depict internal combustion engine depending on engine sizes and environmental conditions. The optimizer 21 evaluates the combustion model 19 with regard to the

 Target torque M (SOLL), the emission limit values, the environmental boundary conditions, for example the humidity phi of the charge air, and the operating situation of the internal combustion engine. The operating situation is defined by the engine speed nIST, the charge air temperature TLL, the

Charge air pressure pLL, etc. The function of the optimizer 21 is now to evaluate the injection system setpoints for controlling the injection system actuators and the gas path setpoints for controlling the gas path actuators. Here, the optimizer 21 selects that solution in which a quality measure is minimized. The quality measure is calculated as the integral of the quadratic nominal-actual deviations within the prediction horizon. For example, in the form:

(1) J = J [wl (NOx (SOLL) -NOx (IST)] ² + [w2 (M (SOLL) -M (IST)] ² + [w3 (....)] + · · ·

Here w1, w2 and w3 denote a corresponding weighting factor. As is known, the nitrogen oxide emission results from the humidity phi of the charge air, the charge air temperature, the start of injection SB and the rail pressure pCR.

The quality measure is minimized by calculating a first quality measure from the optimizer 21 at a first point in time, varying the injection system setpoint values and the gas path setpoints, and using this a second quality measure within the prediction horizon is forecasted. On the basis of the deviation of the two quality measures each other, the optimizer 21 determines a minimum quality measure and sets this as relevant for the

Internal combustion engine. For the example shown in the figure, these are for the injection system the target rail pressure pCR (SL) and the injection start SB and the injection end SE. The desired rail pressure pCR (SL) is the reference variable for the subordinate rail pressure control loop 22. The manipulated variable of the rail pressure control loop 22 corresponds to the PWM signal for acting on the suction throttle. The injector (FIG. 1: 7) is acted upon directly by the start of injection SB and the injection end SE. For the gas path, the optimizer 21 indirectly determines the gas path setpoints. In the illustrated example, these are a lambda setpoint LAM (SL) and an EGR setpoint AGR (SL) for specification for the two subordinate control loops 23 and 24. The returned measured variables MESS are read in by the electronic control unit 10. The measured quantities MESS are to be understood as meaning directly measured physical quantities as well as auxiliary variables calculated therefrom. In the example shown, the actual lambda value LAM (ACTUAL) and the EGR actual value AGR (ACTUAL) are read in.

FIG. 3 shows the method in a program flow chart. After

Initialization at S 1 is checked at S2 whether the starting process is completed. If this still runs, query result S2: no, branches back to point A. If the starting process has ended, then at S3 the setpoint torque M (DESIRED), which can be predetermined by the operator, and the NOx setpoint value NOx (SOLL) are read in. Following this, at S4 the operating situation of the

 Internal combustion engine detected. The operating situation is defined by the measured variables, in particular by the engine speed nIST, the charge air temperature TLL, the charge air pressure pLL, the wet phi of the charge air, etc. At S5, the subroutine optimizer is called and the initial values, for example the injection start SB, are generated at S6. A first quality measure Jl is calculated using equation (1) at S7, and a running variable i is set to zero at S8. After that, the initial values are changed at S9 and calculated as new setpoint values for the manipulated variables. At S10, the run variable i is increased by one. On the basis of the new setpoint values, a second quality measure J2 is then predicted for Si within the prediction horizon, for example for the next 8 seconds. At S12, in turn, the second quality measure J2 is subtracted from the first quality measure J1 and compared with a limit value GW. About the difference of the two quality measures the further progress of the quality measure is checked. Alternatively, it is checked on the basis of the comparison of the run variable i with a limit value iGW, how often an optimization has already been carried out. The two threshold considerations are one

Abort criterion for further optimization. Is further optimization possible, Query result S12: no, it branches back to point C. Otherwise, at S13 the optimizer sets the second quality measure J2 as a minimum quality measure J (min). From the minimum quality measure J (min), the injection system setpoint values and the gas path setpoint values then result for the specification for the corresponding actuators. Following this, S 14 checks whether an engine stop has been initiated. If this is not the case, query result S14: no, branch back to point B. Otherwise, the program schedule is finished.

