Built-in permanent magnet motor rotor air gap side magnetic bridge optimization method and optimization system

Abstract

The invention belongs to the related technical field of motor structure design, and discloses a built-in permanent magnet motor rotor air gap side magnetic bridge optimization method and optimization system, and the method comprises the steps: selecting two edge points P1 and P9, close to one side of an air gap, of a permanent magnet; reference points P4 and P6 are determined, the connecting line P1P4 and the connecting line P6P9 are both perpendicular to the connecting line P1P9, and the distance between the P4 and the air gap boundary and the distance between the P6 and the air gap boundary are both equal to the thickness of the magnetic bridge; position regulation constraints of P2, P3, P5, P7 and P8 are determined, the P2 is located on the connecting line P1P4, the P3, P5 and P7 are located on the connecting line P4P6 and the extension line of the connecting line P4P6, the P3 and P7 are located on the two sides of the P5, and the P8 is located on the connecting line P6P9; and the position of each control point is optimized by taking minimization of the torque ripple as an optimization target, two tangent Bezier curves at P5 are formed, and an air gap side magnetic bridge contour is formed. The method can effectively reduce the torque ripple of the motor.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of motor structure design, and more particularly to a rotor air gap side magnetic bridge optimization method and system for an interior permanent magnet motor. BACKGROUND

[0002] In recent years, permanent magnet synchronous motors have shown significant advantages in new energy vehicles, wind power generation and other fields due to their high power density and high efficiency. However, the rising and fluctuation of the price of rare earth materials has led to an increasing cost of traditional rare earth permanent magnet motors. To address this challenge and improve cost-effectiveness, a large number of studies have focused on high-magnetic-resistance low-rare-earth motors, but the proportion of magnetic resistance torque in such motors is relatively high, which easily leads to large torque ripple. The design of the rotor magnetic barrier structure has a significant impact on key performance parameters such as average torque, torque ripple and mechanical stress intensity of the motor. Therefore, optimizing the design of the rotor magnetic barrier is of great significance to improve the torque ripple of the interior permanent magnet motor.

[0003] The rotor magnetic barrier structure is often divided into a rotor magnetic barrier body and a rotor air gap side magnetic bridge design. Current designs of the rotor magnetic barrier structure often only focus on the design of the magnetic barrier body. Since the shape of the permanent magnet of the interior permanent magnet motor is often rectangular, the rotor magnetic barrier body often has relatively low design freedom, and the effect of suppressing torque ripple is not obvious. SUMMARY

[0004] In view of the above defects or improvement needs of the prior art, the present application provides a rotor air gap side magnetic bridge optimization method and system for an interior permanent magnet motor, which aims to effectively suppress torque ripple by optimizing the design of the rotor air gap side magnetic bridge.

[0005] To achieve the above-mentioned purpose, the following technical solutions are proposed.

[0006] According to a first aspect of the present application, a rotor air gap side magnetic bridge optimization method for an interior permanent magnet motor is provided, the motor rotor contains one or more magnetic barriers, and a permanent magnet is arranged in the magnetic barrier. The optimization method comprises the following steps: Select two edge points P1 and P9 on the side of the permanent magnet close to the air gap as the starting point and the ending point of the air gap side magnetic bridge boundary; Determine reference points P4 and P6, the line P1P4 and the line P6P9 are both perpendicular to the line P1P9, and the distance between P4 and the air gap boundary and the distance between P6 and the air gap boundary are both equal to a preset magnetic bridge thickness; Determine the position of control points P2, P3, P5, P7 and P8, wherein P2 is located on the line P1P4, P3, P5 and P7 are located on the line P4P6 and its extension line, and P3 and P7 are located on both sides of P5, and P8 is located on the line P6P9; The motor performance is improved, the motor performance includes increasing the motor output torque and reducing the motor torque ripple, P2, P3, P5, P7 and P8 are used as optimization variables, an optimization algorithm is executed, two Bessel curves tangent to point P5 are formed, and the two Bessel curves are tangent to the side surface of the permanent magnet at point P1 and point P9 respectively, and the two Bessel curves are spliced to form a complete air gap side magnetic bridge boundary contour.

[0007] According to a second aspect of the present application, a built-in permanent magnet motor rotor air gap side magnetic bridge optimization system is provided, comprising a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the method according to any one of the above.

[0008] According to a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to realize the steps of the method according to any one of the above.

