阅读说明：本技术 内燃发动机控制器 (Internal combustion engine controller ) 是由 G·威廉姆斯 P·拉德洛 R·库瑞蒙 邓宝洋 于 2020-04-20 设计创作，主要内容包括：一种用于内燃发动机的内燃发动机控制器,其包括存储器和处理器。存储器被配置成存储多个控制脉谱图,每个控制脉谱图限定致动器设定点的超曲面,用于基于到所述内燃发动机控制器的多个输入变量来控制内燃发动机的致动器。处理器包括脉谱图更新模块、参数更新模块和发动机设定点模块。脉谱图更新模块被配置成基于内燃发动机的性能目标函数、来自内燃发动机的传感器数据和多个输入变量来计算控制脉谱图中的至少一个控制脉谱图的优化超曲面,其中性能目标函数包括参数。参数更新模块被配置成在确定内燃发动机的运行状况的变化时更新性能目标函数的参数。所述参数包括下列中的一者或两者：与发动机模型相关联的发动机参数；以及与成本函数相关联的成本参数。脉谱图更新模块还被配置成基于优化超曲面来更新控制脉谱图的超曲面。发动机设定点模块被配置成基于由多个输入变量限定的相应控制脉谱图的超曲面上的位置向每个致动器输出控制信号。(An internal combustion engine controller for an internal combustion engine includes a memory and a processor. The memory is configured to store a plurality of control maps, each control map defining a hypersurface of an actuator set point for controlling an actuator of an internal combustion engine based on a plurality of input variables to the internal combustion engine controller. The processor includes a map update module, a parameter update module, and an engine set point module. The map update module is configured to calculate an optimized hypersurface for at least one of the control maps based on a performance objective function of the internal combustion engine, sensor data from the internal combustion engine, and a plurality of input variables, wherein the performance objective function comprises a parameter. The parameter update module is configured to update a parameter of the performance objective function when a change in an operating condition of the internal combustion engine is determined. The parameters include one or both of the following: an engine parameter associated with the engine model; and a cost parameter associated with the cost function. The map update module is further configured to update the hypersurface of the control map based on the optimized hypersurface. The engine set point module is configured to output a control signal to each actuator based on a position on a hypersurface of a respective control map defined by a plurality of input variables.)

1. An internal combustion engine controller for an internal combustion engine, comprising:

a memory configured to store a plurality of control maps, each control map defining a hypersurface of an actuator set point for controlling an actuator of the internal combustion engine based on a plurality of input variables to the internal combustion engine controller; and

a processor, the processor comprising:

a map update module configured to calculate an optimized hypersurface for at least one of the control maps based on a performance objective function of the internal combustion engine, sensor data from the internal combustion engine, and the plurality of input variables, wherein the performance objective function comprises parameters; and

A parameter update module configured to update a parameter of the performance objective function when a change in an operating condition of the internal combustion engine is determined;

wherein the parameters include one or both of: an engine parameter associated with the engine model; and a cost parameter associated with the cost function;

wherein the map update module is configured to update the hypersurface of the control map based on the optimized hypersurface, an

An engine set point module configured to output a control signal to each actuator based on a position on a hypersurface of a respective control map defined by the plurality of input variables.

2. The internal combustion engine controller of claim 1, wherein the map update module is configured to calculate an optimized hypersurface over a period of 1 second.

3. The internal combustion engine controller of any preceding claim, wherein the map update module is configured to simultaneously calculate an optimized hypersurface for each of the control maps; and is

The map update module is configured to update the hypersurface of each of the control maps based on the respective optimized hypersurface.

4. The internal combustion engine controller according to any preceding claim, wherein the map update module comprises:

an optimizer module configured to search for an optimized hypersurface, wherein the optimizer module selects a plurality of candidate sets of actuator setpoints to be evaluated by the performance objective function, and

the optimizer module is configured to output an optimized hypersurface of the at least one control map based on an evaluation of the set of candidate actuator setpoints by the performance objective function.

5. The internal combustion engine controller of any preceding claim, wherein the performance objective function comprises:

an engine modeling module configured to calculate a plurality of engine performance variables associated with each candidate set of actuator setpoints based on the input variables, the sensor data from the internal combustion engine, the engine parameters, and the candidate set of actuator setpoints;

a cost module configured to evaluate the engine performance variables and output a cost associated with each candidate set of actuator setpoints based on the cost parameters.

6. The internal combustion engine controller of claim 5, wherein the engine parameter comprises a time-varying engine parameter based on input from an aftertreatment system connected to the internal combustion engine.

7. The internal combustion engine controller of claim 5, wherein the cost parameter comprises a time-varying cost parameter based on input from an aftertreatment system connected to the internal combustion engine.

8. An internal combustion engine controller according to any preceding claim, wherein the change in the operating condition of the internal combustion engine is based on an observed difference between the model and the internal combustion engine.

9. The internal combustion engine controller according to claim 8, wherein the change in the operating condition is determined based on a change in sensor data output from a sensor of the internal combustion engine with respect to an engine performance variable representing a predicted value of the sensor data; and is

The parameter update module is configured to update an engine parameter of the performance objective function to reduce a difference between the sensor data and an engine performance variable representing a predicted value of the sensor data below a predetermined threshold.

10. The internal combustion engine controller of any preceding claim, wherein the parameter update module is configured to determine a change in the operating condition of the internal combustion engine based on at least one of: the input variables to the internal combustion engine controller, sensor data from the internal combustion engine, and sensor data from an aftertreatment system of the internal combustion engine.

11. A method of controlling an internal combustion engine, comprising:

providing a plurality of control maps, each control map defining a hypersurface of actuator set points for controlling actuators of the internal combustion engine based on a plurality of input variables to the internal combustion engine controller; and

calculating an optimized hypersurface for at least one of the control maps based on a performance objective function of the internal combustion engine, sensor data from the internal combustion engine, and the plurality of input variables, wherein the performance objective function comprises parameters; and

updating a parameter of the performance objective function when a change in an operating condition of the internal combustion engine is determined,

wherein the parameters include one or both of: an engine parameter associated with the engine model; and a cost parameter associated with the cost function;

Wherein the hypersurface of the control map is updated based on the optimized hypersurface, an

Outputting a control signal to each actuator based on a position on the hypersurface of a respective control map defined by the plurality of input variables.

12. The method of claim 11, wherein the optimized hypersurface is calculated within a time period of 1 second.

13. The method of any one of claims 11 or 12, wherein an optimized hypersurface for each of the control maps is calculated simultaneously; and

updating the hypersurface of each of the control maps based on the respective optimized hypersurface.

14. The method of any of claims 11 to 13, wherein computing an optimized hypersurface comprises:

searching for an optimized hypersurface by selecting a plurality of candidate sets of actuator setpoints to be evaluated by said performance objective function, an

Outputting an optimized hypersurface of the at least one control map based on evaluation of each of the candidate set of actuator setpoints by the performance objective function.

15. The method of any of claims 11 to 14, wherein the performance objective function comprises:

An engine model configured to calculate a plurality of engine performance variables associated with each candidate set of actuator setpoints based on the input variables, the sensor data from the internal combustion engine, the engine parameters, and the candidate set of actuator setpoints;

a cost model configured to evaluate the engine performance variables and output a cost associated with each candidate set of actuator setpoints based on the cost parameters.

16. The method of claim 15, wherein the engine parameter comprises a time-varying engine parameter based on input from an aftertreatment system connected to the internal combustion engine.

