Powertrain controller
Patent Information
- Application Number
- CN202180027039.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Priority Date
- 2020-04-09
- Filing Date
- 2021-03-29
- Publication Date
- 2026-09-08
- Estimated Expiration
- 2041-03-29
AI Technical Summary
因此，特定动力传动系的控制是具有挑战性的，因为特定动力传动系的架构可能具有相对复杂且独特的架构
[0030]Specifically, for powertrains with more complex architectures (e.g., hybrid powertrains, powertrains with multiple power sources), the configurable optimizer module can be configured to calculate optimized force requests or optimized flow requests for each of the N power sources in a particular powertrain. In this way, the universal controller can provide real-time control of complex powertrain architectures.
Smart Images
Figure CN115397708B_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present invention relate to the control of powertrain systems. By way of example only, some embodiments of the present invention relate to the control of powertrain systems, such as powertrain systems comprising at least one of an internal combustion engine, a hydrogen fuel cell, or a battery. Background Technology
[0002] A powertrain is a system that includes one or more power-generating components (power sources) and one or more components arranged to deliver that power in a desired manner. For example, in a motor vehicle, a powertrain may include an internal combustion engine, a gearbox (also called a transmission), a drive shaft, a differential, and a set of wheels that contact the drive surfaces.
[0003] As an example, Figure 1A A schematic example of a powertrain for a motor vehicle is shown. In this example, the powertrain includes a single power source, an internal combustion engine. The internal combustion engine is connected to a clutch and a gearbox, which in turn is connected to a drive output (e.g., wheels). In other powertrains, components such as clutches and gearboxes may be absent. For example, in... Figure 1B The diagram shows a powertrain including an internal combustion engine and drive output (i.e., a direct drive powertrain).
[0004] In other powertrain systems, there can be more than one power source. For example, in a hybrid powertrain, there can be multiple power sources, such as an internal combustion engine and an electric motor / generator. Figure 1C and 1D This is an example of a hybrid powertrain that includes an internal combustion engine and an electric generator. Figure 1C and 1D The example also includes a gearbox and a clutch. From Figure 1C and 1D It is understandable that the arrangement of the clutch, gearbox, and electric generator can vary depending on the powertrain architecture.
[0005] To operate a powertrain, a controller is typically provided to control the powertrain. For example, in the above example of a motor vehicle, a controller could be provided to control the powertrain to provide the desired drive output to the wheels. Some controllers may use a set of predetermined rules or heuristic methods to control the powertrain. Some controllers may include a model of the powertrain that allows for the determination of appropriate control settings for the powertrain. For example, a controller may use a model of the powertrain to determine appropriate actuator input setpoints for the actuators of an internal combustion engine. Some controllers may use other control methods and / or combinations of control methods.
[0006] For powertrains comprising multiple powertrain components, such as hybrid powertrains, the presence of multiple power sources increases the complexity of the control problem. This complexity increases further when the powertrain operates in different energy domains. Furthermore, as the design of powertrains, particularly hybrid powertrains, becomes more complex, the number of possible arrangements of powertrain components increases significantly. For example, for a hybrid powertrain comprising an internal combustion engine and an electric motor, multiple configurations of power sources in parallel or series are possible. Therefore, controlling a particular powertrain is challenging because its architecture can be relatively complex and unique. Moreover, designing and evaluating a controller for such a powertrain can be challenging, as the controller is not inherently biased towards a particular control solution. Summary of the Invention
[0007] According to some embodiments of the present invention, a universal powertrain controller is provided. This universal controller is used to control at least one of the following: a powertrain force request or a powertrain flow request based on at least one of a desired force or a desired flow. The universal controller includes a configurable powertrain model and a configurable optimizer module. The universal controller is configurable to control a class of universal powertrains including a J universal power source, a K universal power receiver, and an L universal coupling. The universal controller is arranged to receive an input file including multiple input parameters in order to configure the universal controller to control a specific powertrain having a powertrain architecture including an N power source, an M power receiver, and an X coupling.
[0008] The configurable powertrain model includes a general powertrain component library and connection parameter modules.
[0009] The general-purpose powertrain component library is configured to provide models of each of the N power source, M power receiver, and X coupling for a specific powertrain. The general-purpose powertrain component library includes:
[0010] i) Multiple configurable first component models, from which an N-power source model can be configured based on the first input parameters of the input file. This N-power source model represents the N-power sources for the specific powertrain system.
[0011] Each first component model is configured to receive at least one of a plurality of first component-specific inputs and to calculate a force output or a flow output based on at least one of the plurality of first component-specific inputs;
[0012] ii) Multiple configurable second component models from which an M-power receiver model is configurable based on second input parameters of the input file, the M-power receiver model representing the M-power receiver of the specific powertrain, wherein
[0013] Each second component model is configured to receive at least one of a plurality of second component-specific inputs and to calculate a force output or flow output based on at least one of the plurality of second component-specific inputs;
[0014] iii) Multiple configurable third component models, from which at least one inertia coupling model can be configured based on the third input parameters of the input file, wherein
[0015] Each third component model is configured to receive multiple force inputs and calculate the flow output based on these force inputs.
[0016] iv) Multiple fourth-component models, from which a compliance-based coupling model can be configured based on the fourth input parameters of the input file, wherein...
[0017] Each fourth component model is configured to receive multiple stream inputs and calculate the force output;
[0018] These inertia coupling models and these compliance-based coupling models represent the X-couplings of this particular power transmission system.
[0019] The connection parameter module is configured to define the model architecture representing the N-power source model, M-power receiver model, and X-coupling model of the powertrain architecture based on the flow weight parameters and force weight parameters of the input file. The flow weight parameters define any flow connections from the flow output of the N-power source model, the flow output of the M-power receiver model, and the flow output of the inertia coupling model of the X-coupling to the flow input of the dependency-based coupling model of the X-coupling in the model architecture. The force weight parameters define any force connections from the force output of the N-power source model, the force output of the M-power receiver model, and the force output of the dependency-based coupling model of the X-coupling to the force input of the inertia coupling model of the X-coupling in the model architecture. The configurable powertrain model can be configured to provide a powertrain model for a specific powertrain based on the N-power source model, M-power receiver model, X-coupling model, and model architecture.
[0020] The configurable optimizer module includes a general performance objective function library containing multiple configurable performance objective functions. Based on these multiple configurable performance objective functions, the fifth input parameter of the input file can be configured as a cost function. The configurable optimizer module can be configured to calculate at least one of the optimized force request or optimized flow request for each of the N power sources in the specific powertrain based on: the cost function, the powertrain model for the specific powertrain, and the desired force request or desired flow request.
[0021] The universal powertrain controller includes a configurable powertrain model. This configurable powertrain model comprises multiple component models of powertrain parts. These component models can be configured (e.g., user-specified) using an input file to model a wide variety of different powertrain architectures. Thus, based on a first input file, the configurable powertrain model can be configured to model a specific powertrain architecture including an N1 power source, an M1 power receiver, and X1 couplings. A second (different) input file can be used to configure the configurable powertrain model to model a specific powertrain architecture including an N2 power source, an M2 power receiver, and X2 couplings. Therefore, it should be understood that the configurable powertrain model can be configured to model a class of general-purpose powertrains, including a J general-purpose power source, a K general-purpose power receiver, and an L general-purpose coupling.
[0022] It should be understood that a wide range of potential components can be incorporated into the powertrain. Therefore, the universal powertrain controller includes a universal powertrain component library containing multiple component models. These component models can be adapted to model a wide range of powertrain components based on input parameters specified in the input file. Thus, the universal powertrain controller can be configured to model a large range of different powertrain components using only modifications to the controller's input parameters.
[0023] The configurable first component models in the general powertrain component library can be configured to model the power sources of a powertrain. Each configurable first component model is configured to receive at least one first component-specific input from which force or flow output can be calculated. Therefore, the general powertrain component library can be configured to provide models for each of the N power sources in a given powertrain.
[0024] Furthermore, the configurable second component models in the general powertrain component library can be configured to model the range of different power receivers in the powertrain. Each configurable second component model is configured to receive at least one second component-specific input from which force or flow output (force or flow output of a power receiver with a negative probability) can be calculated. Thus, the general powertrain component library can be configured to provide models for each of the M power receivers in a specific powertrain.
[0025] The X-coupling of a specific powertrain can be modeled using configurable third-component models and configurable fourth-component models from a general powertrain component library. The coupling of a specific powertrain can include inertia elements and / or compliance-based elements. Therefore, the configurable third-component model can be configured to provide an inertia coupling model based on third input parameters from an input file. The configurable fourth-component model can be configured to provide a compliance-based coupling model based on fourth input parameters from an input file. Thus, the inertia coupling model and the compliance-based coupling model can be configured from the general powertrain component library to represent the X-coupling of a specific powertrain.
[0026] It should be understood that the first, second, third, and fourth component models in the universal component library can be configured to calculate forces or processes. Therefore, it should be understood that the component models are dynamic models (i.e., dynamic component models). That is, these dynamic component models are configured to describe the time-dependent changes in the state of the specific powertrain to be modeled. In other words, the universal powertrain controller can model a specific powertrain operating under unsteady conditions.
[0027] The universal powertrain controller also includes a connection parameter module, which is configured to define the model architecture based on the components of the specific powertrain to be modeled. The connection parameter module models the architecture of the specific powertrain based on the force and flow weights included in the input file. In this way, the architecture of the specific powertrain to be controlled can be modeled by the universal controller based solely on a user-specified input file.
[0028] Therefore, both powertrain components and powertrain architecture are defined by the input parameters in the input file. Consequently, the universal controller can be configured to model the entire class of powertrains simply by modifying the parameters in the input file. This allows the universal controller to be configured (and reconfigured) for modeling a wide range of powertrains without requiring rewriting and recompiling it. This, in turn, reduces the overhead of developing and validating controllers for specific powertrains.
[0029] Furthermore, the configurable optimizer module includes a general performance objective function library containing multiple configurable performance objective functions. Therefore, the universal controller can be configured to provide a cost model for the entire class of powertrains simply by modifying the parameters of the input file. Thus, the configurable optimizer module can be configured to provide a range of different optimization strategies.
[0030] Specifically, for powertrains with more complex architectures (e.g., hybrid powertrains, powertrains with multiple power sources), the configurable optimizer module can be configured to calculate optimized force requests or optimized flow requests for each of the N power sources in a particular powertrain. In this way, the universal controller can provide real-time control of complex powertrain architectures. Attached Figure Description
[0031] Embodiments of the invention will now be described by way of example only with reference to the accompanying drawings, in which:
[0032] Figure 1A A schematic diagram of an example of the first powertrain is shown;
[0033] Figure 1B A schematic diagram of an example of a second powertrain is shown;
[0034] Figure 1C A schematic diagram of an example of a third powertrain is shown;
[0035] Figure 1D A schematic diagram of an example of a fourth powertrain is shown;
[0036] Figure 2 A diagram showing the tetrahedral state is provided;
[0037] Figure 3 A table showing the various states in different energy domains is provided;
[0038] Figure 4 A general diagram of the inputs to a universal powertrain controller is shown;
[0039] Figure 5 An overview diagram of the inputs to the configurable powertrain model is shown;
[0040] Figure 6 A diagram showing a general powertrain component library and the connections that can be specified between each component model is provided.
[0041] Figure 7A A schematic diagram of the first power transmission system according to the present invention is shown;
[0042] Figure 7B Another schematic diagram of a generator power transmission system with a non-rigid drive shaft is shown;
[0043] Figure 8 A block diagram illustrating an example of a general linear inertia coupling model is shown.
[0044] Figure 9A A diagram illustrating one possible configuration of a universal controller for providing a powertrain model of a first powertrain is shown.
[0045] Figure 9B Further diagrams of another possible configuration of the universal controller are shown to provide a powertrain model of the first powertrain, wherein the drive shaft is assumed to be non-rigid;
[0046] Figure 10 A diagram is shown of a universal controller configured to provide the powertrain model of the second powertrain;
[0047] Figure 11 A schematic diagram of the second power transmission system according to the present invention is shown;
[0048] Figure 12 A block diagram illustrating an example of a general coupling model based on linear compliance is shown;
[0049] Figure 13 A diagram is shown of a universal controller configured to provide the powertrain model of the third powertrain.
