Parameter identification method and device for motor stator mode, equipment and medium

Abstract

The invention relates to the technical field of vehicle motor system performance development, in particular to a parameter identification method and device for a motor stator mode, equipment and a medium. According to the method, N to-be-identified parameters are determined according to anisotropic material parameters of a motor stator modal, so that the N to-be-identified parameters are used for calculating the frequency of the corresponding modal, M groups of experimental data are determined according to a preset experimental design method, and finite element modal simulation is carried out on the motor stator modal by using each group of experimental data, so that the modal of the motor stator is simulated. Each group of experimental data is used for fitting the agent model, so that the situation that a regression result is insensitive or too sensitive to some coefficients due to the fact that the difference between the upper limit span and the lower limit span of each parameter is too large can be avoided, and the accuracy of coefficient items in the target agent model is improved; therefore, the optimization function is constructed according to the target agent model, and the accuracy of the optimized parameter value is improved when the parameter value of the to-be-identified parameter is optimized and adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle motor system performance development, and in particular to a method, device, equipment and medium for parameter identification of motor stator modes. Background Art

[0002] The electric drive system is a core component of electric vehicles and the main source of noise excitation for electric vehicles. Its NVH level directly affects the customer's overall vehicle driving experience. Therefore, it is particularly important to control the NVH of the motor system, and the modal of the motor assembly is the basis for controlling the NVH level of the motor. How to perform modal simulation analysis of the motor assembly in the early stage of the project is the key to the successful development of the motor and also lays the foundation for the subsequent noise simulation. Since the formula and assembly method of each motor stator core material are different, the modal results are also different. Therefore, the modal analysis of each motor stator faces the problem of identifying its anisotropic material parameters. At this stage, more methods are to refer to the parameters of the previous motor stator or perform simple manual adjustments to obtain its parameters, and the accuracy of the calculated results is low. Therefore, in the process of identifying the anisotropic material parameters of the motor stator, how to improve the accuracy of the motor stator parameter identification has become an urgent problem to be solved. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus, device and medium for identifying parameters of a motor stator mode, which solves the problem of low accuracy in identifying parameters of anisotropic material of a motor stator.

[0004] In a first aspect, an embodiment of the present invention provides a method for identifying parameters of a motor stator mode, comprising: Determining N parameters to be identified according to the motor stator modal anisotropic material parameters, where N is an integer greater than zero; According to a preset experimental design method, determining experimental points corresponding to the preset experimental design method according to the preset experimental design method, and determining M groups of experimental data according to the experimental points, where M is an integer greater than zero; Using each set of experimental data, finite element modal simulation is performed on the stator mode of the motor to obtain the experimental modal frequency corresponding to the experimental data; According to each set of experimental data and the experimental modal frequency of the corresponding experimental data, fitting calculation is performed on the coefficients in the preset proxy model to obtain a fitted target proxy model, wherein the proxy model is constructed by the N parameters to be identified; An optimization function is constructed according to the target proxy model, and parameter values of the N parameters to be identified are optimized according to the optimization function to obtain optimized parameter values.

[0005] In a second aspect, an embodiment of the present invention provides a parameter identification device for a motor stator mode, comprising: A determination module, configured to determine N parameters to be identified based on the modal anisotropic material parameters of the motor stator, where N is an integer greater than zero; A design module is configured to determine, according to a preset experimental design method, experimental points corresponding to the preset experimental design method, and determine M groups of experimental data based on the experimental points, where M is an integer greater than zero; A simulation module, configured to perform finite element modal simulation on the motor stator mode using each set of experimental data to obtain a test modal frequency corresponding to the experimental data; A fitting module is used to perform fitting calculations on the coefficients in the preset proxy model according to each set of experimental data and the experimental modal frequencies of the corresponding experimental data to obtain a fitted target proxy model, wherein the proxy model is constructed by the N parameters to be identified; An optimization module is used to construct an optimization function according to the target proxy model, and optimize the parameter values of the N parameters to be identified according to the optimization function to obtain optimized parameter values.