[0009] According to a fourth aspect of the present application, a computer program product is provided, comprising a computer program or instructions, characterized in that the computer program or instructions are executed by a processor to realize the steps of the method according to any one of the above.

[0010] Overall, compared with the prior art, the above technical solutions conceived by the present application mainly have the following beneficial effects: In the design of the air gap side magnetic bridge curve, the present application does not directly use fixed angles or determined boundary points as the only control constraints, but establishes the curve shape by setting a control point combination with adjusting ability, and uses the positions of these control points as optimization variables. The entire design framework expands the expression ability of the air gap side magnetic bridge shape to a certain extent, improves the adjustable range of the structure, ensures the smooth connection of the magnetic barrier tail and the main body of the magnetic barrier, and the smooth connection of each part inside the air gap side magnetic bridge curve, which is the key to improving the electromagnetic performance. At the same time, the present application specifically uses P2, P3, P5, P7 and P8 as control variables, ensures that the complexity of optimization calculation is still within a controllable range while ensuring the expression of the model, ensures the practical feasibility of the optimization scheme, uses torque ripple as the optimization target when optimizing the position, can obtain an optimal motor scheme, completes parameterized modeling, and finally the structure can effectively reduce the torque ripple of the motor and improve the local stress distribution of the motor. Furthermore, this invention provides a specific embodiment for optimization, employing a multi-objective evolutionary algorithm considering the Pareto front to achieve multi-objective and multi-optimization variable optimization. Moreover, this optimization algorithm also considers manufacturing tolerances. For each parent individual in the population, a series of individuals are randomly collected within a preset manufacturing tolerance range to form an evaluation set for that parent individual. Statistical analysis is performed to obtain the statistical characteristics of the parent individual, and optimization objectives are constructed based on these characteristics. By considering manufacturing tolerances during optimization through the above process, robust optimization and decision support under large-scale manufacturing conditions can be efficiently completed while ensuring accuracy. Furthermore, a pre-trained motor performance prediction model is used to directly predict the motor performance for each individual during optimization, and this model is continuously trained based on new individuals during the optimization period, thereby improving optimization efficiency and ensuring prediction effectiveness. Attached Figure Description

[0011] Figure 1 This is a partial schematic diagram of a built-in permanent magnet motor in one embodiment of the present invention; Figure 2 This is a flowchart of the steps of the method for optimizing the air gap side magnetic bridge of the rotor of a built-in permanent magnet motor in one embodiment of the present invention; Figure 3 This is a schematic diagram of the control point positions of a Bézier curve in one embodiment of the present invention; Figure 4 This is a flowchart of the optimization algorithm in one embodiment of the present invention; Figure 5 A comparison of the torque waveforms of a hybrid permanent magnet motor under one electrical cycle before and after adopting the Bessel air gap side magnetic bridge design in one embodiment under rated operating conditions. Figure 6 A comparison of torque harmonics under one electrical cycle of a hybrid permanent magnet motor with a Bessel air gap side magnetic bridge design under rated operating conditions in one embodiment. Figure 7 A comparison of the line voltage waveforms of the hybrid permanent magnet motor under one electrical cycle before and after adopting the Bessel air gap side magnetic bridge design in one embodiment under rated operating conditions. Figure 8 A comparison of line voltage harmonics under one electrical cycle of a hybrid permanent magnet motor with a Bessel air gap side magnetic bridge design before and after the rated operating condition in one embodiment. Figure 9 This is a stress distribution diagram of a motor using a conventional air-gap side magnetic bridge design in one embodiment; Figure 10 This is a stress distribution diagram of a motor using a Bessel air gap side magnetic bridge design in one embodiment. Detailed Implementation

[0012] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0013] Embodiment 1 As Figure 1 shown is a partial schematic diagram of an internal permanent magnet motor in an embodiment of the present application, which includes a stator and a rotor, and there is an air gap between the two, the rotor has a plurality of groups of magnetic barriers, each group of magnetic barriers has one or more layers of magnetic barriers, and each layer of magnetic barriers is provided with permanent magnets, for example, each layer of magnetic barriers is provided with a central permanent magnet located in the middle and side permanent magnets located on both sides of the central permanent magnet, and the central permanent magnet and the side permanent magnet in each layer of magnetic barriers are symmetrically recessed. In an embodiment, the central permanent magnet is ferrite, and the side permanent magnet is neodymium iron boron. The present application is to optimize the air gap side magnetic bridge profile between the end of the magnetic barrier and the air gap, that is, the tail curve of the magnetic barrier, in order to suppress torque ripple.

