A method and system for extracting electromagnetic force density order values and torque fluctuation order values of a motor

Abstract

The application discloses a kind of electromagnetic force density order value and torque fluctuation order value extraction method and system of motor, method includes: based on the preset slanted pole parameter and multiple working condition parameters, simulation model of motor is called to simulate calculation of motor under different working conditions, corresponding torque and electromagnetic force density simulation data are obtained.These simulation data are adjusted in format, for each slanted pole section and the torque signal synthesized by all slanted pole sections, one-dimensional fast Fourier transform is executed to extract torque fluctuation order value;Meanwhile, electromagnetic force density simulation data is carried out two-dimensional fast Fourier transform, and electromagnetic force density order value is extracted.Through this process, the application can batch, efficiently extract torque fluctuation order value and electromagnetic force density order value under multiple working conditions, avoid manual intervention and repeated operation, improve the accuracy and work efficiency of simulation analysis, significantly shorten the research and development cycle, meet the demand of motor rapid development and optimization.

Description

Technical Field

[0001] This invention belongs to the field of motor design technology, specifically relating to a method and system for extracting the order value of electromagnetic force density and the order value of torque fluctuation of a motor. Background Technology

[0002] With the booming development of the new energy vehicle industry, permanent magnet synchronous motors (PMSMs) have gradually become core components of new energy vehicle drive systems due to their significant advantages such as high efficiency, high power density, and long lifespan. However, as the performance of electric vehicles improves, users' demands for vehicle comfort are also constantly increasing. Among these demands, the noise, vibration, and harshness (NVH) performance of the motor has become an important indicator for measuring motor quality and vehicle comfort. The NVH performance of a motor is mainly affected by electromagnetic excitation force, which can be further divided into radial excitation force and tangential excitation force. Radial excitation force is related to the order characteristics of the motor's electromagnetic force density, while tangential excitation force manifests as torque ripple and is directly related to the order characteristics of torque ripple. Therefore, accurately extracting the order values ​​of electromagnetic force density and torque ripple under different operating conditions is of great significance for optimizing the NVH performance of the motor, improving motor quality, and meeting the design requirements of new energy vehicles.

[0003] Traditionally, the extraction of electromagnetic force density and torque ripple order values ​​for motors has relied heavily on manual operation using simulation software (such as Ansys Maxwell). Engineers typically need to manually set simulation parameters for each operating condition, start the simulation, export the data, and then extract the order values ​​through manual calculation or external software tools. For example, in the prior art, CN117875128A discloses a method for obtaining the order electromagnetic force of stator mesh nodes in a permanent magnet synchronous motor. First, based on a given motor geometric model and given motor parameters, a finite element model for transient electromagnetic field simulation analysis of the motor is established in electromagnetic simulation software. Then, based on given operating conditions, stable speed points within the constant torque and constant power regions are selected, and electromagnetic force simulation is performed on the selected stable speed points. The stator mesh file is read using data processing software, and stator tooth surface mesh nodes are selected as target mesh nodes. Subsequently, based on the simulation results, the time-domain electromagnetic force experienced by the target mesh nodes at each speed point is obtained, and a fast Fourier transform is performed on the time-domain electromagnetic force to obtain the electromagnetic force spectrum. Electromagnetic harmonics of corresponding frequencies are extracted from the electromagnetic force spectrum to obtain the order electromagnetic force of each target mesh node as a function of speed. However, when performing multi-condition analysis (e.g., performance analysis of the motor under different speeds and currents), engineers often need to repeat the above tedious manual setting and calculation steps. This process is not only labor-intensive but also susceptible to human error, thus reducing the accuracy and reliability of the results.

[0004] Furthermore, due to the complexity of the parameters involved in this process and its poor adaptability to various operating conditions, especially during rapid motor development or multi-condition performance evaluation, it often consumes a significant amount of time and labor, severely restricting the efficiency and progress of motor development. For an industry that demands rapid iteration, traditional manual operation is particularly inefficient and fails to meet the real-time and high-efficiency requirements of new energy vehicle motor design and performance optimization.

[0005] Therefore, existing extraction methods have significant shortcomings in multi-condition analysis and rapid development, and an efficient and automated solution is needed to address these issues. Summary of the Invention

[0006] The purpose of this invention is to overcome the defects of the prior art by providing a method and system for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] This invention provides a method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor, comprising the following steps:

[0009] Based on preset skew pole parameters and multiple operating condition parameters, the motor simulation model is called to perform simulation calculations on the motor under multiple operating condition parameters to obtain simulation data for each operating condition.

[0010] The simulation data for each working condition is processed by traversing the data. For each working condition, the torque simulation data and electromagnetic force density simulation data are extracted from the simulation data.

[0011] After adjusting the data format of the torque simulation data, one-dimensional fast Fourier transform processing is performed on the torque signals synthesized from each skewed pole segment and all skewed pole segments to obtain the torque fluctuation order value within the corresponding preset order range.

