Optimization design method, device and equipment of hub motor rotor magnetic isolation bridge and medium

Abstract

The invention discloses an optimization design method, device, equipment and medium for a rotor magnetic isolation bridge of a hub motor, and the method comprises the steps: determining initial optimization parameters of the hub motor, the initial optimization parameters being motor parameters related to the mechanical strength of the magnetic isolation bridge; selecting at least two parameters with high sensitivity of the first motor performance to the initial optimization parameters from the initial optimization parameters as key optimization parameters; screening out a parameter value combination meeting the first motor performance requirement and the allowable stress requirement of the motor rotor punching sheet from the key optimization parameter combinations with different values as a candidate parameter value combination; screening out a parameter value combination meeting the performance requirement of the second motor from the candidate parameter value combinations as a target parameter value combination; and designing the hub motor according to the value corresponding to each key optimization parameter in the target parameter value combination. The method can meet the performance requirements of the motor while ensuring the mechanical strength of the magnetic isolation bridge, and realizes the optimization of the comprehensive performance of the motor.

Description

Technical Field

[0001] The present invention relates to the technical field of motor design, and in particular to an optimization design method, device, equipment and medium for a hub motor rotor magnetic isolation bridge. Background Art

[0002] In the field of hub motor technology, the magnetic isolation bridge, as a vital component of the motor structure, not only undertakes the task of isolating the magnetic field and reducing electromagnetic interference, but is also directly related to the mechanical strength and overall performance of the motor. The traditional magnetic isolation bridge design method mainly focuses on adjusting its size parameters to ensure sufficient mechanical strength and meet the stability requirements of motor operation. However, this method has significant limitations: it ignores the potential impact of changes in the size of the magnetic isolation bridge on the core performance indicators of the motor (such as output power, torque output, energy efficiency ratio, etc.), which are directly related to the operating efficiency and actual application effect of the motor.

[0003] In addition, existing designs often consider the magnetic isolation bridge itself in isolation, and fail to fully consider the interaction between other key parameters inside the motor (such as winding layout, magnetic circuit design, cooling system, etc.) and the performance of the magnetic isolation bridge. This split design approach often makes it difficult to achieve the best balance between mechanical strength, magnetic isolation effect and comprehensive motor performance, and it is difficult to meet the comprehensive requirements of modern motor design for high performance, high efficiency and high reliability.

[0004] Finally, in the prior art, designers usually consider the impact of the magnetic isolation bridge design on the motor performance based on their experience in other motor designs. Designers need to frequently adjust the size of the magnetic isolation bridge and verify its effect through a large number of experiments or simulations. This not only increases the design cost and time, but is also highly dependent on the designer's personal experience and subjective judgment. It is difficult to ensure the optimization and scientific nature of the design results, and the design process is complicated and inefficient.

[0005] Therefore, how to ensure the mechanical strength of the magnetic isolation bridge while meeting the motor performance requirements has become the core problem in the design of the magnetic isolation bridge of the hub motor. Summary of the invention

[0006] In view of the above problems, the present invention is proposed to provide an optimization design method, device, equipment and medium for a hub motor rotor magnetic isolation bridge that overcomes the above problems or at least partially solves the above problems, which can meet the motor performance requirements while ensuring the mechanical strength of the magnetic isolation bridge and achieve optimization of the comprehensive performance of the motor.

[0007] In a first aspect, a method for optimizing the design of a hub motor rotor magnetic isolation bridge is provided, comprising:

[0008] Determining initial optimization parameters of the hub motor, wherein the initial optimization parameters are motor parameters related to the mechanical strength of the magnetic isolation bridge;

[0009] Selecting at least two parameters of the first motor performance having high sensitivity to the initial optimization parameters from the initial optimization parameters as key optimization parameters;

[0010] From the key optimization parameter combinations with different values, a parameter value combination that meets the first motor performance requirement and the motor rotor punching allowable stress requirement is selected as a candidate parameter value combination;

[0011] Selecting a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination;

[0012] The hub motor is designed according to the values ​​corresponding to each of the key optimization parameters in the target parameter value combination.

[0013] Optionally, selecting at least two parameters of the first motor performance having high sensitivity to the initial optimization parameters from the initial optimization parameters as key optimization parameters includes:

[0014] Determining the sensitivity of the first motor performance to each of the initial optimized parameters;

[0015] The first n parameters of the first motor performance that are highly sensitive to the initial optimization parameters are selected as key optimization parameters, where n is a positive integer greater than 1.

[0016] Optionally, determining the sensitivity of the first motor performance to each parameter in the initial optimization parameters includes:

[0017] Inputting each parameter in the initial optimization parameters into a pre-established motor parameterization model in sequence, performing finite element simulation analysis on the motor parameterization model, and outputting first motor performance values ​​corresponding to each parameter in the initial optimization parameters at different values;

[0018] Based on the first motor performance values ​​corresponding to the various parameters in the initial optimization parameters at different values, the sensitivity of the first motor performance to the various parameters in the initial optimization parameters is determined.

