Motor optimization method and related equipment

Abstract

The invention relates to the technical field of motor optimization, in particular to a motor optimization method and related equipment. The method comprises the following steps: acquiring geometric information and material information of a main structural component of the motor; establishing a two-dimensional electromagnetic simulation model according to the geometric information and the material information; determining a constraint range of geometric parameters in the two-dimensional electromagnetic simulation model according to a preset performance optimization target; based on the performance optimization target and the constraint range, a multi-target genetic algorithm is adopted to carry out topological structure optimization on the two-dimensional electromagnetic simulation model, and an optimized motor geometric structure is obtained; constructing a corresponding three-dimensional electromagnetic simulation model according to the optimized geometric structure of the motor; performing three-dimensional transient electromagnetic field simulation according to the three-dimensional electromagnetic simulation model to obtain high-precision motor performance parameters; judging whether the high-precision motor performance parameters meet a performance optimization target or not; and if yes, determining geometric information and material information corresponding to the high-precision motor performance parameters as a target optimization result.

Description

Technical Field

[0001] This invention relates to the field of motor optimization technology, and in particular to a motor optimization method and related equipment. Background Technology

[0002] Embedded permanent magnet synchronous motors are widely used in new energy vehicle drive systems due to their high power density and high efficiency. Their performance directly affects the overall vehicle powertrain performance, making in-depth optimization crucial. Traditional motor optimization relies primarily on designers' experience and repeated prototype manufacturing and testing. This approach is time-consuming, costly, and struggles to systematically find the globally optimal solution.

[0003] With the development of computer simulation technology, finite element analysis has provided a new approach for motor optimization. However, existing simulation methods have significant limitations: while two-dimensional transient electromagnetic field simulation offers fast computation speed, its accuracy is insufficient, making it difficult to meet the demands of high-precision development; while three-dimensional transient electromagnetic field simulation offers higher accuracy, its complex model and large mesh size result in excessively long simulation times, failing to meet the requirements of extensive iterative calculations needed in the optimization process. Furthermore, motor optimization is a typical multi-objective optimization problem, requiring trade-offs between multiple conflicting performance indicators such as torque, efficiency, and torque ripple. Manually adjusting parameters alone is insufficient to effectively achieve global optimization. Therefore, balancing simulation efficiency and accuracy, and effectively solving the multi-objective automatic optimization problem, is a pressing issue that needs to be addressed. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a motor optimization method and related equipment, which can solve the problems existing in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a motor optimization method, comprising: Obtain the geometric and material information of the main structural components of the motor; Based on the geometric and material information, a two-dimensional electromagnetic simulation model is established. The constraint range of the geometric parameters in the two-dimensional electromagnetic simulation model is determined according to the preset performance optimization target; Based on the performance optimization objective and the constraint range, a multi-objective genetic algorithm is used to optimize the topology of the two-dimensional electromagnetic simulation model to obtain the optimized motor geometry. Based on the optimized motor geometry, a corresponding three-dimensional electromagnetic simulation model is constructed. Based on the aforementioned three-dimensional electromagnetic simulation model, a three-dimensional transient electromagnetic field simulation is performed to obtain high-precision motor performance parameters; Determine whether the high-precision motor performance parameters meet the performance optimization target; If the conditions are met, the geometric information and material information corresponding to the high-precision motor performance parameters will be determined as the target optimization result.

[0006] In one possible implementation, the main structural components include one or more combinations of a stator, a rotor, magnets, and windings.

[0007] In one possible implementation, after obtaining the optimized motor geometry, the method further includes: Correlation statistics are performed on the optimized motor geometry and the motor performance parameters obtained through optimization to determine the impact of geometric changes on performance parameters; the impact relationship is used to guide the adjustment of the geometric information in the next round of optimization.

[0008] In one possible implementation, the performance optimization objective includes at least one of peak torque, torque ripple, and efficiency.