[0012] After adjusting the data format of the electromagnetic force density simulation data and converting it into a two-dimensional data form that simultaneously includes spatial and temporal dimensions, two-dimensional fast Fourier transform processing is performed on the electromagnetic force density signals synthesized for each skewed pole segment and all skewed pole segments to obtain the electromagnetic force density order values ​​within the corresponding preset order range.

[0013] Furthermore, the skew pole parameters are used to characterize the skew pole structure features of the motor rotor, and the skew pole parameters include the number of skew pole segments of the rotor and the skew pole angle corresponding to each skew pole segment.

[0014] Furthermore, the operating parameters are used to characterize the motor's operating state, and the operating parameters include the motor's speed parameters, current parameters, current phase parameters, electrical cycle parameters, and simulation step parameters.

[0015] Furthermore, based on preset skew pole parameters and multiple operating condition parameters, the motor simulation model is invoked to perform simulation calculations on the motor under multiple operating condition parameters to obtain simulation data for each operating condition. Specifically, this includes:

[0016] Step A1: Start the simulation software and load the motor simulation model; the motor simulation model is a finite element simulation model used for numerical calculation of the electromagnetic characteristics of the motor;

[0017] Step A2: Write the skew pole parameters and the operating condition parameters corresponding to the current operating condition into the motor simulation model;

[0018] Step A3: Run the motor simulation model under the written skew pole parameters and operating conditions to generate simulation results under the corresponding operating conditions;

[0019] Step A4: Export the simulation results as a data file, which serves as the simulation data corresponding to the operating condition; the simulation data includes torque simulation data and electromagnetic force density simulation data;

[0020] Step A5: Repeat steps A2-A4 according to the preset working condition sequence until the simulation calculation of all working conditions is completed.

[0021] Furthermore, after adjusting the data format of the torque simulation data, a one-dimensional fast Fourier transform is performed on the torque signals synthesized from each skewed segment and all skewed segments to obtain the torque fluctuation order values ​​within a corresponding preset order range. Specifically, this includes:

[0022] Step B1: Obtain the torque simulation data corresponding to each skewed pole segment under a certain operating condition. The torque simulation data is a discretely sampled torque time-domain signal. The format of the torque simulation data is adjusted, including removing redundant data, removing header information, and standardizing the sampling interval, to obtain the format-adjusted torque simulation data. ,in, Indicates the first The first sloping segment in the... Torque value at the sampling time, , This refers to the number of slant pole segments. , This represents the total number of sampling times.

[0023] Step B2: Adjust the format of the torque simulation data Performing a one-dimensional fast Fourier transform yields the frequency domain representation of the torque signal, expressed as:

[0024]

[0025] in, Indicates the first The first sloping segment in the... The complex spectral components corresponding to the order, The imaginary unit;

[0026] Step B3: Calculate the torque fluctuation amplitude corresponding to each order based on the frequency domain expression of the torque signal;

[0027] Step B4: Within the preset order range, extract the torque fluctuation amplitude of each order corresponding to each skewed pole segment, and use it as the torque fluctuation order value of each skewed pole segment.

[0028] Step B5: Perform synthesis processing on the torque time-domain signals of each skewed segment to obtain the synthesized torque time-domain signal of all skewed segments, and repeat steps B2 to B4 on the synthesized torque time-domain signal to obtain the order value of the synthesized torque fluctuation of all skewed segments.

[0029] Furthermore, the torque fluctuation amplitude is expressed as:

[0030]

[0031] in, Indicates the order in the spectrum The corresponding torque fluctuation amplitude, These represent the real part and the imaginary part, respectively.

[0032] Furthermore, after adjusting the data format of the electromagnetic force density simulation data to convert it into a two-dimensional data form that simultaneously includes spatial and temporal dimensions, a two-dimensional fast Fourier transform is performed on the electromagnetic force density signals synthesized for each skewed pole segment and all skewed pole segments to obtain electromagnetic force density order values ​​within a corresponding preset order range. Specifically, this includes:

[0033] Step C1: Obtain electromagnetic force density simulation data corresponding to each slant pole segment under a certain working condition. The electromagnetic force density simulation data is a discretely sampled electromagnetic force density time-domain signal. Adjust the format of the electromagnetic force density simulation data so that the spatial and temporal dimensions of the electromagnetic force density simulation data are represented simultaneously, thus obtaining the format-adjusted electromagnetic force density simulation data. ,in, Indicates the first The first sloping segment in the... The electromagnetic force density value at time t. , This refers to the number of slant pole segments. , This represents the total number of sampling times.

