Motor cogging torque optimization method, device, equipment and storage medium

Abstract

The present application relates to the motor technical field, especially a kind of motor tooth slot torque optimization method, device, equipment and storage medium.It is by constructing motor simulation model, and parameter scanning is carried out to motor simulation model, obtains scanning data;Based on scanning data, fitting function is constructed;According to the geometric parameters of motor simulation model, determine parameter boundary condition;According to parameter boundary condition and fitting function, determine target auxiliary slot parameter.The present application is by constructing motor simulation model first, and parameter scanning is carried out to motor simulation model, obtains scanning data, then according to scanning data, fitting function is constructed, reduces the computing power cost, according to the geometric parameters of motor simulation model, determine parameter boundary condition, finally according to fitting function and parameter boundary condition, determine target auxiliary slot parameter, to make the auxiliary slot opened according to the target auxiliary slot parameter can guarantee the output torque of motor and reduce the tooth slot torque of motor.

Description

TECHNICAL FIELD

[0001] The present application relates to the motor technical field, and particularly relates to a motor cogging torque optimization method, device, equipment and storage medium. BACKGROUND

[0002] Cogging torque is an inherent characteristic of a permanent magnet motor, which causes motor torque fluctuation, brings vibration and noise, and interferes with system control. In actual engineering application, opening auxiliary slots does not need to change motor structure parameters, is a subtractive optimization method, can improve motor performance, meet process requirements and not excessively increase manufacturing cost, and the method of opening auxiliary slots is often used to weaken cogging torque. However, the size and position of the auxiliary slot have a nonlinear and irregular influence on the cogging torque, so it is very difficult to confirm the auxiliary slot parameters. Unsuitable auxiliary slot parameters cannot guarantee that the motor output torque is not excessively lost and still cause large cogging torque.

[0003] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0004] The main purpose of the present application is to provide a motor cogging torque optimization method, device, equipment and storage medium, which aims to solve the technical problem that the confirmation of the existing motor auxiliary slot parameters cannot guarantee that the motor output torque is not excessively lost and still causes large cogging torque.

[0005] To achieve the above purpose, the present application provides a motor cogging torque optimization method, which comprises the following steps:

[0006] Constructing a motor simulation model, and performing auxiliary slot parameter scanning on the motor simulation model to obtain scanning data;

[0007] Based on the scanning data, a fitting function is constructed;

[0008] According to the geometric parameters of the motor simulation model, the parameter boundary conditions are determined;

[0009] According to the parameter boundary conditions and the fitting function, the target auxiliary slot parameters are determined.

[0010] Optionally, the constructing a motor simulation model, and performing auxiliary slot parameter scanning on the motor simulation model to obtain scanning data comprises:

[0011] Constructing a motor simulation model, and determining an auxiliary slot parameter set based on a preset parameter standard, wherein the auxiliary slot parameter value set comprises a plurality of auxiliary slot parameters;

[0012] respectively as operation parameters of the motor simulation model, and running the motor simulation model to obtain a set of cogging torque peak values, wherein the set of cogging torque peak values includes a plurality of cogging torque peak values, and the plurality of cogging torque peak values correspond to the plurality of auxiliary slot parameters;

[0013] The set of auxiliary slot parameters and the set of cogging torque peak values are taken as the scanning data.

[0014] The fitting function is constructed based on the scanning data, including:

[0015] A parameter matrix is constructed based on the set of auxiliary slot parameters, and a torque vector is constructed based on the set of cogging torque peak values;

[0016] The parameter matrix and the torque vector are fitted based on multiple linear regression to obtain a regression model;

[0017] Based on the regression model, fitting parameters are obtained, and the coefficients of the fitting parameters are traversed to obtain the fitting function.

[0018] Optionally, the auxiliary slot parameters include slot depth, slot width, and slot pitch, and the target auxiliary slot parameters are determined according to the parameter boundary conditions and the fitting function, including:

[0019] The maximum output torque function and the minimum cogging torque function are determined according to the fitting function;

[0020] The maximum torque function and the minimum cogging torque function are weighted to obtain an optimization function;

[0021] The value range of the slot depth, the value range of the slot width, and the value range of the slot pitch are determined according to the parameter boundary conditions;

[0022] The target auxiliary slot parameters are determined in the value range of the slot depth, the value range of the slot width, and the value range of the slot pitch based on the optimization function using a preset optimization algorithm, wherein the target auxiliary slot parameters include target slot depth, target slot width, and target slot pitch.

[0023] Optionally, after the target auxiliary slot parameters are determined according to the parameter boundary conditions and the fitting function, the method further includes:

[0024] The initial back electromotive force, the initial cogging torque, and the initial output torque of the motor simulation model are obtained;

[0025] updating the motor simulation model based on the target slot depth, the target slot width and the target slot pitch, and obtaining back EMF, cogging torque and output torque of the updated motor simulation model to obtain updated back EMF, updated cogging torque and updated output torque;

[0026] comparing the initial back EMF and the updated back EMF, the initial cogging torque and the updated cogging torque, and the initial output torque and the updated output torque respectively to verify the target auxiliary slot parameter.

[0027] Optionally, the comparing the initial back EMF and the updated back EMF, the initial cogging torque and the updated cogging torque, and the initial output torque and the updated output torque respectively to verify the target auxiliary slot parameter comprises:

[0028] if the difference between the updated back EMF and the initial back EMF is lower than a preset EMF threshold, determining whether the updated cogging torque is lower than the initial cogging torque;

[0029] if yes, determining whether the fluctuation of the updated output torque is lower than the fluctuation of the initial output torque;

[0030] if yes, determining that the target auxiliary slot parameter passes the verification, and taking the target auxiliary slot parameter as an optimal auxiliary slot parameter.

[0031] Before the constructing the motor simulation model and performing auxiliary slot parameter scanning on the motor simulation model to obtain scanning data, the method further comprises:

[0032] determining a pole-slot matching period according to the number of slots and the number of poles;

[0033] if the pole-slot matching period is an even number, determining the number of auxiliary slots as 2;

[0034] if the pole-slot matching period is an odd number other than 1, determining the number of auxiliary slots as 1.