17. The method of claim 15, wherein the cost parameter comprises a time-varying cost parameter based on input from an aftertreatment system connected to the internal combustion engine.

18. The method of any of claims 11-17, wherein the change in the operating condition of the internal combustion engine is based on an observed difference between the model and the internal combustion engine.

19. The method according to claim 18, wherein the change in the operating condition is determined based on a change in sensor data output from a sensor of the internal combustion engine relative to an engine performance variable representing a predicted value of the sensor data;

Wherein updating engine parameters reduces a difference between the sensor data and the engine performance variable representing a predicted value of the sensor data below a predetermined threshold.

20. A method according to any preceding claim, wherein determining a change in the operating condition of the internal combustion engine is based on at least one of: the input variables to the internal combustion engine controller, sensor data from the internal combustion engine, and sensor data from an aftertreatment system of the internal combustion engine.

Technical Field

The present disclosure relates to control of an internal combustion engine. More specifically, the present disclosure relates to systems and methods for controlling actuators of an internal combustion engine.

Background

Internal combustion engines typically include one or more systems for managing emissions output from the exhaust of the internal combustion engine. For example, internal combustion engines typically include an aftertreatment system for treating exhaust gas produced by the internal combustion engine.

A typical aftertreatment system may include a number of sensors and (control) actuators. Additional sensors and actuators may be provided in the internal combustion engine for monitoring the exhaust gas, performance and/or efficiency of the internal combustion engine. Thus, an internal combustion engine may include a number of independently controllable variables and calibration values. Therefore, the design of engine control systems for internal combustion engines is a multidimensional control problem.

Engine control systems require that set points be provided to the actuators of an internal combustion engine in response to real-time changes in the operating conditions of the internal combustion engine. The need for a high efficiency internal combustion engine that meets emission regulations further limits the design of controllable systems. A further limitation on control system design is that the amount of computing power available to the engine control system may be limited.

Conventionally, control of internal combustion engines and aftertreatment systems is managed by an on-board processor (engine control module). Due to the complexity of internal combustion engines and aftertreatment systems, the engine controls implemented typically utilize open-loop control systems based on a series of "control maps" (maps) that include pre-calibrated, time-invariant engine set points for the internal combustion engine and aftertreatment system. Typically, the engine set points controlled include fuel mass, start of injection (SOI), Exhaust Gas Recirculation (EGR), and Intake Manifold Absolute Pressure (IMAP).

Some simple control maps include a plurality of look-up tables in which a plurality of time-invariant engine set points associated with different engine operating conditions are stored. The engine control module may only read engine set points from the control map associated with the desired engine operation. Some engine control maps may also provide an estimate of one variable as a function of a limited number of other variables. Since memory and map complexity grow exponentially with the addition of additional variables, the engine set point map can only be based on a limited number of input variables. In some cases, system memory may be corrupted, but at the expense of interpolation errors.

One approach for reducing the impact on the performance of an open-loop control scheme is to provide different control maps for different operating conditions. For example, different control maps may be provided for idle operation and wide-open throttle operation or start-up. Providing many different control maps for each internal combustion engine makes calibration of each internal combustion engine expensive and time consuming. Further, these pre-calibrated maps are each time-invariant look-up tables. Therefore, these time-invariant control maps cannot account for inter-component variations in engine components, or unmeasured effects such as humidity. The time-invariant control map is also not adaptable to changes in engine component performance over time.

An alternative approach is to implement real-time, on-board, model-based control of the engine instead of pre-calibrated control maps. Thus, the engine model directly controls one or more set points of the internal combustion engine. Model-based engine control may include dynamic engine models to predict engine performance, emissions, and operating conditions. The predicted engine performance may be fed back into the model to further optimize the engine set point. Thus, model-based control methods effectively incorporate a negative feedback form into the engine control system in order to improve performance and emissions.

Model-based control is difficult to implement because the engine set-point must be calculated in real time. Therefore, model-based engine controllers that include predictive elements ideally also perform their predictions in real time. Thus, many model-based control schemes require significant computational resources to optimize the model output within a suitable time scale for controlling the internal combustion engine.

Disclosure of Invention

According to a first aspect of the present disclosure, an internal combustion engine controller is provided. The internal combustion engine controller includes a memory and a processor. The memory is configured to store a plurality of control maps, each control map defining a hypersurface of an actuator set point for controlling an actuator of an internal combustion engine based on a plurality of input variables to the internal combustion engine controller. The processor includes a map update module, a parameter update module, and an engine set point module. The map update module is configured to calculate an optimized hypersurface for at least one of the control maps based on a performance objective function of the internal combustion engine, sensor data from the internal combustion engine, and a plurality of input variables, wherein the performance objective function comprises a parameter. The parameters of the performance objective function include engine parameters associated with the engine model and/or cost parameters associated with the cost function. The parameter update module is configured to update a parameter of the performance objective function when a change in an operating condition of the internal combustion engine is determined. For example, the parameter update module may update the engine parameters and/or the cost parameters. Additionally, the map update module is configured to update the hypersurface of the control map based on the optimized hypersurface. The engine set point module is configured to output a control signal to each actuator based on a position on a hypersurface of a respective control map defined by a plurality of input variables.

Thus, the internal combustion engine controller includes three processing modules: an engine set point module, a map update module, and a parameter update module. The engine setpoint module is configured to control a plurality of actuators of the internal combustion engine. For example, the engine set point module may control one or more of SOI, EGR, fuel mass, and Intake Manifold Absolute Pressure Request (IMAPR) of the internal combustion engine. The engine set point module controls these actuators based on performance input to the internal combustion engine, such as user demand for torque, engine speed, etc., or specific sensor data from the internal combustion engine (e.g., current intake manifold absolute pressure). Control of each actuator is determined based on the control map of each actuator. Each control map defines a hypersurface for controlling an actuator of the internal combustion engine based on a plurality of input variables to the internal combustion engine controller. Thus, the engine set point module is effectively an open loop control module that controls the actuators using actuator set points stored in a control map.

The map update module operates efficiently independent of the open loop control of the engine set point module. The map update module is configured to optimize control of the internal combustion engine by updating a hypersurface of the control map at a location defined by the input variables. Optimizing a hypersurface is a multidimensional optimization problem since there are multiple actuators to control. The internal combustion engine controller according to the first aspect provides a map update module that aims to solve the multidimensional optimization problem in real time in a computationally efficient manner. Thus, the map update module is designed with the computational resources available to the on-board engine control module of the internal combustion engine under consideration.

By providing a plurality of updatable control maps, a control map-based controller may be provided that may be optimized to a range of different operating points using a limited number of control maps. Thus, the number of control maps that need to be calibrated for an internal combustion engine may be reduced, as the updatable maps of the present disclosure may provide control that covers a range of different operating points for which individual control maps may have been calibrated in the past. Thus, the complexity of initial calibration and setup of the internal combustion engine may be reduced.

Furthermore, the ability of multiple control maps to cover a range of different operating points may be supplemented by a parameter update module according to the first aspect of the present disclosure. The parameter update module may update the performance objective function of the map update module to reflect a change in an operating condition of the internal combustion engine. Thus, the performance objective function of the map update module may be applied to a wider operating point range of the internal combustion engine, thereby reducing the need to calibrate additional control maps for the internal combustion engine.