[0034] Step C2: Adjust the format of the electromagnetic force density simulation data Converting to two-dimensional data format yields the electromagnetic force density signals in the corresponding spatial and temporal dimensions, represented as follows:

[0035]

[0036] in, They represent the real part and the imaginary part, respectively. The imaginary unit;

[0037] Step C3: Perform a two-dimensional fast Fourier transform on the electromagnetic force density signal to calculate its frequency domain representation, obtaining the spatial and temporal spectral expression of the electromagnetic force density signal, as follows:

[0038]

[0039] in, Indicates the first The first sloping segment in the... Time dimension order and number The complex spectral components corresponding to the order; ;

[0040] Step C4: Calculate the electromagnetic force density fluctuation amplitude corresponding to each order based on the frequency domain expression of the electromagnetic force density signal;

[0041] Step C5: Within the preset order range, extract the electromagnetic force density fluctuation amplitude corresponding to each order of each skewed pole segment, and use it as the electromagnetic force density order value of each skewed pole segment.

[0042] Step C6: Synthesize the electromagnetic force density time-domain signal of each skewed pole segment to obtain the synthesized electromagnetic force density time-domain signal of all skewed pole segments, and repeat steps C2 to C4 on the synthesized electromagnetic force density time-domain signal to obtain the electromagnetic force density fluctuation order value of all skewed pole segments.

[0043] Furthermore, the amplitude of the electromagnetic force density fluctuation is expressed as:

[0044]

[0045] in, Represents the corresponding order in the two-dimensional spectrum The amplitude of electromagnetic force density fluctuation.

[0046] Furthermore, the maximum order of the preset order range is less than or equal to half of the number of electrical cycle time steps of the motor.

[0047] Another aspect of the present invention provides a system for extracting the order value of electromagnetic force density and the order value of torque fluctuation of the above-mentioned motor, comprising:

[0048] The simulation module is used to perform multi-condition simulations based on preset skew pole parameters and operating condition parameters through a motor simulation model.

[0049] The first extraction module is used to extract the torque data of each skewed pole segment under a preset order and the torque data synthesized from all skewed pole segments using one-dimensional fast Fourier transform from the simulation data under various working conditions.

[0050] The second extraction module is used to extract the electromagnetic force density data of each skewed pole segment under a preset order and the electromagnetic force density data synthesized from all skewed pole segments using two-dimensional fast Fourier transform on the simulation data under various working conditions.

[0051] In another aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of a motor as described in any of the above-mentioned methods.

[0052] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor as described in any of the above-described methods.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] (1) In the prior art, the extraction of electromagnetic force density order and torque fluctuation order values ​​of motors under multiple operating conditions mainly relies on manual operation using simulation software. This process is cumbersome and time-consuming, increasing workload and making it prone to inaccurate results due to human error. To address this issue, this invention proposes an automated batch extraction method based on a motor simulation model. This method can automatically extract electromagnetic force density order and torque fluctuation order values ​​under multiple operating conditions. Through batch processing, the cumbersome manual operation in traditional methods is eliminated, significantly improving analysis efficiency and avoiding human error, thus ensuring the accuracy of the extraction results.

[0055] (2) In existing technologies, multi-condition analysis requires simulation of each condition separately and manual setting of simulation parameters. Each configuration of simulation parameters and extraction of results requires significant time and manpower, especially when analyzing multiple conditions. This inefficient process severely limits the speed and efficiency of motor development. Therefore, existing technologies cannot meet the needs of rapid development. This invention automatically performs multi-condition simulations using a motor simulation model based on preset skew pole parameters and condition parameters, and extracts corresponding torque and electromagnetic force density data through an automated program, eliminating repetitive manual operations. This method can process multi-condition data in batches quickly, significantly reducing manual intervention and time consumption, thereby achieving high efficiency and real-time performance optimization of motor performance and meeting the needs of rapid motor development.

[0056] (3) Existing electromagnetic force density data is often represented and processed in a single-dimensional form, making it difficult to comprehensively reflect the changes in electromagnetic force density in both spatial and temporal dimensions. This processing method cannot fully utilize the spatial characteristics of electromagnetic force density data, which may lead to an incomplete and inaccurate analysis of motor performance. To solve this problem, this invention converts electromagnetic force density simulation data into a two-dimensional data form that simultaneously includes spatial and temporal dimensions, and processes it using a two-dimensional fast Fourier transform (FFT2D). In this way, the spectral characteristics of electromagnetic force density in both spatial and temporal dimensions can be comprehensively analyzed, thereby accurately extracting the order value of electromagnetic force density. This technical feature effectively improves the accuracy of electromagnetic force density data analysis, enhances the understanding of the overall performance of the motor, and ensures the efficiency and comprehensiveness of the optimization process.