[0035] In addition, to achieve the above-mentioned purposes, the application further provides a motor cogging torque optimization device, which comprises:

[0036] a model construction module configured to construct a motor simulation model and perform auxiliary slot parameter scanning on the motor simulation model to obtain scanning data;

[0037] a fitting function construction module configured to construct a fitting function based on the scanning data;

[0038] a boundary condition determination module configured to determine a parameter boundary condition according to a geometric parameter of the motor simulation model;

[0039] a parameter determination module configured to determine a target auxiliary slot parameter according to the parameter boundary condition and the fitting function.

[0040] Optionally, the model construction module is further configured to construct a motor simulation model, and determine a set of auxiliary slot parameters based on a preset parameter standard, wherein the set of auxiliary slot parameters includes a plurality of auxiliary slot parameters; take each auxiliary slot parameter in the set of auxiliary slot parameters as a running parameter of the motor simulation model, and run the motor simulation model to obtain a set of cogging torque peak values, wherein the set of cogging torque peak values includes a plurality of cogging torque peak values, and the plurality of cogging torque peak values correspond to the plurality of auxiliary slot parameters; and take the set of auxiliary slot parameters and the set of cogging torque peak values as the scanning data.

[0041] Optionally, the fitting function construction module is further configured to construct a parameter matrix based on the set of auxiliary slot parameters, construct a torque vector based on the set of cogging torque peak values, fit the parameter matrix and the torque vector based on multiple linear regression to obtain a regression model, and obtain fitting parameters based on the regression model and iterate coefficients of the fitting parameters to obtain the fitting function.

[0042] Optionally, the parameter determination module is further configured to determine a maximum output torque function and a minimum cogging torque function according to the fitting function, perform weighted processing on the maximum torque function and the minimum cogging torque function to obtain an optimization function, determine a value range of the slot depth, a value range of the slot width, and a value range of the slot pitch according to the parameter boundary condition, and determine the target auxiliary slot parameter in the value range of the slot depth, the value range of the slot width, and the value range of the slot pitch based on the optimization function, wherein the target auxiliary slot parameter includes a target slot depth, a target slot width, and a target slot pitch.

[0043] Optionally, the motor cogging torque optimization device further includes a verification module configured to obtain an initial back EMF, an initial cogging torque, and an initial output torque of the motor simulation model, update the motor simulation model based on the target slot depth, the target slot width, and the target slot pitch, and obtain an updated back EMF, an updated cogging torque, and an updated output torque of the updated motor simulation model, to obtain an updated back EMF, an updated cogging torque, and an updated output torque, and compare the initial back EMF and the updated back EMF, the initial cogging torque and the updated cogging torque, and the initial output torque and the updated output torque, respectively, to verify the target auxiliary slot parameter.

[0044] Optionally, the verification module is further configured to, if the difference between the updated back EMF and the initial back EMF is below a preset back EMF threshold, determine whether the updated cogging torque is below the initial cogging torque; if yes, determine whether the fluctuation of the updated output torque is below the fluctuation of the initial output torque; if yes, determine that the target auxiliary slot parameter passes the verification, and take the target auxiliary slot parameter as the optimal auxiliary slot parameter.

[0045] Optionally, the model construction module is further configured to determine a pole-slot combination period according to the number of slots and the number of poles; if the pole-slot combination period is even, determine the number of auxiliary slots as 2; if the pole-slot combination period is an odd number other than 1, determine the number of auxiliary slots as 1.

[0046] In addition, to achieve the above-mentioned purpose, the application further provides a motor cogging torque optimization device, which comprises a memory, a processor and a motor cogging torque optimization program stored on the memory and executable on the processor, and the motor cogging torque optimization program is configured to implement the steps of the motor cogging torque optimization method as described above.

[0047] In addition, to achieve the above-mentioned purpose, the application further provides a storage medium, which stores a motor cogging torque optimization program, and the motor cogging torque optimization program implements the steps of the motor cogging torque optimization method as described above when executed by a processor.

[0048] The application firstly constructs a motor simulation model, and performs parameter scanning on the motor simulation model to obtain scanning data, thereby improving the efficiency of torque optimization design; then constructs a fitting function according to the scanning data, thereby reducing the cost of computing power; further determines parameter boundary conditions according to the geometric parameters of the motor simulation model; finally determines the target auxiliary slot parameter according to the fitting function and the parameter boundary conditions, so that the auxiliary slot opened according to the target auxiliary slot parameter can ensure the output torque of the motor and reduce the cogging torque of the motor. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a structural schematic diagram of a motor cogging torque optimization device related to a hardware running environment of an embodiment scheme of the application;

[0050] Figure 2 is a flowchart of a first embodiment of the motor cogging torque optimization method of the application;

[0051] Figure 3A schematic diagram of a motor simulation model for the motor tooth slot torque optimization method of the present application.

[0052] Figure 4 A schematic diagram of a parameterized auxiliary slot model for the motor tooth slot torque optimization method of the present application.

[0053] Figure 5 A schematic diagram of a sub-process in the second embodiment of the motor tooth slot torque optimization method of the present application.

[0054] Figure 6 A schematic diagram of another sub-process in the second embodiment of the motor tooth slot torque optimization method of the present application.

[0055] Figure 7 A schematic diagram of another sub-process in the second embodiment of the motor tooth slot torque optimization method of the present application.

[0056] Figure 8 A schematic diagram of a sub-process in the third embodiment of the motor tooth slot torque optimization method of the present application.

[0057] Figure 9 A comparison diagram of back EMF before and after slotting in the motor tooth slot torque optimization method of the present application.

[0058] Figure 10 A comparison diagram of tooth slot torque before and after slotting in the motor tooth slot torque optimization method of the present application.

[0059] Figure 11 A schematic diagram of the principle of constructing a fitting function in the motor tooth slot torque optimization method of the present application.

[0060] Figure 12 A design flowchart of the motor tooth slot torque optimization method of the present application.

[0061] Figure 13 A structural block diagram of the first embodiment of the motor tooth slot torque optimization device of the present application.

[0062] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0063] It should be understood that the specific embodiments described herein are merely intended to explain the present application and are not intended to limit the present application.

[0064] Reference Figure 1 , Figure 1 A schematic diagram of the motor tooth slot torque optimization device structure of the hardware running environment involved in the embodiment scheme of the present application.

