A high-fidelity simulation method for interior permanent magnet synchronous motors for hardware-in-the-loop testing

Abstract

The invention discloses a high-fidelity simulation method for a built-in permanent magnet synchronous motor applied to a hardware-in-the-loop test, comprising: establishing a two-dimensional finite element simulation model of the built-in permanent magnet synchronous motor by using finite element simulation calculation software, and configuring property files of ferromagnetic materials under different temperature conditions; performing finite element simulation calculation to generate first motor electromagnetic data according to the two-dimensional finite element model of the built-in permanent magnet synchronous motor; based on an external d-q axis voltage excitation and the first motor electromagnetic data, respectively constructing a flux calculation submodule, a torque current calculation submodule, an iron loss current calculation submodule, a temperature correction submodule and a torque calculation submodule in Matlab / Simulink according to discrete motor voltage, flux and torque equations; and comprehensively encapsulating the above submodules; and having the advantages of high fidelity and high precision.

Description

Technical Field

[0001] The present invention relates to the technical field of built-in permanent magnet synchronous motor modeling, and in particular to a high-fidelity simulation method of a built-in permanent magnet synchronous motor applied to hardware-in-the-loop testing. Background Art

[0002] Built-in permanent magnet synchronous motors have the advantages of high efficiency, high power density, high torque / inertia ratio, wide speed regulation range and compact structure, and are widely used in new energy vehicles and other fields. Hardware-in-the-loop testing is a technology based on motor models for embedded control system development and testing. It can perform real-time and reliable functional verification and performance evaluation of motor controllers. Both the design of motor control algorithms and the development of motor controller hardware-in-the-loop systems have high requirements for the fidelity of motor models. A high-fidelity motor model can serve as both a theoretical basis for control algorithm design and a basic tool for evaluating algorithm performance.

[0003] The built-in permanent magnet synchronous motor is a multivariable, strongly coupled, nonlinear electromechanical energy conversion system. The built-in permanent magnet synchronous motor often operates in high torque and high current or high speed and high back electromotive force conditions. These operating conditions increase the impact of nonlinear factors such as magnetic circuit saturation and cross coupling on the motor model. At the same time, due to factors such as the asymmetry of the motor magnetic circuit and the cogging effect, the influence of spatial harmonic factors cannot be ignored when modeling the motor. The core loss such as eddy current loss and hysteresis loss in the ferromagnetic material of the motor will affect the dq axis current of the motor, thereby affecting the electromagnetic behavior of the motor. At the same time, the temperature of the ferromagnetic material will also have a non-negligible impact on the electromagnetic behavior of the motor.

[0004] At present, the modeling method of built-in permanent magnet synchronous motor is mainly based on constant parameters, and a linear model is established under the dq axis system. This modeling method is simple and direct, but it cannot accurately characterize the nonlinear electromagnetic behavior of the motor through constant motor parameters. Therefore, the hardware-in-the-loop test system developed based on the linear model still has room for improvement in accuracy and fidelity. Computer simulation software based on the finite element method can perform multi-physics coupling calculations on the stress field, fluid field, electromagnetic field and temperature field of the motor by establishing multi-physics field models such as electromagnetic, mechanical and temperature of the motor. This method can cover the influence of nonlinear factors such as magnetic circuit saturation, spatial harmonics, temperature and core loss mentioned above, and has a high accuracy in calculating motor performance, but this method requires fine and complex meshing and a lot of computing time. At the same time, the motor model in the finite element simulation software cannot be directly used to evaluate the performance of the motor control algorithm, nor can it be directly deployed in the hardware-in-the-loop test equipment. The field-circuit coupling simulation technology that combines Matlab or Simulink with finite element simulation software can provide a relatively accurate motor model basis for the performance evaluation of motor control algorithms. However, this method requires joint debugging between multiple software. A large number of software need to be configured for a simulation calculation, which is time-consuming and difficult to debug. At the same time, this joint simulation model that couples different software cannot be directly deployed to the hardware-in-the-loop test equipment.