[0009] In one possible implementation, a multi-objective genetic algorithm is used to optimize the topology of the two-dimensional electromagnetic simulation model, including: Cases that exhibit geometric errors or fail to generate meshes during the topology optimization process of the multi-objective genetic algorithm are assigned a preset negative evaluation value to reduce the probability that cases with geometric errors or fail to generate meshes will be selected as parent cases in subsequent iterations. In one possible implementation, the constraint range of the geometric parameters is used to prevent mechanical interference in the optimized geometry.

[0010] In one possible implementation, the method further includes, before constructing the 3D model: Efficiency plot simulation and no-load simulation are performed on the optimized motor geometry; the efficiency plot simulation and the no-load simulation are set with corresponding filtering conditions. The results of the efficiency diagram simulation are filtered according to the filtering criteria of the efficiency diagram simulation; and the results of the no-load simulation are filtered according to the filtering criteria of the no-load simulation.

[0011] Secondly, embodiments of the present invention provide a motor optimization device, comprising: The acquisition module is used to acquire the geometric and material information of the main structural components of the motor; The first modeling module is used to establish a two-dimensional electromagnetic simulation model based on the geometric and material information. The determination module is used to determine the constraint range of geometric parameters in the two-dimensional electromagnetic simulation model according to the preset performance optimization target; The processing module is used to perform topology optimization on the two-dimensional electromagnetic simulation model based on the performance optimization objective and the constraint range, and to obtain the optimized motor geometry. The second modeling module is used to construct a corresponding three-dimensional electromagnetic simulation model based on the optimized motor geometry. The simulation module is used to perform three-dimensional transient electromagnetic field simulation based on the three-dimensional electromagnetic simulation model to obtain high-precision motor performance parameters; The judgment module is used to determine whether the high-precision motor performance parameters meet the performance optimization target; The output module is used to determine the geometric information and material information corresponding to the high-precision motor performance parameters as the target optimization result when the judgment module determines that the high-precision motor performance parameters meet the performance optimization target.

[0012] Thirdly, embodiments of the present invention provide an electronic device, comprising: At least one processor; and At least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the method described in the first aspect by calling the program instructions.

[0013] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause the computer to perform the method described in the first aspect.

[0014] In this embodiment of the invention, a preliminary optimal structure is obtained through rapid iterative optimization using a two-dimensional model, and then high-precision verification is performed through three-dimensional simulation. This effectively balances optimization efficiency and result accuracy, and systematically solves the trade-off problem among multiple performance indicators using a multi-objective genetic algorithm. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a motor optimization method provided in an embodiment of the present invention; Figure 2 A schematic diagram of the simulation results of a two-dimensional electromagnetic simulation model provided in an embodiment of the present invention; Figure 3A schematic diagram of the simulation results of a three-dimensional electromagnetic simulation model provided in an embodiment of the present invention; Figure 4 A correlation statistics diagram provided for an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a motor optimization device provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] It should be understood that although terms such as first, second, third, etc., may be used to describe numbers in embodiments of the present invention, these numbers should not be limited to these terms. These terms are only used to distinguish numbers from each other. For example, without departing from the scope of embodiments of the present invention, a first number may also be referred to as a second number, and similarly, a second number may also be referred to as a first number.

[0022] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0023] To address the issues of insufficient accuracy in two-dimensional simulation, low efficiency in three-dimensional simulation, and difficulties in multi-objective optimization in motor optimization simulation, this invention provides a motor optimization method that can efficiently and accurately optimize motor performance. Figure 1 This is a flowchart illustrating a motor optimization method provided in an embodiment of the present invention. Figure 1 As shown, the method includes: Step 101: Obtain the geometric and material information of the main structural components of the motor.

[0024] This invention can be applied to embedded permanent magnet synchronous motors or other motors.

[0025] In some embodiments, the main structural components of the motor described above may include a stator, rotor, magnets, and windings. Geometric information includes key dimensions such as stator inner diameter, core length, stator tooth width, air gap length, magnetic bridge thickness, and permanent magnet cross-sectional area. Material information includes physical properties such as the saturation magnetic flux density of the core material, remanence of the permanent magnet, coercivity of the permanent magnet, and maximum electrical density of the conductor. This information forms the basis for constructing the simulation model.

[0026] Step 102: Based on the geometric and material information, establish a two-dimensional electromagnetic simulation model.