[0065] As Figure 1As shown in the figure, the motor cogging torque optimization device can include a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection communication between the components. The user interface 1003 can include a display, an input unit such as a keyboard, and can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 can be a high-speed random access memory (RAM) memory, or a stable non-volatile memory (NVM) such as a disk memory. The memory 1005 can also be a storage device independent of the aforementioned processor 1001.

[0066] Those skilled in the art can understand that Figure 1 The structure shown in the figure does not constitute a limitation on the motor cogging torque optimization device, and can include more or fewer components than the figure, or combine certain components, or different component arrangements.

[0067] As Figure 1 As shown in the figure, the memory 1005 as a storage medium can include an operating system, a network communication module, a user interface module, and a motor cogging torque optimization program.

[0068] In Figure 1 In the motor cogging torque optimization device shown in the figure, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the motor cogging torque optimization device of the application can be arranged in the motor cogging torque optimization device, and the motor cogging torque optimization device calls the motor cogging torque optimization program stored in the memory 1005 through the processor 1001, and executes the motor cogging torque optimization method provided by the embodiment of the application.

[0069] The embodiment of the application provides a motor cogging torque optimization method, which refers to Figure 2 , Figure 2 The flowchart of the first embodiment of the motor cogging torque optimization method of the application.

[0070] In this embodiment, the motor cogging torque optimization method includes the following steps:

[0071] Step S1: Construct a motor simulation model, and perform auxiliary slot parameter scanning on the motor simulation model to obtain scanning data.

[0072] It should be noted that the execution subject of the method of the embodiment can be a computing service device with data processing, network communication and program running functions, such as the motor tooth slot torque optimization device described above. The embodiment and each of the following embodiments will be described taking the motor tooth slot torque optimization device as an example.

[0073] It should be noted that the motor simulation model is constructed and parameter scanning is performed to obtain the mapping relationship data between the auxiliary slot parameters and the tooth slot torque, which needs to use professional motor simulation software and tools.

[0074] Please refer to Figure 3 and Figure 4 , Figure 3 is a schematic diagram of a motor simulation model for the motor tooth slot torque optimization method of the application; Figure 4 is a schematic diagram of a parameterized auxiliary slot model for the motor tooth slot torque optimization method of the application.

[0075] Specifically, a suitable motor simulation software or tool is selected, and common ones include MATLAB / Simulink, ANSYSElectronics Desktop, JMAG, etc. According to the type (such as permanent magnet synchronous motor, induction motor, etc.), structure and parameters of the motor, the simulation model of the motor is established. The model should include the structure, electromagnetic characteristics, mechanical characteristics and control system of the motor. In this embodiment, MATLAB and a permanent magnet synchronous motor are selected as an example for description.

[0076] Further, the parameter range and step size to be scanned are determined, including the parameters of the auxiliary slot (such as slot depth, slot width, slot pitch, etc.) and other related parameters. In the established motor simulation model, each set of parameter combinations in the parameter range is traversed, the simulation model is run and the tooth slot torque data is recorded. For each set of parameter combinations, the corresponding auxiliary slot parameters and the simulation obtained tooth slot torque data are recorded, and the data is arranged into a suitable format for analysis, such as a matrix or a table (as shown in the following table). The recorded data is analyzed to determine the mapping relationship between the auxiliary slot parameters and the tooth slot torque.

[0077] ck [mm] cs [mm] cx [mm] Tcog [N m] 0.1 0.5 0.1 1.330803015 0.1 1 0.1 1.331723584 0.1 1.5 0.1 1.332725333 … … … … 2.3 2 7 3.626229257 2.3 2.5 7 4.079556793

[0078] In the table, ck represents the slot width of the auxiliary slot, cs represents the slot depth of the auxiliary slot, cx represents the slot pitch of the auxiliary slot, and Tcc represents the tooth slot torque.

[0079] Optionally, a statistical analysis method or a machine learning method (such as regression analysis) can be used to determine the mapping relationship. Verify whether the obtained mapping relationship conforms to the actual situation, and make necessary optimization. Further adjust and optimize the simulation model as needed to improve the accuracy and reliability of the model.

[0080] By the above steps, the determined mapping relationship between the auxiliary slot parameters and the cogging torque is applied to the actual motor design and control to guide the selection and optimization of the auxiliary slot parameters, thereby reducing the cogging torque of the motor.

[0081] Step S2: based on the scanning data, a fitting function is constructed;

[0082] Specifically, the auxiliary slot parameters and the corresponding cogging torque data in the scanning data are obtained. According to the characteristics of the data, a suitable fitting function type is selected, such as linear, polynomial, exponential, etc. The relationship between the parameters and the torque is fitted by using a fitting algorithm to obtain a fitting function. It should be noted that the fitting algorithm that can be used includes the least squares method, curve fitting, etc.

[0083] Optionally, the fitting function is verified to check whether the fitting effect conforms to the actual situation. The fitting function is optimized: according to the verification result, the fitting function is adjusted and optimized to improve the fitting precision and generalization ability.

[0084] The parameter torque fitting function is applied to predict the torque value corresponding to unknown parameters, guide motor design and control.

[0085] Step S3: according to the geometric parameters of the motor simulation model, determine the parameter boundary condition;

[0086] Step S4: according to the parameter boundary condition and the fitting function, determine the target auxiliary slot parameter.

[0087] Specifically, the parameter boundary condition is determined according to the actual geometric parameters of the actual stator. The basic principle is that the auxiliary slot boundary does not exceed the stator tooth boundary, the auxiliary slot depth does not exceed the stator depth, and the auxiliary slot maximum spacing and maximum depth do not exceed the stator tooth boundary. It should be understood that the boundary condition determination principle is explained here, and the specific determination is based on the actual engineering situation.

[0088] The parameter boundary condition is used to limit the value range of the parameter. The goal of the fitting function is to minimize the residual error between the fitting function and the actual data. According to the actual situation, a suitable optimization algorithm is selected, such as genetic algorithm, particle swarm optimization algorithm, mayfly algorithm, raccoon algorithm, sparrow algorithm, etc.

[0089] Further, an optimization algorithm is used to minimize the fitting function to find the optimal parameter combination that makes the fitting function best fit the actual data. Verify whether the optimized parameter combination meets the preset parameter boundary condition, and check whether the fitting effect meets the actual situation. According to the verification result, the parameter boundary condition and the fitting function are adjusted and optimized to further improve the fitting precision and generalization ability.