FIG. 4 shows a time diagram. FIG. 4 comprises FIGS. 4A to 4D. Here, Figure 4A shows the course of the nitrogen oxide emission NOx, Figure 4B

the start of injection SB in degrees crankshaft angle before top dead center (TDC), Figure 4C, the curve of the lambda setpoint LAM (SL) and Figure 4D, the target rail pressure pCR (SL). The time range before to is the past. The prediction horizon, for example 8s, corresponds to the time range t0 to t0 + tp. With ts is a calculation time designated at which a new setpoint, for example, the injection start SB, is output from the electronic control unit. In the example shown, a constant setpoint torque M (SOLL) is assumed.

At the time t0, the initial values of the injection start SB = 8 °, the lambda set value

LAM (SL) = 1.9 and setpoint rail pressure pCR (SL) = 1500 bar. The NOx target value course NOx (SL) in FIG. 4A is predetermined. From these initial values results a correspondingly large desired actual deviation dNOx, see FIG. 4A. The NOx actual value is calculated as a function of the measured air pressures in the air path and the start of injection SB. Using equation (1), the optimizer calculates a first quality measure J1 at time t0. The optimizer then calculates how a change in the start of injection SB, the desired lambda value, is calculated

LAM (SL) and the target rail pressure pCR (SL) within the prediction horizon (tO + tP) would affect the target actual deviation dNOx, for example, by the desired rail pressure is successively increased to pCR (SL) = 2000 bar. The optimizer determines the second quality measure J2 for each of the times shown. About the subtraction of the two quality measures and the

Limit value is then minimized the quality measure, that is, it is checked whether further optimization is promising. For the example shown, the

Optimizer a minimum quality measure for the time tO + 4, which is reflected in the figure 4A in the approximation of the NOx actual value NOx (IST) to the NOx target value NOx (SL). LIST OF REFERENCE NUMBERS

1 internal combustion engine

 2 fuel tank

 3 low pressure pump

 4 suction throttle

 5 high pressure pump

 6 rail

 7 injector

 8 individual memories

 9 rail pressure sensor

 10 electronic control unit

 11 exhaust gas turbocharger

 12 intercooler

 13 throttle

 14 point of entry

 15 inlet valve

 16 exhaust valve

 17 EGR actuator (EGR: exhaust gas recirculation)

18 turbine bypass valve

 19 combustion model

 20 gas path model

 21 optimizers

 22 Rail pressure control loop

 23 lambda control loop

 24 EGR control loop

Claims

1. A method for model-based control and regulation of an internal combustion engine (1), in which, depending on a desired torque (M (SOL)) via a combustion model (19) injection system setpoints for controlling the injection system actuators and a gas path model (20) gas path Setpoints for controlling the gas path actuators are calculated, in which by an optimizer (21) a quality measure (J) as a function of Injector system setpoints and gas path setpoint values are calculated by the optimizer (21) minimizing the measure (J) of changes in injector setpoints and gaspath setpoints within a prediction horizon, and by the optimizer (21) using the minimized measure of goodness (J (min)) the injection system setpoint values and gas path setpoint values are set as decisive for setting the operating point of the internal combustion engine (1). 2. The method according to claim 1, characterized in that the quality measure (J) is minimized by the optimizer (21) at a first time a first quality measure (Jl) is calculated, at a second time a second quality measure (J2) within the prediction horizon is predicted, a deviation from the first (Jl) and second quality measure (J2) is determined and set by the optimizer (21) the second quality measure (J2) as a minimized quality measure (J (min)) in which the deviation is less than a limit ( GW). 3. The method according to claim 1, characterized in that the quality measure (J) is minimized by the optimizer (21) at a first time a first quality measure (Jl) is calculated, at a second time a second quality measure (J2) within the prediction horizon is predicted and the optimizer (21) sets the second quality measure (J2) as a minimized quality measure (J (min)) after passing through a predefinable number (i) of recalculations. 4. The method of claim 2 or 3, characterized in that the optimizer (21) as the injection system setpoint indirectly a rail pressure setpoint (pCR (SL)) for a subordinate rail pressure control loop (22) are specified. 5. The method according to claim 4, characterized in that from the optimizer (21) as the injection system setpoint directly an injection start (SB) and an injection end (SE) for driving an injector (7) are specified. 6. The method according to claim 1, characterized in that the optimizer (21) indirectly gas path setpoint values for subordinate gas path control circuits (23, 24) are specified.