[0006] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the parameter identification method as described in the first aspect when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the parameter identification method as described in the first aspect is implemented.

[0008] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, N parameters to be identified are determined according to the anisotropic material parameters of the motor stator modal, so that the frequency of the corresponding mode is calculated using the N parameters to be identified. According to a preset experimental design method, M groups of experimental data are determined, and each group of experimental data is used to perform finite element modal simulation on the motor stator mode to obtain the experimental modal frequency of the corresponding experimental data. Each group of experimental data is used to fit the proxy model with the experimental modal frequency of the corresponding experimental data. This can avoid the regression result being insensitive or overly sensitive to certain coefficients due to the large gap between the upper and lower limits of each parameter, and improve the accuracy of the coefficient terms in the target proxy model, so that the optimization function is constructed according to the target proxy model, and the parameter values of the parameters to be identified are optimized and adjusted, thereby improving the accuracy of the optimized parameter values. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0010] Figure 1 1 is a flow chart of a method for identifying parameters of a motor stator mode provided in the first embodiment of the present invention; Figure 2 This is a structural block diagram of a parameter identification device for a motor stator mode provided in a second embodiment of the present invention; Figure 3 This is a structural diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0011] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0012] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration and not limitation to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0014] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not necessarily mean the order in which they are executed. The order in which each process is executed should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0020] See also Figure 1 , is a flow chart of a method for identifying parameters of a motor stator mode provided by the first embodiment of the present invention, such as Figure 1 As shown, the parameter identification method of the motor stator mode may include the following steps.

[0021] S101: Determine N parameters to be identified based on the modal anisotropic material parameters of the motor stator, where N is an integer greater than zero.

[0022] In step S101, the modal anisotropic material parameters of the motor stator include the elastic modulus, shear modulus and Poisson's ratio of the motor stator in different directions. The N parameters to be identified are the parameters that need to be identified after the modal anisotropic material parameters of the motor stator are sorted out, so that the elastic modulus, shear modulus and Poisson's ratio of the motor stator in different directions can be determined according to the N parameters to be identified.

[0023] In this embodiment, the modal anisotropic material parameters of the motor stator are the elastic modulus in the x-axis direction, the elastic modulus in the y-axis direction, the elastic modulus in the z-axis direction, the shear modulus on the xy plane, the shear modulus on the yz plane, the shear modulus on the zx plane, the ratio of the transverse strain generated in the y-axis direction to the longitudinal strain of the x-axis when the x-axis is subjected to tension or compression, the ratio of the transverse strain generated in the z-axis direction to the longitudinal strain of the y-axis when the y-axis is subjected to tension or compression, and the ratio of the transverse strain generated in the x-axis direction to the longitudinal strain of the z-axis when the z-axis is subjected to tension or compression.

[0024] The axial direction of the motor stator is the z-axis. Therefore, the elastic modulus in the x-axis direction is equal to the elastic modulus in the y-axis direction, the shear modulus on the yz plane is equal to the shear modulus on the zx plane, and the ratio of the transverse strain generated in the y-axis direction to the longitudinal strain of the x-axis when the x-axis is stretched or compressed, the ratio of the transverse strain generated in the z-axis direction to the longitudinal strain of the y-axis when the y-axis is stretched or compressed, and the ratio of the transverse strain generated in the x-axis direction to the longitudinal strain of the z-axis when the z-axis is stretched or compressed are equal.

[0025] Therefore, the parameters that need to be identified are the elastic modulus in the x-axis direction (or the elastic modulus in the y-axis direction), the elastic modulus in the z-axis direction, the shear modulus on the xy plane, the shear modulus on the yz plane (or the shear modulus on the zx plane), and the ratio of the transverse strain in the y-axis direction to the longitudinal strain of the x-axis when the x-axis is stretched or compressed (or the transverse strain in the z-axis direction when the y-axis is stretched or compressed, or the ratio of the longitudinal strain of the y-axis and the ratio of the transverse strain in the x-axis direction to the longitudinal strain of the z-axis when the z-axis is stretched or compressed).