[0014] As Figure 2 shown is a step flow chart of an internal permanent magnet motor rotor air gap side magnetic bridge optimization method in an embodiment of the present application, as Figure 3 shown is a position schematic diagram of a Bezier curve in an embodiment of the present application. The optimization method will be described in detail below.

[0015] S1, select two edge points P1 and P9 on the side of the permanent magnet close to the air gap as the starting point and the ending point of the air gap side magnetic bridge boundary.

[0016] S2, determine reference points P4 and P6, the line P1P4 and the line P6P9 are both perpendicular to the line P1P9, and the distance between P4 and the air gap boundary and the distance between P6 and the air gap boundary are both equal to the preset magnetic bridge thickness; S3, determine the position control constraints of control points P2, P3, P5, P7, P8, wherein P2 is located on the line P1P4, P3, P5, P7 are all located on the line P4P6 and its extension line, and P3, P7 are located on both sides of P5, and P8 is located on the line P6P9; S4, taking improving the motor performance as the optimization target, improving the motor performance includes increasing the motor output torque and reducing the motor torque ripple, taking the positions of P2, P3, P5, P7, P8 as optimization variables, executing the optimization algorithm, forming two Bezier curves tangent at point P5, and the two Bezier curves are tangent to the side surface of the permanent magnet at point P1 and point P9 respectively, and the two Bezier curves are spliced to form a complete air gap side magnetic bridge boundary profile.

[0017] Specifically, for the air gap side magnetic bridge curve structure of the built-in permanent magnet motor, the positions of the control points P1 and P9 are directly determined by the original arrangement of the permanent magnet in the motor, corresponding to the starting point and the ending point of the curve respectively. The positions of the two points are directly given according to the layout of the embedded permanent magnet in the rotor, and no adjustment is made.

[0018] To ensure that the magnetic bridge thickness meets the design requirements, reference points P4 and P6 are respectively arranged in the direction perpendicular to the line P1P9 and satisfy the distance to the air gap boundary equal to the preset magnetic bridge thickness, thereby playing the role of structural boundary limitation.

[0019] On this basis, point P5 is set on the line P4P6 and used as a common control point for curve connection, which is the continuity control core of the curve splicing.

[0020] Point P3 is located on the line P5P4 or its extension line, and point P7 is located on the line P5P6 or its extension line, to further regulate the curve shape. Points P2 and P8 are respectively located on the lines P1P4 and P6P9, used as intermediate control points for constructing the Bezier curve. Specifically, a control point P2 is selected on the line connecting P1 to P4; another control point P3 is determined in the direction extending from P5 to P4, and these three points together with the starting point P1 and the connection point P5 constitute the geometric control point set of the first segment of the curve. Similarly, a control point P8 is set on the path from P6 to P9, and a control point P7 is set on the extension line of P5 to P6, and these points together with the connection point P5 and the ending point P9 constitute the geometric control point set of the second segment of the curve.

[0021] The finally formed air gap side magnetic bridge curve control point sequence is: P1-P2-P3-P5-P7-P8-P9. This structure can be divided into two Bezier curves, and the first order derivative (tangent) is continuous at the common control point P5, that is, the geometric continuity is realized. Among them: the first curve starts at P1 and ends at P5, and its control points are P1, P2, P3 and P5 in turn; the second curve starts at P5 and ends at P9, and its control points are P5, P7, P8 and P9 in turn. Through this structure setting, not only the smooth transition of the tail of the magnetic barrier and the main magnetic barrier structure can be realized, but also the curvature and shape of the tail can be flexibly adjusted according to the layout of the control points, which is convenient for subsequent motor performance optimization design.

[0022] In an embodiment, direct modeling from the perspective of control points is often difficult to complete. Based on the perspective of motor design modeling, it is decided to model based on the positions of fixed points (P1, P4, P6, P9). Finally, five variable controls can be sorted out: L P1P2 / L P1P4 , L P8P9 / L P6P9 , L P5P6 / LP4P6 , L P5P7 / L P4P6 , L P3P5 / L P4P6 , wherein the subscript represents the connection, and L represents the length of the connection.