[0090] The embodiment constructs a motor simulation model, performs parameter scanning on the motor simulation model, and obtains scanning data; based on the scanning data, a fitting function is constructed; according to the geometric parameters of the motor simulation model, the parameter boundary condition is determined; and according to the parameter boundary condition and the fitting function, the target auxiliary slot parameter is determined. The embodiment first constructs a motor simulation model, performs parameter scanning on the motor simulation model, and obtains scanning data, thereby improving the efficiency of torque optimization design, then constructs a fitting function according to the scanning data, thereby reducing the computing power cost, and finally determines the target auxiliary slot parameter according to the fitting function and the parameter boundary condition, so that the auxiliary slot opened according to the target auxiliary slot parameter can ensure the output torque of the motor and reduce the cogging torque of the motor.

[0091] Reference Figure 5 , Figure 5 is a sub-flow diagram of the motor cogging torque optimization method in the second embodiment of the motor cogging torque optimization method.

[0092] Based on the above first embodiment, in the present embodiment, step S1 comprises:

[0093] S11: Construct a motor simulation model, and determine a set of auxiliary slot parameters based on a preset parameter standard, wherein the set of auxiliary slot parameter values includes a plurality of auxiliary slot parameters;

[0094] S12: Take the auxiliary slot parameters in the set of auxiliary slot parameters as the operating parameters of the motor simulation model respectively, and run the motor simulation model to obtain a set of cogging torque peak values, wherein the set of cogging torque values includes a plurality of cogging torque peak values, and the plurality of cogging torque peak values correspond to the plurality of auxiliary slot parameters;

[0095] S13: Take the set of auxiliary slot parameters and the set of cogging torque peak values as the scanning data;

[0096] Specifically, according to the selected motor type and design requirements, a professional motor simulation software is used to establish a simulation model of the motor. Key parameters in the simulation model are determined, including the structure of the motor, material properties, electromagnetic properties, mechanical properties, and control strategies, etc. According to the design requirements and prior knowledge, the value range of the auxiliary slot parameters is determined. This includes parameters such as the depth, width, and spacing of the auxiliary slots. Within the determined parameter range, a combination set of auxiliary slot parameters is generated according to a certain step or sampling method. Uniform sampling, random sampling, or design sampling methods can be used.

[0097] Further, the generated auxiliary slot parameter set is input into the motor simulation model one by one for simulation running. In each simulation run, the cogging torque peak value output by the motor is recorded, as well as the corresponding auxiliary slot parameter combination. The recorded auxiliary slot parameter combination and the corresponding cogging torque value are arranged in the form of a data table or matrix. The accuracy and completeness of the mapping relationship data are ensured, and the data is preliminarily cleaned and processed.

[0098] Optionally, the arranged mapping relationship data is stored in a suitable data format, such as a CSV file, an Excel table, or a database. The stored mapping relationship data is verified, which can be verified by drawing charts, calculating statistical indicators, etc. to ensure the accuracy and reliability of the data.

[0099] The mapping relationship data of the auxiliary slot parameters and the cogging torque obtained is applied to motor design and control to guide the selection and optimization of auxiliary slot parameters. Through the above steps, a motor simulation model is constructed, and the mapping relationship data (scanning data) of the auxiliary slot parameters and the cogging torque is obtained, which provides strong support for motor design and control.

[0100] Reference Figure 6 , Figure 6 is a sub-process diagram of the second embodiment of the motor cogging torque optimization method of the application.

[0101] Based on the above first embodiment, in this embodiment, step S2 includes:

[0102] S21: constructing a parameter matrix based on the auxiliary slot parameter set and constructing a torque vector based on the cogging torque peak value set;

[0103] S22: fitting the parameter matrix and the torque vector based on multiple linear regression to obtain a regression model;

[0104] S23: obtaining fitting parameters based on the regression model, and traversing the coefficients of the fitting parameters to obtain the fitting function;

[0105] Specifically, according to the auxiliary slot parameter set, a parameter matrix is constructed, where each row represents a combination of auxiliary slot parameters. According to the set of tooth slot torque peaks, a torque vector is constructed, where each element corresponds to a tooth slot torque peak. Using a multiple linear regression method, the parameter matrix and the torque vector are fitted to obtain a regression model. During the fitting process, the regression model calculates the coefficients of the parameters, which represent the degree of influence of each auxiliary slot parameter on the tooth slot torque. The fitting parameters are obtained from the regression model, which are the coefficients in the linear relationship between the parameter matrix and the torque vector. According to the obtained fitting parameters, a fitting function is constructed. It should be noted that the form of the fitting function can be a linear function, a polynomial function or other appropriate function forms.

[0106] Further, the constructed fitting function is verified to check whether the fitting effect meets the actual situation. The fitting function can be used to predict the tooth slot torque value and compared with the actual value to evaluate the accuracy and generalization ability of the fitting. According to the verification result, the fitting function is adjusted and optimized to improve the fitting precision and generalization ability. The fitting effect can be optimized by trying different fitting methods, adjusting the form of the fitting function or increasing the sample data.

[0107] Reference Figure 7 , Figure 7 This is a sub-process diagram for another embodiment of the motor tooth slot torque optimization method.

[0108] Based on the above first embodiment, in this embodiment, step S4 includes:

[0109] S41: determining a maximum output torque function and a minimum tooth slot torque function according to the fitting function;

[0110] S42: performing weighted processing on the maximum torque function and the minimum tooth slot torque function to obtain an optimization function;

[0111] S43: determining the value range of the slot depth, the value range of the slot width and the value range of the slot pitch according to the parameter boundary condition;

[0112] S44: based on the optimization function, using a preset optimization algorithm to determine the target auxiliary slot parameters in the value range of the slot depth, the value range of the slot width and the value range of the slot pitch, wherein the target auxiliary slot parameters include target slot depth, target slot width and target slot pitch.

[0113] Specifically, according to the fitting function, the maximum output torque function and the minimum cogging torque function are determined, which describe the relationship between the auxiliary slot parameters and the output torque and the cogging torque. The maximum output torque function and the minimum cogging torque function are added according to certain weights to obtain an optimization function that comprehensively considers these two factors. Under the condition of the determined parameter boundary conditions, a preset optimization algorithm (such as genetic algorithm, particle swarm algorithm, etc.) is used to search for the auxiliary slot parameter combination that minimizes the optimization function. The target auxiliary slot parameters include the target slot depth, the target slot width, and the target slot pitch. Through the above method, the target auxiliary slot parameter combination that meets the parameter boundary conditions and minimizes the optimization function can be determined.