[0019] Optionally, determining the sensitivity of the first motor performance to each parameter in the initial optimization parameters based on the first motor performance value corresponding to each parameter in the initial optimization parameters at different values ​​includes:

[0020] The sensitivity of the first motor performance to each parameter in the initial optimization parameters is determined according to the following formula:

[0021]

[0022] Among them, G(x i) is the sensitivity, x i is the first parameter value of the i-th parameter in the initial optimization parameters, y i For x i The corresponding first motor performance value; x i ' is the second parameter value of the i-th parameter in the initial optimization parameters, y i ' is x i 'The corresponding first motor performance value.

[0023] Optionally, the step of selecting, from key optimization parameter combinations with different values, a parameter value combination that meets the first motor performance requirement and the motor rotor punching allowable stress requirement as a candidate parameter value combination includes:

[0024] The key optimization parameters are input into a pre-established motor optimization model, and the motor optimization model is used to randomly select and combine values ​​from a preset value range corresponding to each of the key optimization parameters to form a plurality of initial preferred parameter combinations;

[0025] The non-dominated genetic algorithm configured in the motor optimization model is used to iteratively calculate the initial preferred parameter combination, and the initial preferred parameter combination that satisfies preset constraints, preset value ranges, and the first motor performance meets the requirements is output as the candidate parameter value combination; wherein the preset constraints include the allowable stress of the motor rotor punching; the preset value range is the design parameter configured by the motor optimization model.

[0026] Optionally, after screening out the candidate parameter value combinations, the method further includes:

[0027] Obtaining a first motor performance value corresponding to each candidate parameter value combination output by the motor optimization model, recorded as a model first motor performance value;

[0028] Inputting the candidate parameter value combinations into a pre-established motor parameterization model, performing finite element simulation analysis on the motor parameterization model, and outputting first motor performance values ​​corresponding to each candidate parameter value combination, recorded as simulated first motor performance values;

[0029] Based on the model first motor performance value and the simulated first motor performance value, determining whether the motor optimization model needs to be optimized;

[0030] When the motor optimization model needs to be optimized, adjusting the number of iterations of the motor optimization model to optimize the motor optimization model;

[0031] The key optimization parameters are re-input into the optimized motor optimization model, and a new combination of candidate parameter values ​​is output.

[0032] Optionally, the step of selecting a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination includes:

[0033] Inputting the candidate parameter value combinations into a pre-established motor parameterization model, performing finite element simulation analysis on the motor parameterization model, and outputting second motor performance values ​​corresponding to each candidate parameter value combination;

[0034] Based on the second motor performance values ​​corresponding to each of the candidate parameter value combinations, a parameter value combination that meets the second motor performance requirement is screened out from the candidate parameter value combinations as a target parameter value combination.

[0035] In a second aspect, an optimization design device for a hub motor rotor magnetic isolation bridge is provided, comprising:

[0036] An initial determination module, used to determine initial optimization parameters of the hub motor, wherein the initial optimization parameters are motor parameters related to the mechanical strength of the magnetic isolation bridge;

[0037] A key determination module, configured to select at least two parameters of the first motor performance having high sensitivity to the initial optimization parameters from the initial optimization parameters as key optimization parameters;

[0038] A candidate determination module is used to select, from key optimization parameter combinations with different values, a parameter value combination that meets the first motor performance requirement and the motor rotor punching allowable stress requirement as a candidate parameter value combination;

[0039] A target determination module, used to select a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination;

[0040] The design module is used to optimize the design of the hub motor according to the values ​​corresponding to each of the key optimization parameters in the target parameter value combination.

[0041] In a third aspect, an electronic device is provided, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the optimization design method as described in the first aspect by executing the computer instructions.

[0042] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the optimization design method as described in the first aspect.

[0043] The technical solution provided in the embodiments of the present invention has at least the following technical effects or advantages:

[0044] The embodiment of the present invention provides an optimization design method, device, equipment and medium for a hub motor rotor magnetic isolation bridge. By predetermining the motor parameters related to the mechanical strength of the magnetic isolation bridge in the motor as the initial optimization parameters, the sensitivity analysis technology is then used to accurately screen out the key optimization parameters that have a significant impact on the performance of the first motor (such as speed, torque, power, etc.). This process greatly reduces the range of optimization variables, effectively avoiding the complexity and inefficiency of simulation verification caused by too many parameters in traditional optimization methods, thereby greatly improving the design efficiency. At the same time, the present invention divides the motor performance into the first performance and the second performance, and through step-by-step screening, first ensures that the basic requirements such as the first motor performance and the allowable stress of the rotor punching are met, and then further screens the parameter combination that meets the performance requirements of the second motor as the target parameter value combination, and finally optimizes the hub motor according to the values ​​corresponding to each key optimization parameter in the target parameter value combination. This multi-objective optimization strategy not only ensures the mechanical strength of the magnetic isolation bridge, but also takes into account the motor performance, realizes the optimization of the comprehensive performance of the motor, and the fast and efficient optimization design of the motor magnetic isolation bridge.