[0027] Specifically, based on the information obtained in step 101, a two-dimensional axial cross-sectional model of the motor is drawn. This model parametrically represents the motor's geometry, such as stator slots, rotor magnet slots, and air gap, meaning that each key dimension (such as magnet area, air gap width, and slot width) is defined as an independently adjustable parameter variable. Subsequently, corresponding material properties are assigned to each part of the model; for example, silicon steel sheets are specified for the stator and rotor cores, neodymium iron boron permanent magnets are specified for the magnets, and copper is specified for the windings. Next, simulation conditions are set, including the applied torque conditions, whether skewed poles are used, the connection method of the three-phase windings, and the excitation source. Finally, meshes of different densities are generated for different regions of the model (such as stator and rotor cores, air gap, and external air domain), and the required performance response parameters (such as torque and back EMF) are set, completing the construction of the basic two-dimensional electromagnetic simulation model. After constructing the two-dimensional electromagnetic simulation model, its geometry is parameterized for use in the next step.

[0028] Figure 2 This is a schematic diagram illustrating the simulation results of a two-dimensional electromagnetic simulation model provided in an embodiment of the present invention. After constructing the framework of the two-dimensional electromagnetic simulation model, appropriate materials are assigned to the motor stator and rotor, magnets, and windings. Torque conditions, skew stages, and three-phase windings are set. Mesh arrays of different densities are generated for the stator and rotor, air gap, and external air. The output response is set to complete the basic simulation model configuration. Subsequently, the following results are obtained: Figure 2 The two-dimensional simulation results are shown in the figure.

[0029] Step 103: Determine the constraint range of geometric parameters in the two-dimensional electromagnetic simulation model according to the preset performance optimization target.

[0030] The performance optimization objectives include at least one of peak torque, torque ripple, and efficiency. These objectives are often contradictory and require trade-offs. Geometric parameter constraints ensure that the optimal topology found during subsequent optimization does not cause mechanical interference and is structurally sound. For example, the constraints must ensure that the air gap length is not less than the minimum calculated based on the stator inner diameter and core length, the stator tooth width meets the requirements for conductor current density and magnetic flux saturation, and the magnetic bridge thickness is sufficient to prevent irreversible demagnetization of the permanent magnet under a demagnetizing magnetic field. By setting these constraints, the search space can be effectively limited to a physically feasible and reasonable range, avoiding invalid or erroneous design schemes.

[0031] For example, for a specific embedded permanent magnet synchronous motor, according to the preset performance optimization target and the preset simulation error optimization target, the geometric parameters need to meet the requirements of formula (1) to formula (3).

[0032] Formula (1): Formula (2): Formula (3): in, The length of the air gap. Stator inner diameter The length of the iron core. For stator tooth width, The saturation magnetic flux density of the iron core material. Let be the cross-sectional area of ​​the stator teeth. For full tank rate, The maximum electrical density of the conductor, For the thickness of the magnetic bridge, Remanence of permanent magnets. Let be the cross-sectional area of ​​the permanent magnet. The permeability of free space, For the coercivity of permanent magnets.

[0033] Step 104: Based on the performance optimization objectives and constraints, a multi-objective genetic algorithm is used to optimize the topology of the two-dimensional electromagnetic simulation model to obtain the optimized motor geometry.

[0034] Specifically, multi-objective genetic algorithms are used to optimize multiple performance objectives simultaneously while satisfying the constraints mentioned above. The weights of the objective functions can be assigned using an equal-weight method. The total number of generations of the genetic algorithm can be set to ten times the number of variable geometries, and the number of individuals in each generation should be greater than twice the product of the number of objectives to be optimized and the number of variable geometries to ensure sufficient search efficiency.

[0035] A crucial step in the optimization process is handling simulation failures. Due to the random combination of geometric parameters, cases may arise where mesh generation or electromagnetic calculations fail. In this method, these cases with geometric errors or mesh generation failures are assigned a preset range value. By assigning a range value, the probability of these cases with geometric errors or mesh generation failures being selected as parent cases in subsequent iterations can be significantly reduced, thereby guiding the algorithm to evolve towards a design space that is easier to successfully simulate, thus improving optimization efficiency.