[0114] It should be noted that the construction of the fitting function needs to consider the design requirements and performance indicators of the motor, as well as the influence of the auxiliary slot parameters on the torque. According to the preset parameter boundary conditions and design requirements, the value range of the auxiliary slot parameters is determined. This includes parameters such as slot depth, slot width, and slot pitch. The value range of the auxiliary slot parameters needs to consider factors such as motor structure, material properties, manufacturing process, etc., to ensure the reasonableness and feasibility of the parameters.

[0115] Further, according to the characteristics and requirements of the problem, a suitable optimization algorithm is selected. It should be understood that the selected optimization algorithm needs to be able to search for the optimal solution within the given parameter range, and has good convergence and global search ability. Within the determined value range of the auxiliary slot parameters, the selected optimization algorithm is used to minimize the constructed optimization function. The optimization process searches for the auxiliary slot parameters within the given range to find the optimal parameter combination corresponding to the minimum value of the optimization function. These parameters include the target slot depth, the target slot width, and the target slot pitch. The optimal parameter combination is the result of the optimization algorithm search, representing the best choice within the given range.

[0116] Optionally, the obtained target auxiliary slot parameters are verified to ensure that they meet the design requirements and optimization goals. The effectiveness and feasibility of the parameters can be verified through simulation, experiment, or other methods to ensure the reliability of the optimization results.

[0117] Through the above steps, the target auxiliary slot parameters can be determined according to the fitting function and the parameter boundary conditions, providing guidance for motor design and control.

[0118] The embodiment constructs a motor simulation model, performs parameter scanning on the motor simulation model, and obtains scanning data; based on the scanning data, a fitting function is constructed; according to the geometric parameters of the motor simulation model, parameter boundary conditions are determined; and according to the parameter boundary conditions and the fitting function, target auxiliary slot parameters are determined. The embodiment first constructs a motor simulation model, performs parameter scanning on the motor simulation model, and obtains scanning data, thereby improving the efficiency of torque optimization design; then according to the scanning data, a fitting function is constructed, thereby reducing the computing power cost; and finally according to the geometric parameters of the motor simulation model, parameter boundary conditions are determined, and according to the fitting function and the parameter boundary conditions, target auxiliary slot parameters are determined, so that the auxiliary slot opened according to the target auxiliary slot parameters can ensure the output torque of the motor and reduce the cogging torque of the motor.

[0119] Reference Figure 8 , Figure 8 This is a sub-process schematic diagram of the third embodiment of the motor cogging torque optimization method of the application.

[0120] Based on the second embodiment, in this embodiment, after step S4, the following steps are included:

[0121] S4a: obtaining initial back EMF, initial cogging torque and initial output torque of the motor simulation model;

[0122] S4b: updating the motor simulation model based on the target slot depth, the target slot width and the target slot pitch, and obtaining back EMF, cogging torque and output torque of the updated motor simulation model, to obtain updated back EMF, updated cogging torque and updated output torque;

[0123] S4c: comparing the initial back EMF and the updated back EMF, the initial cogging torque and the updated cogging torque, and the initial output torque and the updated output torque, respectively, to verify the target auxiliary slot parameters;

[0124] Reference Figure 9 , Figure 9 This is a back EMF comparison chart before and after slotting of the motor cogging torque optimization method of the application. Figure 10 This is a cogging torque comparison chart before and after slotting of the motor cogging torque optimization method of the application.

[0125] Specifically, the back EMF, cogging torque, air-gap radial flux, and output torque at the initial state are extracted from the motor simulation model. It is noted that these parameters are obtained without applying any auxiliary slots. The target slot depth, target slot width, and target slot pitch are used to update the auxiliary slot parameters in the motor simulation model. This can be achieved by modifying the corresponding parameters in the motor simulation model. The updated motor simulation model is run to obtain the updated back EMF, cogging torque, air-gap radial flux, and output torque. These parameters reflect the motor performance after applying the target auxiliary slot parameters.

[0126] Further, the initial parameters are compared with the updated parameters to evaluate the effectiveness of the target auxiliary slot parameters. The comparison can be made by calculating the difference, relative error, or plotting the curves. For example, the difference between the initial parameters and the updated parameters can be calculated, or their variation trends under different operating conditions can be compared.

[0127] As shown in Figure 9 , the back EMF before and after slotting does not change significantly, ensuring the output performance of the motor. The peak value of the air-gap radial flux after slotting decreases, improving the vibration performance of the motor. The output torque before and after slotting is shown in the following table.

[0128]

[0129] From the table, it can be seen that the output torque fluctuation is reduced compared to before slotting.

[0130] Optionally, based on the comparison results, the effectiveness and advantages and disadvantages of the target auxiliary slot parameters are evaluated. If the updated parameters show significant improvement, such as reduced back EMF, increased output torque, or changed air-gap radial flux, the target auxiliary slot parameters can be effective. These results can be confirmed through further analysis and experimental verification, such as motor performance testing or actual application scenario verification.

[0131] Through the above steps, the effectiveness of the target auxiliary slot parameters can be evaluated in detail, and further adjustments and optimizations can be made based on the verification results. This helps to ensure that the motor can achieve the expected performance indicators in the design stage.

[0132] Based on the above second embodiment, in this embodiment, step S4c includes:

[0133] S4c1: If the difference between the updated back EMF and the initial back EMF is below a preset EMF threshold, determine whether the updated cogging torque is lower than the initial cogging torque;

[0134] S4c2: If yes, determine whether the fluctuation of the updated output torque is lower than the fluctuation of the initial output torque;

[0135] S4c2: If yes, it is determined that the target auxiliary slot parameter passes the verification, and the target auxiliary slot parameter is taken as the optimal auxiliary slot parameter;

[0136] Specifically, a new back EMF value is calculated using the updated auxiliary slot parameter, and then compared with the initial back EMF value to obtain a difference. The calculated back EMF difference is compared with a preset potential threshold. If the back EMF difference is lower than the threshold, it indicates that the performance of the motor is improved after updating the auxiliary slot parameter. In this case, it is necessary to further check whether the updated cogging torque is lower than the initial cogging torque. If the updated cogging torque is lower than the initial value, it is further checked whether the fluctuation amount of the output torque is lower than the initial value. If the updated cogging torque is lower than the initial value and the output torque fluctuation amount is also lower than the initial value, it is determined that the target auxiliary slot parameter passes the verification, and it is taken as the optimal auxiliary slot parameter.