[0045] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0047] Figure 1 is a cross-sectional view of a hub motor provided by an embodiment of the present invention;

[0048] Figure 2 is a partial structural schematic diagram of a rotor provided by an embodiment of the present invention;

[0049] Figure 3 yes Figure 2 A magnified schematic diagram of the middle A area;

[0050] Figure 4 It is a flow chart of an optimization design method of a hub motor rotor magnetic isolation bridge provided by an embodiment of the present invention;

[0051] Figure 5 It is a flow chart of the optimization principle of a motor optimization model provided by an embodiment of the present invention;

[0052] Figure 6 It is a structural block diagram of an optimization design device for a hub motor rotor magnetic isolation bridge provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to better understand the above-mentioned technical scheme, the above-mentioned technical scheme will be described in detail below in combination with the accompanying drawings and specific implementation methods of the specification. It should be understood that the embodiments of the present disclosure and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0054] In order to better understand the present invention, the specific structure of the wheel hub motor is briefly described below:

[0055] Figure 1 is a cross-sectional view of a hub motor provided by an embodiment of the present invention, such as Figure 1 As shown, the hub motor includes a reducer 10 and a high-speed motor body 20. The high-speed motor body 20 includes a stator 21 and a rotor 22. The stator 21 is a fixed part, which contains the coils and permanent magnets of the motor and is responsible for generating a magnetic field. The rotor 22 is a rotating part, which is composed of an iron core, a permanent magnet or a winding and is responsible for receiving the force and torque generated by the motor. The rotor 22 transmits the motor torque to the rim 31 through the reducer 10, thereby driving the tire 30 to rotate.

[0056] Figure 2 is a partial structural schematic diagram of a rotor provided by an embodiment of the present invention, Figure 3 yes Figure 2 The enlarged schematic diagram of area A in the middle is as follows: Figure 2 and Figure 3 As shown, the rotor 22 at least includes an iron core 221, permanent magnets 222, a magnetic isolation bridge 223 and pole shoes 224. The rotor core 221 is usually made of laminated silicon steel sheets and has good magnetic conductivity and low eddy current loss. Its outer circle is punched with evenly distributed grooves for accommodating the permanent magnets 222. The permanent magnets 222 are components that generate a constant magnetic field in the motor and are usually made of high-performance permanent magnetic materials (such as neodymium iron boron). In the slots of the rotor core 221, the permanent magnets 222 are fixed at specific positions to form the required magnetic field distribution. The permanent magnets 222 interact with the rotor core 221 through the magnetic field to generate electromagnetic force to drive the rotor 22 to rotate. The magnetic isolation bridge 223 is a component in the permanent magnet motor used to isolate the magnetic flux path between the permanent magnets 222. It is usually made of non-magnetic material. The magnetic isolation bridge 223 is usually located between the permanent magnets 222 or between the permanent magnets 222 and the rotor core 221. As shown Figure 3As shown, in this embodiment, the magnetic isolation bridge includes a middle magnetic isolation bridge 223a located between two permanent magnets 222, and a top magnetic isolation bridge 223b located on top of the permanent magnets. The pole shoe 224 is a unique structure of the motor pole, usually made of ferromagnetic material. The pole shoe 224 is located around the permanent magnet 222 or at a specific position of the rotor core 221 to improve the magnetic field distribution.

[0057] Due to structural limitations and the need for high integration, the hub motor is usually a flat structure with thinner rotor punchings. Compared with conventional automotive high-speed motors, it also has the need for high speed, but it will be subject to greater centrifugal force. Due to the limited axial space in the rim 31, the rotor 22 is a circular disc structure. Under high-speed rotating working conditions, due to the action of centrifugal force, the rotor's magnetic isolation bridge is subjected to great stress and is prone to large deformation, making the air gap between the stator and rotor uneven, affecting the motor output torque, and more seriously, the rotor will be swept and the motor will be damaged. Therefore, in the design and manufacturing process of the hub motor, it is necessary to fully consider the design and optimization of the magnetic isolation bridge.

[0058] Figure 4 : is a flow chart of an optimization design method for a hub motor rotor magnetic isolation bridge provided by an embodiment of the present invention, such as Figure 4 As shown, the method includes:

[0059] Step S410: determining initial optimization parameters of the hub motor, where the initial optimization parameters are motor parameters related to the mechanical strength of the magnetic isolation bridge.

[0060] Since the mechanical strength of the magnetic isolation bridge is not only related to the size design parameters of the magnetic isolation bridge itself (such as width, shape, material, thickness, etc.), but also related to the design parameters of the permanent magnet and the design parameters of the rotor core. Therefore, in this embodiment, all motor parameters related to the mechanical strength of the magnetic isolation bridge can be calibrated through experiments, and these parameters can be determined as the initial optimization parameters of the motor to better optimize the mechanical strength of the magnetic isolation bridge.