[0026] In this embodiment, N parameters to be identified are determined based on the modal anisotropic material parameters of the motor stator. The N parameters to be identified include all parameters that need to be identified, so that after the N parameters to be identified are identified, the modal anisotropic material parameters of the motor stator can be obtained.

[0027] S102: According to a preset experimental design method, determine experimental points corresponding to the preset experimental design method, and determine M groups of experimental data based on the experimental points, where M is an integer greater than zero.

[0028] In step S102, the default experimental design method is the Box-Behnken design. The Box-Behnken design is an efficient response surface experimental design method. Its core purpose is to analyze the impact of multiple input variables (factors) on the output response using a second-order polynomial model (a nonlinear model) and explore interactions and nonlinear relationships between factors. The Box-Behnken design method requires fewer experiments by arranging experimental points at the center of the edges of a cube. Based on the experimental points, M groups of experimental data are determined, and within each group, the data corresponding to the experimental point where the parameter to be identified is located is selected.

[0029] In this example, the Box-Behnken design method was used to determine the corresponding test points. The test points were the center points of the edges and the center points of the cube. The edge centers represented the high or low levels of each factor, while the center points of the cube represented the intermediate level of the corresponding factor. The high and low levels were the upper and lower limits of the corresponding factor values, and the intermediate level was the value between the upper and lower limits of the corresponding factor values. The factors were the corresponding parameters to be identified.

[0030] Using N parameters to be identified as factors, an N-factor three-level experimental design was conducted. Experimental points corresponding to the pre-defined experimental design method were determined. Specifically, according to the Box-Behnken design method, the center points of the cube edges and the center of the cube were used as experimental points. Based on these experimental points, M sets of experimental data were determined. The parameter to be identified in each set of experimental data was the data at the high, low, and intermediate levels.

[0031] In this embodiment, there are 5 parameters to be identified, that is, N is 5. When a 5-factor three-level experimental design is performed, the number of basic experiments is 40 and the number of repetitions of the center point is 1. Therefore, 41 sets of experimental data are obtained.

[0032] It should be noted that in each set of basic experiments, any two factors in each experimental data set were at extreme levels (i.e., high or low), while the other factors were at intermediate levels. For example, in one set of basic experiments, the elastic modulus in the x-axis direction was set to a low level, the elastic modulus in the z-axis direction was set to a low level, the shear modulus in the xy plane was set to an intermediate level, the shear modulus in the yz plane was set to an intermediate level, and the ratio of the transverse strain in the y-axis direction to the longitudinal strain in the x-axis when the x-axis was subjected to tension or compression was set to an intermediate level. In another set of basic experiments, the elastic modulus in the x-axis direction was set to a high level, the elastic modulus in the z-axis direction was set to a low level, the shear modulus in the xy plane was set to an intermediate level, the shear modulus in the yz plane was set to an intermediate level, and the ratio of the transverse strain in the y-axis direction to the longitudinal strain in the x-axis when the x-axis was subjected to tension or compression was set to an intermediate level. In the center point experiments, all factors were set to intermediate levels.

[0033] In this embodiment, 1 represents a high level, -1 represents a low level, and 0 represents an intermediate level, that is, 1 represents the maximum value that the corresponding factor can take, -1 represents the minimum value that the corresponding factor can take, and 0 represents a value between the maximum and minimum values that the corresponding factor can take.

[0034] The parameter values corresponding to each factor at different levels are shown in the table: in, 、 、 、 、 is the parameter to be identified, is the elastic modulus in the x-axis direction, is the elastic modulus in the z-axis direction, is the shear modulus in the xy plane, is the shear modulus on the yz plane, It is the ratio of the transverse strain in the y-axis direction to the longitudinal strain in the x-axis when the x-axis is stretched or compressed, that is, Poisson's ratio.