[0137] Optionally, it is also possible to judge the air gap radial magnetic flux density variation: if the back EMF variation is not large, it is continued to judge whether the updated air gap radial magnetic flux density is lower than the initial air gap radial magnetic flux density. If the updated air gap radial magnetic flux density is lower than the initial air gap radial magnetic flux density, it indicates that the change of the auxiliary slot parameter leads to the increase of the air gap magnetic flux density, which may reduce the magnetic energy conversion efficiency of the motor.

[0138] Further, if the updated air gap radial magnetic flux density is lower than the initial air gap radial magnetic flux density, it is judged whether the updated output torque is lower than the initial output torque. If the updated output torque is lower than the initial output torque, it indicates that the change of the auxiliary slot parameter leads to the decrease of the motor output torque, which may affect the working performance and efficiency of the motor. If the updated output torque is lower than the initial output torque by a small amount, and other conditions (such as the back EMF difference being less than the preset threshold) are met, the target auxiliary slot parameter can be regarded as the optimal auxiliary slot parameter. The optimal auxiliary slot parameter is the auxiliary slot parameter that can minimize the cogging torque or meet the design requirements while keeping the performance of the motor stable.

[0139] Through the above steps, effective verification can be performed during the auxiliary slot parameter updating process, to ensure that the finally selected auxiliary slot parameter can improve the motor performance to a certain extent and meet the preset conditions, and it can be determined whether the target auxiliary slot parameter is optimal, and the motor can be further adjusted and optimized as needed.

[0140] Based on the above second embodiment, in this embodiment, before step S1, it further includes:

[0141] S1a: determining a pole-slot matching period according to the number of slots and the number of poles of the motor;

[0142] S1b: if the pole-slot matching period is even, determining the auxiliary slot number as 2;

[0143] S1c: if the pole-slot combination period is not an odd number, then determine the auxiliary slot number to be 1;

[0144] Specifically, according to the slot number and the pole number of the motor, the pole-slot combination period can be calculated. This period indicates how many slots there are on a pole. If the pole-slot combination period is even, it means that there are two slots on a pole, and at this time, the auxiliary slot number can be determined to be 2. If the pole-slot combination period is odd, it means that there is only one slot on a pole, and at this time, the auxiliary slot number is determined to be 1. It should be noted that when the auxiliary slot number is 1, there is no slot pitch in the auxiliary slot parameter.

[0145] Further, the pole-slot combination period number calculation formula is as follows:

[0146]

[0147] In the formula, GCD(z,2p) represents the greatest common divisor of the slot number and the pole number. The auxiliary slot number of a single stator tooth is k, and when Np≠1, auxiliary slots need to be opened so that k+1≠m·Np, m is an integer. Take a 14-inch wheel motor as an example to illustrate this embodiment. The 14-inch wheel motor is an outer rotor built-in permanent magnet synchronous motor, which is a fractional slot motor with 18 slots and 16 poles. The pole-slot combination period number is 8, in order to satisfy k+1≠m·Np, k=2.

[0148] Please refer to Figure 11 and Figure 12 , Figure 11 is a schematic diagram of the principle of constructing a fitting function for the motor cogging torque optimization method of the application; Figure 12 is a design flowchart of the motor cogging torque optimization method of the application;

[0149] In this embodiment, a fitting function is established by using the data obtained by scanning the auxiliary slot parameters, as follows: Tcog=interp3(X,Y,Z,V,ck,cs,cx,’spline’);

[0150] The above formula is a matlab code, in which X, Y, Z represent parameter variable grid vectors; V represents the cogging torque result obtained by scanning the parameter variables; interp3 represents a three-dimensional interpolation function; ’spline’ represents using a cubic spline interpolation method, and the interpolation method can be selected according to the matlab help document.

[0151] The optimized mathematical model obtained by the above analysis is:

[0152] y=min(Tcog(ck,cs,cx))

[0153] s.t.{0<ck<3.5;0.5<cs<5;0<cx<7}

[0154] Solving the model represents the cogging torque peak minimum is the optimal auxiliary slot parameters.

[0155] In matlab, the intelligent optimization algorithm is used to optimize the objective function, and the optimal auxiliary slot parameter scheme is obtained. The optimal result is input into the corresponding parameter in ANSYS Electronics Desktop, and the characteristics of the motor after slotting, such as cogging torque, are obtained through simulation and compared with those before slotting, so that the optimal auxiliary slot scheme can be determined.

[0156] In this embodiment, based on the idea of mathematical modeling, an interpolation target function (fitting function) with high precision and high reliability is established by parameter scanning. In the auxiliary slot optimization design, the function relationship between the auxiliary slot parameters and the cogging torque and the function relationship between the auxiliary slot parameters and the output torque are established, and the two targets of minimum cogging torque and maximum output torque are weighted, for example, each target accounts for 50%, and then the multi-objective auxiliary slot optimization design is realized. The sample data (scanning data) is obtained by parameter scanning to establish the function relationship between the auxiliary slot parameters and the cogging torque, on the one hand, the function of the influence of the auxiliary slot parameters on the cogging torque is realized, and on the other hand, a high-reliability and high-accuracy mathematical modeling method for establishing the change rule of the auxiliary slot parameters and the cogging torque is proposed, which effectively improves the optimization design efficiency, avoids the time cost and computing power cost of finite element simulation, and provides reference value for other optimization designs.

[0157] In this embodiment, a motor simulation model is constructed, and parameter scanning is performed on the motor simulation model to obtain scanning data; based on the scanning data, a fitting function is constructed; according to the geometric parameters of the motor simulation model, the parameter boundary conditions are determined; and according to the parameter boundary conditions and the fitting function, the target auxiliary slot parameters are determined. In this embodiment, the motor simulation model is first constructed, and parameter scanning is performed on the motor simulation model to obtain scanning data, which improves the efficiency of torque optimization design, then the fitting function is constructed according to the scanning data, which reduces the computing power cost, and finally the parameter boundary conditions are determined according to the geometric parameters of the motor simulation model, and the target auxiliary slot parameters are determined according to the fitting function and the parameter boundary conditions, so that the auxiliary slot opened according to the target auxiliary slot parameters can ensure the output torque of the motor and reduce the cogging torque of the motor.

