Magnetic force optimization distribution method and system for motor rotor

Abstract

The present application provides a method and system for optimizing the magnetic force distribution of a motor rotor, which relates to the technical field of motor rotors. The operation relationship library is mined through the operation records of interactive motor rotors; a three-dimensional magnetic field simulation model is performed on the target motor rotor, and a magnetic force optimization module is constructed based on the operation relationship library and torque analysis, wherein the magnetic circuit structure-magnet distribution of the target motor rotor is three-dimensionally modeled, and the relationship between the magnet distribution and the torque characteristics-motor performance is used as the analysis target; based on the magnetic force optimization module, a two-step optimization iteration is performed under a two-dimensional magnetic field to determine the optimization distribution strategy. The present application solves the technical problem that the prior art cannot fully consider the complex relationship between the magnetic circuit structure, magnet distribution and motor performance due to the lack of in-depth analysis and mining of the motor rotor operation records, resulting in the inaccurate optimization of the magnetic force distribution of the motor rotor, thereby improving the overall efficiency and torque output characteristics of the motor.

Description

Technical Field

[0001] The present application relates to the technical field of motor rotors, and in particular to a method and system for optimizing magnetic force distribution for motor rotors. Background Art

[0002] The motor rotor is one of the most critical components in the motor, and its magnetic force distribution directly affects the motor's operating performance, efficiency and stability. With the widespread development of electric motor applications, the magnetic force optimization problem of the motor rotor has increasingly become a research focus in motor design. Existing motor rotor magnetic force optimization methods mainly include static optimization based on theoretical models and design optimization based on empirical formulas. These methods mostly start from the characteristics of the magnet itself, such as magnetic field strength, magnetic force size and other factors to adjust the magnet distribution or magnetic circuit structure. However, on the one hand, this method lacks in-depth mining of the motor rotor operating data, fails to fully consider the complex relationship between the magnetic circuit structure, magnet distribution and motor performance, and can often only adjust a certain factor alone, making it difficult to achieve a comprehensive improvement in overall performance. On the other hand, the dynamic changes of the motor under different working conditions are ignored in the optimization process, and the relationship between the torque output characteristics and other factors is not fully utilized. The actual performance of the motor rotor cannot be fully considered, resulting in limited optimization effects. Summary of the invention

[0003] The present application provides a method and system for optimizing the magnetic force distribution of a motor rotor, which solves the technical problem that the traditional magnetic force distribution optimization method lacks in-depth analysis and mining of the motor rotor operation records, and is unable to fully consider the complex relationship between the magnetic circuit structure, magnet distribution and motor performance, resulting in insufficient precision in the optimization of the motor rotor magnetic force distribution, which limits the improvement of the overall performance of the motor. The application achieves the technical effect of comprehensively optimizing the motor rotor magnetic force distribution and improving the overall efficiency and torque output characteristics of the motor.

[0004] In view of the above problems, on the one hand, the present application provides a method for optimizing the magnetic force distribution of a motor rotor, the method comprising: interacting with the operation records of the motor rotor, mining an operation relationship library, wherein the operation relationship library contains multiple mutual influence relationships between magnetic circuit structure-magnet distribution-motor performance; performing three-dimensional magnetic field simulation modeling on the target motor rotor, and constructing a magnetic force optimization module based on the operation relationship library as a benchmark and torque analysis as an analysis method, wherein three-dimensional modeling is performed on the magnetic circuit structure-magnet distribution of the target motor rotor, and the relationship between magnet distribution and torque characteristics-motor performance is used as an analysis target; based on the magnetic force optimization module, a two-step optimization iteration is performed under a two-dimensional magnetic field to determine the optimization distribution strategy.

[0005] On the other hand, the present application also provides a magnetic force optimization distribution system for a motor rotor, the system comprising: an operation relationship mining module, which is used to interact with the operation records of the motor rotor and mine the operation relationship library, wherein the operation relationship library contains multiple mutual influence relationships between magnetic circuit structure-magnet distribution-motor performance; a three-dimensional magnetic field simulation module, which is used to perform three-dimensional magnetic field simulation modeling on the target motor rotor, and construct a magnetic force optimization module based on the operation relationship library and torque analysis, wherein the magnetic circuit structure-magnet distribution of the target motor rotor is three-dimensionally modeled, and the relationship between magnet distribution and torque characteristics-motor performance is used as the analysis target; a distribution optimization module, which is used to perform two-step optimization iteration under a two-dimensional magnetic field based on the magnetic force optimization module to determine the optimization distribution strategy.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: By interacting with the operation records of the motor rotor and mining the operation relationship library, the influence of the magnet layout on the motor performance can be revealed, providing a data basis for subsequent optimization. The potential relationship between various factors can be mined from the actual operation data, so that the optimization scheme is more in line with the actual operation of the motor. By performing three-dimensional magnetic field simulation modeling on the target motor rotor and using torque analysis as the analysis method to build a magnetic optimization module, the relationship between torque output and motor performance under different magnet layouts can be accurately predicted by simulation means, and the motor performance can be considered from a more comprehensive perspective, providing a model basis for finding the optimal magnetic force distribution. The magnetic force distribution of the motor rotor under a two-dimensional magnetic field is optimized through a two-step optimization iteration algorithm. Optimization under a two-dimensional magnetic field can reduce computational complexity and improve computational efficiency. The two-step optimization iteration can more accurately determine the optimization distribution strategy, and continuously approach the optimal magnetic force distribution through multiple iterations, thereby achieving accurate optimization of the magnetic force distribution of the motor rotor.