[0061] Exemplarily, the initial optimization parameters include at least the thickness, width and shape of the rotor magnetic bridge, the radius of the transition circle, the thickness of the connecting ribs, the thickness of the permanent magnet slots, the spacing between the permanent magnet slots, the width of the permanent magnets, the core material, the arrangement of the slots on the core, etc., which can be obtained by technical personnel in this field through experimental calibration, and the present invention is not limited to this.

[0062] Step S420 , selecting at least two parameters of the first motor performance that are highly sensitive to the initial optimization parameters from the initial optimization parameters as key optimization parameters.

[0063] In this embodiment, the first motor performance may include at least one performance parameter of rated power, rated speed, maximum operating speed, peak torque, peak torque allowable time, peak power, peak power operating time, proportion of motor operating high-efficiency zone, motor peak efficiency, cogging torque, torque fluctuation, motor back electromotive force, and maximum temperature of each motor operating condition. The specific performance parameters can be selected according to actual needs for optimization.

[0064] Optionally, step S420 includes:

[0065] Step S421, determining the sensitivity of the first motor performance to each parameter in the initial optimization parameters;

[0066] Step S422: selecting the first n parameters of the first motor performance that are most sensitive to the initial optimization parameters as key optimization parameters, where n is a positive integer greater than 1.

[0067] The value of n can be defined according to actual needs, for example, n = 5. However, the value of n should not be too large or too small, for example, it can be in the range of 3 to 10. If the value of n is too large, the subsequent design process will be more complicated and the design efficiency will be reduced. If it is too small, it will not be possible to achieve a good optimization design effect on the magnetic isolation bridge.

[0068] It should be noted that when the initial optimization parameters include multiple parameters, and the first motor performance includes multiple performance parameters, for each performance parameter, the first n parameters with high sensitivity to each performance parameter can be selected from the initial optimization parameters, and then the overlapping initial optimization parameters are selected from the first n initial optimization parameters corresponding to the multiple performance parameters as key optimization parameters. Exemplarily, when the initial optimization parameters include a1, a2, a3, a4 and a5, and the first motor performance includes b11, b12 and b13, if the first three parameters with high sensitivity to the initial optimization parameters of b11 are a1, a3 and a5, the first three parameters with high sensitivity to the initial optimization parameters of b12 are a1, a2 and a3, and the first three parameters with high sensitivity to the initial optimization parameters of b13 are a1, a3 and a4, then the overlapping parameters a1 and a3 can be selected from the first three initial optimization parameters corresponding to b11, b12 and b13 as key optimization parameters.

[0069] In a specific implementation, step S421 may include:

[0070] The first step is to input each parameter in the initial optimization parameters into a pre-established motor parameterization model in sequence, perform finite element simulation analysis on the motor parameterization model, and output the first motor performance value corresponding to each parameter in the initial optimization parameters under different values;

[0071] The second step is to determine the sensitivity of the first motor performance to each parameter in the initial optimization parameters based on the first motor performance values ​​corresponding to each parameter in the initial optimization parameters at different values.

[0072] In this embodiment, the sensitivity of the first motor performance to each parameter in the initial optimization parameters can be determined according to the following formula (1):

[0073]

[0074] Among them, G(x i ) is the sensitivity, x i is the first parameter value of the i-th parameter in the initial optimization parameters, y i For x i The corresponding first motor performance value; x i ' is the second parameter value of the i-th parameter in the initial optimization parameters, y i ' is x i 'The corresponding first motor performance value; x i '-x i is the change in the parameter value of the i-th parameter in the initial optimization parameters, y i '-y i is the change in the performance value of the first motor.

[0075] In the specific implementation, the i-th parameter in the initial optimization parameters can be input into the pre-established motor parameterization model, and the motor parameterization model can be subjected to finite element simulation analysis to output the i-th parameter at the first parameter value x. i The first performance value y of the first motor performance corresponding to i , and the i-th parameter is at the second parameter value x i 'The second performance value y of the first motor performance corresponding to i '. Then, according to the above formula, the sensitivity G(x) of the first motor performance to each parameter in the initial optimization parameters can be calculated. i ). Sensitivity G(x) i ) is higher, reflecting the optimization design parameter x i The greater the impact on the performance of the first motor to be optimized.

[0076] It should be noted that when the first motor performance includes multiple performance parameters, each parameter in the initial optimization parameters can be input into a pre-established motor parameterization model in turn, and a finite element simulation analysis can be performed on the motor parameterization model to output the values ​​of multiple performance parameters corresponding to each parameter in the initial optimization parameters at different values. Then, the sensitivity of each performance parameter of the motor to each parameter in the initial optimization parameters can be calculated according to the above formula (1).