[0035] The values of the 41 sets of experimental data are as follows: Among them, 1 represents the maximum value that the corresponding factor can take, -1 represents the minimum value that the corresponding factor can take, and 0 represents the value between the maximum and minimum values that the corresponding factor can take.

[0036] It should be noted that the Poisson's ratio of experimental group 34 is limited by the rules for selecting material parameters. The value cannot be 1, so it was changed to 0.667. This means that when the elastic modulus along the x-axis is at its minimum, the ratio of the transverse strain along the y-axis to the longitudinal strain along the x-axis when the x-axis is subjected to tension or compression cannot reach its maximum value. Since the experimental data was used for simulation rather than actual experimentation, there was no need to set up replicates. Therefore, only one replicate of the original center points 41-46 was retained.

[0037] In this embodiment, according to the preset experimental design method, N parameters to be identified are used as factors, and an N-factor three-level experimental design is performed to obtain M groups of experimental data, so that finite element simulation can be performed using the M groups of experimental data to reflect the modal frequencies under different values, thereby fitting the polynomial according to the corresponding modal spectrum.

[0038] S103: Using each set of experimental data, perform finite element modal simulation on the motor stator mode to obtain experimental modal frequencies corresponding to the experimental data, wherein the experimental modal frequencies are frequencies under different modal vibration shapes.

[0039] In step S103, finite element modal simulation is used to simulate the experimental modal frequencies corresponding to the experimental data. The finite element modal simulation is a process of solving the natural frequency and mode shape of the motor stator in a free vibration state.

[0040] In this embodiment, a finite element simulation model of the motor stator is established. For example, the stator outer edge weld layer, the stator inner core, the stator copper wire winding, and the stator insulating varnish layer are discretized into units, and the ends of the stator copper wire winding are flattened. The inner side of the stator outer edge weld layer is bonded to the outer side of the stator inner core, the outer side of the stator insulating varnish layer is bonded to the inner side of the slot in the stator inner core, and the inner side of the stator insulating varnish layer is bonded to the outer side of the stator copper wire winding. The finite element simulation model of the motor stator is established. Each set of experimental data is input into the finite element simulation model to obtain the modal vibration shape corresponding to the experimental data and the experimental modal frequency under the corresponding modal vibration shape.

[0041] It should be noted that for any modal vibration shape, M groups of experimental data are subjected to finite element modal simulation to obtain M experimental modal frequencies.

[0042] In this embodiment, finite element modal simulation is performed on the motor stator mode using each set of experimental data to obtain the experimental modal frequencies corresponding to the experimental data, as shown in the following table: Among them, the first line in the table is the corresponding mode vibration shape, in To indicate that the mode shape presents m-order characteristics in the circumferential direction and n-order characteristics in the axial direction, i is the total order, such as The modal vibration shape presents 2nd order characteristics in the circumferential direction, 0th order characteristics in the axial direction, and a total order of 1. The modal vibration shape presents 2nd order characteristics in the circumferential direction and 0th order characteristics in the axial direction, with a total order of 2. The modal vibration shape presents 2nd order characteristics in the circumferential direction, 1st order characteristics in the axial direction, and a total order of 1. The modal vibration shape presents 2nd order characteristics in the circumferential direction and 1st order characteristics in the axial direction, with a total order of 2. The modal vibration shape presents 3rd order characteristics in the circumferential direction, 0th order characteristics in the axial direction, and a total order of 1. The modal vibration shape presents 3rd order characteristics in the circumferential direction, 1st order characteristics in the axial direction, and a total order of 1. The modal vibration shape presents 4th-order characteristics in the circumferential direction, 0th-order characteristics in the axial direction, and a total order of 1. This is a modal vibration shape that exhibits 4th-order characteristics in the circumferential direction and 1st-order characteristics in the axial direction, with a total order of 1.