[0007] To summarize, the present application comprehensively considers the complex relationship between the magnetic circuit structure, magnet distribution and motor performance, fully utilizes the torque output characteristics, and realizes the comprehensive optimization of the motor rotor magnetic force distribution, thereby effectively improving the overall efficiency and output characteristics of the motor, so that the motor can have more stable and efficient performance under different working conditions.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of a method for optimizing magnetic force distribution for a motor rotor provided in an embodiment of the present application.

[0010] Figure 2 A schematic flow chart of the first optimization distribution strategy in the magnetic force optimization distribution method for the motor rotor provided in an embodiment of the present application.

[0011] Figure 3 A schematic diagram of the structure of a magnetic force optimization distribution system for a motor rotor provided in an embodiment of the present application.

[0012] Description of the reference numerals: operation relationship mining module 10, three-dimensional magnetic field simulation module 20, distribution optimization module 30. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a method and system for optimizing the magnetic force distribution of a motor rotor, thereby solving the technical problem that the conventional magnetic force distribution optimization method lacks in-depth analysis and mining of the motor rotor operation records, and is unable to comprehensively consider the complex relationship between the magnetic circuit structure, magnet distribution and motor performance, resulting in inaccurate optimization of the motor rotor magnetic force distribution and limiting the improvement of the overall performance of the motor. The embodiment of the present application achieves the technical effect of comprehensively optimizing the motor rotor magnetic force distribution and improving the overall efficiency and torque output characteristics of the motor.

[0014] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a method for optimizing magnetic force distribution for a motor rotor, the method comprising: Step S1: Interacting with the operation records of the motor rotor to mine an operation relationship library, wherein the operation relationship library contains a plurality of mutual influence relationships among magnetic circuit structure, magnet distribution and motor performance.

[0015] Specifically, the motor rotor is the rotating part in the motor, which interacts with the stator through magnetic force to generate torque and drive the motor to operate. The operation relationship library is a database containing the influence of different magnetic circuit structures and magnet distribution on motor performance. Among them, the magnetic circuit structure is the magnetic field conduction path in the motor, including the stator, rotor, air gap and other parts. The design of the magnetic circuit affects the strength and distribution of the magnetic field, and thus affects the operating performance of the motor. Magnet distribution refers to the specific layout of magnets on the motor rotor, including the number, position, and pole direction of the magnets, which affects the distribution of the magnetic field and the performance of the motor.

[0016] First, the operating data of the motor rotor under different working conditions are collected. These operating data can be obtained through real-time monitoring by sensors, and then processed and analyzed using data analysis tools to dig out the intrinsic relationship between the magnetic circuit structure, magnet distribution and motor performance, and establish an operating relationship library containing these mutual influence relationships. By building an operating relationship library, it is possible to accurately grasp the relationship between the different magnetic circuit structures and magnet distributions of the motor rotor and the motor performance, providing a theoretical basis and actual operating data for the subsequent optimization process, making the optimization process more targeted and accurate.

[0017] Step S2: Perform three-dimensional magnetic field simulation modeling on the target motor rotor, take the operation relationship library as a benchmark, use torque analysis as an analysis method, and construct a magnetic optimization module, wherein the magnetic circuit structure-magnet distribution of the target motor rotor is three-dimensionally modeled, and the relationship between magnet distribution and torque characteristics-motor performance is used as an analysis target.

[0018] Specifically, torque analysis refers to the analysis of the torque output generated by the motor rotor under different magnetic force distributions through mathematical models and calculation methods. The magnetic force optimization module is an optimization tool developed based on the operation relationship library and magnetic field simulation model. This module uses three-dimensional magnetic field simulation and torque analysis to optimize the motor magnetic force distribution by analyzing and adjusting factors such as magnetic circuit structure and magnet distribution.

[0019] Based on the operation relationship library established in step S1, simulation software (such as ANSYS, Maxwell) is used to perform three-dimensional magnetic field simulation modeling on the target motor rotor. Among them, the target motor rotor can be any motor rotor that needs to be optimized for magnetic force distribution. During the modeling process, the magnetic circuit structure and magnet distribution in the operation relationship library are input into the simulation tool to simulate the magnetic field distribution of the rotor under different working conditions. Subsequently, the torque characteristics of the motor under different magnet distributions are calculated through torque analysis. Combined with the three-dimensional magnetic field simulation model and torque analysis, a magnetic force optimization module is constructed. For example, when modeling, different magnet material properties are set according to the actual size and magnet layout of the target motor rotor, and then the torque output of the motor under different working conditions is calculated through software simulation, and the influence of magnet distribution on torque is analyzed. Through three-dimensional magnetic field simulation modeling, the magnetic field distribution of the motor in actual work can be accurately simulated, providing accurate data support for magnetic force optimization. Torque analysis helps evaluate the influence of different magnet distributions on motor performance, ensuring that the optimization scheme can improve the torque output and overall efficiency of the motor.

[0020] Step S3: Based on the magnetic optimization module, a two-step optimization iteration is performed under a two-dimensional magnetic field to determine an optimization distribution strategy.