[0077] Optionally, the optimization design method further includes:

[0078] Establish a motor parameterized model based on the design parameters of each component in the hub motor;

[0079] The target value, boundary requirements and cooling conditions of the first motor performance are input into the motor parameterization model to obtain the motor initial parameter model.

[0080] In the method for constructing the above-mentioned motor initial parameter model, the target value of the first motor performance can be defined according to the power demand of the passenger car, and the boundary requirements of the hub motor can be given by identifying the outer envelope of the passenger car wheel rim. According to the target value and boundary requirements of the first motor performance, the temperature rise and heat dissipation conditions of the motor under the working state are evaluated, so as to select appropriate cooling conditions and input them into the motor parameterization model to complete the establishment of the motor initial parameter model. Among them, the target value of the first motor performance is the target value corresponding to each performance parameter included in the motor performance, the boundary requirements may include the axial diameter and radial diameter of the hub motor, and the cooling conditions may include the cooling medium, cooling method, cooling system design and other conditions, which are not limited in this embodiment.

[0081] It should be noted that the target value, boundary requirements and cooling conditions of the first motor performance are also related to the optimized design of the magnetic isolation bridge. Therefore, this embodiment constructs a motor initialization parameter model by inputting these conditions into the motor parameterization model in advance, so that the final optimized design result of this scheme can meet the above conditions. At the same time, the constructed motor initialization parameter model can be input into the finite element simulation software for finite element analysis to output the first motor performance value corresponding to the motor initialization parameter model, that is, the actual value of each performance parameter included in the first motor performance.

[0082] Step S430: Filter out, from key optimization parameter combinations with different values, parameter value combinations that meet the first motor performance requirement and the motor rotor punching allowable stress requirement as candidate parameter value combinations.

[0083] Optionally, step S430 includes:

[0084] The key optimization parameters are input into a pre-established motor optimization model, and the motor optimization model is used to randomly select and combine values ​​from a preset value range corresponding to each key optimization parameter to form a plurality of initial optimal parameter combinations;

[0085] The non-dominated genetic algorithm configured in the motor optimization model is used to iteratively calculate the initial preferred parameter combination, and the initial preferred parameter combination that meets the preset constraints and the preset value range and the first motor performance meets the requirements is output as a candidate parameter value combination; wherein the preset constraints include the allowable stress of the motor rotor punching; the preset value range is the design parameters configured by the motor optimization model.

[0086] In the above implementation, the constraints of the motor optimization model can be set more according to actual needs. For example, the constraints can be set to also include rotor punching temperature limits and motor electromagnetic performance requirements, so that the final output candidate parameter value combination meets the above constraints.

[0087] Optionally, the optimization design method further includes:

[0088] A motor optimization model is established. The motor optimization model adopts a non-dominated genetic algorithm, takes the first motor performance as the optimization target, and takes the allowable stress of the motor rotor punching as the constraint condition. The motor optimization model is configured with preset value ranges corresponding to various key optimization parameters designed according to the motor boundary requirements.

[0089] Figure 5 is a flow chart of the optimization principle of a motor optimization model provided by an embodiment of the present invention, such as Figure 5 As shown in the figure, the optimization principle of the motor optimization model includes: first, randomly select values ​​and combine them within the value range of each key parameter to obtain an initialized population (i.e., an initial candidate parameter value combination); secondly, through non-dominated sorting and crowding calculation, the order is compared two by two, and individuals with small order and large crowding are selected for crossover mutation to generate a new population; then the new population is merged with the old population, and non-dominated sorting and crowding calculation are performed again; then a new population is obtained by selecting through an elite strategy, and the elite strategy can obtain individuals with excellent performance and meet the constraints as much as possible; finally, it is determined whether the number of iterations exceeds the set value. If it does not exceed the set value, the above process is repeated to perform a new round of genetic operations until the iteration ends, and a set of preferred solutions (Pareto frontier) is output, which is the above candidate parameter value combination.

[0090] Optionally, after screening out the candidate parameter value combinations, the method further includes:

[0091] Obtain the first motor performance value corresponding to each candidate parameter value combination output by the motor optimization model, recorded as the model first motor performance value; input the candidate parameter value combination into a pre-established motor parameterization model, perform finite element simulation analysis on the motor parameterization model, and output the first motor performance value corresponding to each candidate parameter value combination, recorded as the simulated first motor performance value; determine whether the motor optimization model needs to be optimized based on the model first motor performance value and the simulated first motor performance value; when the motor optimization model needs to be optimized, adjust the number of iterations of the motor optimization model to optimize the motor optimization model; re-input the key optimization parameters into the optimized motor optimization model, and output a new candidate parameter value combination.