[0005] Therefore, the present invention provides a high-fidelity simulation method for a built-in permanent magnet synchronous motor applied to hardware-in-the-loop testing, which can solve the above problems. Summary of the invention

[0006] The technical problem to be solved by the present invention is that there are currently three main methods for modeling built-in permanent magnet synchronous motors, but all of them have certain defects. The first method is based on constant parameters and establishes a linear model under the dq axis system; there is a problem that the nonlinear electromagnetic behavior of the motor cannot be accurately characterized by constant motor parameters; the second computer simulation software based on the finite element method can establish multi-physical field models such as electromagnetic, mechanical and temperature of the motor, and perform multi-physical field coupling calculations on the stress field, fluid field, electromagnetic field and temperature field of the motor; there is a problem that it requires fine and complicated grid division and a lot of computing time, and cannot be directly used to evaluate the performance of the motor control algorithm, nor can it be directly deployed to hardware-in-the-loop. Problems in test equipment; The third field-circuit coupling simulation technology combining Matlab or Simulink with finite element simulation software can provide a relatively accurate motor model basis for motor control algorithm performance evaluation, but this method requires joint debugging between multiple software, and a large number of software need to be configured for a simulation calculation, which is time-consuming and difficult to debug. At the same time, this joint simulation model coupling different software cannot be directly deployed to the hardware-in-the-loop test equipment, so a high-fidelity simulation method of a built-in permanent magnet synchronous motor applied to hardware-in-the-loop testing is provided, and the high-fidelity simulation method of a built-in permanent magnet synchronous motor applied to hardware-in-the-loop testing includes:

[0007] A two-dimensional finite element simulation model of an internal permanent magnet synchronous motor is established through finite element simulation software, and the property files of ferromagnetic materials under different temperature conditions are configured;

[0008] Performing finite element simulation calculation to generate electromagnetic data of the first motor according to a two-dimensional finite element model of the interior permanent magnet synchronous motor;

[0009] Based on external dq axis voltage excitation , and electromagnetic data of the first motor, and constructing a flux calculation submodule, a torque current calculation submodule, an iron loss current calculation submodule, a temperature correction submodule and a torque calculation submodule in Matlab or Simulink respectively according to discrete motor voltage, flux and torque equations;

[0010] The above sub-modules are comprehensively packaged.

[0011] Further, configuring the property files of the ferromagnetic material under different temperature conditions; performing finite element simulation calculation to generate the electromagnetic data of the first motor according to the two-dimensional finite element model of the built-in permanent magnet synchronous motor includes:

[0012] The ferromagnetic material properties are configured to be under T0 temperature condition, the motor speed is set to one third of the rated speed, and a finite element simulation calculation is performed to generate first electromagnetic data, wherein the first electromagnetic data includes flux linkage, electromagnetic torque, and iron loss power data;

[0013] The ferromagnetic material properties are configured to be under T0 temperature condition, the motor speed is set to two-thirds of the rated speed, and a finite element simulation calculation is performed to generate first electromagnetic data, wherein the first electromagnetic data includes flux linkage, electromagnetic torque, and iron loss power data;

[0014] The ferromagnetic material properties are configured to be under T1 temperature condition, the motor speed is set to the rated speed, and a finite element simulation calculation is performed to generate first electromagnetic data, wherein the first electromagnetic data includes flux linkage, electromagnetic torque, and iron loss power data;

[0015] The ferromagnetic material properties are configured to be under T1 temperature conditions, the motor speed is set to one third of the rated speed, and finite element simulation calculations are performed to generate first electromagnetic data, which includes magnetic flux, electromagnetic torque and iron loss power data.

[0016] Furthermore, the magnetic flux calculation submodule specifically includes:

[0017] The basic operation unit in Matlab or Simulink is used to construct the flux calculation submodule, and the calculation is performed through the discrete dq axis voltage equation, which is:

[0018] ,

[0019] ,

[0020] in, and The motor The external dq axis voltage excitation at time, is the motor winding resistance, and The motor The dq axis torque current component at the moment, and The motor The dq-axis iron loss current component at the moment, the iron loss current component and the torque current component together constitute the stator current. It is a motor The electrical angular velocity at the moment, and It is a motor The dq-axis magnetic flux at time, and It is a motor The dq-axis magnetic flux at time, is the discrete sampling period of the motor model.

[0021] Furthermore, the torque current calculation submodule specifically includes:

[0022] The torque current calculation submodule is constructed by using the three-dimensional Look-up Table in Matlab or Simulink, and the calculation is performed through the bidirectional mapping relationship between the current and the motor flux. The expression of the bidirectional mapping relationship is:

[0023] ,

[0024] ,

[0025] in, and It represents the mapping relationship between dq axis current and dq axis flux linkage. and They represent the dq axis flux respectively, and Respectively represent the dq axis torque current components, Indicates the rotor position angle. The content in brackets It means that the d-axis and q-axis fluxes are related to both the current in their own axes and the current in the orthogonal axes, and the d-axis and q-axis fluxes are nonlinear functions of the rotor position angle.