[0021] Specifically, the two-dimensional magnetic field is a simplified magnetic field model that only considers the magnetic field distribution of the motor rotor in the plane, which can improve the calculation efficiency and quickly perform preliminary optimization. The two-step optimization iteration is an optimization algorithm that gradually approaches the optimal solution through two rounds of optimization. The first step is to randomly perturb and evaluate the preliminary optimization plan, and the second step is to further optimize based on the results of the first round and make a convergence judgment to ensure that the optimal magnetic force distribution strategy is found, that is, the optimized distribution strategy. The optimized distribution strategy refers to the magnet distribution scheme determined according to the optimization process to achieve the best motor performance, such as higher torque output, lower energy loss, etc.

[0022] In the magnetic optimization module, the three-dimensional magnetic field simulation results are converted into a two-dimensional model to reduce the computational complexity. Then, a two-step optimization iteration is performed using an optimization algorithm (such as particle swarm optimization or genetic algorithm). The first step of optimization adjusts the magnet distribution through random perturbations and evaluates its impact on the motor performance to obtain a preliminary optimization solution. The second step of optimization is based on the results of the first step of optimization, further optimizing the magnet distribution to meet the convergence conditions and ultimately determining the optimal magnetic force distribution strategy. Through the two-step optimization iteration under the two-dimensional magnetic field, the optimization efficiency can be effectively improved and the final magnet distribution scheme can be ensured to reach the optimal state. Through this step, the optimization process can strike a balance between the amount of calculation and accuracy, and quickly find the best magnetic force distribution strategy that adapts to the actual working conditions of the motor.

[0023] Furthermore, the method described in the embodiment of the present application also includes: Step S41: Acquire the control strategy of the target motor rotor and determine the control electrical data.

[0024] Step S42: Determine a dynamic adjustment strategy for magnetic force distribution according to the control range of the control electrical data, wherein the dynamic adjustment strategy is a dynamic adjustment of the magnetic force distribution corresponding to the control of the electrical data in the control range.

[0025] Step S43: using the optimization distribution strategy and the dynamic adjustment strategy as the magnetic force optimization distribution result of the target motor rotor.

[0026] Specifically, in addition to the influence of the motor rotor distribution on the magnetic force distribution, in the actual motor control, dynamic adjustment operations such as changing the current waveform, phase angle or frequency can also indirectly affect the magnetic force distribution. Therefore, in the optimization of magnetic force distribution, the dynamic adjustment strategy also needs to be optimized.

[0027] The control strategy refers to the control rules or methods set according to the motor operation requirements and environmental conditions during the operation of the motor. The control strategy includes the adjustment plan for the motor parameters (such as speed, current, power, etc.) to adjust the performance and operating status of the motor. Control electrical data refers to the electrical signal data related to the motor performance and operating status generated during the motor control process, such as current, voltage, power, frequency, etc. First, determine the motor control system (such as PLC, single-chip microcomputer, etc.) to which the target motor rotor belongs, and then obtain its control strategy from the control system. This control strategy is stored in the controller of the control system (such as PLC, single-chip microcomputer, etc.). According to these control strategies, the corresponding control electrical data, such as current, voltage, speed and other parameters, are collected.

[0028] The dynamic adjustment strategy is a magnetic force distribution scheme adjusted according to the real-time control data of the motor. It is time-varying and adaptable, and can be adjusted in real time as the working state changes. The acquired control electrical data is analyzed to determine its control range. The control range refers to the value range of these electrical data under normal operation or different working conditions. For example, in a motor speed control system, the control range of the input voltage may be from 0.8 times to 1.2 times the rated voltage. According to this control range, a mapping relationship between magnetic force distribution and electrical data is established. When the control electrical data changes within the control range, the dynamic adjustment strategy of magnetic force distribution is determined according to a pre-set rule (based on the physical model or experimental data of the motor). For example, if the voltage in the control electrical data decreases, according to the electromagnetic relationship of the motor, the magnetization direction or strength of the magnet needs to be adjusted to maintain the performance of the motor. This is a dynamic adjustment strategy for magnetic force distribution. The dynamic adjustment strategy can adjust the magnetic force distribution according to the real-time working data of the motor. This flexibility ensures that the motor can adapt to different loads, speeds and working environments, optimize the performance of the motor, and significantly improve the stability and efficiency of the motor, especially when the operating conditions change frequently.

[0029] The optimized distribution strategy obtained above is combined with the dynamic adjustment strategy to form the final magnetic force optimization distribution result. This result is a comprehensive solution that can provide optimized magnetic force distribution under different working conditions of the motor to optimize the motor performance. For example, the optimized distribution strategy determines the initial distribution and basic parameters of the magnet, and the dynamic adjustment strategy specifies how to fine-tune the magnetic force distribution under different control electrical data such as voltage and current. The combination of the two can comprehensively optimize the magnetic force distribution of the motor rotor.

[0030] By combining the optimized distribution strategy with the dynamic adjustment strategy, the design and operation of the motor rotor can take into account both static performance and dynamic adaptability, which not only improves the efficiency and performance of the motor, but also enhances the motor's adaptability to different working conditions and extends the motor's service life.

[0031] Furthermore, in step S3, the optimization distribution strategy is determined, including: Step S3-11: Setting optimization variables, wherein the optimization variables include at least the anisotropic dimensions of the magnetic poles and the latitude of the magnetic pole layer.