[0057] (4) In existing technologies, the selection of the order range usually requires manual setting, and the preset order range is often fixed and cannot be flexibly adjusted according to the actual situation of different motors. In traditional methods, the selection of the order range may lead to inapplicable results, thereby affecting the accuracy of motor performance analysis. This invention designs an adaptive order range selection mechanism that automatically selects the appropriate maximum order based on the number of electrical cycle time steps of the motor, so that the order range can be flexibly adjusted according to the specific operating conditions of the motor. This technical feature not only improves the applicability of the extraction results, but also ensures that the extraction of order values ​​under different motor operating conditions is more accurate and efficient, avoiding potential errors caused by improper setting of the order range. Attached Figure Description

[0058] Figure 1 A flowchart of a method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor provided by the present invention;

[0059] Figure 2 A flowchart illustrating the method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of a motor, provided for an embodiment.

[0060] Figure 3 A schematic diagram of the motor simulation model in the Maxwell simulation software within the Python script provided for this embodiment;

[0061] Figure 4 The flowchart for Maxwell simulation control in a Python script under the implementation scenario;

[0062] Figure 5 A flowchart of multi-order torque data extraction provided for an embodiment;

[0063] Figure 6 A flowchart for extracting multi-order electromagnetic force density data is provided for this embodiment.

[0064] Figure 7 A block diagram of a system for extracting the order value of electromagnetic force density and the order value of torque fluctuation of a motor is provided for the embodiment.

[0065] Figure 8 This is a schematic diagram of the hardware structure of the electronic device used in this embodiment;

[0066] Figure 9 This is a schematic diagram of the hardware structure of a computer-readable storage medium as an example. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0068] Figure 1 A flowchart of a method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor provided by the present invention is shown below. Figure 1 As shown, the method includes: performing multi-condition simulation using a motor simulation model based on preset skew pole parameters and operating condition parameters;

[0069] For simulation data under various operating conditions, the FFT1D function is used to extract the torque data of each skewed pole segment at a preset order and the combined torque data of all skewed pole segments; and the FFT2D function is used to extract the electromagnetic force density data of each skewed pole segment at a preset order and the combined electromagnetic force density data of all skewed pole segments.

[0070] Understandably, given the deficiencies in the background technology, this invention proposes a method for extracting the order values ​​of electromagnetic force density and torque fluctuation of a motor. This method enables batch extraction of the order values ​​of electromagnetic force density and torque fluctuation of a permanent magnet synchronous motor under multiple operating conditions. Compared to traditional manual operation, it significantly saves labor and time costs and substantially improves processing efficiency. Simultaneously, the method allows for automatic execution of parameter settings, Fourier transforms, and order extraction via scripts, reducing human error and ensuring the accuracy and consistency of results. Furthermore, the simulation conditions can be flexibly added or removed, supporting different versions of simulation software, demonstrating strong adaptability. The final results can also be automatically summarized in a table for easy subsequent analysis, providing efficient and reliable technical support for optimizing the NVH performance of motors.

[0071] Figure 2 The flowchart illustrates a method for extracting the order values ​​of electromagnetic force density and torque fluctuation of a motor in a specific implementation scenario. Figure 2 As shown, in one possible embodiment, a multi-condition simulation of the motor is performed using a Python script file and an Ansys Maxwell simulation model. The Maxwell simulation control statements in the Python script file include: opening the Ansys Maxwell simulation software and the motor simulation model; writing skew parameters; specifying simulation conditions; determining the number of simulation cycles and steps; generating a simulation result report; and exporting the simulation results. It is worth noting that there is no limit to the number of simulation conditions; they can be added directly to the Python script as needed.

[0072] Specifically, based on preset skew pole parameters and operating condition parameters, multi-condition simulations are performed using a motor simulation model, including:

[0073] 1. Configure the Python script to launch the Ansys Maxwell simulation software and open the motor simulation model, for example... Figure 3 The image shows a Maxwell simulation model in a Python script for a specific implementation scenario.

[0074] 2. Write simulation parameters into the motor simulation model. These parameters should include at least the rotor's skewness parameters and parameters for each operating condition. For example... Figure 4 The diagram shows a flowchart of Maxwell simulation control in a Python script for a specific implementation scenario. Figure 3 as well as Figure 4 As shown, the skew pole parameters include the number of skew poles of the rotor and the angle of each skew pole segment. The operating parameters include at least the motor speed, current, current lead angle, number of electrical cycles, and number of steps.

[0075] In this embodiment, there is no mandatory requirement for the version of the Ansys Maxwell simulation software. The Python script can select the software version to call via the API (Application Programming Interface). This embodiment uses a three-phase 8-pole 48-slot permanent magnet synchronous motor as an example. The winding excitation is current source excitation, and the excitation inputs are speed, phase current, and current lead angle. Five operating conditions are set, and the motor rotor has three skew poles with a skew angle of 5°. The maximum extraction order set in the Python script must be less than or equal to half the time step of one electrical cycle. This embodiment simulates two electrical cycles, with 64 steps per electrical cycle, and the maximum order is set to 12.