[0092] It should be noted that when the above-mentioned motor optimization model outputs a candidate parameter value combination, it will also output the first motor performance value corresponding to the candidate parameter value combination (i.e., the model first motor performance value). By comparing the model first motor performance value (denoted as K1) corresponding to the candidate parameter value combination output by the motor optimization model, and the first motor performance value (i.e., the simulated first motor performance value, denoted as K2) corresponding to each candidate parameter value combination output by the finite element simulation analysis, it can be determined whether the motor optimization model needs to be optimized. Specifically, when the difference between K1 and K2 exceeds the preset performance difference, it means that the motor optimization model needs to be optimized. At this time, the motor optimization model can be optimized by adjusting the number of iterations of the motor optimization model until the difference between K1 and K2 is within the preset performance difference range, and the optimization of the motor optimization model is completed.

[0093] Step S440: Filter out a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination.

[0094] Optionally, step S440 includes:

[0095] The candidate parameter value combinations are input into a pre-established motor parameterization model, a finite element simulation analysis is performed on the motor parameterization model, and the second motor performance value corresponding to each candidate parameter value combination is output; based on the second motor performance value corresponding to each candidate parameter value combination, a parameter value combination that meets the second motor performance requirements is screened out from the candidate parameter value combinations as the target parameter value combination.

[0096] Among them, the second motor performance includes at least one performance parameter of rated power, rated speed, maximum operating speed, peak torque, peak torque allowable time, peak power, peak power operating time, motor operating high efficiency zone ratio, motor peak efficiency, cogging torque, torque fluctuation, motor back electromotive force, and maximum temperature of each motor operating condition, and the performance parameters included in the second motor performance do not overlap with the performance parameters included in the first motor performance. This embodiment splits the motor performance into the first performance and the second performance, and through step-by-step screening, first ensures that the basic requirements such as the first motor performance and the allowable stress of the rotor punching are met, and then further screens the parameter combination that meets the second motor performance requirements as the target parameter value combination, which can greatly reduce the computing resources and time required for finite element simulation analysis and effectively improve the design efficiency.

[0097] Step S450: designing the hub motor according to the values ​​corresponding to each key optimization parameter in the target parameter value combination.

[0098] Since the selected target parameter combination meets the requirements of the first motor performance and the second motor performance, as well as the allowable stress of the rotor punching (that is, it meets the mechanical strength requirements of the magnetic isolation bridge), the hub motor is optimized according to the corresponding values ​​of each key optimization parameter in the target parameter value combination. This not only ensures the mechanical strength of the magnetic isolation bridge, but also takes into account the motor performance, thereby achieving the optimization of the comprehensive performance of the motor and the rapid and efficient optimization design of the motor magnetic isolation bridge.

[0099] Based on the same inventive concept, an embodiment of the present invention further provides an optimization design device for a hub motor rotor magnetic isolation bridge, which is used to achieve the optimization design of the hub motor rotor magnetic isolation bridge. Figure 6 : is a structural block diagram of an optimization design device for a hub motor rotor magnetic isolation bridge provided by an embodiment of the present invention, such as Figure 6 As shown, the optimization design device 600 includes an initial determination module 610 , a key determination module 620 , a candidate determination module 630 , a target determination module 640 and a design module 650 .

[0100] An initial determination module 610 is used to determine initial optimization parameters of the wheel hub motor, where the initial optimization parameters are motor parameters related to the mechanical strength of the magnetic isolation bridge;

[0101] A key determination module 620 is used to select at least two parameters of the first motor performance with high sensitivity to the initial optimization parameters from the initial optimization parameters as key optimization parameters;

[0102] A candidate determination module 630 is used to select, from key optimization parameter combinations with different values, a parameter value combination that meets the first motor performance requirement and the motor rotor punching allowable stress requirement as a candidate parameter value combination;

[0103] A target determination module 640 is used to select a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination;

[0104] The design module 650 is used to design the hub motor according to the values ​​corresponding to each key optimization parameter in the target parameter value combination.

[0105] Optionally, the key determination module 620 includes:

[0106] a sensitivity determination unit, configured to determine the sensitivity of the first motor performance to each parameter in the initial optimization parameters;

[0107] The key optimization parameter selection unit is used to select the first n parameters of the first motor performance that are highly sensitive to the initial optimization parameters as key optimization parameters, where n is a positive integer greater than 1.

[0108] Optionally, the sensitivity determination unit is used to:

[0109] Input each parameter in the initial optimization parameters into a pre-established motor parameterized model in sequence, perform finite element simulation analysis on the motor parameterized model, and output a first motor performance value corresponding to each parameter in the initial optimization parameters at different values;

[0110] Based on the first motor performance values ​​corresponding to the various parameters in the initial optimization parameters at different values, the sensitivity of the first motor performance to the various parameters in the initial optimization parameters is determined.

[0111] Optionally, the sensitivity determination unit is used to:

[0112] The sensitivity of the first motor performance to each parameter in the initial optimization parameters is determined according to the following formula:

[0113]

[0114] Among them, G(x i ) is the sensitivity, x i is the first parameter value of the i-th parameter in the initial optimization parameters, y i For x i The corresponding first motor performance value; x i ' is the second parameter value of the i-th parameter in the initial optimization parameters, y i ' is x i 'The corresponding first motor performance value.