[0158] In addition, the embodiment of the present application also provides a storage medium, and the storage medium stores a motor cogging torque optimization program. When the motor cogging torque optimization program is executed by a processor, the steps of the motor cogging torque optimization method described above are realized.

[0159] Please refer to Figure 13 , Figure 13 The structure block diagram of the first embodiment of the motor cogging torque optimization device of the present application is shown in the figure.

[0160] As Figure 13As shown, the motor tooth slot torque optimization device provided by the embodiment of the present application comprises:

[0161] The model construction module 101 is configured to construct a motor simulation model, perform auxiliary slot parameter scanning on the motor simulation model, and obtain scanning data.

[0162] The fitting function construction module 102 is configured to construct a fitting function based on the scanning data.

[0163] The boundary condition determination module 103 is configured to determine a parameter boundary condition according to a geometric parameter of the motor simulation model.

[0164] The parameter determination module 104 is configured to determine a target auxiliary slot parameter according to the parameter boundary condition and the fitting function.

[0165] In this embodiment, the motor simulation model is constructed, and parameter scanning is performed on the motor simulation model to obtain scanning data. Then, a fitting function is constructed based on the scanning data. A parameter boundary condition is determined according to a geometric parameter of the motor simulation model. Finally, a target auxiliary slot parameter is determined according to the parameter boundary condition and the fitting function. In this embodiment, the motor simulation model is constructed, and parameter scanning is performed on the motor simulation model to obtain scanning data, thereby improving the efficiency of torque optimization design. Then, a fitting function is constructed based on the scanning data, thereby reducing the cost of computing power. Then, a parameter boundary condition is determined according to a geometric parameter of the motor simulation model. Finally, a target auxiliary slot parameter is determined according to the fitting function and the parameter boundary condition, so that the auxiliary slot opened according to the target auxiliary slot parameter can ensure the output torque of the motor and reduce the tooth slot torque of the motor.

[0166] Based on the first embodiment of the motor tooth slot torque optimization device of the present application, the second embodiment of the motor tooth slot torque optimization device of the present application is provided.

[0167] In this embodiment, the model construction module 101 is further configured to construct a motor simulation model, determine an auxiliary slot parameter set based on a preset parameter standard, wherein the auxiliary slot parameter set includes a plurality of auxiliary slot parameters, take the auxiliary slot parameters in the auxiliary slot parameter set as the operating parameters of the motor simulation model respectively, and run the motor simulation model to obtain a tooth slot torque peak set, wherein the tooth slot torque peak set includes a plurality of tooth slot torque peaks, and the plurality of tooth slot torque peaks correspond to the plurality of auxiliary slot parameters. The auxiliary slot parameter set and the tooth slot torque peak set are taken as the scanning data.

[0168] Further, the fitting function construction module 102 is further configured to construct a parameter matrix based on the auxiliary slot parameter set, construct a torque vector based on the set of tooth slot torque peaks, perform fitting on the parameter matrix and the torque vector based on multiple linear regression to obtain a regression model, and obtain fitting parameters based on the regression model and traverse coefficients of the fitting parameters to obtain the fitting function.

[0169] Further, the parameter determination module 104 is further configured to determine a maximum output torque function and a minimum tooth slot torque function according to the fitting function, perform weighting processing on the maximum torque function and the minimum tooth slot torque function to obtain an optimization function, determine a value range of the slot depth, a value range of the slot width, and a value range of the slot pitch according to the parameter boundary condition, and determine the target auxiliary slot parameter in the value range of the slot depth, the value range of the slot width, and the value range of the slot pitch based on the optimization function, where the target auxiliary slot parameter includes a target slot depth, a target slot width, and a target slot pitch.

[0170] Further, the motor tooth slot torque optimization apparatus further includes a verification module configured to obtain an initial back electromotive force, an initial tooth slot torque, and an initial output torque of the motor simulation model, update the motor simulation model based on the target slot depth, the target slot width, and the target slot pitch, and obtain an updated back electromotive force, an updated tooth slot torque, and an updated output torque of the updated motor simulation model to obtain an updated back electromotive force, an updated tooth slot torque, and an updated output torque, and compare the initial back electromotive force and the updated back electromotive force, the initial tooth slot torque and the updated tooth slot torque, and the initial output torque and the updated output torque to verify the target auxiliary slot parameter.

[0171] Further, the verification module is further configured to, if a difference between the updated back electromotive force and the initial back electromotive force is lower than a preset electromotive force threshold, determine whether the updated tooth slot torque is lower than the initial tooth slot torque, if yes, determine whether a fluctuation amount of the updated output torque is lower than a fluctuation amount of the initial output torque, if yes, determine that the target auxiliary slot parameter passes the verification, and take the target auxiliary slot parameter as an optimal auxiliary slot parameter.

[0172] Further, the model construction module 101 is further configured to determine a pole-slot matching period according to a number of motor slots and a number of poles, determine an auxiliary slot number as 2 if the pole-slot matching period is an even number, and determine the auxiliary slot number as 1 if the pole-slot matching period is an odd number other than 1.

[0173] The embodiment constructs a motor simulation model, performs parameter scanning on the motor simulation model, and obtains scanning data; a fitting function is constructed based on the scanning data; parameter boundary conditions are determined according to geometric parameters of the motor simulation model; and target auxiliary slot parameters are determined according to the parameter boundary conditions and the fitting function. The embodiment first constructs a motor simulation model, performs parameter scanning on the motor simulation model, and obtains scanning data, thereby improving the efficiency of torque optimization design; then a fitting function is constructed according to the scanning data, thereby reducing the computing power cost; finally, parameter boundary conditions are determined according to geometric parameters of the motor simulation model, and target auxiliary slot parameters are determined according to the fitting function and the parameter boundary conditions, so that auxiliary slots opened according to the target auxiliary slot parameters can ensure the output torque of the motor and reduce the cogging torque of the motor.