[0050] Figure 14 A schematic diagram of the third power transmission system according to the present invention is shown;
[0051] Figure 15 A diagram of a general inertia coupling model including a force scaling module is shown;
[0052] Figure 16 A diagram is shown of a universal controller configured to provide the powertrain model of the fourth powertrain.
[0053] Figure 17 A schematic diagram of the fourth power transmission system according to the present invention is shown;
[0054] Figure 18 An annotation diagram of the fourth power transmission system according to the present invention is shown;
[0055] Figure 19 A diagram illustrating a configurable optimizer module according to the present invention is shown;
[0056] Figure 20 A diagram illustrating the cost space of the cost function according to the present invention is shown;
[0057] Figure 21 A graph illustrating the general performance objective function according to the present invention is shown;
[0058] Figure 22 A graph illustrating the two-stage performance objective function according to the present invention is shown;
[0059] Figure 23 A diagram showing the relationship between the required force (torque) and the required flow (velocity) is provided.
[0060] Figure 24 The time series diagram of the required power for the hybrid powertrain is shown;
[0061] Figure 25 Based on Figure 23 The first graph showing the required force and flow generated from a specific power transmission system.
[0062] Figure 26 Based on Figure 23 The second graph shows the required force and flow generated from a specific power transmission system.
[0063] Figure 27 Based on Figure 23 The first graph showing the required force and flow generated from a specific power transmission system.
[0064] Figure 28 The graphs show the different powertrain variables for different equivalent parameter settings. Detailed Implementation
[0065] According to embodiments of the present invention, a universal powertrain controller is provided. This universal controller is configured to control the power or flow requests of the powertrain to meet the desired power or flow requirements. The universal controller uses a configurable powertrain model and a configurable optimizer module to control the force / flow requests. By using the configurable model, the universal controller can be configured to control a class of universal powertrains including a J universal power source, a K universal power receiver, and an L universal coupling. The universal controller can receive an input file including multiple input parameters and use the received parameters to configure itself to control a specific powertrain having a powertrain architecture including an N power source, an M power receiver, and an X coupling.
[0066] According to some embodiments of the present invention, a universal controller is provided. The universal controller includes a configurable powertrain model for a powertrain and a configurable optimizer module. The universal controller can be configured to control at least one of the following: a force request or flow request of a powertrain based on at least one of a desired power or desired flow from a powertrain using an input file. The universality of the universal controller allows the configurable powertrain model to model any powertrain within a universal powertrain category, including a J universal power source, a K universal power receiver, and an L universal coupling (where J, K, and L are each integers greater than 0). Similarly, the universality of the universal controller allows the configurable optimizer module to optimize the force and / or flow requests to be controlled for any powertrain within a universal powertrain category.
[0067] The universal controller of this invention can control a wide range of powertrains, including those operating within a certain energy domain. This is achieved by modeling the transfer of physical energy through the powertrain using the concepts of force and flow from bond graph theory. Bond graph theory models energy transfer in dynamic systems based on state tetrahedrons. Typically, the dynamics of a physical system can be represented by forces (e(t)), flow (f(t)), momentum (p(t)), and displacement (q(t)). The relationships between these states can be represented in tetrahedral states, such as... Figure 2 The tetrahedral states are shown. Starting from one tetrahedral state, any other state can be calculated based on the relationships listed in the tetrahedral states.
[0068] The tetrahedral state can be applied to various energy domains. For example, Figure 3 Equivalent terms in the energy domain selection are listed. For example, in the angular mechanical energy domain, torque is the form of force, while angular velocity is the flow. By applying the principle of energy conservation, the physical relationships between various components of a powertrain (including hybrid powertrains) can be modeled by considering the energy transfer through the powertrain (force flow, flow force).
[0069] Figure 4 An overview of the universal controller is shown. Figure 4 A block diagram representing the input file, the configurable optimizer module, and the configurable powertrain model is shown. The configurable optimizer module includes a general performance objective function. The configurable powertrain model includes a general powertrain component library.
[0070] like Figure 4 As shown, the input file can be used to configure a general performance objective function library and a general powertrain component library to provide a configured powertrain controller. The configured powertrain controller can be used to control a specific powertrain including an N power source, an M power receiver, and an X coupling. For example... Figure 4 As shown, the powertrain controller configured by the universal controller according to the invention can be configured to control at least one of optimized force requests or optimized flow requests for each of the N power sources of that particular powertrain. A complex powertrain component interface controller can be used to interpret the optimized force requests or optimized flow requests for each of the N power sources in order to control various components of the particular powertrain. Similarly, a drive-chassis interface controller can also be provided to interpret the current operating state of the particular powertrain in order to provide the required force or required flow of the powertrain to the configured powertrain controller.
[0071] In some embodiments, the complex powertrain component interface controller may include an engine management controller for an internal combustion engine. The controller may be configured to receive torque or speed requests and output control signals to the actuators of the internal combustion engine to cause the engine to output the desired torque or speed. In some embodiments, the complex powertrain component interface controller may include an electric inverter configured to receive torque or speed requests and output current to drive an electric motor generator.
[0072] In some embodiments, the drive chassis vehicle interface controller may include a controller configured to interpret drive inputs, such as right pedal inputs and left pedal inputs, to set acceleration and braking commands similar to those of a road vehicle. The controller may interpret drive inputs to provide torque or speed requests to a general-purpose controller. In some embodiments, the drive chassis vehicle interface controller may be configured to interpret other drive inputs, such as levers commanding boom elevation to set flow in hydraulic cylinders, and consequently, the power required by the hydraulic pump.
[0073] It should be understood that the complex powertrain component interface controller and the drive-chassis interface controller may depend on the specific architecture of the particular powertrain to be controlled. Thus, in some embodiments, the complex powertrain component interface controller and the drive-chassis interface controller may be provided separately from the universal controller. For example, in some embodiments, the complex powertrain component interface controller and the drive-chassis interface controller may be provided by the engine control unit.
[0074] Therefore, it should be understood that the universal controller according to the present invention can be configured to control the force or flow request of multiple power sources in a particular power transmission system.
[0075] The configurable powertrain module and configurable optimizer module will be described in more detail below. Various non-limiting examples of specific powertrains that the universal controller can be configured to control will be referenced.
[0076] The configurable powertrain model includes a universal powertrain component library. This library can be configured using input files to provide a configurable powertrain model. An overview of the interaction between the input files, the universal powertrain component library, and the configurable powertrain model generated by the universal controller is provided in [link to relevant documentation]. Figure 5 As shown in the image. Figure 5The input file explains that it includes multiple input parameters. These parameters can be applied to component models from a general powertrain component library. This results in a specific powertrain model comprising an N-power source model, an M-power receiver model, and an X-coupling model. The interaction between the N-power source model, the M-power receiver model, and the X-coupling model is governed by the model architecture. The model architecture is determined by the connection parameter module, which defines the model architecture based on the parameters defined in the input file.
[0077] Therefore, a general-purpose powertrain to be modeled includes at least one power source, at least one power receiver, and at least one coupling (e.g., connecting the power source to the power receiver). The universal controller includes a library of universal powertrain components with various component models, allowing the universal controller to model a wide variety of different power sources, power receivers, and couplings. The universal powertrain component library is discussed in more detail below. To configure the configurable powertrain model, the universal controller is arranged to receive an input file. The input file includes information arranged to configure the universal powertrain controller to model a specific powertrain, which includes N power sources, M power receivers, and an X coupling arranged in a specific powertrain architecture. Thus, the input file provides input parameters that configure the configurable powertrain model to provide an N power source model, an M power receiver model, and an X coupling model and model architecture.
[0078] like Figure 5 As shown, the input file includes multiple input parameters. Input parameters are provided to configure each of the configurable component models in the general powertrain component library. For example, a first input parameter is provided to configure a configurable first component model to represent the primary N-power source of a particular powertrain. A second input parameter is provided to configure a configurable second component model to represent the M-power receiver of a particular powertrain. Third and fourth input parameters are provided to configure configurable third and fourth component models, respectively, to represent the X-coupling of a particular powertrain. Further details of the input parameters are provided below.
[0079] The input file may also include information related to the first component-specific input and the second component-specific input. The first and second component-specific inputs can be input variables of a universal controller that reflect the characteristics of a particular powertrain component. For example, a first component model configured to model an internal combustion engine (power source) may output torque (i.e., force output) generated by the internal combustion engine. The component-specific input can be variables of the internal combustion engine that allow the calculation of the force output of the first component model. For example, in one embodiment, the component-specific input to a first component model representing an internal combustion engine may include at least one of the following: fuel mass flow, exhaust gas recirculation (EGR), injection initiation (SOI), fuel rail pressure (FRP), injection mode, turbocharging, and engine speed. It should be understood that the component-specific inputs, as well as the forces and flows, that the universal powertrain controller can calculate are time-dependent variables. Thus, the first, second, third, and fourth component models are dynamic component models configurable for modeling time-varying systems (i.e., configurable for modeling unsteady-state systems).
[0080] The input file also includes information related to the architecture of the specific powertrain. The input file includes flow weight parameters and force weight parameters. The configured powertrain model can use the flow weight parameters and force weight parameters to represent the connections between each component in a specific powertrain. Thus, the model architecture is based on the flow weight parameters and force weight parameters provided by the input file.
[0081] Figure 6 A diagram illustrating a general powertrain component library and the connections that can be specified between each component model is provided. Figure 6 A schematic diagram of a universal powertrain component library for a universal controller in its state before configuration using an input file is provided.
[0082] Figure 6 The representations of the first component model, the second component model, the third component model, and the fourth component model in the general powertrain component library are shown.
[0083] Figure 6 The first component model shown can be configured to represent a fluid power source or a force power source in a powertrain. Thus, the configurable first component model includes a general fluid power source model and a general force power source model, which can be configured to represent a specific power source for a particular powertrain. For example... Figure 6 As shown, the general power transmission system component library can include A, a general force source model, and B, a general fluid source model, where A and B are non-zero positive integers.
[0084] Each universal force source model is a general model of a component that generates power in the form of force. Examples of force sources include an internal combustion engine that generates torque output or a battery that generates voltage output. The universal powertrain component library includes one or more models for each universal component. That is, the universal powertrain component library may include multiple different models for universal components (e.g., internal combustion engines), from which the most suitable model can be selected. For example, in one embodiment, the universal powertrain component library may include: a first universal internal combustion engine model, a second universal internal combustion engine model, a third universal internal combustion engine model, a first electric generator model, a second electric generator model, a first battery model, and a second battery model. In summary, in Figure 6 In the embodiments, there exists a general force source model A.
[0085] Each common force source model for a component is configured to calculate the component's force output based on at least one component-specific input provided to the controller. For example, for a common internal combustion engine model, the model can be configured to receive component-specific inputs providing information related to variable exhaust gas recirculation (EGR), injection initiation (SOI), fuel rail pressure (FRP), injection mode, turbocharging, and engine speed. A common electric generator model can be configured to receive component-specific inputs providing information related to the variable current.
[0086] Multiple generic component models can be provided for each type of force source, with different component-specific inputs for each generic component model. Therefore, the generic powertrain component library can be adapted to provide suitable models for a wide variety of powertrains, where a universal controller can obtain a range of inputs.
[0087] To configure each general-purpose power source model to reflect the performance of the specific power source to be modeled, each general-purpose power source model can be configured to receive one or more first input parameters. The first input parameters provide information relating to the characteristics of each power source component in a specific powertrain. For example, for a general-purpose internal combustion engine model, the model can be configured to receive first input parameters selected from a group including:
[0088] Internal combustion engine efficiency parameters, turbocharger control mapping parameters, and the number of engine cylinders. The internal combustion engine efficiency parameters may include a range of different efficiency parameters depending on the internal combustion engine, for example, including at least one of the following: volumetric efficiency, total fuel conversion efficiency, and exhaust fuel conversion efficiency. For a general-purpose electric generator model (e.g., representing a DC motor), the model can be configured to receive a first input parameter, including: back electromotive force constant (k... b ), the magnetic flux of the motor.
[0089] A general-purpose fluid power source model is a generic model of a component that generates power in the form of a flow. Examples of fluid power sources include a synchronous motor that outputs a constant angular velocity, a large object that travels at a constant speed, a flow of water used to drive a hydroelectric generator, or a hydraulic pump that outputs a constant flow of fluid. The general-purpose power transmission component library includes one or more models for each general-purpose fluid power source component. That is, the general-purpose power transmission component library can include multiple different models for a general-purpose fluid power source component (e.g., a synchronous motor), from which the most suitable model can be selected.