The performance objective function includes engine parameters and cost parameters that may be updated by a parameter update module. The engine parameters may be updated to compensate for uncertainty in engine performance in the performance objective function. For example, uncertainties in engine performance may be caused by manufacturing variations between internal combustion engines, degradation of internal combustion engines, and/or uncertainties in the operating environment (e.g., atmospheric conditions) of internal combustion engines. Accordingly, a time-varying difference between the observed performance of the internal combustion engine and the modeled performance of the internal combustion engine may be determined by the parameter update module as a change in the operating condition of the internal combustion engine. The parameter update module may update engine parameters associated with the performance objective function to reduce an uncertainty associated with the performance objective function.

In some embodiments, the internal combustion engine to be controlled may include an aftertreatment system. Accordingly, the sensor data provided from the internal combustion engine to the internal combustion engine controller may include sensor data from the aftertreatment system.

The cost parameter may be updated to reflect a change in the performance objective of the performance objective function. For example, tailoring the performance goals of regeneration of the aftertreatment system may be achieved through changes in cost parameters. Further, the performance targets of the internal combustion engine controller may be updated to reflect changes in the emission requirements and/or operating environment of the internal combustion engine.

Accordingly, the parameter update module may be configured to determine the operating condition of the internal combustion engine based on at least one of: input variables to an internal combustion engine controller, sensor data from the internal combustion engine, sensor data from an aftertreatment system of the internal combustion engine, a performance target of the internal combustion engine, and an output of a real-time performance model.

The map update module may include an optimizer module configured to search for an optimized hypersurface, wherein the optimizer module selects a plurality of candidate sets of actuator setpoints to be evaluated by the performance objective function. The optimizer module may be configured to output an optimized hypersurface of the at least one control map based on an evaluation of the set of candidate actuator setpoints by the performance objective function.

The performance objective function may include an engine modeling module and a cost module. The engine modeling module may be configured to calculate a plurality of engine performance variables associated with each candidate set of actuator setpoints based on the input variables, sensor data from the internal combustion engine, the engine parameters, and the candidate set of actuator setpoints. The cost module is configured to evaluate an engine performance variable and output a cost associated with each candidate set of actuator setpoints based on a cost parameter.

The change in the operating condition of the internal combustion engine may be based on an observed difference between the model and the internal combustion engine. The change in the operating condition may be determined based on a change in sensor data output from a sensor of the internal combustion engine relative to an engine performance variable representing a predicted value of the sensor data. The parameter update module may be configured to update the engine modeling module to reduce a difference between the sensor data and an engine performance variable representing a predicted value of the sensor data below a predetermined threshold.

The engine parameters may include time-varying engine parameters based on input from an aftertreatment system coupled to the internal combustion engine. For example, time-varying engine parameters may be updated to calibrate for uncertainties associated with sensors providing input from the aftertreatment system. By reducing the uncertainty associated with the performance objective function, the map update module may calculate an optimized hypersurface, which results in improved performance of the internal combustion engine.

The cost parameter may include a time-varying cost parameter based on input from an aftertreatment system coupled to the internal combustion engine. For example, a time-varying cost parameter may be updated to compensate for time-varying changes in the efficiency of the aftertreatment system. Generally, the conversion efficiency of a selective catalytic reduction filter (SCR) of an aftertreatment system may vary over time due to a number of factors. To maintain tailpipe NOx when SCR conversion efficiency is low, engine out NOx constraints may be reduced by changing the associated cost function parameters.

Drawings

The invention will now be described with reference to the following non-limiting figures. Further advantages of the present disclosure will become apparent by reference to the detailed description considered in conjunction with the following drawings, in which:

figure 1 shows a block diagram of a system comprising an internal combustion engine and an internal combustion engine controller according to an embodiment of the present disclosure;

figure 2a is an example of a look-up table control map, and figure 2b is a graphical representation of a hypersurface defined by values in the look-up table control map of figure 2 a;

figure 3 shows a block diagram of an internal combustion engine controller according to an embodiment of the present disclosure;

figures 4a, 4b and 4c show graphical representations of suitable functions of the operational objective function, the emission function and the engine constraint function, respectively;

Figure 5 shows a detailed block diagram of a part of a parameter update module and a map update module according to an embodiment of the present disclosure;

FIG. 6 is a graphical representation of the time-varying variation of the NOx calibration parameter in response to the observed variation of the operating conditions of the internal combustion engine.

Detailed Description

Fig. 1 shows an overall system diagram of an internal combustion engine 1 and an internal combustion engine controller 10 according to an embodiment of the present disclosure.

The internal combustion engine controller 10 may include a processor and memory (not shown). Accordingly, the internal combustion engine controller 10 may be implemented on any suitable computing device known in the art. The internal combustion engine module may be provided on a dedicated engine control unit (e.g., an engine control module) that includes one or more processors and integrated memory. The internal combustion engine controller 10 may be connected to various inputs and outputs in order to implement the control scheme of the present disclosure. Accordingly, the internal combustion engine controller 10 may be configured to receive various input variable signals, sensor data, and any other signals that may be used in a control scheme. For example, the internal combustion engine controller 10 may be configured to receive engine sensor data such as engine speed, barometric pressure, ambient temperature, IMAP, Intake Manifold Air Temperature (IMAT), EGR mass rate (or a sensor used to derive an EGR mass estimate), fuel rail pressure, air system valve position, and/or fuel mass estimate. The internal combustion engine controller may also be configured to receive aftertreatment sensor data, such as engine out NOx (e.g., net indicated specific NOx), tailpipe NOx, diesel particulate filter soot sensors (differential pressure sensors and/or RF soot sensors), diesel oxidation catalyst inlet temperature, and/or SCR inlet temperature.

As shown in FIG. 1, the actuators of an internal combustion engine are controlled by a plurality of engine actuator set points. The engine actuator set points are controlled by the internal combustion engine controller 10. In the embodiment of FIG. 1, the engine actuators to be controlled are EGR, SOI, fuel quality, and IMAP. Of course, in other embodiments, the engine actuators to be controlled may be varied.

As shown in FIG. 1, an internal combustion engine controller includes an engine set point module 20. The engine set point module 20 is configured to output a control signal to each actuator based on a plurality of control maps 30 and input variables to the engine set point module 20. Thus, the operation of the engine set point module 20 is similar to open-loop, engine map-based control schemes known in the art. Such open-loop control schemes have relatively small computational requirements compared to more complex model-based control schemes.

The input variables to the engine set point module 20 may be a combination of different variables derived from the current operation of the internal combustion engine. Some of the input variables may be based on the performance requirements of the internal combustion engine. Some of the input variables may be based on current operating conditions of the internal combustion engine, such as current operating conditions measured by various sensors. Since the input variables are used to determine the actuator set points based on the control maps, it should be appreciated that the total number of input variables per control map may be limited by the computational resources available to internal combustion engine controller 10.

In the embodiment of fig. 1, the input variables are requested torque (TqR), current engine speed (N), and current IMAP. In other embodiments, other input variables may be used, such as current EGR (i.e., current position of the EGR valve).

In general, it should be appreciated that some control actuators associated with an internal combustion engine may have some time lag associated with them. Thus, there may be some degree of time delay between a requested change in the actuator set point (e.g., the requested IMAP) and the change recorded by the sensor (i.e., the current IMAP sensor reading).

Each of the plurality of control maps 30 defines a relationship between one or more input variables and an actuator set point. In the embodiment of fig. 1, four control maps 30 are provided, one for each of EGR, SOI, fuel quality, and request imap (imapr). Each of the control maps 30 may define an engine actuator set point based on one or more of TqR, N, and the current imap (imapc). For example, the EGR control map may define a hypersurface for the actuator set point based on TqR, N and IMAPC. Thus, TqR, the combination of N and IMAPC defines the position of the hypersurface from which the actuator set point for EGR can be calculated. Similarly, the SOI and fuel quality control map 30 may also be defined by a hypersurface that is a function of TqR, N and IMAPC. The control map of IMAPR in the embodiment of fig. 1 may be defined by a hypersurface which is a function of TqR and N. Thus, different control maps may have different numbers of dimensions.