[0032] Step S3-12: Introduce a penalty term and determine the objective function with the torque characteristic, wherein the torque characteristic at least includes the self-spin torque and the tilt torque, and the penalty term is determined based on the optimization disadvantage and the preset optimization degree.

[0033] Step S3-13: Based on the optimization variables and the objective function, a multi-objective optimization search is performed under finite element analysis to determine an optimization distribution strategy.

[0034] Specifically, in the process of determining the optimization distribution strategy, the optimization variables must be set first, that is, the parameters that need to be adjusted during the optimization process. In the magnetic optimization of the motor rotor, the optimization variables include the anisotropic dimensions of the magnetic poles (such as the width, length, thickness, etc. of the magnetic poles) and the latitude of the magnetic pole layer (that is, the hierarchical distribution structure of the magnets inside the rotor). The anisotropic dimensions of the magnetic poles determine the specific size of the magnets inside the rotor, while the latitude of the magnetic pole layer determines the distribution level of the magnets. By adjusting these parameters, the magnetic force distribution of the rotor can be affected, thereby optimizing the performance of the motor.

[0035] At the same time, in order to avoid some solutions that do not meet the design requirements or process limitations during the optimization process, it is necessary to introduce penalty terms to constrain the objective function. The penalty term is used to penalize solutions that do not meet the constraints, making these solutions less fit, thereby guiding the optimization algorithm to find a better solution. The size of the penalty term is related to the optimization direction (bad or good) and the preset degree of optimization (such as error tolerance). It can be set as an additional term for some undesirable situations, which usually increases the value of the objective function. The objective function refers to the function that needs to be minimized or maximized during the optimization process. It is a mathematical expression of the optimization goal and is used to evaluate the quality of the solution. In the motor rotor magnetic optimization, the objective function can be a function of the torque characteristics (such as spin torque and tilt torque), and can also include efficiency or other performance indicators. By introducing penalty terms and determining the objective function, it can be ensured that the optimization process proceeds in the expected direction and avoid solutions that do not meet the design requirements.

[0036] Use finite element analysis software (such as ANSYS, Maxwell) to simulate the magnetic field distribution of the motor rotor. Combine the objective function and optimization variables, and use multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization) to find the optimal magnetic force distribution strategy. Finite element analysis can simulate the distribution of the motor rotor magnetic field and analyze the impact of different magnetic force distribution schemes on motor performance. When performing multi-objective optimization, consider multiple optimization goals at the same time, such as maximizing torque output, minimizing eddy current losses, and optimizing magnet distribution, to find the optimal solution that meets all goals.

[0037] Finite element analysis combined with a multi-objective optimization algorithm can accurately evaluate the impact of different magnet distributions on motor performance and find the best balance between multiple performance targets, ensuring that the motor rotor is optimized in performance and improving the motor's torque output, efficiency and stability.

[0038] Furthermore, in step S3, a two-step optimization iteration is performed under a two-dimensional magnetic field, including: Step S3-21: Perform two-dimensional conversion on the three-dimensional simulated magnetic field to determine the radial magnetic field and the transverse magnetic field.

[0039] Step S3-22: Perform a double-step optimization search using the radial magnetic field and the transverse magnetic field to determine a first optimization distribution strategy.

[0040] Step S3-23: Perform optimization iterative constraints based on preset convergence conditions until the Nth optimization distribution strategy is determined, integrate the first optimization distribution strategy and the Nth optimization distribution strategy, and select the best option as the optimization distribution strategy.

[0041] Specifically, the three-dimensional magnetic field of the motor rotor is simplified in the simulation software and converted into a two-dimensional model. In the two-dimensional conversion process, some dimensional information that is not important in the two-dimensional analysis is ignored by projection or simplification, and only the magnetic field information of the two main dimensions related to the analysis target is retained, namely the radial magnetic field and the transverse magnetic field. Among them, the radial magnetic field refers to the distribution of the magnetic field along the radial direction of the motor rotor, which is usually parallel to the rotation axis of the motor rotor and controls the torque and magnetic flux density of the motor rotor. The transverse magnetic field refers to the magnetic field component in the direction perpendicular to the radial magnetic field. In the design of the motor rotor, the transverse magnetic field can have an important impact on the stability and efficiency of the motor. Simplifying the complex three-dimensional magnetic field into radial magnetic field and transverse magnetic field in a two-dimensional plane reduces the complexity of the analysis, while focusing on the two magnetic field components that have a greater impact on the magnetic force distribution of the motor rotor, laying the foundation for the subsequent two-step optimization.

[0042] The radial and transverse components (radial magnetic field and transverse magnetic field) of the rotor magnetic field are optimized, and a two-step optimization strategy is used to find a preliminary optimization distribution strategy, namely the first optimization distribution strategy. During the optimization, the radial and transverse magnetic fields are perturbed to explore possible optimization directions. For example, the strength of the radial magnetic field can be adjusted first, and then the transverse magnetic field can be adjusted to gradually approach the optimal magnet distribution. Through two-step optimization, the effects of the radial magnetic field and the transverse magnetic field on the magnetic force distribution are considered separately, and a preliminary optimization distribution strategy is gradually determined. This strategy can comprehensively consider the effects of the two important magnetic field components on the motor performance (such as torque characteristics), and can obtain more comprehensive optimization results compared to single-step optimization.