[0026] Furthermore, the temperature correction submodule specifically includes:

[0027] Use the two-dimensional Look-up Table in Matlab or Simulink to store the reference value data of the dq axis current correction amount, and then build the temperature correction submodule through the basic operation unit in Matlab or Simulink;

[0028] According to the set motor operating temperature, the output torque current is temperature corrected. The specific principle expression is:

[0029] ,

[0030] ,

[0031] Among them, the left side of the equal sign and They are the dq axis torque current after temperature correction, and the right side of the equal sign is and are the dq axis torque currents without temperature correction output by the torque current calculation submodule, represents the initial motor temperature in the finite element calculation, represents the operating temperature set for the constructed nonlinear motor model; and They are the reference values ​​of dq axis current correction respectively; is the corresponding temperature reference value.

[0032] Furthermore, the torque calculation submodule includes:

[0033] Use the three-dimensional Look-up Table in Matlab or Simulink to build the torque calculation submodule;

[0034] Based on the mapping relationship between torque current and electromagnetic torque, torque calculation is performed. The specific mapping relationship expression is:

[0035] ,

[0036] in, and are the dq axis torque current after temperature correction, is the rotor position angle, is the electromagnetic torque.

[0037] Furthermore, the iron loss current calculation submodule includes:

[0038] The one-dimensional Look-up Table in Matlab or Simulink is used to store the mapping data from the electrical angular velocity to the iron loss power, and the iron loss current is calculated by the basic operation unit in Matlab or Simulink to construct the iron loss current calculation submodule;

[0039] The iron loss current calculation equation is as follows:

[0040] ,

[0041] ,

[0042] Among them, the left side of the equal sign and Respectively represent the dq axis iron loss current, the right side of the equal sign is the motor iron loss power, the content in brackets It means that the iron loss power is a function of the electrical angular velocity of the motor.

[0043] Furthermore, the two-dimensional finite element simulation model of the interior permanent magnet synchronous motor is deployed to the hardware-in-the-loop test system through the HDL Coder tool software.

[0044] The implementation of the present invention has the following beneficial effects:

[0045] (1) The present invention provides a high-fidelity simulation method for built-in permanent magnet synchronous motors for hardware-in-the-loop testing; the built-in permanent magnet synchronous motor model is decomposed into five submodules: flux calculation, torque current calculation, iron loss current calculation, temperature correction and torque calculation. In each submodule, the data basis for characterizing the electromagnetic behavior of the motor is obtained through finite element simulation calculation; compared with the linear motor model with constant parameters, the finite element simulation calculation can consider the influence of nonlinear factors such as magnetic circuit saturation, cross coupling, temperature, core loss and spatial harmonics, thereby improving the fidelity, effectiveness and correctness of the constructed model; the method of the present invention improves the accuracy and fidelity of the built-in permanent magnet synchronous motor model, and the modeling idea can be further extended to other types of motors such as surface-mounted permanent magnet synchronous motors, switched reluctance motors and axial flux motors; the construction of each submodule in the motor model of the present invention is realized through basic operations and storage units in Matlab or Simulink, and can be directly deployed to the hardware-in-the-loop test system through other tool software such as HDL Coder for performance verification of the motor controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the method of the present invention;

[0047] Figure 2 The structure diagram of the magnetic flux calculation submodule constructed according to the method of the present invention;

[0048] Figure 3 A structural diagram of a torque current calculation submodule constructed according to the method of the present invention;

[0049] Figure 4 A temperature correction submodule structure diagram is constructed according to the method of the present invention;

[0050] Figure 5 A structural diagram of a torque calculation submodule constructed according to the method of the present invention;

[0051] Figure 6 The structure diagram of the iron loss current calculation submodule constructed by the method of the present invention;

[0052] Figure 7 The overall structure diagram of the nonlinear motor model constructed according to the method of the present invention;

[0053] Figure 8 Calculate the test results of the submodule for torque current component;

[0054] Fig. 9 This is the test result of the torque calculation submodule;

[0055] Fig.10 The test results of the external characteristics of the nonlinear motor model considering the influence of temperature factors;

[0056] Fig.11 Comparative test results of iron loss factors of nonlinear motor model. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0058] Please refer to the instruction manual Figure 1-11 This embodiment provides a high-fidelity simulation method for a built-in permanent magnet synchronous motor for hardware-in-the-loop testing. The high-fidelity simulation method for a built-in permanent magnet synchronous motor for hardware-in-the-loop testing includes:

[0059] A two-dimensional finite element simulation model of the built-in permanent magnet synchronous motor is established through finite element simulation software, and the property files of ferromagnetic materials under different temperature conditions are configured to introduce the influence of temperature factors in the finite element calculation process.