Each of the control maps 30 of fig. 1 may be implemented as a lookup table. Look-up table control maps 30 for engine controllers are well known in the art. An exemplary look-up table control map 31 is shown in fig. 2 a. The look-up table control map 31 shown in fig. 2a has two input dimensions and a single output dimension. Thus, in the embodiment of fig. 2a, the control map 31 is a two-dimensional control map, wherein the number of referenced dimensions is determined by the number of input dimensions. The control map 31 of fig. 2a comprises input variables 1 (i.e. first input variables) and input variables 2 (second input variables). The look-up table defines a plurality of values (actuator set points) for different combinations of input variables 1 and 2. Thus, the look-up table control map 31 may be used to select an actuator set point based on the values of the input variables 1 and 2. Fig. 2b is a graphical representation of a hypersurface defined by values in the look-up table control map 31. Interpolation of the set points defined in the lookup table may be used, as is known in the art, to find locations on the hypersurface where one or more input variables do not exactly match the values stored in the lookup table.

In other embodiments, the hyper-surface of each control map 30 may be described using alternative approaches. For example, a hypersurface may be defined as a function of the input variables. A suitable multi-dimensional function for defining a hypersurface may be a general approximator function. Suitable generic approximator functions may include: artificial neural networks (e.g., radial basis functions, multi-layer perceptrons), multivariate polynomials, fuzzy logic, irregular interpolation, Kriging (Kriging).

The plurality of control maps 30 may be stored in a memory of the internal combustion engine controller 10 such that various processing modules of the internal combustion engine controller 10 may access the control maps 30.

As shown in fig. 1, the internal combustion engine controller 10 also includes a map update module 40. The map update module 40 is configured to calculate an optimized hypersurface for at least one of the control maps 30. In the embodiment of fig. 1, the map update module 40 may simultaneously calculate an optimized hypersurface for each of the control maps 30. The map update module 40 is configured to update the hypersurface of the control map 30 based on the calculated optimized hypersurface. Thus, during operation of the internal combustion engine 1, the hypersurface of one or more control maps 30 may be updated. By providing an updatable set of control maps 30, a set of control maps 30 may be provided that may be optimized to different operating point ranges. Therefore, the number of control maps that need to be calibrated for the internal combustion engine 1 may be reduced, because the updatable set of control maps 30 of the present disclosure may control the internal combustion engine 1 over different operating point ranges for which a separate set of control maps (i.e., a plurality of sets of control maps) may have been calibrated in the past.

The map update module 40 is configured to compute an optimized hypersurface based on the performance objective function. The performance objective function may be evaluated in real time rather than, for example, off-line calculations of historical engine data. The performance objective function uses sensor data from the internal combustion engine 1 and a plurality of input variables (i.e., real-time input variables to the internal combustion engine 1) to calculate an optimized hypersurface. The performance objective function includes engine parameters associated with the engine model and/or cost parameters associated with the cost function, which are used to calculate the optimized hypersurface. Thus, the performance objective function may be a multi-dimensional function. In fact, the internal combustion engine controller 10 of the present disclosure incorporates additional variables (direct and/or indirect sensor data variables) into the control of the internal combustion engine 1 in a manner that does not significantly increase the computational complexity of map-based control.

Map update module 40 may use a performance objective function to search for an optimized hypersurface. For example, the map update module 40 may search for an optimized hypersurface by modeling real-time performance of the internal combustion engine 1 based on engine parameters associated with the engine model and calculating a cost associated with the modeled real-time performance. The map update module 40 may repeat this process for a plurality of candidate sets of actuator setpoints and then determine an optimized hypersurface based on the lowest cost candidate set of actuator setpoints.

For example, the map update module 40 may be configured to compute an optimized hypersurface of the IMAPR control map. The IMAPR control map 30 may be based on input variables: engine speed (N) and requested torque (TqR). The map update module 40 may model real-time performance of the internal combustion engine 1 for a plurality of candidate sets of engine actuator setpoints. For example, a set of candidate engine actuator setpoints may include: SOI, fuel quality, requested EGR, and IMAPR. The map update module 40 may change one or more of the engine actuator setpoints between each candidate set of engine actuator setpoints in order to search for an optimized hypersurface of the IMAPR control map 30. In one embodiment, in which only the IMAPR control map 30 is updated, the engine actuator set point for IMAPR may be varied between each of the candidate sets of engine actuator set points. Based on the output of the performance objective function for each candidate set, the map update module 40 may determine an optimized hypersurface for the IMAPR control map. As described above, the optimized hypersurface may be part of the total hypersurface defined by the control map 30 (i.e., only part of the total hypersurface defined by the control map may be updated).

As shown in fig. 1, the internal combustion engine controller 10 also includes a parameter update module 50. The parameter update module 50 is configured to update one or more parameters of the performance objective function. In particular, the parameter update module 50 is configured to update the engine parameters and/or the cost parameters of the performance objective function.

The parameter update module 50 is configured to update the parameters of the performance objective function when a change in the operating condition of the internal combustion engine is determined. The operating condition of the internal combustion engine may be based on at least one of: input variables to an internal combustion engine controller, sensor data from the internal combustion engine, and sensor data from an aftertreatment system of the internal combustion engine. By monitoring one or more of these variables, the parameter update module may determine that a change in operating conditions has occurred and select one or more parameters (cost parameters and/or engine parameters) that update the performance objective function in response to the change. Determining a change in the operating condition of the internal combustion engine by the parameter update module 50 will be discussed in more detail below with reference to FIG. 5.

The optimized hypersurface calculated by map update module 40 may account for changes in the operating conditions of the internal combustion engine by updating parameters of the performance objective function. Thus, the map update module may be more sensitive to time-varying changes in the performance of the internal combustion engine. For example, the parameter update module may detect a change in an operating condition of the internal combustion engine associated with a change in calibration of one or more sensors of the internal combustion engine and/or the aftertreatment system, and continue to update the associated performance parameters to account for the change in sensor calibration over time. Alternatively, changes and/or uncertainties over time between the modeled performance of the internal combustion engine and the actual real-time performance of the internal combustion engine may be detected by the parameter update module as changes in the operating conditions of the internal combustion engine.

FIG. 3 shows a more detailed block diagram of the internal combustion engine controller 10 according to an embodiment of the present disclosure. The block diagram indicates in dashed lines that the map update module 40 includes a performance objective function and optimizer module 42. To further explain the performance objective function, the performance objective function is represented in FIG. 3 as including an engine modeling module 44 and a cost module 46. Of course, it will be understood that the engine modeling module 44 and the cost module 46 may also be provided as a combined "black box" function (i.e., as the performance objective function of FIG. 1). Therefore, the internal combustion engine controller 10 has an overall structure similar to that shown in fig. 1.

The internal combustion engine controller 10 of fig. 3 also includes an engine set point module 20. Referring to fig. 1 and the corresponding description, it will be understood that the engine set point module 20 of fig. 3 is configured to output a plurality of actuator set points based on positions on a hypersurface of a respective control map 30 defined by a plurality of input variables.