[0174] Other embodiments or specific implementations of the motor cogging torque optimization device of the present application can refer to the above-mentioned method embodiments, which will not be described here.

[0175] It should be noted that in this paper, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or system. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or system including the element.

[0176] The above-mentioned embodiment numbers of the present application are only for description, not representing the advantages and disadvantages of the embodiments.

[0177] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and necessary general hardware platform, of course, they can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, an optical disk), and includes a plurality of instructions for making a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in various embodiments of the present application.

[0178] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation based on the content of the specification and drawings of the present application, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method of motor cogging torque optimization, characterized by, The method comprises the following steps: constructing a motor simulation model and determining an auxiliary slot parameter set based on a preset parameter standard, wherein the auxiliary slot parameter set comprises a plurality of auxiliary slot parameters, and the auxiliary slot parameters comprise slot depth, slot width and slot pitch; taking the auxiliary slot parameters in the auxiliary slot parameter set as operation parameters of the motor simulation model respectively, and running the motor simulation model to obtain a set of cogging torque peak values, wherein the set of cogging torque peak values comprises a plurality of cogging torque peak values, and the plurality of cogging torque peak values correspond to the plurality of auxiliary slot parameters; taking the auxiliary slot parameter set and the set of cogging torque peak values as scanning data; constructing a parameter matrix based on the auxiliary slot parameter set and constructing a torque vector based on the set of cogging torque peak values; based on multiple linear regression, fitting the parameter matrix and the torque vector to obtain a regression model; based on the regression model, obtaining fitting parameters and traversing the coefficients of the fitting parameters to obtain a fitting function; determining parameter boundary conditions according to the geometric parameters of the motor simulation model; determining a maximum output torque function and a minimum cogging torque function according to the fitting function; performing weighted processing on the maximum output torque function and the minimum cogging torque function to obtain an optimization function; determining the value range of the slot depth, the value range of the slot width and the value range of the slot pitch according to the parameter boundary conditions; based on the optimization function, determining a target auxiliary slot parameter in the value range of the slot depth, the value range of the slot width and the value range of the slot pitch by using a preset optimization algorithm, wherein the target auxiliary slot parameter comprises a target slot depth, a target slot width and a target slot pitch.

2. The method of electric machine cogging torque optimization according to claim 1, characterized in that, After determining the target auxiliary slot parameter according to the parameter boundary conditions and the fitting function, the method further comprises the following steps: obtaining an initial back electromotive force, an initial cogging torque and an initial output torque of the motor simulation model; updating the motor simulation model based on the target slot depth, the target slot width and the target slot pitch, and obtaining the back electromotive force, the cogging torque and the output torque of the updated motor simulation model to obtain an updated back electromotive force, an updated cogging torque and an updated output torque; comparing the initial back electromotive force and the updated back electromotive force, the initial cogging torque and the updated cogging torque, and the initial output torque and the updated output torque respectively to verify the target auxiliary slot parameter.

3. The method of electric machine cogging torque optimization according to claim 2, characterized in that, The step of comparing the initial back electromotive force and the updated back electromotive force, the initial cogging torque and the updated cogging torque, and the initial output torque and the updated output torque respectively to verify the target auxiliary slot parameter comprises the following steps: if the difference between the updated back electromotive force and the initial back electromotive force is lower than a preset electromotive force threshold, determining whether the updated cogging torque is lower than the initial cogging torque; if yes, determining whether the fluctuation of the updated output torque is lower than the fluctuation of the initial output torque; if yes, determining that the target auxiliary slot parameter passes the verification, and taking the target auxiliary slot parameter as an optimal auxiliary slot parameter.

4. The method of electric machine cogging torque optimization of claim 1, wherein, Before the motor simulation model is constructed and the motor simulation model is subjected to auxiliary slot parameter scanning to obtain scanning data, the method further includes: determining a pole-slot matching period according to the number of slots and the number of poles of the motor; if the pole-slot matching period is an even number, determining the number of auxiliary slots to be 2; if the pole-slot matching period is an odd number other than 1, determining the number of auxiliary slots to be 1.

5. An electric motor cogging torque optimization apparatus characterized by comprising: The motor cogging torque optimization device includes: A model construction module is configured to construct a motor simulation model and determine an auxiliary slot parameter set based on a preset parameter standard, wherein the auxiliary slot parameter set includes a plurality of auxiliary slot parameters, and the auxiliary slot parameters include slot depth, slot width, and slot pitch. The auxiliary slot parameters in the auxiliary slot parameter set are used as operating parameters of the motor simulation model, and the motor simulation model is run to obtain a set of cogging torque peak values, wherein the set of cogging torque peak values includes a plurality of cogging torque peak values, and the plurality of cogging torque peak values correspond to the plurality of auxiliary slot parameters. The auxiliary slot parameter set and the set of cogging torque peak values are used as scanning data. A fitting function construction module is configured to construct a parameter matrix based on the auxiliary slot parameter set, construct a torque vector based on the set of cogging torque peak values, and obtain a regression model by fitting the parameter matrix and the torque vector based on multiple linear regression. The fitting function construction module is further configured to obtain fitting parameters based on the regression model and obtain a fitting function by traversing the coefficients of the fitting parameters. A boundary condition determination module is configured to determine a parameter boundary condition based on the geometric parameters of the motor simulation model. A parameter determination module is configured to determine a maximum output torque function and a minimum cogging torque function based on the fitting function, perform weighted processing on the maximum output torque function and the minimum cogging torque function to obtain an optimization function, determine the value range of the slot depth, the value range of the slot width, and the value range of the slot pitch based on the parameter boundary condition, and determine a target auxiliary slot parameter based on the optimization function within the value range of the slot depth, the value range of the slot width, and the value range of the slot pitch, wherein the target auxiliary slot parameter includes a target slot depth, a target slot width, and a target slot pitch.

6. An electric motor cogging torque optimization apparatus characterized by comprising: The device includes a memory, a processor, and a motor cogging torque optimization program stored on the memory and executable on the processor, and the motor cogging torque optimization program is configured to implement the steps of the motor cogging torque optimization method according to any one of claims 1 to 4.

7. A storage medium, characterized by The storage medium stores a motor cogging torque optimization program, and the motor cogging torque optimization program is executed by the processor to implement the steps of the motor cogging torque optimization method according to any one of claims 1 to 4.

Images