[0060] According to the two-dimensional finite element model of the built-in permanent magnet synchronous motor, a finite element simulation calculation is performed to generate electromagnetic data of the first motor; the simulation calculation mainly scans different current operating points, rotor position angles, motor speeds and temperatures to obtain electromagnetic data such as magnetic flux, torque, iron loss power, etc. of the motor at different rotor position angles, different current operating points, different speeds and different temperatures;

[0061] Based on external dq axis voltage excitation , and electromagnetic data of the first motor, and constructing a flux calculation submodule, a torque current calculation submodule, an iron loss current calculation submodule, a temperature correction submodule and a torque calculation submodule in Matlab or Simulink respectively according to discrete motor voltage, flux and torque equations;

[0062] The above sub-modules are comprehensively packaged.

[0063] The data basis of the nonlinear motor model constructed in this embodiment is the motor electromagnetic data calculated by the finite element method. Compared with constant motor parameters, the electromagnetic data obtained by the finite element method covers the influence of factors such as magnetic circuit saturation, cross-coupling, temperature, core loss and spatial harmonics, and has good fidelity in characterizing the electromagnetic characteristics of the motor. The present invention directly constructs the model according to the discrete motor equation, and the constructed motor model is implemented by Matlab or Simulink discrete modules, which can be directly deployed to the hardware-in-the-loop test system.

[0064] The flux calculation submodule specifically includes: constructing a flux calculation submodule using a basic operation unit in Matlab or Simulink, and performing calculations through a discrete dq axis voltage equation, the equation being:

[0065] ,

[0066] ,

[0067] in, and The motor The external dq axis voltage excitation at time, is the motor winding resistance, and The motor The dq axis torque current component at the moment, and The motor The dq-axis iron loss current component at the moment, the iron loss current component and the torque current component together constitute the stator current. It is a motor The electrical angular velocity at the moment, and It is a motor The dq-axis magnetic flux at time, and It is a motor The dq-axis magnetic flux at time, is the discrete sampling period of the motor model.

[0068] The main calculation process of the magnetic flux calculation submodule is input The external dq axis voltage excitation, dq axis torque current, dq axis iron loss current, motor electrical angular velocity and dq axis flux linkage at the moment, output The dq-axis magnetic flux at time.

[0069] Compared with the linear motor model that does not consider the influence of core loss, the flux calculation submodule decomposes the stator current into two parts: torque current component and iron loss current component. The motor electromagnetic torque is calculated through the torque current component, and the influence of core loss is introduced into the motor model through the iron loss current component. The calculation of the torque current component and the iron loss current component is explained in the subsequent steps.

[0070] The torque current calculation submodule specifically includes:

[0071] The torque current calculation submodule is constructed by using the three-dimensional Look-up Table in Matlab or Simulink, and the calculation is performed through the bidirectional mapping relationship between the current and the motor flux. The expression of the bidirectional mapping relationship is:

[0072] ,

[0073] ,

[0074] in, and It represents the mapping relationship between dq axis current and dq axis flux linkage. and They represent the dq axis flux respectively, and Respectively represent the dq axis torque current components, Indicates the rotor position angle. The content in brackets It means that the d-axis and q-axis fluxes are related to both the current in their own axes and the current in the orthogonal axes, and the d-axis and q-axis fluxes are nonlinear functions of the rotor position angle.

[0075] The main process of the torque current calculation submodule is to input the rotor position angle and the dq axis flux output by the flux calculation submodule, and output the dq axis current. The bidirectional mapping data between the current and flux stored in this submodule can be obtained through finite element simulation calculation in.

[0076] Compared with calculating the current through constant parameters in the linear motor model, the torque current calculation submodule stores and implements online table lookup of the bidirectional mapping data between the motor current and flux under different rotor position angle conditions through the Look-up Table. The mapping data is based on the finite element simulation calculation results, and naturally covers the influence of magnetic circuit saturation, cross-coupling and spatial harmonic factors. In terms of flux and current calculation, the nonlinear relationship between flux and current is characterized as accurately as possible, thereby improving the fidelity of the motor model.

[0077] The temperature correction submodule specifically includes:

[0078] Use the two-dimensional Look-up Table in Matlab or Simulink to store the reference value data of the dq axis current correction amount, and then build the temperature correction submodule through the basic operation unit in Matlab or Simulink;

[0079] According to the set motor operating temperature, the output torque current is temperature corrected. The specific principle expression is:

[0080] ,

[0081] ,

[0082] Among them, the left side of the equal sign and They are the dq axis torque current after temperature correction, and the right side of the equal sign is and are the dq axis torque currents without temperature correction output by the torque current calculation submodule, represents the initial motor temperature in the finite element calculation, represents the operating temperature set for the constructed nonlinear motor model; and They are the reference values ​​of dq axis current correction respectively; is the corresponding temperature reference value.