[0043] The preset convergence condition is the standard for stopping iterations in advance during the optimization iteration process. It can be set according to factors such as the amount of change in the optimization result, the range of change in the objective function value, or the number of iterations. For example, when the objective function value of the optimization distribution strategy in two adjacent iterations changes less than a certain threshold (such as 0.01), it is considered that the convergence condition is met and the iteration is stopped. During the optimization iteration process, each iteration will be judged according to the preset convergence condition. If the convergence condition is not met, the next round of optimization iteration will continue, and the optimization variables will be continuously adjusted to obtain a new optimization distribution strategy (the Nth optimization distribution strategy). When the convergence condition is met, the optimization distribution strategies obtained in each round (i.e., the first optimization distribution strategy to the Nth optimization distribution strategy) are integrated, and the advantages and disadvantages of all optimization distribution strategies are compared. The optimal item is selected as the final optimization distribution strategy. This optimal item is the optimization distribution strategy with the largest or smallest objective function among all optimization distribution strategies.

[0044] Through iterative optimization and convergence condition constraints, it can be ensured that the optimal distribution strategy found is fully optimized, which can comprehensively improve the optimization effect of the magnetic force distribution of the motor rotor, thereby maximizing the overall performance of the motor, while ensuring the stability and reliability of the optimization process.

[0045] Further, such as Figure 2 As shown, step S3-22 includes: Step S3-221: Determine a first magnetic field, wherein the first magnetic field is any one of the radial magnetic field and the transverse magnetic field.

[0046] Step S3-222: Based on the first magnetic field, randomly perturb the optimization variable to determine a one-step optimization strategy, wherein a preset single-step perturbation amount is used as a constraint.

[0047] Step S3-223: Based on the objective function, the one-step optimization strategy is evaluated to determine the optimization coefficient.

[0048] Step S3-224: Based on the optimization coefficient, randomly perturb the optimization variables of the second magnetic field to determine a two-step optimization strategy.

[0049] Step S3-225: Combining the one-step optimization strategy and the two-step optimization strategy as the first optimization distribution strategy.

[0050] Specifically, a radial magnetic field and a transverse magnetic field are randomly selected as the first magnetic field. Then, based on the selected first magnetic field, the optimization variables (magnetic pole size, layer latitude, magnet distribution, etc.) are randomly perturbed to generate a one-step optimization strategy. The one-step optimization strategy is a set of new values ​​of the optimization variables obtained after randomly perturbing the optimization variables of the first magnetic field. This random perturbation is a small random adjustment of the variables under the constraint of a preset single-step perturbation amount. The preset single-step perturbation amount cannot be set too large to avoid skipping possible better solutions. For example, the preset single-step perturbation amount can be ±0.1 mm of the isotropic size of the magnetic pole or ±1 degree of the latitude of the magnetic pole layer, etc.

[0051] Substitute the one-step optimization strategy into the objective function for evaluation to confirm whether the new magnetic force distribution after the disturbance improves the performance of the motor, such as torque output, efficiency, etc. Based on the evaluation results, an optimization coefficient is obtained, which indicates the degree of improvement of the current optimization strategy relative to the previous strategy. If the optimization coefficient is high, it means that the magnetic force distribution after the disturbance is better than the original distribution. Exemplarily, the optimization coefficient can be the ratio of the objective function value after optimization to the objective function value before optimization.

[0052] On the basis of the one-step optimization strategy, the optimization coefficient is used to guide the optimization of the second magnetic field. The random perturbation of the optimization variable is also constrained by the preset single-step perturbation amount. The perturbation at this time is adjusted according to the effect of the one-step optimization to generate a two-step optimization strategy. The two-step optimization strategy is a set of new values ​​of the optimization variables obtained after the random perturbation of the second magnetic field.

[0053] Combining the one-step optimization strategy and the two-step optimization strategy, the first optimization distribution strategy is obtained. Then, according to the same two-step optimization iteration process mentioned above, the new optimization distribution strategy is determined by cyclic iteration through random perturbation and objective function evaluation.

[0054] Further, after the optimization coefficient is determined in step S3-223, the following steps are included: Identify the optimization coefficient, determine the optimization direction, and optimize the second magnetic field; if it is an optimization inferior direction, perform optimization fallback processing on the one-step optimization strategy; if it is an optimization superior direction and it is greater than the preset optimization degree, taboo lock the optimization method of the one-step optimization strategy, and unlock the taboo with a preset number of iterations.

[0055] Specifically, after determining the optimization coefficient, it is also necessary to determine the optimization direction according to the value of the optimization coefficient, and guide the optimization process of the second magnetic field according to the determination result. The optimization direction judgment can be based on the positive or negative or size of the optimization coefficient to determine whether the optimization strategy is moving in the direction of improving performance. If the optimization coefficient indicates that the current optimization strategy causes performance degradation, the optimization direction of the one-step optimization strategy is determined to be the optimization inferior direction. If the optimization coefficient indicates that the current optimization strategy causes performance improvement, the optimization direction of the one-step optimization strategy is determined to be the optimization superior direction. Exemplarily, according to the optimization coefficient of the aforementioned example, when the optimization coefficient is greater than 1, it means that the current strategy has brought about a significant improvement, that is, the current optimization direction is the optimization superior direction. When the optimization coefficient is less than 1, it means that the current optimization strategy is less effective, that is, the current optimization direction is the optimization inferior direction.