[0036] Through the above steps, the topology of the motor can be rapidly iterated without changing the material properties, and one or more optimized solutions that achieve the best balance among multiple performance objectives can be found, i.e., the optimized motor geometry.

[0037] Step 105: Based on the optimized motor geometry, construct the corresponding three-dimensional electromagnetic simulation model.

[0038] After obtaining a suitable geometric structure through 2D model optimization, a more accurate 3D electromagnetic simulation model needs to be built based on this structure. The 3D model can more completely reflect the actual geometric characteristics of the motor, especially considering factors that cannot be simulated by the 2D model, such as the end effect of flat wire windings. Before building the 3D model, it may be necessary to perform appropriate simplification, such as removing fillets and chamfers that have a minor impact on electromagnetic performance, to ensure smooth mesh generation and computational efficiency.

[0039] Figure 3 This is a schematic diagram illustrating the simulation results of a three-dimensional electromagnetic simulation model provided in an embodiment of the present invention. The motor geometry obtained after topological optimization based on a two-dimensional model is then subjected to three-dimensional simulation to obtain... Figure 3 The three-dimensional simulation results are shown in the figure.

[0040] Step 106: Perform three-dimensional transient electromagnetic field simulation based on the three-dimensional electromagnetic simulation model to obtain high-precision motor performance parameters.

[0041] Using the three-dimensional model constructed in step 105, a three-dimensional transient electromagnetic field simulation was performed. Because the three-dimensional model considers three-dimensional factors such as end effects, the accuracy of its simulation results is much higher than that of the two-dimensional simulation, and it is closer to the performance of the real motor.

[0042] Step 107: Determine whether the high-precision motor performance parameters meet the performance optimization target.

[0043] The high-precision performance parameters obtained from the 3D simulation, such as peak torque and efficiency, are compared with the preset performance optimization targets in step 103 to determine whether they meet the requirements.

[0044] Step 108: If satisfied, the geometric information and material information corresponding to the high-precision motor performance parameters are determined as the target optimization result.

[0045] If the 3D simulation results confirm that the performance meets or even exceeds the optimization target, then the optimization is successful. At this point, the final geometric dimensions and material information corresponding to the optimized scheme can be determined as the target optimization result, used to guide the motor's production and manufacturing. As a preferred implementation, when the performance results exceed the optimization target, the amount of magnets used can be appropriately reduced, thereby further reducing the motor's cost while ensuring performance.

[0046] If the judgment result is not satisfactory, the material properties should be improved (for example, by selecting a higher strength permanent magnet or silicon steel sheet), and the optimization method should be executed again from step 102 until the optimization result that meets the requirements is obtained.

[0047] In some embodiments, in order to perform a more comprehensive performance evaluation and screening during the two-dimensional optimization stage, after obtaining the optimized motor geometry and before constructing the three-dimensional model, the method also includes performing efficiency graph simulation and no-load simulation on the optimized case.

[0048] Specifically, efficiency plot simulation is used to evaluate the efficiency distribution of a motor under different speed and torque conditions. Efficiency plot simulations have corresponding filtering criteria, such as requiring peak efficiency to exceed a certain value, or requiring the area of ​​regions with efficiency greater than 85% to account for a certain proportion. Based on these filtering criteria, the results of the efficiency plot simulation can be filtered, eliminating cases with poor efficiency performance.

[0049] No-load simulation is used to evaluate the performance of a motor when it is not powered on, mainly outputting parameters such as cogging torque, peak line back EMF, and harmonic distortion rate. No-load simulation also has corresponding screening criteria, such as requiring cogging torque to be less than a certain limit, or the harmonic content of the line back EMF to be below a specific threshold. Filtering the results of the no-load simulation according to these criteria can ensure the low-speed smoothness of the motor and the quality of the output voltage waveform.

[0050] By combining efficiency diagram simulation and no-load simulation, a small number of solutions with the best overall performance in terms of load performance, efficiency characteristics and electromagnetic compatibility can be further selected from a large number of cases generated by two-dimensional optimization. These solutions then enter the more time-consuming three-dimensional simulation verification stage, thereby greatly improving the overall optimization efficiency while ensuring the quality of the results.