[0023] In an embodiment, a third-order Bezier curve is selected, a geometric modeling method based on combination of third-order Bezier curve segments is used to construct the tail curve structure, and the focus is on improving the flexibility of parameter adjustment under the premise of ensuring smooth connection and structural continuity, so as to adapt to multi-objective design requirements of different electromagnetic and mechanical properties.

[0024] Taking the first curve B1 as an example, the modeling formula of the third-order Bezier curve can be expressed as:

[0025] Similarly, taking the second curve B2 as an example, the modeling formula of the third-order Bezier curve can be expressed as:

[0026] After arrangement, the overall air gap side magnetic bridge curve C based on the Bezier curve can be defined as:

[0027] In the formula, t represents a trajectory point.

[0028] It should be noted that increasing the design freedom will increase the complexity of the optimization design, if the design freedom is not high enough, the air gap side magnetic bridge curve cannot be effectively optimized, and if the design freedom is too high, the optimal solution cannot be obtained due to the high complexity, and the method cannot be applied to practice.

[0029] In the curve construction, the present application does not directly use fixed angles or determined boundary points as the only control constraints, but establishes the curve shape by setting control point combinations with adjustment ability, and takes the positions of the control points and their combination modes as optimization variables, so that the design framework expands the expression ability of the air gap side magnetic bridge shape to a certain extent, improves the adjustable range of the structure, ensures the smooth connection of the magnetic barrier tail and the main body of the magnetic barrier, and the parts inside the air gap side magnetic bridge curve are also smoothly connected, which is the key to improving the electromagnetic performance; meanwhile, the present application determines five control variables P2, P3, P5, P7 and P8, ensures that the optimization calculation complexity is still within a controllable range while ensuring the model expression, i.e., within the range of normal optimization solution, so as to ensure the practical feasibility of the optimization scheme. Finally, the torque ripple is taken as the optimization target to obtain the optimal motor scheme and complete the parameterized modeling. The structure can effectively reduce the torque ripple of the motor and improve the local stress distribution of the motor.

[0030] In an embodiment, the following optimization algorithm is also proposed, as shown in Figure 4 The steps of the optimization algorithm in an embodiment of the application are shown in the flow chart, which includes the following processes.

[0031] S41, randomly generate an initial population, the population including N individuals, the i-th individual Vi of which represents the i-th set of optimization variables, i=1, 2, 3, …, N, N being the number of individuals contained in the population.

[0032] Specifically, the size of the population can be flexibly set.

[0033] S42, for each individual Vi in the current population, a series of individuals are randomly collected within the preset manufacturing tolerance range to form an evaluation individual set of the individual Vi, the motor performance corresponding to each individual in the evaluation individual set is predicted based on the motor performance prediction model, and statistical analysis is performed to obtain the average output torque, the average torque ripple, and the variation coefficient of the output torque and the torque ripple of the individual Vi considering the manufacturing tolerance.

[0034] Specifically, this step considers the manufacturing tolerance of each individual, and for each individual Vi, a series of individuals are randomly collected within its manufacturing tolerance range, for example, a series of individuals can be collected by Latin hypercube sampling to form an evaluation individual set for statistical analysis, and statistical analysis is performed to obtain the average output torque, the average torque ripple, and the variation coefficient of the output torque and the torque ripple of the individual Vi considering the manufacturing tolerance.

[0035] Specifically, the motor performance prediction model is a pre-trained model. In an embodiment, different surrogate models can be trained using training samples, and the best-performing surrogate model is selected as the motor performance prediction model; wherein the surrogate models include BP neural network model, Kriging model, polynomial model, and k-nearest neighbor model, and the training samples are optimization variables and their corresponding motor performance. Using the motor performance prediction model, motor performance prediction can be quickly realized, thereby improving the optimization efficiency.

[0036] S43, a multi-objective evolutionary algorithm considering the Pareto front is executed with the optimization objectives of maximizing the average output torque, minimizing the average torque ripple, and minimizing the variation coefficient of the output torque and the torque ripple to obtain a new generation of population.

[0037] Specifically, the optimization objectives can be expressed as:

[0038] In the formula, represents the output torque, represents the torque ripple, represents the average, This indicates the calculation of the standard deviation.