[0056] When it is determined to be an optimization failure, it means that the optimization result of the current one-step optimization strategy is not ideal, and it is necessary to perform an optimization fallback process on the one-step optimization strategy, that is, fall back to the previous unoptimized state and try other optimization variable adjustments. The specific fallback method can be to select an optimization variable value or optimization strategy version that performed well before based on the previously recorded optimization process information, and then restart the optimization on this basis. When it is determined to be an optimization failure, the optimization coefficient and the preset optimization degree are compared. When the optimization coefficient is greater than the preset optimization degree, it means that the current one-step optimization strategy has a significant effect. The optimization method of the one-step optimization strategy is taboo locked, that is, the same optimization method is used again within a certain time (or number of iterations). This locking helps to explore more optimization space and prevent the optimization process from converging to the local optimal solution too early. Among them, the preset optimization degree is a pre-set performance improvement threshold. When this threshold is exceeded, the optimization strategy is considered to be effective.

[0057] The taboo is unlocked with a preset number of iterations, that is, after a certain number of iterations, the taboo restrictions on the previous optimization method are lifted. The preset number of iterations is a value set according to the specific optimization problem and experience. For example, if it is set to unlock after 10 iterations, then after 10 iterations, the optimization method that was previously tabooed can be considered again. This avoids falling into a local optimal solution too early, and does not completely exclude possible effective optimization paths.

[0058] By identifying the optimization coefficients and determining the optimization direction, it is ensured that the optimization of the motor rotor magnetic force distribution can proceed in the right direction. When the optimization direction is correct and reaches the preset optimization degree, the taboo locking strategy is used to avoid falling into the local optimum; when the optimization direction is not ideal, the fallback processing strategy is used for adjustment. These steps ensure the globality and stability of the optimization process, so that the final optimization solution can greatly improve the performance of the motor rotor and avoid unnecessary performance degradation.

[0059] Furthermore, the method described in the embodiment of the present application also includes: Step S51: determining the permanent magnetic material of the target motor rotor as the first physical constraint.

[0060] Step S52: determining the magnetic pole structure and magnetic barrier structure of the target motor rotor as the second physical constraint.

[0061] Step S53: performing an optimization decision for magnetic force optimization distribution based on the first physical constraint and the second physical constraint.

[0062] Specifically, when optimizing the magnetic force distribution, the material, pole structure and magnetic barrier structure of the target motor rotor are already determined and cannot be changed. Therefore, when making optimization decisions, corresponding physical constraints need to be set to avoid optimization distribution strategies that cannot be practically applied.

[0063] Permanent magnetic materials determine the strength and stability of the motor's magnetic field. Different permanent magnetic materials have different magnetic properties, such as remanence, coercive force, and magnetic energy product. In the optimization decision-making of magnetic force optimization distribution, the characteristics of permanent magnetic materials will limit the direction and scope of optimization. For example, since the remanence strength of permanent magnetic materials is fixed, when optimizing magnetic force distribution, the maximum magnetic flux range that the permanent magnetic material can provide cannot be exceeded. According to the performance limitations of the permanent magnetic material of the target motor rotor, the first physical constraint is set.

[0064] The magnetic pole structure refers to the shape, size and arrangement of the magnetic poles in the motor rotor. The magnetic barrier structure is a structure used to guide and limit the magnetic field path, which is used to optimize the magnetic field distribution and reduce magnetic force loss. The magnetic pole structure and the magnetic barrier structure directly affect the magnetic field distribution of the motor rotor. For example, when the shape of the magnetic pole is arc-shaped, its magnetic field distribution will be more uniform or have special magnetic field characteristics in certain directions compared with the square magnetic pole. The magnetic barrier structure can affect the distribution of the magnetic field by changing the path of the magnetic circuit. For example, the existence of the magnetic barrier may change the leakage of the magnetic field. When the magnetic pole structure and the magnetic barrier structure are determined, constraints will be formed on the optimized distribution of the magnetic force, and the change of the magnetic force distribution cannot be separated from the existing magnetic pole structure and magnetic barrier structure framework. In the process of optimal decision-making, the value range of the optimization variable is also limited. For example, if the magnetic pole structure is a specific symmetrical structure, then when optimizing the magnetic force distribution, it must be ensured that the optimized magnetic force distribution still meets the requirements of this symmetrical structure. Therefore, the magnetic pole structure and the magnetic barrier structure of the target motor rotor are set as the second physical constraints.

[0065] In the two-step optimization iteration process, it is necessary to combine the first physical constraint and the second physical constraint. Under the constraints of the magnetic property range that the permanent magnet material can provide and the pole structure and magnetic barrier structure, by changing the optimization variables (such as the anisotropic dimensions of the poles, the latitude of the pole layer, etc.), find the magnetic force distribution scheme that maximizes the motor torque or the highest efficiency, to ensure that the final magnetic force optimization distribution scheme not only meets the design requirements but also has practical physical feasibility.