[0051] In some embodiments, in order to accumulate empirical knowledge from each optimization process, after obtaining the optimized motor geometry, correlation statistics can be performed between the optimized motor geometry and the motor performance parameters obtained through optimization to determine the influence relationship between the geometric changes and the performance parameters.

[0052] Specifically, through statistical data analysis, the correlation between various geometric parameters (such as air gap length, magnet thickness, stator tooth width, etc.) and key performance indicators (such as peak torque, torque ripple, and efficiency) can be quantified. For example, the analysis results may show that an increase in magnet thickness is strongly positively correlated with peak torque, but also positively correlated with cost. Conversely, a decrease in stator slot width is closely related to a reduction in torque ripple.

[0053] Figure 4 This is a correlation statistics diagram provided as an embodiment of the present invention. Figure 4 The parameters on the horizontal and vertical axes and their physical meanings are as follows: angle (angle of 1 / 2V type magnet), length (magnet length), width (magnet width), NO_LOAD: angle (no-load simulation: angle of 1 / 2V type magnet), NO_LOAD: length (no-load simulation: magnet length), NO_LOAD: width (no-load simulation: magnet width), LOAD: angle (load simulation: angle of 1 / 2V type magnet), LOAD: length (magnet length), LOAD: width (magnet width), Trange_noload (cogging torque), Voltage_Difference (amplitude of linear reaction potential), THD (harmonics of linear reaction potential), fundamental (fundamental of linear reaction potential), Torque<Whole Model> (Torque <Full Model>), Tmax (Peak Torque), Trange_load (Load Torque Fluctuation), mag_S (Magnet Area).

[0054] like Figure 4As shown, different geometric parameters have different influence relationships with different performance indicators. These relationships can be used to guide the adjustment of geometric information in the next round of optimization. When a new round of optimization is needed (e.g., due to unsatisfactory 3D verification or the change of materials), the focus of the next round of optimization can be clarified based on the previously summarized correlation statistics. This makes the optimization process more targeted, accelerates the convergence of optimization, and improves optimization efficiency.

[0055] In the above embodiments, the optimization objectives are verified by collecting motor geometric and material information, constructing a parameterized two-dimensional set, building a two-dimensional simulation model, performing load performance optimization simulation, efficiency diagram optimization simulation, no-load performance simulation, and constructing a three-dimensional electromagnetic simulation model. If the objectives are met, the optimization ends; otherwise, the material properties are improved, and the optimization process is repeated from the load performance optimization simulation steps until the optimization objectives are met.

[0056] In a specific example, an embedded permanent magnet synchronous motor for new energy vehicles is optimized. First, initial geometric and material information of its stator, rotor, magnets, and windings is collected. Next, a parameterized two-dimensional electromagnetic simulation model is established, setting eight key dimensions, including magnet area, air gap width, and slot width, as optimizable variables. The optimization objective is set to minimize torque ripple and improve average efficiency while ensuring that the peak torque is not lower than the initial value. Constraints on each variable are set to prevent structural inconsistencies.

[0057] Subsequently, a multi-objective genetic algorithm was used for optimization. The total number of generations was set to 80 (10 times the number of variables), with 50 individuals per generation. During the optimization process, approximately 5% of cases were assigned poor evaluation values ​​due to grid generation failures, which were effectively suppressed. After iterations, the algorithm selected three solutions.

[0058] Then, efficiency plot simulation and no-load simulation were performed on these three schemes. The efficiency plot simulation eliminated a scheme with a peak efficiency below 96%. The no-load simulation eliminated a scheme with excessive cogging torque. Finally, the optimal two-dimensional optimization case remained.

[0059] Based on the geometry of this case, a three-dimensional electromagnetic simulation model was constructed, and three-dimensional transient electromagnetic field simulation was performed. The high-precision performance parameters obtained from the simulation show that the peak torque slightly exceeds the target, the torque ripple is reduced by 25% compared to the initial design, and the efficiency meets the requirements. Therefore, this scheme is determined to meet all optimization objectives, and its geometric and material information is identified as the target optimization result. Simultaneously, correlation statistics revealed a significant negative correlation between slot width and torque ripple; this conclusion was recorded to guide future optimization work for similar motors.