[0076] 3. Start / run the motor simulation model under a single working condition, generate a simulation result report, and output the simulation data under the corresponding working condition in the form of an Excel spreadsheet. The simulation data should include at least torque simulation data and electromagnetic force density simulation data.

[0077] 4. Iterate through all working conditions. It should be noted that there is no limit to the number of simulation working conditions; you can add them directly in the Python script as needed. It is particularly important to emphasize that only one working condition is written at a time during simulation. The script uses a for loop to check if all preset working conditions have been simulated. If not, it continues to simulate the next working condition. If all working conditions have been simulated, the simulation model and software are closed, and the simulation results for all working conditions are summarized and exported to an Excel spreadsheet.

[0078] In one possible embodiment, such as Figure 5 The flowchart shown is a flowchart of the extraction process for multi-order torque data in a Python script. The FFT1D function is used to extract torque data for each skewed pole segment at a preset order, as well as the combined torque data for all skewed pole segments, including:

[0079] 1. Import the torque simulation data for each skewed pole segment under a specific operating condition. This torque simulation data is one-dimensional torque time-domain data (i.e., a sequence of torque values ​​that change over time) exported during motor simulation. Adjust the format of the torque simulation data, for example, by deleting header information to remove redundant information from the data, and adjusting the data format to meet the requirements of Fourier transform.

[0080] 2. Perform a one-dimensional Fast Fourier Transform (FFT) on the torque simulation data after format adjustment to calculate the spectrum of the torque signal and obtain the amplitude corresponding to different frequency components.

[0081] Understandably, the FFT1D function is a signal processing function implemented based on the one-dimensional Fast Fourier Transform (FFT) algorithm. It is primarily used to convert one-dimensional time-domain signals into frequency-domain signals, thereby extracting the signal's frequency characteristics (such as order and amplitude). Its core principle is to rapidly calculate the Discrete Fourier Transform (DFT) to transform signals that are difficult to analyze intuitively in the time domain into the frequency domain, clearly displaying the frequency components contained in the signal and their corresponding amplitudes or energy.

[0082] 3. Based on the preset maximum order (usually related to the number of motor electrical cycle steps), the torque fluctuation amplitude of the corresponding order is selected from the torque signal spectrum. The torque fluctuation amplitude of the corresponding order includes the torque values ​​of each order in each skew segment, as well as the torque values ​​of each order synthesized from each skew segment. The torque fluctuation amplitudes extracted and stored in this step mainly include the average torque, the peak-to-peak torque, and the corresponding order value. The extracted torque fluctuation amplitudes of each order can be used as key parameters for subsequent NVH analysis.

[0083] The FFT1D function extracts both the torque values ​​of each skew segment and the combined torque values ​​of the skew segments. For example... Figure 5 As shown, for each slanted pole segment, the `fft` function of the `scipy.fftpack` module in the simulation software is used for data transformation. Based on the maximum order set in the Python script, the torque fluctuation amplitude corresponding to each order is extracted and stored in an Excel list for each slanted pole segment. For the extraction of torque values ​​of each order synthesized from each slanted pole segment, firstly, the vector mean of the torque of each slanted pole segment is calculated, and then the `fft` function of the `scipy.fftpack` module in the simulation software is used for data transformation. Based on the maximum order set in the Python script, the torque fluctuation amplitude corresponding to each order is extracted and stored in a synthesized Excel list.

[0084] Compared to the traditional Fourier transform, the FFT1D function significantly reduces computation through optimized algorithms, making it particularly suitable for processing large-scale time-domain data (such as torque sequences in multi-condition motor simulations), thus significantly improving processing efficiency while maintaining accuracy. In one possible implementation, such as... Figure 6 The flowchart shown is a flowchart of the extraction process for multi-order electromagnetic force density data in a Python script. The FFT2D function is used to extract the electromagnetic force density data for each skewed pole segment at a preset order, as well as the electromagnetic force density data synthesized from all skewed pole segments, including:

[0085] 1. Import the electromagnetic force density simulation data of each skewed pole segment exported from the motor simulation under a certain operating condition, and adjust the format of the electromagnetic force density simulation data to convert it into a two-dimensional matrix format in which the horizontal and vertical axes correspond to spatial position and time, respectively. Remove redundant information (such as table headers) from the data to ensure the integrity and standardization of the two-dimensional matrix.

[0086] 2. Perform a two-dimensional fast Fourier transform on the electromagnetic force density simulation data after format adjustment to calculate the two-dimensional spectrum of the electromagnetic force density signal and obtain the amplitude of different spatial frequency (corresponding to spatial order) and time frequency (corresponding to time order) components.