[0043] The first column is the corresponding experimental group number. The number of experiments is equal to the number of experimental data groups, that is, each group of experimental data corresponds to one experiment. The same experimental data may have different frequencies under different modal vibration modes, such as and When the minimum value is taken, and the other parameters to be identified take the middle value, The test modal frequency under the modal vibration shape is 653.09. The experimental modal frequency under the modal vibration mode is 739.68. Different experimental data may have different frequencies under the same modal vibration mode, such as and When the minimum value is taken and the other parameters to be identified take the middle value, The test modal frequency under the modal vibration shape is 653.09, Take the minimum value, When the maximum value is taken and the other parameters to be identified take the intermediate values, The experimental modal frequency under the modal vibration shape is 682.52.

[0044] In this embodiment, each set of experimental data is used to perform finite element modal simulation on the motor stator mode to obtain the experimental modal frequency corresponding to the experimental data, so that the corresponding experimental modal frequency can be used as the response value to fit the coefficients in the preset proxy model.

[0045] S104: According to each set of experimental data and the experimental modal frequency of the corresponding experimental data, the coefficients in the preset proxy model are fitted and calculated to obtain a fitted target proxy model. The proxy model is constructed by N parameters to be identified.

[0046] In step S104, the preset proxy model is a second-order polynomial model, the independent variables in the proxy model are the corresponding N parameters to be identified, and the dependent variable is the experimental modal frequency. According to each set of experimental data and the experimental modal frequency of the corresponding experimental data, the coefficients in the preset proxy model are fitted and calculated to determine the coefficients in the proxy model and obtain the fitted target proxy model.

[0047] In this embodiment, a second-order polynomial is constructed based on N parameters to be identified, and the corresponding second-order polynomial is determined as a preset proxy model. Since the number of parameters to be identified in this embodiment is 5, the formula of the proxy model is shown as follows: in, is the parameter to be identified, is the frequency of the corresponding mode shape, , , is the coefficient of the surrogate model. They are respectively 、 、 、 and One of them.

[0048] According to the experimental modal frequency of each set of experimental data and the corresponding experimental data, the coefficients in the preset proxy model are fitted and calculated. It should be noted that when fitting, the regress(y,X) function of matlab can be called to complete it, where y is the corresponding ,X is( ).

[0049] It should be noted that when fitting the surrogate model, the test modal frequencies under different modal vibration modes are different. Therefore, the target surrogate models under different modal vibration modes are different. The coefficients of the target surrogate models under different modal vibration modes are shown in the following table: Among them, the first column - are the coefficients in the surrogate model. The values in each column are the values after fitting the corresponding experimental modal frequencies under different modal vibration shapes.

[0050] In this embodiment, when fitting the proxy model, the parameter values of the parameters to be identified are not actual physical values, but substitute values in the Box-Behnken design, that is, -1 and 1 represent the maximum and minimum values, respectively. This processing can avoid the large gap between the upper and lower limits of each parameter, which may cause the regression results to be insensitive or overly sensitive to certain coefficients.

[0051] S105: Constructing an optimization function according to the target proxy model, optimizing the parameter values of the N parameters to be identified according to the optimization function, and obtaining optimized parameter values.

[0052] In step S105 , the optimization function is used to optimize the parameter values of the parameters to be identified so as to obtain optimized parameter values.

[0053] In this embodiment, an optimization function is constructed according to the target proxy model, and a mean square error function can be used as the corresponding optimization function. The optimization function formula is as follows: in, is the dependent variable, is the independent variable, i.e. the corresponding parameter to be identified, i is the corresponding modal order, K is the number of modal orders, is the experimental modal frequency of the i-th mode, is the modal frequency calculated based on the target surrogate model.

[0054] According to the optimization function, the parameter values of N parameters to be identified are optimized. During the optimization, the parameters to be identified are taken in turn according to the corresponding experimental data, and the values are calculated. ,when When the value of is less than the preset optimization threshold, the optimization is terminated, and it is not necessary to take all the parameter values of the parameters to be identified to improve the optimization efficiency. When the value of is less than the preset optimization threshold The value of is taken as the optimized parameter value. The preset optimization threshold is pre-set based on experience and is not limited in this embodiment.