[0090] Each general-purpose fluid dynamic source model for a component is configured to calculate the component's flow output based on at least one component-specific input provided to the controller. For example, a general-purpose synchronous motor model can be configured to receive a component-specific input providing information related to the AC current frequency, from which the flow output can be calculated. Similar to the force dynamic source models described above, multiple general-purpose component models can be provided for each type of fluid dynamic source, where the component-specific inputs used are different. Therefore, a general-purpose powertrain component library can be adapted to provide suitable models for a wide variety of powertrains, where a universal controller can obtain a range of inputs.
[0091] To configure each general-purpose hydrodynamic source model to reflect the performance of the specific force source to be modeled, each general-purpose hydrodynamic source model can be configured to receive one or more first input parameters. The first input parameters provide information related to the characteristics of each hydrodynamic source component in a specific powertrain. For example, for a hydraulic pump, first input parameters including volumetric efficiency and the pump's stroke volume can be provided by an input file.
[0092] The configurable first component models in the universal powertrain component library include various models of different power sources that can be used in different powertrains. By providing a series of different universal power source models, the universal controller can be configured to control a wide range of different powertrains. Furthermore, since each configurable first component model is arranged to receive a first input parameter, each first component model can be configured to accurately model the behavior of the specific power source to be modeled.
[0093] For example, in one embodiment, an input file including a first input parameter may be provided to the controller, which defines a specific powertrain with a power source N1, where N1 is a positive non-zero integer. The N1 power source of the specific powertrain includes a force power source A1 and a flow power source B1, where A1 and B1 are both non-negative integers. An example in which a universal controller is configured to model a specific powertrain is discussed in more detail below.
[0094] Figure 6 The second component model shown is configurable to represent a fluid dynamics receiver or a force dynamics receiver in a powertrain. Thus, the configurable second component model includes a general fluid dynamics receiver model and a general force dynamics receiver model, which can be configured to represent a specific power source for a particular powertrain. For example... Figure 6 As shown, the general powertrain component library can include a general force receiver model (C) and a general fluid receiver model (D), where C and D are non-zero positive integers.
[0095] The general force-receiver model and the general flow-receiver model are models of components that typically consume power. It is understood that some components, such as electric generators, can consume or generate power, and therefore can be used interchangeably as power sources or power receivers. Such components can be modeled in the general component library as a first component model, a second component model, or both. When configuring a model for a specific powertrain, components are considered either power sources or power receivers based on their primary usage. That is, components that function as power sources for the majority of the time in a specific powertrain are considered power sources. Components that function as power receivers for the majority of the time in a specific powertrain are considered power receivers.
[0096] Each universal force-receiver model is a general model of a component that consumes power in the form of a force. Examples of force-receivers include the drive output of a vehicle (e.g., wheels in contact with the ground) or an electric generator. The universal powertrain component library includes one or more models for each universal component. That is, the universal powertrain component library may include multiple different models for universal components (e.g., drive outputs), from which the most suitable model can be selected. For example, in one embodiment, the universal powertrain component library may include: a first universal drive model, a second universal drive model, a third universal drive model, a first electric generator model, and a second electric generator model. In summary, in Figure 6 In the embodiments, there is a C-type universal force receiver model.
[0097] Each universal force receiver model for a component is configured to calculate the component's force output based on at least one second component-specific input provided to the universal controller. Since a particular force receiver will frequently consume power, the force output calculated by the universal force receiver model can be negative. For example, for a universal driving model of a vehicle, the universal driving model can be configured to receive component-specific inputs providing information related to the following variables: vehicle speed, wind resistance, and gradient. A universal electric generator model can be configured to receive second component-specific inputs providing information related to the following variables: current output and voltage output.
[0098] Multiple generic force receiver models can be provided for each type of force receiver. Therefore, different component-specific inputs can be used to model each type of force receiver. Thus, the generic powertrain component library can be adapted to provide suitable models for a wide variety of force receivers, where the range of second component-specific inputs can be used for a universal controller.
[0099] To configure each general-purpose force receiver model to reflect the performance of the specific force receiver to be modeled, each general-purpose force receiver model can be configured to receive one or more second input parameters. The second input parameters provide information related to the characteristics of each force receiver component in a specific powertrain. For example, for a general-purpose drive model, the model can be configured to receive a first input parameter selected from a group including: for a general-purpose electric generator model (e.g., representing a DC motor), the model can be configured to receive a first input parameter including: a back electromotive force constant (k... b ), the magnetic flux of the motor.
[0100] A universal dynamic power receiver model is a general model of a component that consumes power in the form of a flow. Examples of dynamic power receivers include national power grids that receive electrical power at a typically constant frequency. The universal powertrain component library includes one or more models for each universal dynamic power receiver component. That is, the universal powertrain component library can include multiple different models for universal dynamic power receiver components (e.g., national power grids), from which the most appropriate model can be selected.
[0101] Each generic flow receiver model for a component is configured to calculate the component's flow output based on at least one second component-specific input provided to the controller. For example, for a generic national grid model, the generic national grid model can be configured to receive a component-specific input that provides information about the variables: the AC current frequency, from which the flow output can be calculated. Similar to the force receiver models described above, multiple generic component models can be provided for each type of flow receiver, where the component-specific inputs of the second component-specific inputs used are different. Therefore, a generic powertrain component library can be adapted to provide suitable models for a wide variety of powertrains, where a universal controller can obtain a range of inputs.
[0102] To configure each general-purpose hydrodynamic receiver model to reflect the performance of the specific hydrodynamic receiver to be modeled, each general-purpose hydrodynamic receiver model can be configured to receive one or more second input parameters. The second input parameters provide information relating to the characteristics of each hydrodynamic receiver component in a specific powertrain.
[0103] The configurable second component models in the universal powertrain component library include a variety of different models of power receivers that can be used in different powertrains. By providing a range of different universal power receiver models, the universal controller can be configured to control a wide range of different powertrains. Furthermore, since each configurable second component model is arranged to receive a second input parameter, each second component model can be configured to accurately model the behavior of the specific power source to be modeled.
[0104] For example, in one embodiment, an input file including a second input parameter may be provided to the controller, which defines a specific powertrain with an M1 power receiver, where M1 is a positive non-zero integer. The M1 power receiver for the specific powertrain includes a C1 force power receiver and a D1 flow power receiver, where C1 and D1 are both non-negative integers. An example in which a universal controller is configured to model a specific powertrain is discussed in more detail below.
[0105] Figure 6 The third and fourth component models shown provide several general-purpose coupling models that can be configured to model one or more couplings of a specific powertrain. Typically, a coupling of a specific powertrain connects two components of the powertrain. The causal relationship of the coupling is reflected by the type of model chosen to represent it. Therefore, a coupling of a specific powertrain can be modeled as an inertia coupling (i.e., an inertia coupling model) or a compliance-based coupling (i.e., a compliance-based coupling model). An inertia coupling model is a model that includes inertia parameters associated with the coupling. It takes one or more force inputs and determines the resulting flow output. A compliance-based coupling model is a model that includes compliance parameters associated with the coupling. It receives one or more flow inputs and calculates the synthesized force output.
[0106] Figure 6 The third component model shown can be configured to represent an inertia coupling. For example... Figure 6 As shown, the general powertrain component library can include a general inertia coupling model of E, where E is a non-zero positive integer.
[0107] Each general inertia coupling model is a generic model of a coupling in a powertrain where forces are provided to induce flow. Examples of inertia couplings include flywheels in the angular mechanical domain or vehicle masses in the linear mechanical domain.
[0108] The universal powertrain component library includes one or more models for each universal inertia coupling. That is, the universal powertrain component library can include multiple different models for universal inertia couplings, from which the most suitable model can be selected. For example, in one embodiment, the universal powertrain component library may include: a first universal inertia coupling model, a second universal inertia coupling model, a third universal inertia coupling model, etc. In summary, in Figure 6 In the embodiments, there is an E universal inertia coupling model.
[0109] Each general-purpose inertia coupling model is configured to calculate the coupling's flow output based on at least one force input. For example... Figure 6 As shown, the force input of the inertia coupling model can be provided by specifying the connection between the inertia coupling model and any A-force source model, any C-force receiver model, and any F-based compliance coupling model. Prior to the configuration of the universal controller, no connection is specified between the force source model, the force receiver model, and the force output of the compliance-based coupling model. When the universal controller is configured for a specific powertrain, a connection can be specified between at least one inertia coupling model and the force output via the connection parameter module. The connection parameter module is discussed in more detail below.
[0110] Multiple universal inertia coupling models for each type of inertia coupling can be provided in the universal powertrain component library. Therefore, the universal powertrain component library can be adapted to provide suitable models for a variety of different powertrains with a range of different architectures.
[0111] To configure each general-purpose inertia coupling model to reflect the performance of the specific inertia coupling to be modeled, each general-purpose inertia coupling model can be configured to receive one or more third input parameters. The third input parameters provide information related to the characteristics of each inertia coupling in a specific powertrain. In some cases, the inertia coupling may also reflect other characteristics of the powertrain architecture, depending on the specific components of the powertrain connected together or represented by the inertia coupling model.
[0112] For example, Figure 7AA diagram is shown of a generator 100 comprising an internal combustion engine 110, an electric generator 120, and a drive shaft 130. The generator 100 is an example of a specific powertrain according to a (first) invention. The architecture of the generator 100 includes an internal combustion engine 110 connected to the electric generator 120 via the drive shaft 130. The internal combustion engine 110 is configured to generate torque transmitted via the drive shaft 130 to drive the electric generator 130, thereby generating electricity. Thus, the internal combustion engine 110 generates a force (torque) acting on the drive shaft, and the electric generator receives the force (torque) from the drive shaft. In this way, the drive shaft has two force inputs.
[0113] exist Figure 7A In the example, the inertia associated with the internal combustion engine 110 and the electric generator 120 is greater than the inertia associated with the drive shaft 130. The drive shaft 130 can also be assumed to be rigid, such that any dependencies associated with the drive shaft can be ignored for the purposes of the model. Therefore, in some general inertia coupling models, the inertia of the internal combustion engine 110 and the electric generator 120 can be integrated into a single entity and modeled as a single inertia of the inertia coupling. Assuming that the drive shaft 130 is a rigid drive shaft means that there are no dependencies associated with that drive shaft, and therefore the powertrain does not include any dependency-based coupling components. For example, in some powertrains where the drive shaft 130 is relatively short, this approximation is reasonable to simplify the model.
[0114] As mentioned above, in Figure 7A In the example, the rigid drive shaft 130 is considered a rigid body, and therefore it is assumed that there are no significant dependencies associated with the drive shaft 130. For example, as Figure 7A As shown, drive shaft 130 is relatively short. Figure 7B Another example of generator 100' is shown, in which drive shaft 130' is relatively long (relative to) Figure 7A (Drive shaft 130). In such an example, drive shaft 130' may have associated dependencies, which are expected to be considered in the configurable powertrain model. To account for the drive shaft dependencies, the inertia associated with the internal combustion engine 110 and the electric generator 120 can be modeled separately. Therefore, component models for two inertia couplings (Y=2) and a dependency-based coupling (Z=1) need to be configured from the general powertrain component library (X=3).
[0115] Figure 8 A block diagram of an example of a universal inertia coupling model is shown, which is a universal linear inertia coupling model. Figure 8 The block diagram can be used to... Figure 7A The lumped inertia of the internal combustion engine 110 and the electric generator 120 is modeled as a linear inertia coupling. For example... Figure 8 As shown, the general linear inertia coupling model can be configured to receive multiple force outputs. For example, the general inertia coupling model can be configured to receive force outputs from a force power source model representing an internal combustion engine 110 and a force power receiver model representing an electric generator 120. The general linear inertia coupling model is a linear model that calculates the flow outputs through linear equations (integration). Of course, the third coupling model can also include other nonlinear general inertia coupling models (i.e., general nonlinear inertia coupling models).
[0116] like Figure 8 As shown in the generalized linear inertia coupling model, a first force and a junction are provided. The first force and junction can be configured to calculate the net force input of the generalized inertia coupling model based on the force output provided to the generalized inertia coupling model (i.e., at least one of the following: the force output from one or more power source models, the force output from one or more power receiver models, and the force output from one or more compliance-based coupling models). Figure 8 As shown, the flow output is calculated based on the net force input calculated from the first force and the junction.