[0066] In summary, the magnetic force optimization distribution method for a motor rotor provided in the embodiments of the present application has the following technical effects: The embodiment of the present application significantly improves the accuracy of the magnetic force distribution of the motor rotor and the overall performance of the motor through a multi-level optimization strategy. First, through the operation record of the interactive motor rotor, an operation relationship library containing the relationship between the magnetic circuit structure, magnet distribution and motor performance is excavated, providing data support and theoretical basis for subsequent optimization. Based on the operation relationship library, three-dimensional magnetic field simulation modeling is carried out, combined with torque analysis, to accurately simulate the relationship between magnet distribution and motor performance, thereby providing a scientific analysis framework for optimization. Subsequently, optimization is performed under a two-dimensional magnetic field through a two-step optimization iteration method to ensure the efficiency and accuracy of the optimization distribution strategy. On this basis, a dynamic adjustment strategy is further introduced to achieve dynamic adjustment of the magnetic force distribution for the motor control strategy and the control range, so that the motor can adaptively improve performance under different working conditions. In addition, during the optimization process, by introducing optimization variables, objective functions and penalty terms, multi-objective optimization is performed in combination with finite element analysis technology to improve the optimization accuracy. Ultimately, the convergence and stability of the optimization process were ensured through random perturbations and optimization back-off mechanisms, and the optimal magnetic force distribution strategy was ultimately determined by comprehensively considering physical constraints such as permanent magnet materials and magnetic pole structures.

[0067] Overall, the embodiments of the present application achieve comprehensive optimization of the magnetic force distribution of the motor rotor, which not only effectively improves the torque output and operating efficiency of the motor, but also reduces eddy current losses, improves the overall performance and adaptability of the motor, and enables the motor to have more stable and efficient performance under different operating conditions.

[0068] Embodiment 2, as Figure 3 As shown, an embodiment of the present application provides a magnetic force optimization distribution system for a motor rotor, the system comprising: The operation relationship mining module 10 is used to interact with the operation records of the motor rotor and mine the operation relationship library, wherein the operation relationship library contains a plurality of mutual influence relationships of magnetic circuit structure-magnet distribution-motor performance.

[0069] The three-dimensional magnetic field simulation module 20 is used to perform three-dimensional magnetic field simulation modeling on the target motor rotor, and construct a magnetic optimization module based on the operation relationship library and torque analysis. Among them, the magnetic circuit structure-magnet distribution of the target motor rotor is three-dimensionally modeled, and the relationship between the magnet distribution and the torque characteristics-motor performance is used as the analysis target.

[0070] The distribution optimization module 30 is used to perform a double-step optimization iteration under a two-dimensional magnetic field based on the magnetic optimization module to determine an optimization distribution strategy.

[0071] Furthermore, the system described in the embodiment of the present application further includes a dynamic adjustment module, and the dynamic adjustment module is used to perform the following steps: Acquire the control strategy of the target motor rotor and determine the control electrical data; determine the dynamic adjustment strategy of the magnetic force distribution according to the control range of the control electrical data, wherein the dynamic adjustment strategy is the dynamic adjustment of the corresponding magnetic force distribution under the control of the electrical data in the control range; use the optimized distribution strategy and the dynamic adjustment strategy as the magnetic force optimization distribution result of the target motor rotor.

[0072] Furthermore, the distribution optimization module 30 in the embodiment of the present application is also used to perform the following steps: The optimization variables are set, wherein the optimization variables include at least the anisotropic dimensions of the magnetic poles and the latitude of the magnetic pole layer; a penalty term is introduced to determine the objective function based on the torque characteristics, wherein the torque characteristics include at least the spin torque and the tilt torque, and the penalty term is determined based on the optimization disadvantage and the preset optimization degree; based on the optimization variables and the objective function, a multi-objective optimization search is performed under finite element analysis to determine the optimization distribution strategy.

[0073] Furthermore, the distribution optimization module 30 in the embodiment of the present application is also used to perform the following steps: The three-dimensional simulated magnetic field is converted into two dimensions to determine the radial magnetic field and the transverse magnetic field; the radial magnetic field and the transverse magnetic field are used for two-step optimization to determine the first optimization distribution strategy; optimization iterative constraints are performed based on preset convergence conditions until the Nth optimization distribution strategy is determined, the first optimization distribution strategy and the Nth optimization distribution strategy are integrated, and the best option is selected as the optimization distribution strategy.

[0074] Further, a double-step optimization is performed using the radial magnetic field and the transverse magnetic field to determine a first optimization distribution strategy, and the execution steps include: Determine a first magnetic field, wherein the first magnetic field is any one of the radial magnetic field and the transverse magnetic field; based on the first magnetic field, randomly perturb the optimization variables to determine a one-step optimization strategy, wherein a preset single-step perturbation amount is used as a constraint; based on the objective function, evaluate the one-step optimization strategy to determine an optimization coefficient; based on the optimization coefficient, randomly perturb the optimization variables of the second magnetic field to determine a two-step optimization strategy; combine the one-step optimization strategy and the two-step optimization strategy as the first optimization distribution strategy.

[0075] Furthermore, after determining the optimization coefficient, the distribution optimization module 30 is further configured to perform the following steps: Identify the optimization coefficient, determine the optimization direction, and optimize the second magnetic field; if it is an optimization inferior direction, perform optimization fallback processing on the one-step optimization strategy; if it is an optimization superior direction and it is greater than the preset optimization degree, taboo lock the optimization method of the one-step optimization strategy, and unlock the taboo with a preset number of iterations.

[0076] Furthermore, the system described in the embodiment of the present application further includes a physical constraint module, and the physical constraint module is used to perform the following steps: The permanent magnet material of the target motor rotor is determined as a first physical constraint; the magnetic pole structure and the magnetic barrier structure of the target motor rotor are determined as a second physical constraint; and the optimization decision of the magnetic force optimization distribution is performed based on the first physical constraint and the second physical constraint.