[0055] It should be noted that The initial value is , the upper limit is , the lower limit is .

[0056] In this embodiment, the mean square error function is used as the optimization function. The square can amplify larger errors, so that more attention is paid to points with large errors during optimization, thereby adjusting parameter values more quickly to reduce these errors and improve optimization efficiency.

[0057] Optionally, construct an optimization function based on the target surrogate model, including: The N parameters to be identified are taken as independent variables, and the sum of the percentages of the differences between the corresponding experimental modal frequencies and the modal frequencies calculated based on the target surrogate model in each mode is taken as the dependent variable to construct an optimization function.

[0058] In this embodiment, when constructing the optimization function, N parameters to be identified can also be used as independent variables, and the sum of the percentages of the difference between the corresponding test modal frequency and the modal frequency calculated based on the target surrogate model in each mode in the corresponding test modal frequency can be used as the dependent variable. The corresponding optimization function formula is as follows: in, is the dependent variable, is the independent variable, i.e. the corresponding parameter to be identified, i is the corresponding modal order, is the experimental modal frequency of the i-th mode, is the modal frequency calculated based on the target surrogate model, is the weighting coefficient of the i-th order mode, which can be set according to experience. In this embodiment, Set to 1.

[0059] In this embodiment, when optimizing the parameter values of N parameters to be identified according to the corresponding optimization function, the values are taken in sequence according to the corresponding experimental data. When the value of is less than the preset optimization threshold, the optimization is terminated, and it is not necessary to take all parameter values of the parameters to be identified, so as to improve the optimization efficiency. The preset optimization threshold is pre-set based on experience and is not limited in this embodiment.

[0060] In this embodiment, when optimizing the parameter values of the parameters to be identified, a genetic algorithm can be used for iterative optimization. The initial value is , the upper limit is , the lower limit is .

[0061] In this embodiment, N parameters to be identified are used as independent variables, and the sum of the percentages of the difference between the corresponding test modal frequency and the modal frequency calculated based on the target proxy model in each order mode in the corresponding test modal frequency is used as the dependent variable. When the value of is less than the preset optimization threshold, the optimization is terminated, and there is no need to take all parameter values of the parameters to be identified, so as to improve the optimization efficiency.

[0062] Optionally, an optimization function is constructed according to the target proxy model, and parameter values of the N parameters to be identified are optimized according to the optimization function to obtain optimized parameter values, including: With the goal of minimizing the value of the dependent variable in the optimization function, the parameter values of N parameters to be identified are optimized to obtain the optimized parameter values.

[0063] In this embodiment, the goal is to minimize the value of the dependent variable in the optimization function, that is, The goal is to minimize the value of , and the parameter values of N parameters to be identified are optimized so that the modal frequencies calculated in the target surrogate model with the optimized parameter values are close to the experimental modal frequencies under the corresponding optimized parameter values, thereby improving the accuracy of the parameter values of the parameters to be identified.

[0064] Optionally, after obtaining the optimized parameter value, the following steps are further included: The optimized parameter value is fine-tuned to obtain a fine-tuned parameter value, so that the difference between the modal frequency calculated by the target surrogate model and the test modal frequency under the corresponding mode is less than a preset threshold.

[0065] In this embodiment, in order to improve the accuracy of the parameter value to be identified, the optimized parameter value is fine-tuned so that the difference between the modal frequency calculated by the target proxy model and the test modal frequency under the corresponding mode is less than a preset threshold. When fine-tuning, a fine-tuning range can be set so that the value can be taken within the fine-tuning range. For example, the fine-tuning range is (optimized parameter value + Optimized parameter values - ),in, The value of can be preset based on experience and is not limited in this example. During fine-tuning, a binary approach can be used to fine-tune the value until the difference between the modal frequency calculated using the target surrogate model and the experimental modal frequency under the corresponding mode after fine-tuning is less than a preset threshold.