The map update module 40 includes an optimizer module 42, an engine modeling module 44, and a cost module 46. As described above, the map update module 40 is configured to calculate an optimized hypersurface for one or more of the control maps 30. In this embodiment, the map update module 40 is configured to calculate an optimized hypersurface for the plurality of control maps 30. For example, in the embodiment of FIG. 3, a control map for each of SOI, fuel quality, requested EGR, and IMAPR is provided. The control map 30 of SOI, fuel mass, and requested EGR is a function of the input variables engine speed (N), requested torque (TqR), and IMAPC, respectively. The control map of IMAPR is a function of engine speed (N) and requested torque (TqR).

The optimizer module 42 is configured to search for an optimized hypersurface of at least one of the control maps 30. In this embodiment, the optimizer module 42 is configured to search the optimized hypersurface of each of the control maps 30 for the SOI, fuel mass and EGR requested simultaneously. The optimizer module 42 may be configured to search the optimized hypersurface of IMAPR at different times. Thus, it can be appreciated that the map update module 40 need not update all of the control maps at the same time. In other embodiments, it will be appreciated that the map update module 40 may update all of the control maps simultaneously.

The optimizer module 42 is configured to search for an optimized hypersurface where the optimizer module 42 provides a plurality of candidate sets of actuator setpoints to the engine modeling module 44. Each candidate set of actuator setpoints is effectively a vector of actuator setpoints. The candidate set of actuator setpoints may include an actuator setpoint for each control map 30 to be updated. The candidate set of actuator set points may also include actuator set points of the control map 30 that are not currently updated by the map update module 40. For example, in the embodiment of FIG. 3, the set of candidate actuator setpoints includes setpoints for each of SOI, fuel mass, requested EGR, and IMAPR. By including the IMAPR actuator set point in the candidate set, the real-time performance model accuracy may be improved even if the control map 30 is not updated. Essentially, in the embodiment of fig. 3, the IMAPR setpoint is considered to be a time-invariant setpoint. Control maps not updated by the optimizer module 42 (e.g., control maps for IMAPR) may be updated in other ways. As discussed further below, a plurality of different optimizer functions may be provided to update different control maps.

The optimizer module 42 outputs each candidate set of actuator setpoints to an engine modeling module 44 that forms part of the performance objective function. The optimizer module 42 may select the set of candidate actuator set points to be evaluated by the performance objective function in various ways. For example, the optimizer module 42 may randomly select each actuator setpoint within the candidate set of actuator setpoints from a predefined range of allowable actuator setpoints. Thus, the candidate set of actuator setpoints may be a substantially randomized set of actuator setpoints. Thus, the optimizer module 42 may randomly select a candidate set of actuator setpoints (randomized search strategy). Alternative search strategies may also be utilized, as discussed in more detail below.

The number of candidate sets of actuator setpoints selected by the optimizer module 42 may be predetermined according to the computational resources available for computing the optimized hypersurface. The map update module 40 is configured to output an optimized hypersurface to optimize a position on the control map corresponding to a current operating point of the internal combustion engine. Thus, the map update module 40 may update the control map in real-time, thereby placing a limit on the amount of processing time available to compute the optimized hypersurface. For example, in the embodiment of FIG. 3, the map update module is configured to output the optimized hypersurface within 60 ms. The processing time consumed in evaluating a single candidate set of engine actuator set points using the performance objective function will place an upper limit on the number of possible candidate sets that can be evaluated in a single 60ms period. The processing time consumed to evaluate a single set of candidate engine actuator set points will depend on the computational complexity of the performance objective function.

In the embodiment of FIG. 3, the processing time may depend on the computational complexity of the engine modeling module 42 and the cost module 44, which will be explained in more detail below. Typically, using a performance objective function to evaluate a single candidate set of engine actuator setpoints may take approximately 0.1 ms. Thus, in the embodiment of FIG. 3, approximately 200 candidate sets of engine actuator set points may be evaluated by the map update module 40, consuming approximately 20 ms. Thus, for a map update module 40 configured to output an optimized hypersurface in 60ms, the remaining processing may be allocated a processing time budget of about 30ms and a relaxation time of about 10 ms.

As an alternative to a randomized search strategy, the optimizer module 42 may employ other search strategies. For example, a set of candidate actuator set points may be selected according to an iterative search strategy. As part of the iterative search strategy, a first set of candidate sets of actuator setpoints may be identified and analyzed as described above to determine an associated cost. The optimizer module 42 may then select the second set of candidate sets of actuator setpoints based on the first set of actuator setpoints and the associated costs (i.e., based on the lowest cost candidate set of the first set of candidate sets). Examples of suitable search iterative search strategies include genetic algorithms, Simplex algorithms (Simplex), random optimization, and/or swarm algorithms.

The engine modeling module 44 is configured to calculate a plurality of engine performance variables associated with each candidate set of actuator setpoints. Inputs to the engine modeling module 44 are a plurality of input variables of the control map, as well as sensor inputs from the internal combustion engine and a set of candidate actuator set points. Thus, the engine modeling module 44 is provided with a plurality of input variables associated with the real-time operating point of the internal combustion engine. Thus, the plurality of engine performance variables calculated by the engine modeling module 44 may represent actual real-time performance of the internal combustion engine.

In the embodiment of FIG. 3, the engine modeling module 44 is provided with candidate actuator set point sets of SOI, fuel quality, requested EGR, and IMAPR. The engine modeling module is also provided with real-time data from a plurality of sensors of the internal combustion engine. The sensor data from the internal combustion engine 1 may include information from various sensors associated with the internal combustion engine 1. The sensor data may also include variables derived from data from one or more sensors of the internal combustion engine. For example, the sensor data may include intake manifold absolute pressure, intake manifold temperature, fuel rail pressure, back pressure valve position, mass EGR flow, total air mass flow, Fuel Rail Pressure (FRP).

The engine modeling module 44 may include one or more engine models configured to calculate a plurality of engine performance variables associated with each candidate set of actuator set points. It should be appreciated that since the inputs to the engine modeling module 44 include input variables to the internal combustion engine as well as sensor data, the engine performance variables will represent the real-time performance of the internal combustion engine at those actuator set points. The calculated engine performance variables may include: engine torque, mass airflow, Brake Mean Effective Pressure (BMEP), net mean indicated effective pressure (IMEP), Pump Mean Effective Pressure (PMEP), Friction Mean Effective Pressure (FMEP), exhaust manifold temperature, peak cylinder pressure, NOx amounts (e.g., net indicated ratio NOx (nisnox), brake indicated ratio NOx), soot amounts (e.g., net indicated ratio soot, brake indicated ratio soot), NOx/soot ratio, minimum fresh charge, and EGR potential.

Where applicable, the internal combustion engine controller calculates a net indicated ratio engine performance variable (e.g., IMEP, NISNOx) instead of a brake indicated ratio performance variable. IMEP reflects the average effective pressure of an internal combustion engine over the entire engine cycle. In contrast, BMEP is the mean effective pressure calculated from the brake torque. In some embodiments, net indicated ratios (e.g., IMEP, NISNOX) may be used because these values are non-zero even when the engine is idling.

In the present disclosure, the net indicated ratio NOx (nisnox) and brake indicated ratio NOx are further intended to represent the amount of NOx output by the internal combustion engine prior to any treatment in the aftertreatment system. Of course, those skilled in the art will appreciate that the amount of NOx (e.g., tailpipe NOx) may also be estimated downstream of the aftertreatment system.