[0083] The temperature correction submodule inputs the set model operating temperature, the dq axis torque current without temperature correction output by the torque current calculation submodule, and the dq axis flux output by the flux calculation submodule, and outputs the dq axis torque current corrected by considering the temperature factor.

[0084] The torque calculation submodule includes:

[0085] Use the three-dimensional Look-up Table in Matlab or Simulink to build the torque calculation submodule;

[0086] Based on the mapping relationship between torque current and electromagnetic torque, torque calculation is performed. The specific mapping relationship expression is:

[0087] ,

[0088] in, and are the dq axis torque current after temperature correction, is the rotor position angle, is the electromagnetic torque.

[0089] The torque calculation submodule inputs the rotor position angle and the temperature-corrected dq-axis torque current output by the temperature correction submodule, and outputs the electromagnetic torque of the motor. The data of the current-to-torque mapping under different rotor position angles stored in the torque calculation submodule can be obtained through finite element simulation calculation.

[0090] Since traditional linear motor models cannot take into account the influence of motor operating temperature factors, the constructed temperature correction submodule realizes the function of correcting the torque current component according to the set motor operating temperature; the corrected torque current is used as the input of the torque calculation submodule, thereby adding consideration of the motor operating temperature conditions in the torque calculation; at the same time, the rotor position angle is introduced as an input variable in the torque calculation submodule, thereby adding consideration of spatial harmonic factors in the model; in terms of torque calculation, the nonlinear relationship between torque and current is characterized as accurately as possible, thereby improving the fidelity of the motor model.

[0091] The iron loss current calculation submodule includes:

[0092] The one-dimensional Look-up Table in Matlab or Simulink is used to store the mapping data from the electrical angular velocity to the iron loss power, and the iron loss current is calculated by the basic operation unit in Matlab or Simulink to construct the iron loss current calculation submodule;

[0093] The iron loss current calculation equation is as follows:

[0094] ,

[0095] ,

[0096] Among them, the left side of the equal sign and Respectively represent the dq axis iron loss current, the right side of the equal sign is the motor iron loss power, the content in brackets It means that the iron loss power is a function of the electrical angular velocity of the motor.

[0097] The input of the iron loss current calculation submodule is the motor electrical angular velocity and the dq axis magnetic flux output by the magnetic flux calculation submodule, and the output is the dq axis iron loss current.

[0098] Compared with the linear motor model that cannot consider the influence of iron loss factors, the iron loss current calculation submodule solves the iron loss power obtained by finite element calculation into the corresponding iron loss current, and then introduces this part of the iron loss current into the magnetic flux calculation, thereby increasing the consideration of core loss factors in the motor model and improving the fidelity of the model.

[0099] High-fidelity modeling of interior permanent magnet synchronous motors for hardware-in-the-loop testing The model is deployed to hardware-in-the-loop testing equipment using HDL Coder and Vivado Design Suite tool software. Example

[0100] Please refer to the instruction manual Figure 1-11This embodiment provides a high-fidelity simulation method for a built-in permanent magnet synchronous motor for hardware-in-the-loop testing. The high-fidelity simulation method for a built-in permanent magnet synchronous motor for hardware-in-the-loop testing includes:

[0101] A two-dimensional finite element simulation model of the interior permanent magnet synchronous motor is established in the finite element simulation calculation software.

[0102] In order to introduce the influence of temperature factors in the finite element simulation calculation, the properties of the ferromagnetic material are modified according to the data manual of the motor ferromagnetic material properties, and a corresponding low temperature condition is configured. Ferromagnetic material property files, one for high temperature conditions Ferromagnetic material property file, temperature and It needs to be consistent with the temperature data given in the ferromagnetic material property data sheet.

[0103] The ferromagnetic material of the motor finite element model is configured as The material properties corresponding to the temperature conditions, set the motor speed to one-third of the rated speed, and then set the scanning range and step size of the current and rotor position angle. The current scanning range can be considered to be set to 1.5 times the rated motor current, and the current scanning step size can be considered to be set to one-tenth of the rated motor current. The rotor position angle scanning range is naturally determined to be an electrical cycle angle range (0° to 360°), and the rotor position angle step size can be considered to be set to the angle corresponding to a motor tooth slot to reflect the simulation results of spatial harmonic factors; after the current and rotor position angle scanning range and step size are set, the motor finite element model is scanned and calculated to obtain Under temperature conditions and one-third of the rated speed, a series of data on dq-axis flux, electromagnetic torque, iron loss power, etc. under different rotor position angles and different dq-axis current excitations.