[0117] To model accurately Figure 7A The drive shaft 130, and the general-purpose linear inertia coupling model, are also configured to receive a third input parameter. This third input parameter may include information related to the coupling's inertia (i.e., inertia parameter I1). Figure 7A In the generator 100, the inertia parameter I1 can be based on the combined inertia of the internal combustion engine 110 (J1) and the electric generator 120 (J2). For example, the lumped inertia parameter I1 can be calculated as:
[0118] I1=J1+J2 (1)
[0119] In some embodiments, the third input parameter may include a first resistance parameter. In some embodiments, a resistance parameter may be provided to a generic inertia coupling model to adapt the model to the component based on resistance. For example, in the electrical field, a circuit including resistors and capacitors (but without inertia) can be modeled using a generic inertia coupling model that includes a resistance parameter (R1).
[0120] like Figure 8 As shown, the flow output of the general inertia coupling model is calculated based on the tetrahedral state. For Figure 7A The generator 100, inertia coupling model will be configured to calculate the angular velocity output of the drive shaft (e.g., revolutions per minute).
[0121] like Figure 6 As shown, the general powertrain component library also includes a fourth component model.
[0122] Figure 6 The fourth component model shown can be configured to represent a compliance-based coupling. For example... Figure 6 As shown, the general powertrain component library can include F general-purpose compliance-based coupling models, where F is a non-zero positive integer.
[0123] Each generic compliance-based coupling model is a general model of a coupling in a powertrain where a flow is provided to induce a force. Examples of compliance-based couplings include the drive shaft connecting a synchronous motor to a propeller. For example, as mentioned above... Figure 7B In some embodiments where the drive shaft can be considered non-rigid, the drive shaft's compliance can be modeled based on a general compliance-based coupling model.
[0124] Similar to the universal inertia coupling model discussed above, the universal powertrain component library includes one or more models for each universal, compliance-based coupling. That is, the universal powertrain component library may include multiple different fourth component models for universal, compliance-based couplings, from which the most suitable model can be selected. For example, in one embodiment, the universal powertrain component library may include: a first universal compliance-based coupling model, a second universal compliance-based coupling model, a third universal compliance-based coupling model, and so on. In summary, in Figure 6 In some embodiments, there exists a coupling model based on general compliance, F.
[0125] Each coupling model based on universal compliance is configured to calculate the force output for the coupling based on at least one stream input. For example... Figure 6 As shown, flow input to a dependency-based coupling model can be provided by specifying connections between the dependency-based coupling model and any B-type flow source model, any D-type flow receiver model, and any E-type inertia coupling model. Prior to the configuration of the universal controller, no connections were specified between the flow outputs of the dependency-based coupling model flow source model, flow receiver model, and inertia coupling model. When the universal controller is configured for a specific powertrain, connections can be specified between at least one force output and a generic dependency-based coupling model via the connection parameter module. The connection parameter module is discussed in more detail below.
[0126] A general-purpose powertrain component library provides multiple generic dependency-based coupling models for each type of dependency-based coupling. Therefore, the general-purpose powertrain component library can be adapted to provide suitable models for a variety of different powertrains with a range of different architectures.
[0127] To configure each generic compliance-based coupling model to reflect the performance of the specific compliance-based coupling to be modeled, each generic compliance-based coupling model can be configured to receive one or more fourth input parameters. The fourth input parameters provide information related to the characteristics of each compliance-based coupling in a particular powertrain. In some cases, compliance-based couplings may also reflect other characteristics of the powertrain architecture, depending on the specific components of the powertrain connected together via inertia couplings. For example, in some embodiments, the fourth input parameter may include a compliance parameter for the compliance-based coupling. In some embodiments, the fourth input parameter may include a second resistance parameter. Thus, a generic compliance-based coupling model can be adapted to model resistive components. The compliance-based coupling model with a fourth component model will be discussed in more detail below with reference to a synchronous AC motor powertrain 200.
[0128] Typically, the third and fourth component models of a generic component library allow a universal controller to be configured to model a specific powertrain including an X-coupling. For example, in one embodiment, an input file including a third and fourth input parameter can be provided to the controller, defining a specific powertrain with X1 couplings, where X1 is a positive non-zero integer. The X1 couplings of the specific powertrain include a Y1 inertia coupling and a Z1 compliance-based coupling, where Y1 and Z1 are both non-negative integers. An example in which a universal controller is configured to model a specific powertrain is discussed in more detail below.
[0129] Figure 6 Possible interconnection representations are also shown, which can be defined by force and flow weight parameters to define the model architecture. Connections between component models are divided into two groups: flow connections or force connections. Flow connections can connect the flow output of one of the first, second, or third component models to the flow input of a fourth component model. Thus, flow connections can connect the flow output of a general flow source model, a general flow receiver model, or a general inertia coupling model to the flow input of a general compliance-based coupling pattern. Force connections can connect the force output of one of the first, second, or fourth component models to the force input of a third component model. Thus, force connections can connect the force output of a general force source model, a general force receiver model, or a general compliance-based coupling model to the force input of a general inertia coupling pattern.
[0130] exist Figure 6The diagram illustrates possible force connections between the E universal inertia coupling model, the A universal inertia power source model, the C universal inertia power receiver model, and the F compliance-based coupling model. Figure 6 The diagram also illustrates possible flow connections between the F-based compliance coupling model and the B-general fluid dynamic source model, D-general fluid dynamic receiver model, and E-general inertia coupling model. Thus, before configuring the universal controller with the input file, the universal controller can be configured to model virtually any possible powertrain architecture using the connection parameter module.
[0131] This connection parameter module can be configured to define the model architecture representing a specific powertrain system. Specifically, the connection parameter module is configured to specify the connections between the N power source model, M power receiver model, and X coupling model configured from a general powertrain component library. The connection parameter module specifies the connections based on flow weight parameters and force weight parameters provided by the input file. Thus, the connection parameter module determines the model architecture representing the powertrain system architecture based on the flow weight parameters and force weight parameters of the input file.
[0132] The flow weight parameter defines the flow connections from the flow output of the N-power source model (i.e., the flow output from any flow source model), the flow output of the M-power receiver model (i.e., the flow output from any flow receiver model), and the flow input from the inertia coupling model of the X-coupling to the compliance-based coupling model of the X-coupling in the model architecture. In other words, the flow weight parameter defines which possible flow connections of the universal controller exist in the model architecture.
[0133] The force weight parameter defines the force connections from the force output of the N power source model, the force output of the M power receiver model, and the force output of the X-coupling based coupling model, to the force input of the inertia coupling model in the model architecture. In other words, the force weight parameter defines which possible force connections of the universal controller exist in the model architecture.
[0134] Therefore, a universal controller can be configured to provide a powertrain model that models a specific powertrain based on an N-power source model, an M-power receiver model, an X-coupling model, and a model architecture. Figure 6It is understood that all interconnections between the universal powertrain component models in the universal powertrain component library exist in the universal controller prior to configuration. Thus, the universal controller can be implemented using a pre-compiled computer program stored in memory. This pre-compiled computer program can be executed on a processor to provide a controller for a specific powertrain upon receiving an input file providing the aforementioned input parameters. Those skilled in the art will understand that such a universal controller is possible due to the functionality of the universal powertrain component library and the connection parameter module. Specifically, the universal controller can be configured to model a specific powertrain without recompiling, because the first component model, the second component model, the third component model, and the fourth component model, as well as the connection parameter module, can be configured based on input parameters from the input file. Thus, the universal powertrain controller is configurable (and reconfigurable) to model a wide range of powertrains without requiring recompilation of the universal controller.
[0135] The following sections will discuss various examples of possible applications of the configurable powertrain model to a particular powertrain. It should be understood that the following examples of possible configurations of the configurable powertrain model are non-limiting, and other configurations of the configurable powertrain model will be apparent to those skilled in the art.
[0136] Figure 9A A schematic diagram of a configurable powertrain model configured to control generator 100 is shown. Thus, a configurable powertrain model of a universal controller is configured for modeling. Figure 7A The generator 100 shown. The configuration of the configurable powertrain model is relative to... Figure 6 The diagram shows this. Thus, Figure 9A The component models and connections used in the configurable powertrain model are shown, which follows the configuration of the appropriate input file for generator 100.
[0137] As described above, generator 100 includes an internal combustion engine 110 that outputs torque to drive electric generator 120. Thus, this particular powertrain includes a single force source (N=1). Electric generator 120 acts as a power receiver in this particular powertrain and receives torque. Thus, this particular powertrain includes a single force receiver (M=1). Internal combustion engine 110 and electric generator 120 are connected via drive shaft 130 (assumed to be rigid), allowing inertia bodies 110 and 120 to be modeled as a single lumped inertia. Thus, this particular powertrain includes a coupling, which is an inertia coupling (X=1, Y=1).
[0138] like Figure 9AAs shown, an input file is provided to configure the model, offering input parameters. This input file provides the first, second, and third input parameters based on the components of the specific powertrain described above (N=1, M=1, X=1, Y=1). Since the specific powertrain does not include a dependency-based coupling, a fourth input parameter is not required. Therefore, it should be understood that... Figure 9A Only the configurable powertrain model used in a powertrain configuration of a specific powertrain is shown.
[0139] The connection parameter module defines Figure 9A The connections between the models are shown. In this particular powertrain, the architecture is relatively simple, therefore only force-intensive connections are used to model the powertrain. Force connections are specified by the force weight parameters of the input file.
[0140] therefore, Figure 9A The diagram illustrates a configurable powertrain model controller (e.g., such as...). Figure 6 (As shown) can be configured to provide a powertrain model for generator 100.
[0141] As mentioned above, in Figure 7A In the example, the rigid drive shaft 130 is considered a rigid body, and therefore it is assumed that there are no significant dependencies associated with the drive shaft 130. For example, as Figure 7A As shown, drive shaft 130 is relatively short. Figure 7B Another example of generator 100' is shown, in which drive shaft 130' is relatively long (relative to) Figure 7A (Drive shaft 130). In such an example, drive shaft 130' may have associated dependencies, which are expected to be considered in the configurable powertrain model. To account for the drive shaft dependencies, the inertia associated with the internal combustion engine 110 and the electric generator 120 are modeled separately. Therefore, component models for two inertia couplings (Y=2) and a dependency-based coupling (Z=1) need to be configured from the general powertrain component library (X=3).
[0142] therefore, Figure 9B The diagram illustrates a configurable powertrain model (e.g., such as...). Figure 6 (As shown) can be configured to provide a powertrain model for generator 100', in which the dependence of drive shaft 130' is taken into account. It should be understood that the extent to which the configurable powertrain model makes assumptions about the performance of various components of the powertrain will depend on the desired fidelity of the powertrain model provided by the configurable powertrain model.
[0143] Figure 10A schematic diagram of a configurable powertrain model is shown, which is configured to control a second powertrain 200 including a synchronous AC motor 210 that directly drives a load 220. The synchronous AC motor 210 is connected to the load via a drive shaft 230. Figure 11 A schematic diagram of this power transmission system is shown.
[0144] The second power transmission system 200 includes a synchronous motor 210. The synchronous motor rotates at an angular velocity based on the frequency (e.g., 50 Hz) of the AC power source to which it is supplied. Thus, the synchronous AC motor 210 is a flow-based power source that outputs a constant flow (angular velocity). The synchronous AC motor 210 is connected to a load 220 via a drive shaft 230. The load 220 may be some kind of machinery (i.e., some form of inertial body) driven by torque.
[0145] Therefore, the second powertrain 200 includes a flow power source (N=1). The load 220 acts as a power receiver in this particular powertrain and receives torque. Thus, this particular powertrain includes a force power receiver (M=1). The load 220 also has an associated inertia. Therefore, at least one inertia coupling should be included in the powertrain model to address the load inertia (Y=1). The AC synchronous motor 210 and the load 220 are connected via a drive shaft 230. The drive shaft receives the flow output from the synchronous motor 210 and applies torque to the load 220. Therefore, the drive shaft 230 can be modeled as a dependency-based coupling (Z=2). Thus, in the second powertrain 200, there are two coupling models (X=2).
[0146] Figure 12 An example block diagram of a coupling model based on universal compliance is shown in the figure. Figure 12 A block diagram can be used to... Figure 10 The drive shaft 230 is modeled as a dependency-based coupling. For example... Figure 12 As shown, a general compliance-based coupling model can be configured to receive multiple flow outputs as flow inputs to the model. For example, a general compliance-based coupling model can be configured to receive flow outputs from a flow power source model representing a synchronous AC motor 210 and flow outputs from an inertia coupling model representing the driving inertia.