[0115] Optionally, the candidate determination module 630 is used to:

[0116] The key optimization parameters are input into a pre-established motor optimization model, and the motor optimization model is used to randomly select and combine values ​​from a preset value range corresponding to each key optimization parameter to form a plurality of initial optimal parameter combinations;

[0117] The non-dominated genetic algorithm configured in the motor optimization model is used to iteratively calculate the initial preferred parameter combination, and the initial preferred parameter combination that meets the preset constraints and the preset value range and the first motor performance meets the requirements is output as a candidate parameter value combination; wherein the preset constraints include the allowable stress of the motor rotor punching; the preset value range is the design parameters configured by the motor optimization model.

[0118] Optionally, the device further comprises a model optimization model, which is used to:

[0119] Obtaining a first motor performance value corresponding to each candidate parameter value combination output by the motor optimization model, recorded as a model first motor performance value;

[0120] Inputting the candidate parameter value combinations into a pre-established motor parameterization model, performing finite element simulation analysis on the motor parameterization model, and outputting the first motor performance value corresponding to each candidate parameter value combination, recorded as the simulated first motor performance value;

[0121] Determining whether the motor optimization model needs to be optimized based on the model first motor performance value and the simulated first motor performance value;

[0122] When the motor optimization model needs to be optimized, the number of iterations of the motor optimization model is adjusted to optimize the motor optimization model;

[0123] The key optimization parameters are re-input into the optimized motor optimization model, and a new combination of candidate parameter values ​​is output.

[0124] Optionally, the target determination module 640 is used to:

[0125] Inputting the candidate parameter value combinations into a pre-established motor parameterization model, performing finite element simulation analysis on the motor parameterization model, and outputting the second motor performance value corresponding to each candidate parameter value combination;

[0126] Based on the second motor performance values ​​corresponding to each candidate parameter value combination, a parameter value combination that meets the second motor performance requirement is screened out from the candidate parameter value combinations as a target parameter value combination.

[0127] The specific details of the optimization design method involved in the above-mentioned optimization design device can be understood by corresponding to the relevant descriptions and effects in the above-mentioned optimization design method embodiment, and will not be repeated here.

[0128] Based on the same inventive concept as the above-mentioned optimization design method, the present invention also provides an electronic device, which may include a processor and a memory, wherein the processor and the memory may be connected to each other by bus or other means. The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components and other chips, or a combination of the above-mentioned various types of chips. The memory, as a non-transient computer-readable storage medium, may be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the optimization design method in the embodiment of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory, that is, realizing the optimization design of the magnetic isolation bridge in the above-mentioned method embodiment.

[0129] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. The one or more modules are stored in the memory, and when executed by the processor, the execution is as follows: Figure 4 The optimization design method in the illustrated embodiment.

[0130] For details of the above electronic equipment, please refer to Figure 4 The corresponding related descriptions and effects in the illustrated embodiments can be understood and will not be repeated here.

[0131] Based on the same inventive concept as the optimization design method, the present invention also provides a computer-readable storage medium, on which computer instructions are stored, and the computer program instructions are used to enable a computer to execute the optimization design method in the above embodiment.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0133] The technical solutions provided in the above embodiments of the present application have at least the following technical effects or advantages:

[0134] The embodiment of the present invention provides an optimization design method, device, equipment and medium for a hub motor rotor magnetic isolation bridge. The motor parameters related to the mechanical strength of the magnetic isolation bridge in the motor are pre-determined as initial optimization parameters, and then the sensitivity analysis technology is used to accurately screen out the key optimization parameters that have a significant impact on the performance of the first motor (such as efficiency, power density, etc.). This process greatly reduces the range of optimization variables, effectively avoids the complexity and inefficiency of simulation verification caused by too many parameters in traditional optimization methods, thereby greatly improving the design efficiency. At the same time, the present invention divides the motor performance into the first performance and the second performance, and through step-by-step screening, first ensures that the basic requirements such as the first motor performance and the allowable stress of the rotor punching are met, and then further screens the parameter combination that meets the performance requirements of the second motor as the target parameter value combination, and finally optimizes the hub motor according to the values ​​corresponding to each key optimization parameter in the target parameter value combination. This multi-objective optimization strategy not only ensures the mechanical strength of the magnetic isolation bridge, but also takes into account the motor performance, realizes the optimization of the comprehensive performance of the motor, and the fast and efficient optimization design of the motor magnetic isolation bridge.

[0135] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0136] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the intention that the claimed invention requires more features than those explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0137] It should be noted that the above-mentioned embodiments illustrate rather than limit the invention and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims.