[0060] In this embodiment of the invention, by combining rapid optimization of a parametric two-dimensional model with high-precision verification of a three-dimensional model, and by utilizing a multi-objective genetic algorithm and intermediate screening process, the optimization efficiency and result accuracy are effectively balanced. The optimal balance point can be found between conflicting performance objectives, thereby achieving efficient and high-precision optimization of motor performance.

[0061] Corresponding to the above-described motor optimization method, this embodiment of the invention provides a motor optimization device. Figure 5 This is a schematic diagram of a motor optimization device provided in an embodiment of the present invention. Figure 5 As shown, the motor optimization device includes: an acquisition module 501, a first modeling module 502, a determination module 503, a processing module 504, a second modeling module 505, a simulation module 506, a judgment module 507, and an output module 508.

[0062] The acquisition module 501 is used to acquire the geometric and material information of the main structural components of the motor.

[0063] The first modeling module 502 is used to establish a two-dimensional electromagnetic simulation model based on geometric and material information.

[0064] The determination module 503 is used to determine the constraint range of geometric parameters in the two-dimensional electromagnetic simulation model according to the preset performance optimization target.

[0065] The processing module 504 is used to perform topology optimization on the two-dimensional electromagnetic simulation model based on the performance optimization objectives and constraints, and to obtain the optimized motor geometry.

[0066] The second modeling module 505 is used to construct a corresponding three-dimensional electromagnetic simulation model based on the optimized motor geometry.

[0067] Simulation module 506 is used to perform three-dimensional transient electromagnetic field simulation based on the three-dimensional electromagnetic simulation model to obtain high-precision motor performance parameters.

[0068] The judgment module 507 is used to determine whether the high-precision motor performance parameters meet the performance optimization target.

[0069] The output module 508 is used to determine the geometric information and material information corresponding to the high-precision motor performance parameters as the target optimization result when the judgment module 507 determines that the high-precision motor performance parameters meet the performance optimization target.

[0070] Figure 5 The motor optimization device provided in the illustrated embodiment can be used to execute this specification. Figure 1-4 The implementation principle and technical effects of the method embodiment shown can be further referred to the relevant description in the method embodiment.

[0071] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 6 As shown, the aforementioned electronic device may include at least one processor and at least one memory communicatively connected to the processor, wherein the memory stores program instructions executable by the processor, and the processor can execute this specification by calling the program instructions. Figure 1-4 The embodiment shown provides a motor optimization method.

[0072] like Figure 6 As shown, the electronic device is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors 610, communication interface 620 and memory 630, and a communication bus 640 connecting different system components (including memory 630, communication interface 620 and processor 610).

[0073] Communication bus 640 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MAC) buses, Enhanced ISA buses, Video Electronics Standards Association (VESA) local buses, and Peripheral Component Interconnect (PCI) buses.

[0074] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0075] Memory 630 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 630 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments described herein.

[0076] A program / utility having a set (at least one) of program modules can be stored in memory 630. Such program modules include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this specification.

[0077] Processor 610 executes various functional applications and data processing by running programs stored in memory 630, such as implementing the functions described in this specification. Figure 1-4 The embodiment shown provides a motor optimization method.

[0078] This specification provides a computer program product, which includes a computer program that, when executed by a processor, performs the functions described in this specification. Figure 1-4 The embodiment shown provides a motor optimization method.

[0079] This specification provides a computer-readable storage medium storing computer instructions that cause a computer to execute this specification. Figure 1-4 The embodiment shown provides a motor optimization method.

[0080] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program that may be used by or in connection with an instruction execution system, apparatus, or device.

[0081] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0082] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0083] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this specification, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0084] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this specification includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which the embodiments of this specification pertain.

[0085] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0086] It should be noted that the devices involved in the embodiments of this specification may include, but are not limited to, personal computers (PCs), personal digital assistants (PDAs), wireless handheld devices, tablet computers, mobile phones, MP3 displays, MP4 displays, etc.