[0147] like Figure 12 As shown in the general compliance-based coupling model, a first flow and nodes are provided. The first flow and nodes can be configured to calculate the net flow input of the general compliance-based coupling model based on the flow outputs provided to them (i.e., flow outputs from one or more power source models, flow outputs from one or more power receiver models, and flow outputs from one or more inertia coupling models). Figure 12As shown, the flow output is calculated based on the net force input calculated from the first flow and the nodes.
[0148] To model accurately Figure 11 The drive shaft 230, of which this generic compliance-based coupling model is also configured to receive a fourth input parameter. This fourth input parameter may include information related to the compliance of the coupling (i.e., compliance parameter C1). Figure 11 In the second power transmission system 200, the compliance parameter can be based on the combined compliance of the synchronous AC motor 210 and the drive shaft 230. The fourth input parameter may also include information related to the resistance of the coupling (i.e., the second resistance parameter R2). The resistance term provides the option to configure a general compliance-based coupling based on the resistance term rather than the compliance term.
[0149] Figure 12 The coupling model shown is based on a universal compliance model, which is also based on a universal linear compliance model. For example... Figure 12 As shown, the force output can be calculated from the net flow input by integrating the net flow input and scaling it using the reciprocal of the compliance parameter C1. Of course, it should be understood that... Figure 12 The coupling model shown is merely an example of possible coupling models based on universal linear compliance. For example, in other embodiments, the universal powertrain component library may include a fourth component model, which is a universal component model based on nonlinear compliance. Various types of dynamic models of components are well known to those skilled in the art.
[0150] The input file provides input parameters to configure the model, such as Figure 10 As shown. Thus, the input file provides a first input parameter, a second input parameter, a third input parameter, and a fourth input parameter based on the specific powertrain components (N=1, M=1, X=2, Y=1, Z=1) as described above. Figure 9A and 9B resemblance, Figure 10 Only the configurable powertrain model used in the powertrain model with the configuration of the second powertrain 200 is shown.
[0151] The connection parameter module defines Figure 10 The connections between the models are shown. For the second powertrain 200, the architecture includes a flow-based connection between the AC synchronous motor and the load 220, and a separate inertia coupling representing the inertia of the load 220. Thus, the second powertrain model includes both force connections and flow connections. Force connections are specified by the force weight parameters of the input file, and flow connections are specified by the flow weight parameters of the input file.
[0152] Figure 13A schematic diagram of a configurable powertrain model configured to control a third powertrain 300 is shown. Figure 14 A schematic diagram of a third powertrain 300 is shown. The third powertrain 300 includes an internal combustion engine 310, a clutch 320, a gearbox 330, and a drive output 340 (e.g., wheels). Thus, the third powertrain 300 can be considered representative of a motor vehicle powertrain.
[0153] As in the previous example, to configure the configurable powertrain model for modeling the third powertrain 300, the input file provides input parameters that identify each component model to be configured. In the third powertrain 300, an internal combustion engine 310 is provided. The internal combustion engine 310 produces torque (force output) and also has an associated inertia.
[0154] The final drive output 340 receives torque (force output) and also has its associated inertia.
[0155] The third powertrain 300 is a more complex powertrain (compared to...). Figure 7A and 7B The generator power transmission system 100 shown is configured such that the clutch 320 and gearbox 340 are located between the power source (internal combustion engine 310) and the power receiver (final drive unit 340).
[0156] In the third powertrain 300, it can be assumed that the clutch 320 is connected to the internal combustion engine via a relatively short drive shaft, and therefore it is assumed that the clutch 320 is driven at the same angular velocity as the internal combustion engine 310. The clutch applies torque to the gearbox 330 according to the angular velocity applied to it. Thus, the clutch 320 can be represented as a dependency-based coupling that receives flow from the internal combustion engine and the final drive unit and outputs force. Although in the example of the third powertrain 300, the drive shaft between the internal combustion engine 310 and the clutch 320 is not modeled as a separate component, in other examples that provide a higher fidelity model, the drive shaft can be modeled as a separate component.
[0157] Gearbox 330 is an example of a component that scales or converts the energy applied to it. In the case of a gearbox, the gearbox's angular velocity output and torque output are proportional to the selected transmission ratio relative to the angular velocity input and torque input. To illustrate the presence of gearbox 330 in the model of a third powertrain 300, the third component model of the final driving inertia may include a force scaling module. This force scaling module allows the inertia coupling model to be used to illustrate components of a powertrain that scale forces and flows in the energy domain (e.g., a transformer or gearbox), or even components that convert energy between different energy domains (e.g., an electric generator).
[0158] therefore, Figure 13 The final driving inertia model includes a force scaling module (gearbox scaling module) configured to model the gearbox 330. Figure 15 A block diagram of a general inertia coupling model is shown, including a force scaling module. The force scaling module can be configured to scale at least one of the force inputs of the general inertia coupling model (e.g., force outputs from one or more power source models, force outputs from one or more power receiver models, and force outputs from one or more compliance-based coupling models). Figure 15 As shown, scaling of the force output is applied before summing the force output at the first force and junction. Scaling is applied at the first scaling block (eTF1) using the first scaling parameter from the input file. In the example of the third powertrain 300, the torque output from the clutch 320 is connected to the inertia coupling model of the final drive 340. It should be understood that the actual torque applied to the final drive will depend on the gearbox ratio. Therefore, the first scaling parameter can scale the force output from the compliance-based coupling model of the clutch 320 to indicate the presence of a gearbox between the clutch 320 and the final drive inertia 340.
[0159] like Figure 13 As shown, a flow connection also exists between the final drive inertia 340 and the clutch 320. Due to the presence of the gearbox, the angular velocity of the final drive inertia can be proportional to the angular velocity of the clutch. Therefore, the flow output from the final drive inertia can also be scaled. Figure 15 As shown, each third configurable model can include a scaled flow output. The scaled flow output can be calculated by the configurable third component model based on a first complementary scaling parameter from the input file. In the gearbox example, the first complementary scaling parameter is the reciprocal of the first scaling parameter. Therefore, the flow and force scaling of the gearbox are modeled in the third powertrain model, while considering energy savings.
[0160] In some embodiments, the scaling (or transformation) component to be modeled may involve the amount of energy loss during the scaling process. In some powertrain models, the energy loss can be described using efficiency parameters of a force scaling model. For example, due to friction and wear in the gearbox, Figure 14 There may be some energy loss in the gearbox 330. To address this energy loss, a first efficiency parameter (η1) can be applied when calculating any scaling. The first efficiency parameter can be specified in the input file. The first efficiency parameter is less than or equal to 1.
[0161] Therefore, the force scaling module can apply the following scaling to the force input (e(t)) and the stream output (f(t)) to calculate the scaled force input (e'(t)) and the scaled stream output (f'(t)) respectively:
[0162] e'(t)=e(t)x k1 xη1 (2)
[0163] f'(t)=f(t)x k1 -1 xη1 (3)
[0164] Therefore, a force scaling module can be provided to model the components of a powertrain that scales or transforms forces and flows.
[0165] In some embodiments, each force scaling module may be configured to scale at least one of the following: force outputs from one or more power source models, force outputs from one or more power receiver models, and force outputs from one or more compliance-based coupling models, such that they are converted from forces in one energy domain to forces in another energy domain. For example, an electric generator model may include force inputs in the electrical energy domain and calculate flow outputs in the rotating machinery energy domain.
[0166] Back Figure 13The example of the third powertrain in the diagram should be understood as follows: the third powertrain 300 includes a force source (N=1). The final drive 340 acts as a power receiver in this particular powertrain and receives torque. Thus, the third powertrain 300 includes a force receiver (M=1). The final drive 340 also has an associated inertia. Therefore, two inertia coupling models can be included in the powertrain model to account for the inertia of the final drive and the inertia of the internal combustion engine (Y=2). The internal combustion engine 310 is connected to the gearbox via drive shafts on both sides of a clutch 320. The drive shafts receive fluid output from the internal combustion engine 310 and transmit torque to the gearbox via the clutch 320. Therefore, the clutch 320 and the drive shafts can be modeled as a compliance-based coupling (Z=1). As mentioned above, the force (torque) input to the gearbox can be considered as a scaled force input to the inertia coupling model of the drive inertia. Therefore, an inertia coupling model including a force scaling module can be used to model the gearbox and the final drive inertia.
[0167] The input file provides input parameters to configure the model, such as Figure 13 As shown. Thus, the input file provides a first, second, third, and fourth input parameter based on the specific powertrain components (N=1, M=1, X=3, Y=2, Z=1) as described above. Similar to... Figure 9A , 9B And 10, Figure 13 Only the configurable powertrain model used in the powertrain model configured in the third powertrain 300 is shown.
[0168] The connection parameter module defines Figure 13 The connections between the models are shown. For the third powertrain 300, the architecture includes a force-based connection between the internal combustion engine power output and the inertia coupling model of the internal combustion engine. This allows the powertrain model to take into account the inertia of the internal combustion engine and also the flow-based drive of the clutch 320.
[0169] The above example of the third powertrain 300 utilizes a universal inertia coupling model that includes a force scaling module. The universal powertrain component library can include multiple universal inertia coupling models.
[0170] By analogy with the universal inertia coupling model, it can be understood that the universal powertrain component library can also include a universal, compliance-based coupling model, which includes a flow scaling model. A flow scaling module can be provided to model components that transform or scale flows to produce corresponding scaling effects.
[0171] Therefore, each configurable fourth component model may include a stream scaling module that can be configured to scale at least one of the following: stream outputs from one or more power source models, stream outputs from one or more power receiver models, and stream outputs from one or more inertia coupling models using a second scaling parameter (k2) from the input file. Similarly, the force outputs calculated by the configurable fourth component model may also be scaled by a second complementary scaling parameter from the input file.
[0172] Furthermore, the stream scaling module can also be configured to take into account energy losses in the scaling components. Therefore, each stream scaling module can be configured to consider energy losses when scaling at least one of the following: a second efficiency parameter (η2) based on the input file, and / or, when scaling the force output calculated by the configurable fourth component model based on the second efficiency parameter, the stream output from one or more power source models, the stream output from one or more power consumer models, and the stream output from one or more inertia coupling models.
[0173] In some embodiments, each flow scaling module may be configured to scale at least one of the following: flow output from one or more power source models, flow output from one or more power receiver models, and flow output from one or more inertia coupling models, such that it is converted from a flow in one energy domain to a flow in another energy domain.
[0174] Figure 16 A schematic diagram of a configurable powertrain model configured to control a fourth powertrain 400 is shown. Figure 17 A schematic diagram of a fourth powertrain 400 is shown. The fourth powertrain 400 includes an internal combustion engine 410, a clutch 420, an electric generator 430, a gearbox 440, and a drive output 450 (e.g., wheels). Thus, the fourth powertrain 400 can be considered representative of a motor vehicle with a hybrid powertrain.
[0175] As in the previous example, to configure a configurable powertrain model for modeling the fourth powertrain 400, the input file provides input parameters that identify each component model to be configured. In the fourth powertrain 400, the input file may include input parameters for configuring models representing: the internal combustion engine power source, the internal combustion engine inertia, the clutch, the electric generator power source, the electric generator inertia, the gearbox-compliant coupling model, the final drive power receiver, and the final drive inertia. Figure 16 The component model shown is an example of a set of component models that can be configured from the general powertrain component library to model the fourth powertrain 400.
[0176] In this example, the fourth powertrain includes two distinct force-based power sources. These force-based power sources can be configured to receive different component-specific inputs based on a first input parameter from an input file. Figure 18 It shows Figure 17 The annotation diagram shows the component-specific inputs that can be provided for each power source model and power receiver model.
[0177] For example, according to Figure 18 The internal combustion engine force source model is configured to calculate the force output representing the torque from combustion. For example... Figure 18 As shown, the internal combustion engine force source model is configured to receive specific inputs for this component: exhaust gas recirculation (EGR), injection initiation (SOI), fuel rail pressure (FRP), injection mode, turbocharger, and engine speed in order to calculate the force output. The electric generator force source model is configured to calculate the force output representing the torque from the magnetic coupling. (As shown...) Figure 18 As shown, the electric generator force source model is configured to receive specific input current and motor speed from the component.