[0104] The motor speed is set to two-thirds of the rated speed, the scanning range and step size of the current and rotor position angle remain unchanged, and the scanning calculation process is repeated. Under temperature conditions and two-thirds of the rated speed, a series of data on dq-axis flux, electromagnetic torque, iron loss power, etc. under different rotor position angles and different dq-axis current excitations.

[0105] Set the motor speed to the rated speed, keep the current and rotor position angle scanning range and step size unchanged, and repeat the scanning calculation process. Under temperature conditions and rated speed conditions, a series of data on dq-axis flux, electromagnetic torque, iron loss power, etc. at different rotor position angles, different dq-axis current excitations.

[0106] The ferromagnetic material of the motor finite element model is configured as The material properties corresponding to the temperature conditions are set to one-third of the rated speed and the sweep calculation process is repeated. Under temperature conditions and one-third of the rated speed, a series of data on dq-axis flux, electromagnetic torque, iron loss power, etc. under different rotor position angles and different dq-axis current excitations.

[0107] In Matlab or Simulink, the flux calculation submodule is constructed according to the flux calculation equations shown in equations (1) and (2). The module structure diagram is shown in the following figure: Figure 2 shown.

[0108] ,

[0109] ,

[0110] Among them, the left side of the equal sign and yes The dq axis flux is used as the output signal of the flux calculation submodule; and The motor The external dq axis voltage excitation at the moment, these two signals are directly input from the outside of the motor model; is the discrete sampling period of the motor model. This time variable is set manually and implemented in Matlab or Simulink through a multiplication gain unit; is the phase resistance of the motor winding. This variable is also set manually and implemented through a multiplication unit. , , and They are The dq axis torque current component and iron loss current component at the moment. These four signals are calculated in the subsequent torque current calculation and iron loss current calculation modules. Here, these signals can be introduced through the time delay unit in Matlab or Simulink. for The motor electrical angular velocity at the moment, this signal is directly input from the outside of the motor model; and They are The dq axis flux at the moment, these two signals can be obtained by delaying the output of the flux calculation submodule by one sampling period through the time delay unit in Matlab or Simulink.

[0111] According to the mapping relationship from flux linkage to current direction shown in equations (3) and (4), the torque current calculation submodule is constructed. The module structure diagram is shown in Figure 3 shown.

[0112] ,

[0113] ,

[0114] in, and It is the output of the flux calculation submodule, and is used as the input signal of the torque current calculation submodule. is the motor rotor position angle, which can be calculated based on the motor speed signal. The data of the mapping relationship in equations (3) and (4) can be stored and implemented through the three-dimensional Look-up Table unit in Matlab or Simulink. Because the mapping relationship from flux linkage to current shown in equations (3) and (4) is independent of the motor speed, the "external dq axis voltage excitation" can be selected. and And the simulation results of the motor electromagnetic data obtained by finite element calculation serve as the data basis. and is the output signal of the torque current calculation submodule. Indicates that the electromagnetic data corresponds to Temperature conditions, that is, the influence of temperature factors is not introduced at this time.

[0115] The temperature correction submodule is constructed according to the temperature correction equations shown in equations (5) and (6). The module structure is shown in the following figure: Figure 4 The function of this submodule is to correct the torque current obtained in the torque current calculation submodule according to the set motor operating temperature.

[0116] ,

[0117] ,

[0118] in, and It is the output of the flux calculation submodule, and is used here as the input signal of the temperature correction submodule. and It is the output of the torque current calculation submodule and is used as the input signal here. and It is the mapping relationship between two flux linkages and currents. As the data benchmark for temperature correction, the two mapping data can be obtained by comparing the finite element simulation results under the same speed and different temperature conditions. In Matlab or Simulink, two two-dimensional Look-up Table units are used for storage and online table lookup. is the corresponding temperature reference value, Since the temperature reference value is fixed after the material properties are configured and the finite element simulation is completed, it can be implemented through a constant unit in Matlab or Simulink. and is the output signal of the temperature correction submodule, subscript Indicates the set motor operating temperature.

[0119] According to the mapping relationship from current to torque shown in equation (7), the torque calculation submodule is constructed. The module structure diagram is shown in Figure 5 The function of this submodule is to obtain the corresponding motor torque according to the corrected torque current.

[0120] ,

[0121] in, and They are the dq axis torque current after temperature correction respectively. These two signals are the outputs of the temperature correction submodule and are used as signal inputs here; is the rotor position angle, is the electromagnetic torque, which is the output of the torque calculation submodule. The torque mapping data can be stored and looked up online through a two-dimensional Look-up Table unit.