To calculate one or more of the engine performance variables described above based on inputs to the engine modeling module 44, one or more engine parameters may be used. The engine parameters may be used to define a relationship between one or more of the above-described performance variables and inputs to the engine modeling module. For example. Various physical relationships between the above-described performance variables and the inputs provided to the engine modeling module are known to those skilled in the art. Accordingly, the engine modeling module may provide one or more physics-based models to calculate one or more of the above-described performance variables. As an alternative to the physics-based models, the engine modeling module 44 may also calculate one or more of the above-described performance variables using empirical/black-box models or a combination of empirical and physics-based models (i.e., semi-physical/gray-box models).

For example, the engine modeling module 44 may include a mean engine model. Mean engine models are well known to those skilled in the art for modeling engine performance parameters such as BMEP, engine torque, airflow, etc. Further explanation of a Mean engine model suitable for use in the present disclosure may be found in "Event-Based Mean-Value Modeling of DI Diesel Engines for Controller Design" in Urs Christen et al, SAE Technical Paper Series. Accordingly, the mean engine model may be used to calculate engine performance variables based on the inputs of the engine modeling module 44.

In addition to or in lieu of using a mean model, the engine modeling module 44 may include one or more neural network-based models for calculating one or more engine performance variables. For example, a net indicated ratio nox (nisnox) engine performance variable may be calculated from sensor data using a suitably trained neural network. Further explanation of suitable techniques for calculating engine performance variables, such as NOx amounts (e.g., NISNOx), using neural networks can be found in Michele Steyskal et al, "Development of PEMS Models for Predicting NOx Emissions from Large Bore Natural gases Engineers" in SAE Technical Paper Series.

A physics-based model of one or more internal combustion engine components may be provided. For example, a compressor model, a turbine model, or an exhaust gas recirculation cooler model may be provided to help calculate the appropriate engine performance variables.

The engine modeling module 44 outputs the engine performance variables to a cost module 46. The cost module 46 is configured to evaluate an engine performance variable and output a cost associated with each candidate set of actuator setpoints based on the performance variable. In the embodiment of FIG. 3, the cost module 46 is configured to output the cost associated with each candidate set of actuator setpoints to the optimizer module 42. In other embodiments, the evaluation of the cost associated with each candidate set of actuator setpoints may be performed by an additional module separate from the optimizer module 42.

Cost module 46 may include a plurality of cost functions configured to assign costs to various performance goals in order to evaluate the modeled performance of the internal combustion engine at the set of candidate actuator setpoints. Each cost function may determine a cost based on one or more engine performance variables and one or more cost parameters. For example, the plurality of cost functions may include one or more operational objective functions, one or more emissions functions, and one or more engine constraint functions. Each of the plurality of cost functions may be configured to output a cost based on a function of one or more engine performance variables and one or more cost parameters. The cost parameter may determine a magnitude of the cost associated with each engine performance variable. The cost parameter may also determine the relative cost of each cost function relative to the other cost functions. In the embodiment of FIG. 3, the cost function is configured such that lower costs are associated with better performance.

The operating objective function may be a cost function configured to optimize the internal combustion engine to meet certain objectives for operating the internal combustion engine. For example, one goal may be to operate the internal combustion engine while minimizing Brake Specific Fuel Consumption (BSFC) or Net Indicated Specific Fuel Consumption (NISFC). Another operational goal may be to minimize torque error (i.e., the difference between the actual output torque and the requested torque). The form of such a running objective function may be represented by a function having a weighted square law relationship (i.e., the form: cost ═ weight ^ (performance variable) ^ 2). Thus, for a running objective function, the weight of the running objective function is a cost parameter. A graphical representation of a suitable operational objective function is shown in fig. 4 a. For example, the cost (cost) associated with the operational objective of the NISFC_NISFC) Can be as follows:

cost of_NISFCWeight-weight_NISFC*NISFC^2

The emissions function may be a function configured to optimize the internal combustion engine in order to meet certain objectives related to emissions produced by the internal combustion engine. For example, one or more emission functions may be provided based on engine performance variables related to emissions produced by the internal combustion engine. Thus, one or more emission functions may be based on NOx mass (NISNOx, soot (NISCF), NOx-to-soot ratio, minimum fresh charge, and/or EGR potential.

For example, the emissions function may include a target upper limit (T)_U). The target upper limit may define a value for the engine performance variable above which significant costs may be incurred, while for values below the target upper limit no or minimal costs may be incurred. For example, for some internal combustion engines, the upper target limit for NISNOx may be 4 g/kWh. Thus, for an emissions function, the target upper limit and/or weight may be a cost parameter. In other embodiments, a target limit may be provided as the target lower limit.

Thus, the emission function (cost) based on the engine performance variable NISNOx_NOx) Can be as follows:

when: NISNOx<T_UCost of_NOx＝0

NISNOx≥T_UCost of_NOxWeight-weight_NOx*(NISNOx–T_U)^2

Some emission functions may also be limited by a minimum or target lower limit (T)_L) To be defined. For example, an emission function (cost) based on an engine performance variable Exhaust Minimum Temperature (EMT)_EMT) Can be defined as:

when: EMT>T_LCost of_EMT＝0

EMT≤T_LCost of_EMTWeight-weight_EMT*(EMT–T_L)^2

The engine constraint function may be a function configured to reflect constraints associated with operation of the internal combustion engine. Accordingly, one or more engine constraint functions may be provided to prevent or prevent the controller from operating the internal combustion engine at certain engine actuator set points. For example, one or more engine constraint functions may be based on engine performance variables having fixed limits that cannot be exceeded due to the physical requirements of the internal combustion engine. Accordingly, one or more engine constraint functions may be based on Peak Cylinder Pressure (PCP), exhaust manifold temperature, compressor outlet temperature. Additional engine performance variables having desired fixed limits, such as maximum allowable torque error, may also have corresponding engine constraint functions. Each engine constraint function may use any suitable function to define the relationship between the cost and one or more engine performance variables. The engine constraint function may also include a cost parameter. For example, in the embodiment of fig. 3, the engine constraint function may be provided in the form of a cost of 1/engine performance variable. A graphical representation of a suitable engine constraint function is shown in figure 4 c.

For example, an engine constraint function for the engine performance variable PCP may be provided based on the PCP upper limit L, and the cost calculated by the engine constraint function may increase asymptotically as the PCP upper limit L is approached. Therefore, the limit L may also be a cost parameter. Therefore, engine constraint function (cost) based on engine performance variable PCP_PCP) Can be as follows:

cost of_PCP＝1/(L–PCP)

Since the engine constraint function generally involves engine performance variables having fixed limits based on the physical requirements of the internal combustion engine, in some embodiments, the parameter update module may not update the cost parameters associated with the engine constraint function. For example, the PCP upper limit L may be a time-invariant cost parameter.

As described above, various cost parameters have been described with respect to an operational objective function, an emissions function, and an engine constraint function. The cost parameter may be stored by cost module 46, for example, as a cost parameter vector.

Accordingly, cost module 46 may calculate a total cost associated with each candidate set of actuator setpoints based on the costs calculated by each of the cost functions calculated above. The total cost associated with each candidate set of actuator setpoints may be provided to the optimizer module 42 for further processing.

As shown in FIG. 3, one or more of the cost parameters may be updated by a parameter update module 50. Updating of the cost parameter is discussed in more detail below.

The optimizer module 42 is configured to output an optimized hypersurface for the at least one control map 30 based on the set of candidate actuator setpoints and the associated costs. Thus, based on the total cost of each candidate set of actuator setpoints, the optimizer module 42 may identify the set of actuator setpoints having the best performance. For example, the set of candidate actuator setpoints with the lowest total cost may provide optimal performance. Thus, the optimizer module 42 may determine that the candidate set of actuator setpoints having the lowest overall cost is the optimized set of actuator setpoints. The map update module may update one or more hypersurfaces of the control map at positions defined by the input variables based on the optimized set of actuator set points.