[0087] Understandably, the FFT2D function is a two-dimensional signal processing function implemented based on the 2D Fast Fourier Transform (2DFastFourierTransform) algorithm. It is primarily used to convert two-dimensional time-space domain signals into frequency domain signals, thereby extracting the spatial and temporal frequency features (such as order and amplitude) contained in the signal. Its core principle is to rapidly calculate the 2D Discrete Fourier Transform (2DDFT) to transform signals that are difficult to analyze directly in the two-dimensional time-space domain into the two-dimensional frequency domain, clearly displaying the amplitude or energy distribution corresponding to different spatial and temporal orders in the signal.

[0088] 3. Based on the preset maximum order, select the electromagnetic force density amplitude of a specific spatial order and time order combination (e.g., (8,8) order represents a spatial order of 8 and a time order of 8) from the two-dimensional spectrum of the electromagnetic force density signal. The electromagnetic force density amplitude of the specific spatial order and time order combination includes the electromagnetic force density values ​​of each order of each skewed pole segment, as well as the electromagnetic force density values ​​of each order synthesized by each skewed pole segment, providing key parameters for the NVH performance analysis of the motor.

[0089] like Figure 6 As shown, for each slanted pole segment, the `fftn` function of the `scipy.fftpack` module in the simulation software is used for data transformation. Based on the maximum order set in the Python script, the electromagnetic force density amplitude corresponding to each order is extracted and stored in an Excel list for each slanted pole segment. For the extraction of electromagnetic force density amplitudes of each order synthesized for each slanted pole segment, firstly, the average electromagnetic force density amplitude of all slanted pole segments is calculated. Then, the `fftn` function of the `scipy.fftpack` module in the simulation software is used for data transformation. Based on the maximum order set in the Python script, the electromagnetic force density amplitude corresponding to each order is extracted and stored in a synthesized Excel list.

[0090] Compared to one-dimensional Fourier transform, the FFT2D function in this embodiment can process signal features in both spatial and temporal dimensions simultaneously. It is particularly suitable for analyzing physical quantities such as electromagnetic force density in permanent magnet synchronous motors, which are related to both spatial position (e.g., the angle of the inner circumference of the stator) and time, and can more comprehensively reflect their order characteristics.

[0091] Figure 7 A structural diagram of a system for extracting the order value of electromagnetic force density and the order value of torque fluctuation of a motor, provided in an embodiment of the present invention, is shown below. Figure 7 As shown, a system for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor includes a simulation module, a first extraction module, and a second extraction module, wherein:

[0092] The simulation module is used to perform multi-condition simulations based on preset skew pole parameters and operating condition parameters through a motor simulation model.

[0093] The first extraction module is used to extract the torque data of each skewed pole segment and the combined torque data of all skewed pole segments under the preset order from the simulation data under various working conditions using the FFT1D function.

[0094] The second extraction module is used to extract the electromagnetic force density data of each skewed pole segment under a preset order and the electromagnetic force density data synthesized from all skewed pole segments using the FFT2D function for simulation data under various working conditions.

[0095] It is understood that the electromagnetic force density order value and torque fluctuation order value extraction system for motors provided by the present invention corresponds to the electromagnetic force density order value and torque fluctuation order value extraction methods for motors provided in the foregoing embodiments. The relevant technical features of the electromagnetic force density order value and torque fluctuation order value extraction system for motors can be referred to the relevant technical features of the electromagnetic force density order value and torque fluctuation order value extraction methods for motors, and will not be repeated here.

[0096] Please see Figure 8 , Figure 8 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 8 As shown, this embodiment of the invention provides an electronic device, 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, it performs the following steps: based on preset skew pole parameters and operating condition parameters, it performs multi-operating condition simulation using a motor simulation model; for the simulation data under each operating condition, it uses the FFT1D function to extract the torque data of each skew pole segment at a preset order and the torque data synthesized from all skew pole segments; and it uses the FFT2D function to extract the electromagnetic force density data of each skew pole segment at a preset order and the electromagnetic force density data synthesized from all skew pole segments.

[0097] Please see Figure 9 , Figure 9 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by the present invention. (See diagram below.) Figure 9 As shown, this embodiment provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program performs the following steps:

[0098] Based on preset skew pole parameters and operating condition parameters, multi-condition simulation is performed using a motor simulation model;

[0099] For simulation data under various operating conditions, the FFT1D function was used to extract torque data for each skewed pole segment at a preset order, as well as the synthesized torque data for all skewed pole segments; and,

[0100] The FFT2D function is used to extract the electromagnetic force density data of each skewed pole segment at a preset order, as well as the electromagnetic force density data synthesized from all skewed pole segments.

[0101] The present invention provides a method, system, and storage medium for extracting the order value of electromagnetic force density and the order value of torque fluctuation of a motor, which has the following advantages compared with the prior art:

[0102] 1. Significantly improves multi-condition processing efficiency, reduces costs, and automates batch processing: The entire AnsysMaxwell simulation process (including model startup, parameter writing, simulation execution, and data export) and subsequent Fourier transform and order extraction are controlled by Python scripts, replacing traditional manual operation for each condition and avoiding repetitive work.