[0122] According to the iron loss current equations shown in equations (8) and (9), the iron loss current calculation submodule is constructed. The module structure diagram is shown in Figure 6 shown.

[0123] ,

[0124] ,

[0125] in, and It is the output of the flux calculation submodule, and is used here as the input signal of the iron loss current calculation submodule. is the motor electrical angular velocity. This signal is directly input from the motor model. The mapping relationship between iron loss and power can be obtained by summarizing the power simulation results at different speeds, and can be stored and implemented through an online lookup table in Matlab or Simulink through a one-dimensional Look-up Table unit. and It is the output signal of the iron loss current calculation submodule.

[0126] Each submodule in the above steps is encapsulated, and the signal flows of each submodule are connected through the Goto Tag and From Tag in Matlab or Simulink to construct a nonlinear motor model such as Figure 7 As shown; further, the two-dimensional finite element simulation model of the built-in permanent magnet synchronous motor is deployed to the hardware-in-the-loop test equipment through HDL Coder and Vivado Design Suite tool software.

[0127] In order to further verify the effect of the present invention, a 35kW built-in permanent magnet synchronous motor is used as a test prototype. According to the method of the present invention, its nonlinear model is constructed in Matlab or Simulink and deployed to a hardware-in-the-loop test device. The performance of the motor controller is tested in a hardware-in-the-loop based on the nonlinear motor model.

[0128] Figure 8 The calculation results of the torque current calculation submodule in the method of this paper show that the d-axis torque current is not only related to its own axial magnetic flux, but also to the orthogonal axial magnetic flux, and shows a nonlinear change relationship between these two magnetic flux variables; the q-axis torque current also shows the same characteristics as the d-axis current. The calculation results show that the modeling method invented in this paper reflects the influence of magnetic circuit saturation and cross-coupling factors in the motor model.

[0129] Fig. 9 It is a comparison of the torque test results of the linear motor model and the nonlinear motor model constructed by the modeling method proposed in this paper. From the comparison results, it can be found that compared with the linear motor model, which can only calculate a constant torque result that is independent of the rotor position angle, in the calculation results of the nonlinear model, the motor torque shows periodic torque pulsation. The results show that the influence of spatial harmonic factors is reflected in the motor model.

[0130] Fig.10 Different operating temperature conditions are set for the nonlinear motor model constructed by the present invention, and the motor external characteristics test results. It can be seen from the figure that with the increase of the set motor operating temperature, the motor output torque and power both show a downward trend, which is consistent with the physical reality and the finite element calculation results. The results show that the influence of temperature on the motor operating characteristics is reflected in the motor model.

[0131] Fig.11It is a comparison of the torque calculation results of the motor model considering iron loss current and not considering iron loss current. It can be seen from the figure that when the iron loss factor is not considered, for different speed conditions, the same current is given and the motor output torque is the same. This is because there is no iron loss current component at this time, and the stator current is fully used for torque calculation; when the iron loss current is considered, as shown in the local enlarged part of the figure, for the same current, as the motor speed increases, the motor output torque decreases. This is because the stator current amplitude is fixed, the iron loss current component increases with the increase of the motor speed, and the corresponding torque current component decreases, and the motor torque decreases, which is consistent with the physical reality and the finite element calculation results. The comparison results show that the influence of the iron loss factor on the motor operating characteristics is reflected in the motor model.