[0077] Through the above-mentioned detailed description of the magnetic force optimization distribution method for the motor rotor in this specification, those skilled in the art can clearly understand the magnetic force optimization distribution system for the motor rotor in this embodiment. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional modules and beneficial effects, the relevant parts can be referred to the description of the method part.

[0078] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing magnetic force distribution for a motor rotor, characterized in that: The method comprises: Interact with the operation records of the motor rotor to mine the operation relationship library, wherein the operation relationship library contains the mutual influence relationship between multiple magnetic circuit structures, magnet distribution and motor performance; The target motor rotor is subjected to three-dimensional magnetic field simulation modeling, and a magnetic force optimization module is constructed based on the operation relationship library and torque analysis, wherein the magnetic circuit structure-magnet distribution of the target motor rotor is subjected to three-dimensional modeling, and the relationship between the magnet distribution and the torque characteristics-motor performance is taken as the analysis target; Based on the magnetic optimization module, a two-step optimization iteration is performed under a two-dimensional magnetic field to determine an optimization distribution strategy.

2. The method for optimizing magnetic force distribution for a motor rotor according to claim 1, characterized in that: The method further comprises: Acquire the control strategy of the target motor rotor and determine the control electrical data; Determine a dynamic adjustment strategy for magnetic force distribution according to the control range of the control electrical data, wherein the dynamic adjustment strategy is a dynamic adjustment of the magnetic force distribution corresponding to the control of the electrical data in the control range; The optimized distribution strategy and the dynamic adjustment strategy are used as the magnetic force optimization distribution result of the target motor rotor.

3. The method for optimizing magnetic force distribution for a motor rotor according to claim 1, characterized in that: Determine the optimal distribution strategy, including: Setting optimization variables, wherein the optimization variables at least include the magnetic pole anisotropic dimensions and the magnetic pole layer latitude; A penalty term is introduced to determine the objective function based on the torque characteristic, wherein the torque characteristic at least includes the self-spin torque and the tilt torque, and the penalty term is determined based on the optimization disadvantage and the preset optimization degree; Based on the optimization variables and the objective function, a multi-objective optimization search is performed under finite element analysis to determine the optimization distribution strategy.

4. The method for optimizing magnetic force distribution for a motor rotor as claimed in claim 3, characterized in that: Perform a two-step optimization iteration in a two-dimensional magnetic field, including: Perform two-dimensional conversion on the three-dimensional simulated magnetic field to determine the radial magnetic field and the transverse magnetic field; Performing a double-step optimization search using the radial magnetic field and the transverse magnetic field to determine a first optimization distribution strategy; Based on the preset convergence condition, iterative optimization constraints are performed until the Nth optimization distribution strategy is determined, the first optimization distribution strategy and the Nth optimization distribution strategy are integrated, and the best option is selected as the optimization distribution strategy.

5. The method for optimizing magnetic force distribution for a motor rotor as claimed in claim 4, characterized in that: The radial magnetic field and the transverse magnetic field are used to perform a double-step optimization to determine a first optimization distribution strategy, including: Determine a first magnetic field, wherein the first magnetic field is any one of the radial magnetic field and the transverse magnetic field; Based on the first magnetic field, randomly perturb the optimization variable to determine a one-step optimization strategy, wherein a preset single-step perturbation amount is used as a constraint; Based on the objective function, the one-step optimization strategy is evaluated to determine an optimization coefficient; Based on the optimization coefficient, randomly perturb the optimization variable to the second magnetic field to determine a two-step optimization strategy; The one-step optimization strategy and the two-step optimization strategy are combined as the first optimization distribution strategy.

6. The method for optimizing magnetic force distribution for a motor rotor as claimed in claim 5, characterized in that: After determining the optimization coefficient, including: Identifying the optimization coefficient, determining the optimization direction, and optimizing the second magnetic field; Among them, if it is an optimization inferiority, the one-step optimization strategy is subjected to optimization and fallback processing; if it is an optimization superiority and is greater than the preset optimization degree, the optimization method of the one-step optimization strategy is taboo locked and the taboo is unlocked with a preset number of iterations.

7. The method for optimizing magnetic force distribution for a motor rotor according to claim 1, characterized in that: The method further comprises: Determining the permanent magnetic material of the target motor rotor as a first physical constraint; Determining the magnetic pole structure and magnetic barrier structure of the target motor rotor as a second physical constraint; The first physical constraint and the second physical constraint are used to make an optimal decision on the magnetic force optimization distribution.

8. A magnetic force optimization distribution system for a motor rotor, characterized in that: The system is used to execute the magnetic force optimization distribution method for a motor rotor according to any one of claims 1 to 7, comprising: An operation relationship mining module is used to interact with the operation records of the motor rotor and mine the operation relationship library, wherein the operation relationship library contains multiple mutual influence relationships of magnetic circuit structure-magnet distribution-motor performance; A three-dimensional magnetic field simulation module is used to perform three-dimensional magnetic field simulation modeling on the target motor rotor, and to construct a magnetic force optimization module based on the operation relationship library and torque analysis, wherein the magnetic circuit structure-magnet distribution of the target motor rotor is three-dimensionally modeled, and the relationship between the magnet distribution and the torque characteristics-motor performance is used as the analysis target; The distribution optimization module is used to perform double-step optimization iteration under a two-dimensional magnetic field based on the magnetic optimization module to determine the optimization distribution strategy.

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