Thus, it is possible to provide the internal combustion engine controller 10 according to the map shown in fig. 3.

Fig. 5 shows a more detailed block diagram of the parameter update module 50 and a portion of the map update module 40. The parameter update module 50 is directed to updating one or more engine parameters and/or cost parameters of the performance objective function. The parameters to be updated are typically used for one of two purposes. The engine parameters associated with the engine model (i.e., forming part of the engine modeling module 44) may be updated to reduce the uncertainty in the engine model of the engine modeling module 44. The cost parameters associated with the cost function described above may be updated in order to effect a change in the priority of the internal combustion engine controller (i.e. change the operating mode of the internal combustion engine 1).

As described above, the engine modeling module 44 of the performance objective function utilizes a model of the internal combustion engine to determine engine performance variables. It should be appreciated that there will be some uncertainty associated with the calculated engine performance variables. During the life of the internal combustion engine, it should be appreciated that, for example, aging of the internal combustion engine and/or variations in the manufacturing of the internal combustion engine may cause the actual performance of the internal combustion engine to be slightly different from the performance modeled by the engine modeling module 44. In particular, age-related uncertainties may be time-varying. A parameter update module 50 is provided for updating engine parameters over time in an attempt to offset the effects of time-varying uncertainties on the engine modeling module 44.

As described above, the performance objective function (engine modeling module 44) uses the sensor data and the plurality of input variables to calculate one or more engine performance variables. Some of these engine performance variables may be related to physical characteristics of the internal combustion engine, which may be observed by additional engine sensors. The parameter update module 50 is configured to make model observations of given engine performance variables and physical observations of the engine performance variables based on sensor data obtained from the internal combustion engine. By comparing the model observations and the physical observations, the parameter update module 50 is configured to determine engine parameters to reduce any differences between the model observations and the physical observations. It should be appreciated that the engine model used by the parameter update module is time invariant. Therefore, any difference occurring over time between the model observation of the engine performance variable and the physical observation of the engine performance variable is effectively considered to be due to a change in the operating condition of the internal combustion engine 1.

As shown in FIG. 5, the parameter update module 50 outputs the engine parameters to the performance objective function of the map update module 40. The performance objective function uses the engine parameters to update the corresponding engine performance variables calculated by the engine modeling module 44 to reduce the uncertainty. The updated engine performance variables are then input to the cost module 46 portion of the performance objective function.

For example, in one embodiment, engine performance variables representing the amount of NOx may be calculated by the engine modeling module 44 based on sensor data. However, there may be some uncertainty associated with this calculated NOx amount. The uncertainty may be caused by manufacturing variations of the internal combustion engine, degradation of the internal combustion engine, and/or environmental uncertainty. For example, the actual amount of NOx produced by a given internal combustion engine may depend on unmeasured disturbances such as engine wear or humidity. In an attempt to counteract this uncertainty, the parameter update module 50 may update one or more engine parameters to reduce the uncertainty in the calculation of the engine performance variables.

The parameter update module 50 may be provided with additional sensor data from an aftertreatment system connected to the internal combustion engine 1, from which the actual NOx amount may be determined. For example, sensor data from a NOx sensor coupled to the aftertreatment system may be provided to the parameter update module. The parameter update module 50 may also be configured with the same sensor data and input parameters as the map update module 40, and the parameter update module may calculate the amount of NOx based on the input parameters using the same engine model as the engine modeling module 44.

The parameter update module 50 is configured to determine the NOx calibration parameters to reduce any difference between the amount of NOx calculated by the engine modeling module and the actual amount of NOx observed by the sensors connected to the internal combustion engine 1.

Fig. 6 shows an example of a time-varying variation of the NOx calibration parameter in response to an observed variation of the operating conditions of the internal combustion engine 1. In the example of fig. 6, the internal combustion engine 1 is operated under steady-state conditions under the control of the internal combustion engine controller 10 according to the present disclosure. At time t 120s, an artificial mass error in the EGR sensor is introduced. The EGR sensor data is used by one of the sensor data inputs used by the engine modeling module 44 to calculate the NOx amount of the engine performance variable. As shown in graph 1) of FIG. 6, the disturbance in the EGR sensor results in a disturbance in the NOx amount engine performance variable calculated by the engine modeling module 44. FIG. 6 also shows a graph of the amount of NOx measured by a NOx sensor coupled to the aftertreatment system over the same time period. As shown in fig. 6, the actual NOx amount output by the internal combustion engine is not changed at time t-120 s.

Fig. 2) of fig. 6 shows a plot of the NOx calibration parameter over the corresponding time period of plot 1). Before time t 120s, the internal combustion engine is operating at steady state, so the NOx calibration parameter is set at about 1.23. Upon introducing a disturbance in the EGR sensor at time t-120 s, the parameter update module 50 observes the difference between the model observations of the NOx amount engine performance variables and the physical observations of the NOx amount by the NOx sensor. The parameter update module adjusts the NOx calibration parameters over time to reduce the difference between the model observations and the physical observations, as shown in FIG. 6. Accordingly, the parameter update module 50 is configured to calibrate the disturbances introduced at the EGR mass sensor and reduce the difference between the model observations of the NOx amount and the actual NOx amount detected by the sensor.

It should be appreciated that the example of FIG. 6 provides for applying jamming to the EGR sensor to aid in understanding the present disclosure, and it should not be understood that the present disclosure is limited to canceling short-term transient disturbances. Further, while in the example of fig. 6, disturbances in the sensors are used to demonstrate the effect of the parameter update module 50, it will be understood that the present disclosure is not limited to counteracting sensor errors. For example, the parameter update module 50 may also be configured to account for input sensitivity of the internal combustion engine that results in differences between the performance and/or emissions of the internal combustion engine relative to the values calculated by the engine modeling module 44.

As further shown in FIG. 5, the parameter update module 50 may update one or more cost parameters associated with one or more cost functions of the performance objective function when a change in operating conditions of the internal combustion engine is determined. As described above, the cost function of the performance objective function may include an operational objective function, an emissions function, and/or an engine constraint function. Each of these types of cost functions may have one or more cost parameters associated with them. The parameter update module 50 may update the relative values of these cost parameters to adjust the relative importance of each cost function to the total cost calculated for each candidate set of actuator setpoints. Thus, the parameter update module 50 may effectively provide time-varying adjustments to the strategy of the map update module 40 when searching for an optimized hypersurface. This in turn allows the internal combustion engine controller 10 to operate over a range of different environments and at different operating points using a reduced number of control maps.

For example, the parameter update module 50 may utilize data from the aftertreatment system in order to determine that a regeneration of the aftertreatment system is to be performed (e.g., an indication from the aftertreatment system that regeneration is needed). Such an indication may be based on a determination that the DPF soot loading has risen above a threshold. Accordingly, one or more cost parameters may be updated such that the map update module 40 changes the strategy, for example, from prioritizing low fuel consumption to prioritizing high exhaust temperature. Accordingly, the parameter update module 50 may update some cost parameters of the performance objective function to achieve regeneration of the aftertreatment system.