[0132] In the description of the present invention, it is necessary to understand that the terms "coaxial", "bottom", "one end", "top", "middle", "the other end", "upper", "one side", "top", "inside", "front", "center", "both ends" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0133] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation" and "screw-on" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0134] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-fidelity simulation method for an internal permanent magnet synchronous motor for hardware-in-the-loop testing, characterized in that: include: A two-dimensional finite element simulation model of an internal permanent magnet synchronous motor is established through finite element simulation software, and the property files of ferromagnetic materials under different temperature conditions are configured; Performing finite element simulation calculations based on a two-dimensional finite element model of the interior permanent magnet synchronous motor to generate electromagnetic data of the first motor; Based on external dq axis voltage excitation , and electromagnetic data of the first motor, and constructing a flux calculation submodule, a torque current calculation submodule, an iron loss current calculation submodule, a temperature correction submodule and a torque calculation submodule in Matlab or Simulink respectively according to discrete motor voltage, flux and torque equations; Comprehensively encapsulate the above sub-modules; Among them, building the magnetic flux calculation submodule in Matlab or Simulink specifically includes: The basic operation unit in Matlab or Simulink is used to construct the flux calculation submodule, and the calculation is performed through the discrete dq axis voltage equation, which is: ， ， in, and The motor The external dq axis voltage excitation at time, is the motor winding resistance, and The motor The dq axis torque current component at the moment, and The motor The dq axis iron loss current component at the moment, the iron loss current component and the torque current component together constitute the stator current; It is a motor The electrical angular velocity at time, and It is a motor The dq-axis magnetic flux at time, and It is a motor The dq-axis magnetic flux at time, is the discrete sampling period of the motor model; Among them, the construction of the torque current calculation submodule in Matlab or Simulink specifically includes: The torque current calculation submodule is constructed by using the three-dimensional Look-up Table in Matlab or Simulink, and the calculation is performed through the bidirectional mapping relationship between the current and the motor flux. The expression of the bidirectional mapping relationship is: ， ， in, and It represents the mapping relationship between dq axis current and dq axis flux linkage. and They represent the dq axis flux respectively, and Represent the dq axis torque current components respectively, Indicates the rotor position angle. The content in brackets It means that the d-axis and q-axis fluxes are related to the current in their own axes and the current in the orthogonal axes at the same time, and the d-axis and q-axis fluxes are nonlinear functions of the rotor position angle; Among them, building a temperature correction submodule in Matlab or Simulink specifically includes: Use the two-dimensional Look-up Table in Matlab or Simulink to store the reference value data of the dq axis current correction amount, and then build the temperature correction submodule through the basic operation unit in Matlab or Simulink; According to the set motor operating temperature, the output torque current is temperature corrected. The specific principle expression is: ， ， Among them, the left side of the equal sign and They are the dq axis torque current after temperature correction, and the right side of the equal sign is and are the dq axis torque currents without temperature correction output by the torque current calculation submodule, represents the initial motor temperature in the finite element calculation, represents the operating temperature set for the constructed nonlinear motor model; and They are the reference values ​​of dq axis current correction respectively; is the corresponding temperature reference value; Among them, the torque calculation submodule constructed in Matlab or Simulink includes: Use the three-dimensional Look-up Table in Matlab or Simulink to build the torque calculation submodule; Based on the mapping relationship between torque current and electromagnetic torque, torque calculation is performed. The specific mapping relationship expression is: ， in, and are the dq axis torque current after temperature correction, is the rotor position angle, is the electromagnetic torque; Among them, the iron loss current calculation submodule constructed in Matlab or Simulink includes: The one-dimensional Look-up Table in Matlab or Simulink is used to store the mapping data from the electrical angular velocity to the iron loss power, and the iron loss current is calculated by the basic operation unit in Matlab or Simulink to construct the iron loss current calculation submodule; The iron loss current calculation equation is as follows: ， ， Among them, the left side of the equal sign and Respectively represent the dq axis iron loss current, the right side of the equal sign is the motor iron loss power, the content in brackets It means that the iron loss power is a function of the electrical angular velocity of the motor.

2. The method for high-fidelity simulation of an internal permanent magnet synchronous motor for hardware-in-the-loop testing according to claim 1, characterized in that: Configure the property files of ferromagnetic materials under different temperature conditions; According to the two-dimensional finite element model of the built-in permanent magnet synchronous motor, the finite element simulation calculation is performed to generate the electromagnetic data of the first motor, including: Configure the ferromagnetic material properties as Under the temperature condition, the motor speed is set to one third of the rated speed, and a finite element simulation calculation is performed to generate first electromagnetic data, wherein the first electromagnetic data includes flux linkage, electromagnetic torque, and iron loss power data; Configure the ferromagnetic material properties as Under the temperature condition, the motor speed is set to two-thirds of the rated speed, and a finite element simulation calculation is performed to generate first electromagnetic data, wherein the first electromagnetic data includes flux linkage, electromagnetic torque, and iron loss power data; Configure the ferromagnetic material properties as Under the temperature condition, the motor speed is set to the rated speed, and a finite element simulation calculation is performed to generate first electromagnetic data, wherein the first electromagnetic data includes flux linkage, electromagnetic torque, and iron loss power data; Configure the ferromagnetic material properties as Under the temperature condition, the motor speed is set to one third of the rated speed, and a finite element simulation calculation is performed to generate first electromagnetic data, which includes magnetic flux, electromagnetic torque and iron loss power data.

3. The method for high-fidelity simulation of an interior permanent magnet synchronous motor for hardware-in-the-loop testing according to claim 2, characterized in that: The two-dimensional finite element simulation model of the interior permanent magnet synchronous motor is deployed to the hardware-in-the-loop test system through the HDL Coder tool software.

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