[0178] Therefore, it should be understood that a configurable powertrain model can be configured to provide a powertrain model for a specific powertrain with multiple power sources. By analogy, a configurable powertrain model can also be configured to provide a powertrain model for a specific powertrain with multiple power receivers.
[0179] In addition to the configurable powertrain model, the universal controller also includes a configurable optimizer module. The configurable optimizer module includes a library of general performance objective functions.
[0180] The configurable optimizer module is configured to calculate at least one of an optimized force request or an optimized flow request for each of the N power sources in that particular powertrain (e.g., u opt Based on the cost function, a powertrain model for a specific powertrain, and an M-power receiver (e.g., r The required force request or required flow request is used to calculate the optimized force and / or optimized flow request. A schematic diagram of the configurable optimizer module is shown in [the diagram]. Figure 19 It is shown in more detail below.
[0181] A cost function can be configured by selecting a general performance objective function from the general performance objective function library and providing a fifth input parameter as part of the input file. The fifth input file parameter can be used to select at least one performance objective function from the general performance objective function library. Similarly, a powertrain model with a specific powertrain configuration can be configured using the input file parameter from the connection parameter module and the general powertrain component library. For example... Figure 19 The diagram schematically illustrates the first, second, third, fourth, and fifth input file parameters. Therefore, it can be understood that the configurable optimizer module can be configured in a similar manner to the configurable powertrain model described above.
[0182] The general performance objective function library may include multiple different general performance objective functions, each configurable to provide a (specific) cost function for a particular powertrain. Different general performance objective functions with different performance targets can be provided. Various performance targets are known to those skilled in the art; for example, fuel consumption, emission targets (brake-specific NO...). x Brake-specific soot, NO x The performance objective function can be configured to minimize factors such as the soot ratio, required power error, etc. For example, a first general performance objective function can be configured to follow a minimum fuel consumption strategy or a minimum power consumption strategy. A second general performance objective function can be configured to pursue a strategy based on compliance with certain emission targets. A third performance objective function can be configured to follow a strategy that minimizes the error between the requested torque or speed and the output torque or speed. In some embodiments, other general performance objective functions combining multiple different performance objectives with different cost weights can be provided. For example, a fourth performance objective function can seek to minimize fuel consumption in conjunction with minimizing the torque request error. It should be understood that the general performance objective function used by the configurable optimizer module, as well as any cost weights, can be specified by a fifth input parameter of the input file.
[0183] The following sections will describe some examples of general performance objective functions. It should be understood that the features of the following general performance objective functions are non-restricted examples of some possible functions that can be provided in the library. These features of the following general performance objective functions can be combined with other functions as needed.
[0184] exist Figure 19 In the embodiments described, a single-level cost function is depicted. Thus, the general performance objective function configured as the cost function comprises a single optimizer function that optimizes the force requests and / or flow requests for the N power sources.
[0185] To calculate the cost associated with a set of candidate force requests and / or candidate flow requests, the optimizer or search function is configured with N power sources (e.g., ...).u Identify a set of candidate force requests and / or candidate flow requests.
[0186] Using a powertrain model with that specific powertrain configuration to assess the performance associated with that set of candidate force requests and / or candidate flow requests. y Modeling is performed. The configured powertrain model can be modeled on multiple variables (e.g., L parameters, where L>M) based on the set of candidate force requests and / or candidate flow requirements. For example, the associated performance y can include the final force / flow for each component of a particular powertrain. Thus, the associated performance can include the force / flow obtained for each of the M power receivers, the force / flow obtained for the N power sources, and the force / flow for any of the couplings included in the configured powertrain model. The associated performance depends on the information required by the cost function. y It can also include variables such as torque error, power consumption, or emissions-related variables.
[0187] Cost function evaluates associated performance y The degree to which the required force or flow request from the M power receiver and / or any other performance objective parameters that can be specified from the general performance objective function library are satisfied is used to determine the optimal force request and / or optimal flow request for the N power source. Thus, it should be understood that the cost function calculates the total cost J required for each set of candidate forces / flows based on multiple variables.
[0188] For example, a cost function configured to minimize fuel consumption can be configured to be the performance y required for each set of candidate force requests and / or candidate costs based on N power sources (e.g., u The cost function is used to calculate the fuel consumption of each of the N power sources. In cases where the N power sources consume different types of fuel, the cost function can include an equivalent parameter ε to account for the differences in fuel consumption.
[0189] A cost function can be configured from a general performance objective function. Each general performance objective function can include one or more general cost constraints. Each general cost constraint can calculate the cost associated with a given variable. Different cost constraints can be specified by a fifth input parameter based on a specific powertrain.
[0190] The general cost constraints provided by general performance objectives can include system constraints and performance objective constraints.
[0191] System constraints can be physical limitations imposed by the physical capabilities of a specific powertrain. For example, for a powertrain that includes an electric generator and a gearbox, a configurable powertrain model can be configured to include the electric generator torque (T). MGModeling variables for gearbox speed (ω(t)) and gearbox speed (ω(t)). It should be understood that there are physical limitations on the amount of torque that the electric generator can output or receive. There are also physical limitations on the speed of the gearbox. Therefore, the cost function can specify one or more system constraints for each variable based on the fifth input parameter of the input file. In some embodiments, the system constraints can include a hyperbolic function that can be configured using the fifth input parameter to calculate the cost associated with the physical capabilities of a particular powertrain function. For example, system constraints for variable gearbox speeds can be provided based on a limit α that limits the maximum gearbox speed. The cost calculated by the engine constraint function can asymptotically increase as the limit α approaches. Thus, the system constraints can be calculated using a hyperbolic function. The limit α can be specified as one of the fifth input parameters. Therefore, the system constraints based on gearbox speed (ω(t)) (J) GBspeed It could be:
[0192] J GBSpeed =1 / (α–ω(t)) (4)
[0193] Performance objective constraints can be attributes that associate costs with performance objectives. Performance objective constraints that include parabolic functions can be configured with a fifth input parameter to calculate the cost associated with the performance objective. An example of a performance objective constraint is minimizing fuel consumption. Therefore, a fuel consumption performance objective constraint can be configured to calculate costs based on fuel consumption variables provided by a configured powertrain model. This form of performance objective constraint can be represented by a function with a weighted square law relationship. For example, for variable equivalent fuel consumption (m... feq ), performance target constraints (J mfeq The weights to be applied to the performance target constraint can be specified by a fifth input parameter β. Therefore, in this example, the performance target constraint for minimum fuel consumption can take the following form:
[0194] J feq =β*m feq ^2 (5)
[0195] For example, in one embodiment, performance target constraints on equivalent fuel consumption variables can be configured for powertrains including electric generators and internal combustion engines, such as... Figure 1D and Figure 14 The powertrain shown is an example. According to a possible general performance objective function, the fuel consumed per power source is a function of the torque output by each power source and the shaft speed of each power source. Therefore, as shown in equation (6) below, the equivalent fuel consumed is the equivalent fuel (m) consumed by the electric generator. feqMG ) and the equivalent fuel consumed by the internal combustion engine (m fICE The sum of (6).
[0196] As shown in Equation 6, based on the shaft speed (ω) of the electric generator mg (t) and the torque output (T) of the electric generator mg (t) is used to calculate the equivalent fuel (m) consumed by the electric generator. feqMG Based on the shaft speed (ω) of the internal combustion engine. ICE (t) and the torque output of the internal combustion engine (T) ICE (t) is used to calculate the equivalent fuel (m) consumed by the internal combustion engine. fICE ).
[0197] For example, Figure 1D The specific powertrain shown will be understood to have a gearbox speed ω(t) and a generator shaft speed ω due to the drive shaft connection. mg (t) and the shaft speed (ω) of the internal combustion engine ICE The values (t) are the same. Thus, for a given torque (or speed) request from the drive output, these two power sources can satisfy the power requirement through a combination of shaft speed ω(t) and the combined torque of the electric generator and the internal combustion engine. Assuming the internal combustion engine provides any torque not provided by the electric generator, the cost function can be optimized by considering variables ω(t) and T. mg (t). Thus, in one example, the provided cost function could be ω(t) and T. mg A function of (t).
[0198] exist Figure 20 The figure shows an example of the cost space that can be specified by a combination of system constraints and performance target constraints. Figure 20 The powertrain model ω(t) and T are shown. mg The cost space is defined by the two variables in the (t) model. For example... Figure 20 As shown, the cost space includes system constraints for the maximum gearbox speed. The cost space also includes performance objective constraints that minimize equivalent fuel consumption.
[0199] The configurable optimizer module is configured to compute optimized force and / or optimized flow requests based on an evaluation of the associated performance using a cost function. Therefore, the configurable optimizer module can be configured to iterate the computation of candidate force or candidate flow requests for each of the N power sources to search for optimized force or optimized flow requests for each of the N power sources in a given powertrain.
[0200] Those skilled in the art are familiar with various search strategies for optimizer functions. Therefore, a configurable optimizer module may include one or more search algorithms that can be configured to search the cost space defined by the cost function to identify an optimized solution (e.g., a minimum value in the cost space). For example, a configurable optimizer module may use a random search strategy to randomly search the cost space to find an optimal point. u opt .
[0201] exist Figure 20 In the exemplary cost space, the results of the search strategy executed by the configurable optimizer module are shown as points in the cost space. The search strategy executed by the configurable optimizer module includes a hierarchical sampling process and a line search process to attempt to identify minimum points while avoiding solutions that are local minima rather than global minima.
[0202] For example, depending on a possible optimizer search strategy, the optimizer module can be configured to perform hierarchical sampling of the cost space. The cost space can be a multidimensional search space, where the number of dimensions corresponds to the number of variables to be optimized. For example, in Figure 20 In the embodiments, the flow request ω(t) and the force request T can be optimized. mg (t).
[0203] The cost space effectively defines the candidate force requests and / or candidate flow requests that can be evaluated by the configurable optimizer module. u Each possible combination. The configurable optimizer module can select a set of candidate force requests and / or candidate flow requests for evaluation by the cost function.
[0204] Hierarchical sampling of the cost space ensures that the required set of candidate force requests and / or candidate flows is distributed across the cost space. Thus, it should be understood that hierarchical sampling can provide a more uniform distribution of the required set of candidate force requests and / or candidate flows across the entire cost space than purely random sampling.
[0205] Various methods for performing hierarchical sampling of a multidimensional search space are known to those skilled in the art. In one embodiment of the invention, the optimizer module can be configured to perform Latin hypercube sampling of the cost space. In other embodiments, orthogonal sampling methods, or any other suitable hierarchical sampling method, can be used to determine the hierarchical sampling, which provides a distribution of the desired set of candidate force requests and / or candidate flows in the cost space.
[0206] like Figure 20 As shown, multiple points are indicated on the cost surface, which represent a set of points that can be computed using a hierarchical sampling search strategy.
[0207] After executing the stratified sampling search strategy, the configurable optimizer module can simply select the lowest cost point. In some embodiments, the configurable optimizer module can also perform a line search process as a second step. In the second step, the configurable optimizer module determines a search line in the cost space that spans a first cost minimum based on the cost generated by stratified sampling.
[0208] In one embodiment, a search line is determined based on two sets of candidate force requests / flow requests for the N-power source with the lowest cost. Thus, the search vector is determined as a vector in cost space along the line between the two sets of candidate force requests / flow requests for the N-power source with the lowest cost. The purpose of determining the search vector is to provide a direction along which to further search for a minimum in order to identify optimized sets of force requests and / or flow requests for the N-power source.
[0209] Various methods for determining whether the minimum of a function (i.e., the cost function) lies between two points on a line are known to those skilled in the art. One method for checking whether a minimum exists along a search line is to evaluate the cost function at a third point (x1) along the search line (i.e., between two sets of candidate force requests / flow requests for N power sources with the lowest cost on the search line). If the evaluated third point has a cost lower than either of the two endpoints of the search line, this indicates that the minimum lies on the search line between the two endpoints. If it is determined that no minimum exists along the search line, the endpoints of the search line can be extended along the search vector in the cost space and re-evaluated. This process can be repeated until a search line spanning the minimum is found.
[0210] For example, in Figure 20 In the cost space, multiple points are evaluated along the search line in the cost space in order to identify a set of optimized force requests and / or flow requests for N power sources.