[0103] Time and manpower savings: For multi-operating scenarios (such as different speeds, currents, and skew pole parameter combinations), simulation and data processing for all operating conditions can be completed without manual intervention. Compared with manual operation, it can save more than 70% of time and manpower costs, significantly improving the efficiency of motor R&D.

[0104] 2. Improve the accuracy and consistency of results and reduce human error: Simulation parameter settings, data export, Fourier transform and order extraction are all executed automatically by the script, avoiding result fluctuations caused by manual operation (such as parameter input errors, data format adjustment deviations), and ensuring the consistency of analysis results under different working conditions and different batches.

[0105] Standardized processing flow: By presetting parameters such as the maximum order and data format conversion rules, the order extraction process is standardized, the results are highly repeatable, and reliable data support is provided for the optimization of motor NVH performance.

[0106] 3. Enhanced technical flexibility and scalability: Operating conditions and parameters can be flexibly adjusted. There is no limit to the number of simulation operating conditions. Operating condition parameters (such as speed, current, and skew pole angle) can be added or modified directly in the Python script without rebuilding the simulation model. This adapts to the analysis needs of different motor models (such as 8-pole 48-slot, multi-segment skew pole, etc.).

[0107] Strong software compatibility: Supports selecting different versions of AnsysMaxwell via API interface, adapting to different simulation environments and reducing dependence on specific software versions.

[0108] 4. Results integration facilitates automated summarization and export for subsequent analysis: Electromagnetic force density order values ​​and torque fluctuation order values ​​(including individual and composite results for each skewed pole segment) for all operating conditions are automatically summarized into an Excel spreadsheet, which includes key parameters such as operating condition information, average torque, and peak-to-peak value, presenting data correlations intuitively and facilitating engineers to quickly compare and analyze motor characteristics under multiple operating conditions.

[0109] Supporting NVH performance optimization: The precisely extracted order values ​​(such as spatial order and temporal order) can be directly used for motor vibration and noise source analysis, providing a clear direction for structural optimization (such as skewed pole design and winding layout adjustment).

[0110] Therefore, this invention, through automated and standardized technical solutions, improves efficiency and ensures accuracy while enhancing the flexibility and practicality of motor electromagnetic characteristic analysis, which is of great value for the research and development and performance optimization of permanent magnet synchronous motors (especially motors for new energy vehicles).

[0111] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor, characterized in that, Includes the following steps: Based on preset skew pole parameters and multiple operating condition parameters, the motor simulation model is called to perform simulation calculations on the motor under multiple operating condition parameters to obtain simulation data for each operating condition. The simulation data for each working condition is processed by traversing the data. For each working condition, the torque simulation data and electromagnetic force density simulation data are extracted from the simulation data. After adjusting the data format of the torque simulation data, one-dimensional fast Fourier transform processing is performed on the torque signals synthesized from each skewed pole segment and all skewed pole segments to obtain the torque fluctuation order value within the corresponding preset order range. After adjusting the data format of the electromagnetic force density simulation data and converting it into a two-dimensional data form that simultaneously includes spatial and temporal dimensions, two-dimensional fast Fourier transform processing is performed on the electromagnetic force density signals synthesized for each skewed pole segment and all skewed pole segments to obtain the electromagnetic force density order values ​​within the corresponding preset order range.

2. The method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 1, characterized in that, The skew pole parameters are used to characterize the skew pole structure features of the motor rotor. The skew pole parameters include the number of skew pole segments of the rotor and the skew pole angle corresponding to each skew pole segment.

3. The method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 1, characterized in that, The operating parameters are used to characterize the operating state of the motor. The operating parameters include the motor speed parameters, current parameters, current phase parameters, electrical cycle parameters, and simulation step parameters.

4. The method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 1, characterized in that, The process involves calling a motor simulation model based on preset pole slant parameters and multiple operating condition parameters to perform simulation calculations on the motor under various operating conditions, obtaining simulation data for each operating condition. Specifically, this includes: Step A1: Start the simulation software and load the motor simulation model; the motor simulation model is a finite element simulation model used for numerical calculation of the electromagnetic characteristics of the motor; Step A2: Write the skew pole parameters and the operating condition parameters corresponding to the current operating condition into the motor simulation model; Step A3: Run the motor simulation model under the written skew pole parameters and operating conditions to generate simulation results under the corresponding operating conditions; Step A4: Export the simulation results as a data file, which serves as the simulation data corresponding to the operating condition; the simulation data includes torque simulation data and electromagnetic force density simulation data; Step A5: Repeat steps A2-A4 according to the preset working condition sequence until the simulation calculation of all working conditions is completed.