[0066] In this embodiment, fine-tuning is performed to obtain corresponding fine-tuned parameter values, which are used as parameter values of parameters to be identified, thereby obtaining the modal anisotropic material parameters of the motor stator.

[0067] In this embodiment, the optimized parameter values are fine-tuned so that the modal frequencies calculated by the target proxy model using the fine-tuned parameter values are closer to the experimental modal frequencies under the corresponding modes, thereby improving the accuracy of the parameter values of the parameters to be identified.

[0068] In the present invention, N parameters to be identified are determined according to the anisotropic material parameters of the motor stator modal, so that the frequency of the corresponding mode is calculated using the N parameters to be identified. According to a preset experimental design method, M groups of experimental data are determined, and each group of experimental data is used to perform finite element modal simulation on the motor stator mode to obtain the experimental modal frequency of the corresponding experimental data. Each group of experimental data is used to fit the proxy model with the experimental modal frequency of the corresponding experimental data. This can avoid the regression result being insensitive or overly sensitive to certain coefficients due to the large gap between the upper and lower limits of each parameter, and improve the accuracy of the coefficient terms in the target proxy model, so that the optimization function is constructed according to the target proxy model, and the parameter values of the parameters to be identified are optimized and adjusted, thereby improving the accuracy of the optimized parameter values.

[0069] See also Figure 2 , Figure 2 This is a block diagram of a parameter identification device for a motor stator mode provided by the second embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 2 The parameter identification device 20 includes a determination module 21 , a design module 22 , a simulation module 23 , a fitting module 24 , and an optimization module 25 .

[0070] The determination module 21 is configured to determine N parameters to be identified based on the modal anisotropic material parameters of the motor stator, where N is an integer greater than zero.

[0071] The design module 22 is used to determine the experimental points corresponding to the preset experimental design method according to the preset experimental design method, and determine M groups of experimental data according to the experimental points, where M is an integer greater than zero.

[0072] The simulation module 23 is used to perform finite element modal simulation on the motor stator mode using each set of experimental data to obtain the experimental modal frequency corresponding to the experimental data.

[0073] The fitting module 24 is used to fit the coefficients in the preset proxy model according to each set of experimental data and the experimental modal frequency of the corresponding experimental data to obtain a fitted target proxy model. The proxy model is constructed by N parameters to be identified.

[0074] The optimization module 25 is used to construct an optimization function according to the target proxy model, and optimize the parameter values of the N parameters to be identified according to the optimization function to obtain optimized parameter values.

[0075] Optionally, the optimization module 25 includes: A construction unit is used to construct an optimization function by taking N parameters to be identified as independent variables and the sum of the percentages of the differences between the corresponding test modal frequencies and the modal frequencies calculated based on the target surrogate model in each mode as dependent variables.

[0076] Optionally, the optimization module 25 further includes: The optimization unit is used to optimize the parameter values of N parameters to be identified with the goal of minimizing the value of the dependent variable in the optimization function to obtain optimized parameter values.

[0077] Optionally, the parameter identification device 20 further includes: The fine-tuning module is used to fine-tune the optimized parameter value to obtain the fine-tuned parameter value, so that the difference between the modal frequency calculated by the target agent model after the fine-tuning parameter value and the test modal frequency under the corresponding mode is less than a preset threshold.

[0078] It should be noted that the information interaction, execution process and other contents between the above modules are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0079] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store thermal management indicator information of each thermal management indicator of the thermal management component in each preset cycle. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a parameter identification method is implemented.

[0080] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the parameter identification method in the above embodiment is implemented, for example Figure 1 The functions of the parameter identification method shown, or the functions of each module / unit in the embodiment of the parameter identification device are realized when the processor executes the computer program, such as Figure 2 The functions of the parameter identification device shown are not described here in detail to avoid repetition.