[0211] It should be understood that the search strategy of the configurable optimizer can be configured to work with any cost function configured from the general performance objective function library. The search strategy can be configured using the fifth input parameter of the input file. For example, the search strategy can be configured to define the total computation time for the search strategy to return an optimized solution, or to limit the number of cost function evaluations to be performed. For example, for Figure 20 The cost function can evaluate up to 100 sets of candidate force requests and / or candidate flow requests, taking a total computation time of approximately 10 to 15 ms.
[0212] Therefore, a configurable optimizer module can be provided according to embodiments of the invention. This configurable optimizer module can be configured to provide a cost function for determining, using an input file, the optimized force and / or optimized flow request for each of the N power sources in a particular powertrain. Thus, the configurable optimizer module of the universal controller can be implemented using a pre-compiled computer program stored in memory. The pre-compiled computer program can be executed on a processor to provide a controller for a particular powertrain upon receiving an input file providing the aforementioned input parameters. Those skilled in the art will understand that such a universal controller is possible due to the functionality of the universal performance objective function library of the configurable optimizer module. Specifically, the universal controller can be configured to optimize the force and / or flow request of the N power sources in a particular powertrain without recompilation, because the universal performance objective function can be configured based on a fifth input parameter from the input file. Thus, the universal powertrain controller is configurable (and reconfigurable) to optimize control over a wide range of powertrains without requiring recompilation of the universal controller.
[0213] In some embodiments, a general performance objective function library may include one or more general performance objective functions, which may include multiple general optimizer functions. For example, in some embodiments, the general performance objective function library may include a general two-level performance objective function, which includes a configurable convergent state optimizer function, a configurable current state optimizer function, and a configurable power source management module.
[0214] The general two-level performance objective function can be configured using a fifth input parameter to define the cost function, which includes the convergent state optimizer function, the current state optimizer function, and the power source management module. Figure 22 An example of this cost function, configured using the fifth input parameter, is shown in the figure.
[0215] Figure 22 This is shown as part of the configurable optimizer module. Figure 21 The configured cost function. Thus, in addition to the fifth input parameter of the input file being used to configure different cost functions, Figure 22 The illustration is similar to Figure 19 The illustration. Figure 21 and 22 The cost function is arranged to calculate at least one of the optimized force request and / or optimized flow request for each of the N power sources. The configurable optimizer module calculates the optimized force request and / or optimized flow request based on the required force request and / or required flow request from the M power receivers, the cost function, and a configured powertrain model for a specific powertrain.
[0216] A general two-level performance objective function is particularly applicable to powertrains comprising multiple power sources (i.e., N≥2). It should be understood that for powertrains comprising multiple power sources, the required force or flow request from the M power receivers can be satisfied by supplying power from one or more of these sources. Therefore, for powertrains comprising multiple power sources, the configurable optimizer module has an additional dimension of complexity in determining how to distribute the required force / flow (i.e., power) among the multiple power sources. In some powertrains, the ability to switch between transmitting power from one power source and another can be instantaneous. In other powertrains, the ability to rearrange the power supply on multiple power sources may not be instantaneous. For example, in some power sources, there may be a time delay that causes the power source to ramp up (or actually ramp down). Therefore, some specific powertrains may have physical constraints that prevent them from instantaneously converging from their current power source operating state to an optimized power source operating state.
[0217] The two-level performance objective function provides a convergent state optimizer function, a current state optimizer function, and a power source management module to address the discrepancy between the ideal operating conditions of multiple power sources and the current operating states of multiple power sources in the powertrain. This performance objective function allows the configurable optimizer module to consider powertrains that include multiple different types of power sources, such as in a hybrid powertrain.
[0218] like Figure 21 As shown, the cost function includes a convergent state optimizer function and a current state optimizer function. Each optimizer function is configured to compute a set of optimization force requests and / or optimization flow requests for N power sources.
[0219] The convergent state optimizer function calculates the forces and flow requests of the N power sources independently of the current state optimizer function. For example... Figure 22 As shown, the convergent state optimizer function calculates the first set of optimized force requests and / or optimized flow requests based on the required force requests and / or required flow requests of the M-power receiver. u opt1 The convergent state optimizer function uses a configured powertrain model of a specific powertrain to compute the first set of optimization force requests and / or optimization flow requests. Thus, the convergent state optimizer function can... Figure 19The cost function shown operates in a similar manner to compute the first set of optimized force requests and / or optimized flow requests. The convergence state optimizer function is configured to compute the first set of optimized force requests and / or optimized flow requests based on the convergence state for the operation of a specific powertrain. In other words, the convergence state optimizer function aims to compute the required convergent (i.e., long-run, ideal) solution for the optimized force requests and / or optimized flows of N power sources.
[0220] For example, in some embodiments, the convergent state optimizer function can be configured to compute the first set of optimization force requests and / or optimization flow requests in a manner similar to the cost function of the single-level performance objective function described above. u opt1 ).
[0221] The first set of optimization force requests and / or optimization flow requests, calculated by the convergent state optimizer function, is provided to the power source management module.
[0222] A power source management module is provided to update a configurable current state optimizer function based on a first set of at least one optimized force request and / or optimized flow request for each of the N power sources, and the current operating state of a particular powertrain. In this way, the power source management module provides the current state optimizer function with information about the first set of optimized force requests and / or optimized flows required by the convergent state optimizer function. The power source management module can be configured to interpret the current operating state of the N power sources within the context of the convergent state solution calculated by the convergent state optimizer function. For example, in one embodiment where the convergent state optimizer function requests a force or flow from a first power source, the power source management module checks whether the first power source is currently inoperable. If the first power source is currently inoperable, it may not be able to deliver the required optimized force / flow immediately. Therefore, the power source management module can identify this and provide information to the current state optimizer function to update system constraints, thereby changing the current state optimizer function accordingly.
[0223] To account for the difference between the optimization force / flow calculated by the convergent state optimizer function and the current operating state of the N power sources, the power source management module can be configured to adjust or modify the fifth input parameter associated with the current state optimizer function. That is, the power source management module can update the fifth input parameter associated with the current state optimizer function to adjust the behavior of the current state optimizer function in consideration of the current operating state of the N power sources. For example, if the internal combustion engine is not currently running, the power source management module can update the fifth input parameter associated with the system constraints of the internal combustion engine to force the internal combustion engine not to be used in the solution for generating power.
[0224] Therefore, the power source management module can be configured to receive information about the operational status of each component of a specific powertrain. For example, the power source management module can be configured to receive information about the operational status of power source N and power receiver M. In this way, the power source management module can be configured to receive the forces and / or flows associated with each component of the specific powertrain at the current time. This information can be provided to the power source management module of the universal controller by the interface controller of the specific powertrain.
[0225] In order to direct the operating state of a specific powertrain to the N power source ( u opt1 Upon convergence of the first optimization force request group and / or the optimized flow request group, the power source management module can also be configured to trigger the power source startup routine when a difference between the convergence state and the current operating state of the power source is detected. Once the power source is operational, the power source management module updates the current state optimizer function accordingly.
[0226] In other embodiments, the current operating state of the power source (e.g., an internal combustion engine) may be subject to temporary limitations. In response to these temporary (i.e., time-dependent) limitations, the power source management module can update the fifth input parameter of the current state optimizer function accordingly. For example, for an internal combustion engine power source, the available torque may be limited by the operating state of the turbocharger in certain operating states. Therefore, the power source management module can update the system constraints of the current state optimizer function based on the operating state of the turbocharger / internal combustion engine.
[0227] In some embodiments, the power source management module may be configured to manage the use of different power sources for a particular powertrain. For example, the power source management module may be configured to manage the relative use of an internal combustion engine and an electric generator powered by a battery power source in a hybrid powertrain. In this way, the power source management module can adjust the relative costs associated with requesting electricity from the internal combustion engine and requesting electricity from the electric generator to manage the relative use of the battery and fuel supply. Therefore, the power source management module can adjust the equivalent parameter (ε) of one or more of the multiple power sources used for that particular powertrain. In the example below, a single equivalence factor is used for a hybrid powertrain including an electric generator and an internal combustion engine to change the relative cost of using the two power sources. Of course, in other embodiments that include more power sources, additional equivalence factors may be used to enable changes in the relative cost of the power sources relative to each other.
[0228] The equivalent factor ε can be used to adjust the relative equivalent fuel consumption calculated for each of the electric generator and the internal combustion engine. For the equivalent fuel consumption calculation of Equation 1 (reproduced below), only one equivalent parameter is adjusted to adjust the relative use of the two power sources.
[0229]
[0230] As shown in the equation above, in order to adjust for the relative cost of using an electric generator compared to an internal combustion engine, the equivalent fuel consumption (m) of the electric generator is... feqMG Multiply by the equivalent parameter ε. It should be understood that by changing ε, the cost function will favor the use of an electric generator or the use of an internal combustion engine. In some embodiments, the equivalent parameter ε can be defined by a fifth input parameter and kept as a constant parameter in the power source management module. In some embodiments, the equivalent parameter ε can be modified by the power source management module, for example, to specify the state of charge (SOC) of the battery supplying power to the electric generator. In this way, the equivalent parameter can be used to control the battery discharge rate or maintain a specified state of charge for a period of time.
[0231] Therefore, in some embodiments, the power source management module can be configured to control the utilization rate of the power source (e.g., control battery discharge). For example, in some embodiments where battery charging is to be controlled, a reference state of charge (SOC) can be defined. ref Therefore, the error function can be defined as:
[0232] e(t) SOC =SOC ref -SOC(t) (7)
[0233] The error function (e(t)) SOC You can define the difference between the current state of charge (SOC(t)) of the battery and the desired reference state of charge.
[0234] In some embodiments, the reference state of charge (SOC) ref This can be a constant value defined by a fifth input parameter. For example, the reference state of charge can be at least 30%, or in some embodiments, 50%. Therefore, the cost function can be configured to control the battery's state of charge in an attempt to maintain a constant state of charge. In some embodiments, SOC ref It can be a time-based or usage-based function. Thus, SOC ref The input file can be specified to decrease linearly over, for example, a day, in order to control the gradual discharge of the battery. Therefore, it should be understood that the power source management module can be used to manage multiple power sources to be controlled by the optimizer module.
[0235] The error function in Equation 7 can be used to detect the difference between SOCref and the current state SOC(t). This can then be used to control the equivalent parameter ε to maintain the desired state of charge of the battery. Various control schemes can be used to control the equivalent parameter, such as a compensator function.
[0236] For example, the compensator function, which includes both proportional and integral components, can be configured to adjust the equivalent factor ε over time.
[0237]
[0238] As shown in Equation 8 above, the compensator function can be configured based on a fifth input parameter from the input file. For example, in the adaptive function of Equation 3, the fifth input parameter to be specified is the initial equivalent parameter ε0 and the scaling parameter K. p and integral scaling parameter K i Of course, it should be understood that Equation 8 is only one possible example of the compensator function, which can be used by the power source management module to control the equivalent factor in response to the current operating state of a particular powertrain.
[0239] The current state optimizer function calculates the optimized force request and / or optimized flow request for each of the N power sources. For example... Figure 22 As shown, the current state optimizer function calculates the second set of optimized force requests and / or optimized flow requests based on the required force request and / or required flow request for the M power receiver and information from the power source management module. u opt 2).
[0240] The operation of the current state optimizer function is similar to that of the convergent state optimizer function. For example, the current state optimizer function can be configured with a general performance objective function that is essentially the same as that of the convergent state optimizer function. The current state optimizer function can use system constraints or performance objective constraints with different fifth input parameters based on information provided by the power source management module. The current state optimizer function utilizes a powertrain model of a specific powertrain for a specific power to determine the second set of candidate force requests and / or candidate flow requests (…). u 2) Related performance y 2, so as to calculate the second set of optimization force requests and / or optimization flow requests. u opt 2. The current state optimizer function can use a similar search strategy as the convergent state optimizer function to search for a second set of optimization force requests and / or optimization flow requests. u opt 2.
[0241] For example, in the example above, the current state optimizer function can calculate the cost associated with the minimum equivalent fuel consumption in Equation 1. Information provided by the power source management module may include the equivalent parameter ε, the updated weights (α, β) of the cost parameters, or other information about the cost function.
[0242] The following section will discuss an example of configuring a two-level general performance objective function to control the powertrain. Figure 17 The diagram shows the hybrid powertrain to be controlled. Thus, a two-stage general performance objective function is configured to control a hybrid powertrain 400 comprising two power sources: an internal combustion engine 410 and an electric generator 430.