For example, an emissions function may be provided to assign a cost to include an associated exhaust gas minimum temperature cost parameter (T)_L) Exhaust gas minimum temperature engine performance variable. To regenerate the aftertreatment system (e.g., to regenerate a Diesel Particulate Filter (DPF)), the parameter update module 50 may update the cost parameter T_LFrom a negligible value (e.g., -273.15 ℃) to a higher value (e.g., 400 ℃). The internal combustion engine may not be able to reach such exhaust gas temperatures, but will be encouraged to find a solution that minimizes the deviation from this value, thereby increasing the exhaust gas temperature so that the aftertreatment system can be regenerated. Thus, the cost parameter T _LMay be used to trigger an aftertreatment thermal management mode in which the temperature of the exhaust gas output from the internal combustion engine is increased. When aftertreatment thermal management is no longer needed (e.g., once the regeneration process is complete), parameter update module 50 may update parameter T_LTo a negligible value (e.g., -180 ℃). Thus, when aftertreatment thermal management is not required, the importance of the emissions function of the EMT is reduced relative to other cost functions.

To determine whether the DPF should be regenerated, an engine performance variable indicative of the DPF soot loading may be provided to the parameter update module 50. Alternatively, the DPF soot loading can be derived by the parameter update module 50 from sensor data provided by the internal combustion engine. For example, DPF soot loading may be an engine performance variable derived by an internal combustion engine controller from sensor data representative of DPF soot loading, such as a comparison of an expected DPF differential pressure at a given mass flow rate to a measured DPF differential pressure to infer DPF soot loading.

In some operating environments, the actual DPF soot loading may vary, for example, due to soot accumulation on the DPF. The parameter update module 50 may update the cost parameter T in response to determining that the DPF soot load has exceeded the DPF soot load upper threshold _L. Thus, a change in DPF soot loading indicates a change in the operating conditions of the internal combustion engine. Thus, in some embodiments, the parameter update module 50 may determine that the DPF should be regenerated when the DPF soot load rises above a DPF soot load upper threshold. Thus, the parameter update module 50 may update the cost parameter T_LFrom a negligible value (e.g., -273.15 ℃) to a higher value (e.g., 400 ℃). Once the DPF is regenerated (i.e., soot is burned off of the DPF to reduce DPF soot loading), the parameter update module 50 may update the parameter T_LAdjust to a negligible value (e.g., -180 ℃). The parameter update module 50 may determine that the DPF is regenerated based on determining that the DPF soot load has dropped below the lower soot load threshold. In addition to or instead of the lower DPF soot loading indicator, the parameter update module 50 may determine that the DPF is regenerated after a predetermined period of time has expired. The predetermined threshold may vary in other embodiments depending on the specific requirements of the internal combustion engine and DPF. For example, the DPF soot loading threshold may be at least: 85%, 90% or 95%.

In other embodiments, the parameter update module 50 may update the relative values of the weights of the cost function to cause regeneration of the aftertreatment system. Thus, the weighting function of the cost function may be updated from prioritizing low fuel consumption to, for example, prioritizing high exhaust temperature by changing one or more weighting functions associated with the cost function(s).

In some embodiments, the parameter update module 50 may include more than one function for updating the parameters of the performance objective function. For example, in some embodiments, the parameter update module 50 may include an SCR temperature function for updating the exhaust minimum temperature T based on sensor data indicative of an SCR catalyst temperature (e.g., an SCR inlet temperature)_L. Such functionality may alternatively or additionally be provided to a parameter update module 50 that determines whether the DPF should be thermally managed as described above. The SCR temperature function of the parameter update module is configured to represent an SCR catalyst temperature (T) in response to determining_SCR) Is below the threshold SCR lower temperature (k)_SCR1) While increasing the exhaust minimum temperature cost parameter T_L. To increase the SCR catalyst temperature, the parameter update module 50 may update the cost parameter T_LFrom a negligible value (e.g., -273.15 ℃) to a higher value (e.g., 400 ℃). Thus, the SCR temperature function may also update the cost parameter T_LTo provide an aftertreatment thermal management mode. T is_LCan be maintained up to T_SCRExceeds the upper threshold temperature, at which point T_LMay be updated to a negligible value. Effectively, the SCR temperature function may be at k_SCR1And k_SCR2In the form of hysteresis, so that T is measured as a function of SCR temperature _LThe update frequency of (2) is smoothed.

The parameter update module 50 may store emission data received from the aftertreatment system relating to emissions of the internal combustion engine. The parameter update module 50 may utilize the emission data to monitor the emission performance of the internal combustion engine. In some embodiments, the parameter update module 50 may adjust one or more of the emission functions based on the monitored emission performance. Accordingly, the internal combustion engine controller 10 may be configured to control the internal combustion engine 1 in a manner that complies with various emission regulations. It should be appreciated that emission legislation may vary depending on the operating location of the internal combustion engine. Unlike time-invariant control maps, which may be individually calibrated to pre-meet specific emission targets, the parameter update module 50 of the internal combustion engine may be updated to properly meet local emission regulations. Thus, the calibration requirements of the internal combustion engine controller 10 may be further reduced.

For example, the parameter update module 50 may update the emissions function (cost) in response to changes in SCR conversion efficiency_NOx) An associated cost parameter. The parameter update module 50 may update the cost parameter target upper limit T in response to a change in SCR conversion efficiency_U. Thus, the parameter update module 50 may change Cost parameter T_UIn an attempt to offset the variation in SCR efficiency so that any variation in tailpipe NOx amounts is reduced or eliminated.

In one embodiment, the parameter update module 50 may include a target update function to update the cost parameter T_UThe cost parameter takes into account variations in the SCR conversion efficiency. According to this embodiment, the parameter update module may determine or be provided with a desired upper limit D for the amount of NOx_U. For example, the parameter update module may be calibrated at a desired upper NOx amount limit based on the internal combustion engine to be controlled. For example, in the embodiment of FIG. 5, D_UIt may be 4 g/kWh. Parameter update module 50 may be based on D_UCalculating T_UAnd calculating a scaling factor (k) based on the SCR conversion efficiency_CE)：

T_U＝D_U*k_CE

Scaling factor k_CEThe difference between the expected SCR conversion efficiency (e.g., the expected SCR conversion efficiency assumed by the engine modeling module) and the actual SCR conversion efficiency determined by the parameter update module 50 may be reflected in real-time. Scaling factor k_CEThere may be an upper limit of 1 corresponding to when the actual SCR conversion efficiency is equal to or greater than the expected SCR conversion efficiency. Scaling factor k when the actual SCR conversion efficiency is less than or equal to the SCR conversion efficiency threshold_CEThere may be a lower limit X, which may be less than 1 and greater than about 0.4, for example, the lower limit X may be 0.4, 0.5, 0.6, or 0.7. Thus, in one embodiment, the target update function may scale the target upper limit for the amount of NOx from 4g/kWh when the SCR catalyst is operating at 95% efficiency to 2g/kWh when the SCR catalyst is operating at 90% efficiency. The scaling over the range between these values may be linear or any other form of suitable relationship.

In some embodiments, the parameter update module 50 may adjust one or more of the emission functions by updating the scaling factors used to calculate the cost parameters based on the monitored emission performance. For example, the parameter update module may update the scaling factor k by updating the scaling factor k based on the monitored emissions performance_CETo update the cost parameter T_U。

INDUSTRIAL APPLICABILITY

The internal combustion engine controller 10 of the present disclosure may be configured to control an internal combustion engine in a variety of configurations.

One application may be for controlling an actuator set point of an internal combustion engine as shown in fig. 1. The internal combustion engine may be mounted on, for example, a vehicle or machine, or may form part of an electrical generator.