Claims

1. An optimization design method for a hub motor rotor magnetic isolation bridge, characterized in that: include: Determining initial optimization parameters of the hub motor, wherein the initial optimization parameters are motor parameters related to the mechanical strength of the magnetic isolation bridge; Selecting at least two parameters of the first motor performance having high sensitivity to the initial optimization parameters from the initial optimization parameters as key optimization parameters; From the key optimization parameter combinations with different values, a parameter value combination that meets the first motor performance requirement and the motor rotor punching allowable stress requirement is selected as a candidate parameter value combination; Selecting a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination; The hub motor is designed according to the values ​​corresponding to each of the key optimization parameters in the target parameter value combination.

2. The method according to claim 1, characterized in that The selecting at least two parameters of the first motor performance having high sensitivity to the initial optimization parameters as key optimization parameters from the initial optimization parameters includes: Determining the sensitivity of the first motor performance to each of the initial optimized parameters; The first n parameters of the first motor performance that are highly sensitive to the initial optimization parameters are selected as key optimization parameters, where n is a positive integer greater than 1.

3. The method according to claim 2, characterized in that The determining the sensitivity of the first motor performance to each parameter in the initial optimization parameters includes: Inputting each parameter in the initial optimization parameters into a pre-established motor parameterization model in sequence, performing finite element simulation analysis on the motor parameterization model, and outputting first motor performance values ​​corresponding to each parameter in the initial optimization parameters at different values; Based on the first motor performance values ​​corresponding to the various parameters in the initial optimization parameters at different values, the sensitivity of the first motor performance to the various parameters in the initial optimization parameters is determined.

4. The method according to claim 3, characterized in that The determining, based on the first motor performance values ​​corresponding to the various parameters in the initial optimization parameters at different values, the sensitivity of the first motor performance to the various parameters in the initial optimization parameters comprises: The sensitivity of the first motor performance to each parameter in the initial optimization parameters is determined according to the following formula: Among them, G(x i ) is the sensitivity, x i is the first parameter value of the i-th parameter in the initial optimization parameters, y i For x i The corresponding first motor performance value; x i ' is the second parameter value of the i-th parameter in the initial optimization parameters, y i ' is x i 'The corresponding first motor performance value.

5. The method according to claim 1, characterized in that The method of selecting, from the key optimization parameter combinations with different values, a parameter value combination that meets the first motor performance requirement and the motor rotor punching sheet allowable stress requirement as a candidate parameter value combination includes: The key optimization parameters are input into a pre-established motor optimization model, and the motor optimization model is used to randomly select and combine values ​​from a preset value range corresponding to each of the key optimization parameters to form a plurality of initial preferred parameter combinations; The non-dominated genetic algorithm configured in the motor optimization model is used to iteratively calculate the initial preferred parameter combination, and the initial preferred parameter combination that satisfies preset constraints, preset value ranges, and the first motor performance meets the requirements is output as the candidate parameter value combination; wherein the preset constraints include the allowable stress of the motor rotor punching; the preset value range is the design parameter configured by the motor optimization model.

6. The method according to claim 5, characterized in that After screening out the candidate parameter value combinations, the method further includes: Obtaining a first motor performance value corresponding to each candidate parameter value combination output by the motor optimization model, recorded as a model first motor performance value; Inputting the candidate parameter value combinations into a pre-established motor parameterization model, performing finite element simulation analysis on the motor parameterization model, and outputting first motor performance values ​​corresponding to each candidate parameter value combination, recorded as simulated first motor performance values; Based on the model first motor performance value and the simulated first motor performance value, determining whether the motor optimization model needs to be optimized; When the motor optimization model needs to be optimized, adjusting the number of iterations of the motor optimization model to optimize the motor optimization model; The key optimization parameters are re-input into the optimized motor optimization model, and a new combination of candidate parameter values ​​is output.

7. The method according to claim 1, characterized in that The step of selecting a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination includes: Inputting the candidate parameter value combinations into a pre-established motor parameterization model, performing finite element simulation analysis on the motor parameterization model, and outputting second motor performance values ​​corresponding to each candidate parameter value combination; Based on the second motor performance values ​​corresponding to each of the candidate parameter value combinations, a parameter value combination that meets the second motor performance requirement is screened out from the candidate parameter value combinations as a target parameter value combination.

8. An optimized design device for a hub motor rotor magnetic isolation bridge, characterized in that: include: An initial determination module, used to determine initial optimization parameters of the hub motor, wherein the initial optimization parameters are motor parameters related to the mechanical strength of the magnetic isolation bridge; A key determination module, configured to select at least two parameters of the first motor performance having high sensitivity to the initial optimization parameters from the initial optimization parameters as key optimization parameters; A candidate determination module is used to select, from key optimization parameter combinations with different values, a parameter value combination that meets the first motor performance requirement and the motor rotor punching allowable stress requirement as a candidate parameter value combination; A target determination module, used to select a parameter value combination that meets the second motor performance requirement from the candidate parameter value combinations as a target parameter value combination; The design module is used to design the hub motor according to the values ​​corresponding to each of the key optimization parameters in the target parameter value combination.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the optimization design method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the optimization design method according to any one of claims 1 to 7.

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