5. The method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 1, characterized in that, After adjusting the data format of the torque simulation data, a one-dimensional fast Fourier transform is performed on the torque signals synthesized from each skewed pole segment and all skewed pole segments to obtain the torque fluctuation order values ​​within a corresponding preset order range. Specifically, this includes: Step B1: Obtain the torque simulation data corresponding to each skewed pole segment under a certain operating condition. The torque simulation data is a discretely sampled torque time-domain signal. The format of the torque simulation data is adjusted, including removing redundant data, removing header information, and standardizing the sampling interval, to obtain the format-adjusted torque simulation data. ,in, Indicates the first The first sloping segment in the... Torque value at the sampling time, , This refers to the number of slant pole segments. , This represents the total number of sampling times. Step B2: Adjust the format of the torque simulation data Performing a one-dimensional fast Fourier transform yields the frequency domain representation of the torque signal, expressed as: in, Indicates the first The first sloping segment in the... The complex spectral components corresponding to the order, The imaginary unit; Step B3: Calculate the torque fluctuation amplitude corresponding to each order based on the frequency domain expression of the torque signal; Step B4: Within the preset order range, extract the torque fluctuation amplitude of each order corresponding to each skewed pole segment, and use it as the torque fluctuation order value of each skewed pole segment. Step B5: Perform synthesis processing on the torque time-domain signals of each skewed segment to obtain the synthesized torque time-domain signal of all skewed segments, and repeat steps B2 to B4 on the synthesized torque time-domain signal to obtain the order value of the synthesized torque fluctuation of all skewed segments.

6. The method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 5, characterized in that, The torque fluctuation amplitude is expressed as: in, Indicates the order in the spectrum The corresponding torque fluctuation amplitude, These represent the real part and the imaginary part, respectively.

7. The method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 1, characterized in that, After adjusting the data format of the electromagnetic force density simulation data to a two-dimensional data form that simultaneously includes spatial and temporal dimensions, a two-dimensional fast Fourier transform is performed on the electromagnetic force density signals synthesized from each skewed pole segment and all skewed pole segments to obtain electromagnetic force density order values ​​within a corresponding preset order range. Specifically, this includes: Step C1: Obtain electromagnetic force density simulation data corresponding to each slant pole segment under a certain working condition. The electromagnetic force density simulation data is a discretely sampled electromagnetic force density time-domain signal. Adjust the format of the electromagnetic force density simulation data so that the spatial and temporal dimensions of the electromagnetic force density simulation data are represented simultaneously, thus obtaining the format-adjusted electromagnetic force density simulation data. ,in, Indicates the first The first sloping segment in the... The electromagnetic force density value at time t. , This refers to the number of slant pole segments. , This represents the total number of sampling times. Step C2: Convert the formatted electromagnetic force density simulation data Converting to two-dimensional data format yields the electromagnetic force density signals in the corresponding spatial and temporal dimensions, represented as follows: in, They represent the real part and the imaginary part, respectively. The imaginary unit; Step C3: Perform a two-dimensional fast Fourier transform on the electromagnetic force density signal to calculate its frequency domain representation, obtaining the spatial and temporal spectral expression of the electromagnetic force density signal, as follows: in, Indicates the first The first sloping segment in the... Time dimension order and number The complex spectral components corresponding to the order; ; Step C4: Calculate the electromagnetic force density fluctuation amplitude corresponding to each order based on the frequency domain expression of the electromagnetic force density signal; Step C5: Within a preset order range, extract the electromagnetic force density fluctuation amplitude corresponding to each order of each skewed pole segment, and use it as the electromagnetic force density order value of each skewed pole segment. Step C6: Synthesize the electromagnetic force density time-domain signal of each skewed pole segment to obtain the synthesized electromagnetic force density time-domain signal of all skewed pole segments, and repeat steps C2 to C4 on the synthesized electromagnetic force density time-domain signal to obtain the electromagnetic force density fluctuation order value of all skewed pole segments.

8. The method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 7, characterized in that, The amplitude of the electromagnetic force density fluctuation is expressed as: in, Represents the corresponding order in the two-dimensional spectrum The amplitude of electromagnetic force density fluctuation.

9. A method for extracting the order value of electromagnetic force density and the order value of torque fluctuation of an electric motor according to claim 5 or 7, characterized in that, The maximum order of the preset order range is less than or equal to half the number of electrical cycle time steps of the motor.

10. A system for extracting the order value of electromagnetic force density and the order value of torque fluctuation of the motor according to any one of claims 1 to 9, characterized in that, include: The simulation module is used to perform multi-condition simulations based on preset skew pole parameters and operating condition parameters through a motor simulation model. The first extraction module is used to extract the torque data of each skewed pole segment under a preset order and the torque data synthesized from all skewed pole segments using one-dimensional fast Fourier transform on the simulation data under various working conditions. The second extraction module is used to extract the electromagnetic force density data of each skewed pole segment under a preset order and the electromagnetic force density data synthesized from all skewed pole segments using two-dimensional fast Fourier transform on the simulation data under various working conditions.

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