[0081] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the parameter identification method in the above embodiment is implemented, for example Figure 1 The functions of the parameter identification method shown are not described here in detail to avoid repetition. Alternatively, when the computer program is executed by the processor, the functions of each module / unit in the embodiment of the parameter identification device are realized, for example Figure 2 The functions of each module in the parameter identification device shown are not described here in detail to avoid repetition.

[0082] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0083] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for identifying parameters of a motor stator mode, characterized in that: include: Determining N parameters to be identified according to the motor stator modal anisotropic material parameters, where N is an integer greater than zero; According to a preset experimental design method, determining experimental points corresponding to the preset experimental design method, and determining M groups of experimental data according to the experimental points, where M is an integer greater than zero; Using each set of experimental data, finite element modal simulation is performed on the stator mode of the motor to obtain the experimental modal frequency corresponding to the experimental data; According to each set of experimental data and the experimental modal frequency of the corresponding experimental data, fitting calculation is performed on the coefficients in the preset proxy model to obtain a fitted target proxy model, wherein the proxy model is constructed by the N parameters to be identified; An optimization function is constructed according to the target proxy model, and parameter values of the N parameters to be identified are optimized according to the optimization function to obtain optimized parameter values.

2. The parameter identification method according to claim 1, wherein: The preset experimental design method is the Box-Behnken design method.

3. The parameter identification method according to claim 1, wherein: The constructing of an optimization function according to the target proxy model includes: An optimization function is constructed by taking the N parameters to be identified as independent variables and the sum of the percentages of the difference between the corresponding test modal frequency and the modal frequency calculated based on the target proxy model in each mode as the dependent variable.

4. The parameter identification method according to claim 3, wherein: The step of constructing an optimization function according to the target proxy model and optimizing the parameter values of the N parameters to be identified according to the optimization function to obtain optimized parameter values includes: With the goal of minimizing the value of the dependent variable in the optimization function, the parameter values of the N parameters to be identified are optimized to obtain optimized parameter values.

5. The parameter identification method according to claim 1, wherein: After obtaining the optimized parameter value, the method further includes: The optimized parameter value is fine-tuned to obtain a fine-tuned parameter value, so that the difference between the modal frequency calculated by the target proxy model for the fine-tuned parameter value and the test modal frequency under the corresponding mode is less than a preset threshold.

6. A parameter identification device for a motor stator mode, characterized in that: include: A determination module, configured to determine N parameters to be identified based on the modal anisotropic material parameters of the motor stator, where N is an integer greater than zero; A design module is configured to determine, according to a preset experimental design method, experimental points corresponding to the preset experimental design method, and determine M groups of experimental data based on the experimental points, where M is an integer greater than zero; A simulation module, configured to perform finite element modal simulation on the motor stator mode using each set of experimental data to obtain a test modal frequency corresponding to the experimental data; A fitting module is used to perform fitting calculations on the coefficients in the preset proxy model according to each set of experimental data and the experimental modal frequencies of the corresponding experimental data to obtain a fitted target proxy model, wherein the proxy model is constructed by the N parameters to be identified; An optimization module is used to construct an optimization function according to the target proxy model, and optimize the parameter values of the N parameters to be identified according to the optimization function to obtain optimized parameter values.

7. The parameter identification device according to claim 6, characterized in that: The fitting module includes: A construction unit is used to construct an optimization function using the N parameters to be identified as independent variables and the sum of the percentages of the differences between the corresponding test modal frequencies and the modal frequencies calculated based on the target proxy model in each mode as dependent variables.

8. The parameter identification device according to claim 6, characterized in that: The parameter identification device also includes: A fine-tuning module is used to fine-tune the optimized parameter value to obtain a fine-tuned parameter value, so that the difference between the modal frequency calculated by the target proxy model for the fine-tuned parameter value and the test modal frequency under the corresponding mode is less than a preset threshold.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the parameter identification method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the parameter identification method according to any one of claims 1 to 5 is implemented.

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