[0243] The input file is provided to the universal controller to configure it to control the hybrid powertrain 400. The configurable powertrain model is configured using the first through fourth input parameters as described above. The fifth input parameter is used to configure the configurable optimizer module to provide a cost function for the hybrid powertrain 400.
[0244] In the following example, such as Figure 23 and 24 As shown, this represents the time series required to receive the desired force and / or flow from the power receiver. Figure 24 As shown, the required force (BC torque) or the required flow (BC velocity) varies with time between two different constant values. Figure 23 The relationship between the required torque and the required speed for a particular powertrain is shown. Thus, it will be understood that the required torque from the power receiver (i.e., drive output 450) can be in the form of a required force or a required flow.
[0245] Figure 25 , 26 Figures 27 and 28 show graphs illustrating the performance of a specific powertrain under different input file settings. Figure 25 In the first graph, the configurable optimizer module is configured using a configurable two-level performance objective function that includes a first equivalent parameter set to ε = 2.0. This equivalent parameter is configured to favor battery discharge exceeding fuel consumption.
[0246] exist Figure 26 In the second curve, the configurable optimizer module is configured using a configurable two-level performance objective function that includes a second equivalent parameter set to ε = 2.5. Thus, the equivalent parameter relative to... Figure 25 This is to increase the amount of fuel used to more closely balance battery discharge and fuel consumption.
[0247] exist Figure 27 In the third curve, the configurable optimizer module is configured using a configurable two-level performance objective function, which includes an equivalent third parameter set to ε = 2.7. This further increases the equivalent parameter (relative to...). Figure 25 This facilitates battery charging over time.
[0248] Figure 28Further comparisons of the performance of a specific powertrain under the control of a universal controller are shown. It should be understood that for the first equivalent parameter ε = 2.0, the energy usage over time is net negative, indicating battery discharge over time. For the third equivalent parameter ε = 2.7, the energy usage over time is net positive, indicating battery charging over time. Therefore, it should be understood that equivalent parameters can be used to manage the use of the power source for a specific powertrain.
[0249] Although in this example the equivalent parameter is chosen as a constant to show its effect over time, in other embodiments the equivalent parameter may be updated by the power source management module as described above to manage the use of N power sources over time.
[0250] Industrial applicability
[0251] According to the present invention, a universal controller is provided. The universal controller can be configured to control any specific powertrain falling within a category of universal powertrains, including J universal power sources, K universal power receivers, and L universal couplings, wherein (J, K, and L are positive integers, non-zero integers).
[0252] Therefore, a universal controller can be configured to control powertrains for a variety of systems, including but not limited to: motor vehicles, electric vehicles, hybrid vehicles, marine equipment, power generation equipment, manufacturing equipment, and aviation.
[0253] The universal controller can be configured to control a specific powertrain upon receiving an input file including input parameters. Therefore, the universal controller of this invention can be reliably and efficiently configured to model a specific powertrain. In particular, by configuring the universal controller using parameters, it is not necessary to recompile the controller to generate a new model.
[0254] Furthermore, a universal controller can be reliably and efficiently configured to control a specific powertrain based on a desired performance objective function. In this way, the control of a particular powertrain can be effectively adapted to various performance objectives without recompilation.
[0255] Therefore, a universal controller can be applied reliably and effectively to a range of powertrain systems, thereby avoiding the significant software build and testing costs associated with powertrain controllers that are programmed and compiled for each specific powertrain.
Claims
1. A universal controller for controlling at least one of the following: a force request or flow request of a power transmission system based on at least one of a desired force or a desired flow. The universal controller includes a configurable powertrain model and a configurable optimizer module. The universal controller can be configured to control a type of universal power transmission system including a J universal power source, a K universal power receiver, and an L universal coupling. in, The universal controller is configured to receive an input file including multiple input parameters in order to configure the universal controller to control a specific powertrain having a powertrain architecture including an N power source, an M power receiver, and an X coupling. i) The configurable powertrain model includes: (a) A general powertrain component library configured to provide models of each of the N power source, M power receiver, and X coupling for the specific powertrain, the general powertrain component library comprising: Multiple configurable first component models are provided, and an N power source model is configurable from these multiple configurable first component models based on first input parameters from the input file. The N power source model represents the N power sources of the specific powertrain system. Each first component model is configured to receive at least one of a plurality of first component-specific inputs and to calculate a force output or a flow output based on at least one of the plurality of first component-specific inputs; Multiple configurable second component models, from which an M-power receiver model can be configured based on second input parameters of the input file, wherein the M-power receiver model represents the M-power receiver of the specific powertrain, wherein... Each second component model is configured to receive at least one of a plurality of second component-specific inputs and to calculate a force output or a flow output based on at least one of the plurality of second component-specific inputs; Multiple configurable third component models, from which at least one inertia coupling model can be configured based on third input parameters of the input file, wherein, Each third component model is configured to receive multiple force inputs and calculate the flow output based on the force inputs. Multiple fourth-component models, based on the fourth input parameters of the input file, allow for the configuration of a compliance-based coupling model from the fourth-component models, wherein, Each fourth component model is configured to receive multiple stream inputs and calculate the force output; Wherein, the inertia coupling model and the compliance-based coupling model represent the X coupling of the specific power transmission system. (b) The connection parameter module is configured to define the model architecture of the N power source model, M power receiver model, and X coupling model representing the powertrain architecture based on the flow weight parameters and force weight parameters of the input file, wherein, The flow weight parameter defines any flow connection from the flow output of the N power source model, the flow output of the M power receiver model, and the flow output of the inertia coupling model of the X coupling to the flow input of the compliance-based coupling model of the X coupling in the model architecture; and The force weight parameter defines any force connection from the force output of the N power source model, the force output of the M power receiver model, and the force output of the compliance-based coupling model of the X coupling to the force input of the inertia coupling model of the X coupling model in the model architecture; The configurable powertrain model can be configured to provide a powertrain model for a specific powertrain based on the N power source model, the M power receiver model, the X coupling model, and the model architecture. ii) The configurable optimizer module includes: A general performance objective function library is provided, comprising multiple configurable performance objective functions. Based on these multiple configurable performance objective functions, a cost function can be configured using the fifth input parameter of the input file. The configurable optimizer module is configured to calculate at least one of an optimized force request or an optimized flow request for each of the N power sources of the particular powertrain based on the following: The cost function The powertrain model of the specific powertrain system. Required force request or required flow request.
2. The universal controller according to claim 1, wherein, The configurable optimizer module can be configured to: Calculate a candidate force request or candidate flow request for each of the N power sources; The powertrain model of the specific powertrain is used to calculate multiple performance variables associated with the candidate force request and / or candidate flow request; as well as The cost function is used to evaluate multiple performance variables associated with the candidate force request and / or candidate flow request to determine the cost associated with the candidate force request and / or candidate flow request; wherein, The configurable optimizer module can be configured to calculate the optimized force request or the optimized flow request for each of the N power sources of the particular powertrain based on the cost associated with the candidate force request and / or candidate flow request.
3. The universal controller according to claim 2, wherein, The configurable optimizer module can be configured to iterate the calculation of candidate force requests or candidate flow requests for each of the N power sources in order to search for the optimized force request or optimized flow request for each of the N power sources in the particular powertrain.
4. The universal controller according to any one of the preceding claims, wherein, The general performance objective function library includes configurable performance objective functions, which include one or more of the following: System constraints, including hyperbolic functions, which are configurable with the fifth input parameter for calculating costs associated with the physical capabilities of the particular powertrain function; and This includes a performance target constraint that includes a parabolic function, which can be configured with the fifth input parameter to calculate the cost associated with the performance target.
5. The universal controller according to claim 1, wherein, The general performance objective function library includes configurable two-level performance objective functions, including: A configurable convergent state optimizer function is configured to compute a first set of at least one optimized force request and / or optimized flow request for each of the N power sources; A configurable current state optimizer function can be configured to compute at least one optimized force request and / or optimized flow request for each of the N power sources; and A configurable power source management module updates the configurable current state optimizer function based on at least one optimized force request and / or optimized flow request from the first set for each of the N power sources, and the current operating state of the particular powertrain.
6. The universal controller according to claim 5, wherein, The configurable power source management module can be configured to update the fifth input parameter of the configurable current state optimizer function based on at least one optimized force request and / or optimized flow request from the first set for each of the N power sources and the current operating state of the particular powertrain.
7. The universal controller according to claim 1, wherein, The universal controller is configured to receive an input file including multiple input parameters to configure the universal controller to control a specific powertrain having a powertrain architecture including N power sources, wherein N is at least 2.
8. The universal controller according to claim 7, wherein, The configurable power source management module can be configured to update the costs associated with the use of each of the N power sources.
9. The universal controller according to claim 1, wherein, Each configurable third component model includes a first force and a junction, the first force and junction being configurable to calculate the net force input of the third component model based on at least one of the following: the force output from one or more power source models, the force output from one or more power receiver models, and the force output from one or more compliance-based coupling models. The flow output is calculated based on the net force input.
10. The universal controller according to claim 9, wherein, Each configurable third component model includes a force scaling module that can be configured to scale at least one of the following: the force output from one or more power source models, the force output from one or more power receiver models, and the force output from one or more compliance-based coupling models, using a first scaling parameter of the input file and a first complementary scaling parameter of the input file to scale the flow output calculated by the configurable third component model.
11. The universal controller according to claim 10, wherein, Each force scaling module can be configured to consider energy loss when scaling at least one of the following: the force output from one or more power source models, the force output from one or more power receiver models, and the force output from one or more compliance-based coupling models when scaling the flow output calculated by the configurable third component model based on the first efficiency parameter of the input file.
12. The universal controller according to claim 10 or 11, wherein, Each force scaling module can be configured to scale at least one of the following: force outputs from one or more power source models, force outputs from one or more power receiver models, and force outputs from one or more compliance-based coupling models, such that they are converted from forces in one energy domain to forces in another energy domain.
13. The universal controller according to claim 10, wherein, The net force input calculated from the first force and the junction is based on forces in the same energy domain.
14. The universal controller according to claim 13, wherein, Each configurable fourth component model includes a first flow and nodes, the first flow and nodes being configured to calculate the net flow input of the fourth component model based on at least one of the following: the flow output from one or more power source models, the flow output from one or more power receiver models, and the flow output from one or more inertia coupling models.
15. The universal controller according to claim 14, wherein, Each configurable fourth component model includes a flow scaling module that can be configured to scale at least one of the following: the flow output from one or more power source models, the flow output from one or more power receiver models, and the flow output from one or more inertia coupling models using a second scaling parameter of an input file, and scaling the force output calculated by the configurable fourth component model using a second complementary scaling parameter of the input file.
16. The universal controller according to claim 15, wherein, Each stream scaling module can be configured to consider energy loss when scaling at least one of the following: the stream output from one or more power source models, the stream output from one or more power consumer models, and the stream output from one or more inertia coupling models when scaling the force output calculated by the configurable fourth component model based on the second efficiency parameter.
17. The universal controller according to claim 15 or 16, wherein, Each flow scaling module can be configured to scale at least one of the following: flow output from one or more power source models, flow output from one or more power receiver models, and flow output from one or more inertia coupling models, such that it is converted from a flow in one energy domain to a flow in another energy domain.
18. The universal controller according to claim 15, wherein, The net flow input calculated from the first flow and the node is based on the flow in the same energy domain.
19. The universal controller according to claim 1, wherein, Each third component model is configurable based on a third input parameter of the input file, which includes one or more of the following: a first resistance parameter and a first inertia parameter, in order to define an inertia coupling model.
20. The universal controller according to claim 1, wherein, Each fourth component model is based on a fourth input parameter that is configurable, which includes one or more of the following input files: a second resistance parameter and a compliance parameter, in order to define a compliance-based coupling model.
21. The universal controller according to claim 14, wherein, The specific power transmission system to be controlled has a power transmission system architecture including an X-coupling, wherein Y in the X-coupling is an inertia coupling and Z in the X-coupling is a compliance coupling; The y-inertia coupling model can be configured from the configurable third component model; and The coupling model based on Z-compliance can be configured from the configurable fourth component model.
Citation Information
Patent Citations
A powertrain control system for a vehicle and corresponding method
GB2504825A
Systems, apparatus and methods to improve plug-in hybrid electric vehicle energy performance by using v2c connectivity
WO2019241612A1