[0087] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms. Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0088] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, a connector, or a network device, etc.) or a processor to execute some steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0089] The above description is merely a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of protection of this specification.

[0090] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

Claims

1. A motor optimization method, characterized in that, include: Obtain the geometric and material information of the main structural components of the motor; Based on the geometric and material information, a two-dimensional electromagnetic simulation model is established. The constraint range of the geometric parameters in the two-dimensional electromagnetic simulation model is determined according to the preset performance optimization target; Based on the performance optimization objective and the constraint range, a multi-objective genetic algorithm is used to optimize the topology of the two-dimensional electromagnetic simulation model to obtain the optimized motor geometry. Based on the optimized motor geometry, a corresponding three-dimensional electromagnetic simulation model is constructed. Based on the aforementioned three-dimensional electromagnetic simulation model, a three-dimensional transient electromagnetic field simulation is performed to obtain high-precision motor performance parameters; Determine whether the high-precision motor performance parameters meet the performance optimization target; If the conditions are met, the geometric information and material information corresponding to the high-precision motor performance parameters will be determined as the target optimization result.

2. The method according to claim 1, characterized in that, The main structural components include one or more combinations of stator, rotor, magnets and windings.

3. The method according to claim 1, characterized in that, After obtaining the optimized motor geometry, the method further includes: Correlation statistics are performed on the optimized motor geometry and the motor performance parameters obtained through optimization to determine the impact of geometric changes on performance parameters; the impact relationship is used to guide the adjustment of the geometric information in the next round of optimization.

4. The method according to claim 1, characterized in that, The performance optimization targets include at least one of peak torque, torque pulsation, and efficiency.

5. The method according to claim 1, characterized in that, A multi-objective genetic algorithm is used to optimize the topology of the two-dimensional electromagnetic simulation model, including: Cases that exhibit geometric errors or fail to generate meshes during the topology optimization process of the multi-objective genetic algorithm are assigned a preset negative evaluation value to reduce the probability that cases with geometric errors or fail to generate meshes will be selected as parent cases in subsequent iterations.

6. The method according to claim 1, characterized in that, The constraint range of the geometric parameters is used to ensure that the optimized geometry does not experience mechanical interference.

7. The method as described in claim 1, characterized in that, Before constructing the 3D model, the method further includes: Efficiency plot simulation and no-load simulation are performed on the optimized motor geometry; the efficiency plot simulation and the no-load simulation are set with corresponding filtering conditions. The results of the efficiency diagram simulation are filtered according to the filtering criteria of the efficiency diagram simulation; and the results of the no-load simulation are filtered according to the filtering criteria of the no-load simulation.

8. A motor optimization device, characterized in that, include: The acquisition module is used to acquire the geometric and material information of the main structural components of the motor; The first modeling module is used to establish a two-dimensional electromagnetic simulation model based on the geometric and material information. The determination module is used to determine the constraint range of geometric parameters in the two-dimensional electromagnetic simulation model according to the preset performance optimization target; The processing module is used to perform topology optimization on the two-dimensional electromagnetic simulation model based on the performance optimization objective and the constraint range, and to obtain the optimized motor geometry. The second modeling module is used to construct a corresponding three-dimensional electromagnetic simulation model based on the optimized motor geometry. The simulation module is used to perform three-dimensional transient electromagnetic field simulation based on the three-dimensional electromagnetic simulation model to obtain high-precision motor performance parameters; The judgment module is used to determine whether the high-precision motor performance parameters meet the performance optimization target; The output module is used to determine the geometric information and material information corresponding to the high-precision motor performance parameters as the target optimization result when the judgment module determines that the high-precision motor performance parameters meet the performance optimization target.

9. An electronic device, characterized in that, include: At least one processor; as well as At least one memory communicatively connected to the processor, wherein: The memory stores program instructions that can be executed by the processor, and the processor can execute the method according to any one of claims 1 to 7 by calling the program instructions.

10. A computer-readable storage medium storing computer instructions that cause the computer to perform the method according to any one of claims 1 to 7.

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