[0039] In one embodiment, a multi-objective evolutionary algorithm considering Pareto fronts is used, specifically a differential evolution algorithm considering Pareto fronts. Differential evolution is a heuristic random search algorithm that can be used to solve... D One parameter to be optimized N obj The algorithm first randomly generates the optimization variables within a preset range. N Each group of parameters constitutes a population, serving as the initial generation for evolution. Each group contains... D There are several parameters to be optimized. N The group parameters are differentially analyzed to generate mutant individuals. These mutant individuals are then crossbred with the parent population (here, the initial generation) according to a certain mapping relationship to generate new individuals. N Group parameters. The objective function of each parent individual and this batch of new individuals is calculated as the competition standard. Through natural selection, those with better fitness are selected. N The parameters of each group form a new generation of the population, which acts as new parents in the differential and crossover operations of the next generation. This process is repeated to approximate the optimal solution to the parameters to be optimized. The Pareto front technique is an important method for evaluating population fitness in multi-objective optimization. This method is based on the dominance relationships among all individuals in the population to obtain a Pareto front. Individuals on the front are the preferred individuals with high fitness in the population, also known as Pareto optimal individuals.

[0040] In one embodiment, a multi-objective evolutionary algorithm considering the Pareto front is executed to obtain a new generation of population, including: Evolutionary operations are performed on N parent individuals in the current population to generate N offspring individuals. The N parent individuals and N offspring individuals form a set of individuals to be selected. Using the Pareto front technique, individuals in the set to be selected are first divided into multiple non-dominated fronts with different priorities based on the non-dominated degree ranking principle. N individuals from the non-dominated fronts are selected according to their priority to form a new generation of population. When it is necessary to select some individuals from the non-dominated front of the j-th priority to make up N individuals with the individuals from the first j-1 non-dominated fronts, the individuals with the highest crowding degree are further selected from the non-dominated front of the j-th priority based on the crowding degree calculation. The crowding degree of an individual is the perimeter of the largest rectangle generated by that individual without touching other individuals.

[0041] In this embodiment, it is considered that offspring individuals obtained through differential and mutation operations are not necessarily superior to all parent individuals. To avoid the loss of highly fit individuals from the parent generation, offspring and parents need to be merged and selected before the next generation evolves. If the population size is N, the number of individuals to be selected is 2N, and N individuals are selected from these 2N individuals to continue participating in the next generation of evolution.

[0042] To effectively solve the problem of selecting these N individuals, non-dominated ranking and crowding calculation are introduced to implement this elite retention strategy. The Pareto front found among all individuals in a population is the first non-dominated front Y1, which contains... N Y1 Individuals. To further find the second non-dominated frontier, the first non-dominated frontier is discarded, and then the Pareto frontier of the current population is searched as the second non-dominated frontier Y2. The frontier contains... N Y2 Individuals, in this cycle, will eventually reach 2 N Individuals are divided into i There are i non-dominated fronts. However, the i non-dominated fronts defined above may not necessarily select exactly N individuals. That is, it is very likely that the number of individuals on the first j-1 non-dominated fronts is less than N, while the number of individuals on the first j non-dominated fronts is greater than N. This means that each individual on the j-th non-dominated front needs to be sorted, and a portion of the individuals should be selected to participate in the next generation of evolution. For this purpose, the crowding degree of the individuals on the j-th non-dominated front needs to be calculated. In this embodiment, the crowding degree of an individual is defined as the perimeter of the largest rectangle generated in the target space without touching other individuals. The larger the crowding degree, the greater the difference between this individual and other individuals in the population, and the more it reflects the diversity of the population. The individuals on the j-th non-dominated front are sorted by crowding degree, and the individuals with higher crowding degree are selected and combined with the individuals in the first j-1 non-dominated fronts to form N individuals as the next generation of the population.

[0043] S44. Determine if the preset algebra has been reached; if not, proceed to S45; if yes, jump to S46.

[0044] Specifically, the preset number of generations can be flexibly set according to the actual situation, for example, it can be set to 100 generations.

[0045] S45. Obtain the motor performance corresponding to each individual in the new population through the finite element analysis algorithm, form new training data, continue training the motor performance prediction model, and then jump to S42.

[0046] Specifically, the step is to continuously optimize the motor performance prediction model in the population evolution process, so that the model can adapt to the new population and more accurately predict the motor performance of each individual when performing statistical feature analysis.

[0047] S46, selecting the optimal scheme of the optimization variable in the Pareto frontier.

[0048] In the following, the technical solutions of the present application are verified through specific experiments.

[0049] The hybrid permanent magnet motor provided by the present example has the motor parameters shown in Table 1.

[0050]

[0051] When performing air gap side magnetic bridge optimization design, a differential evolution algorithm is used, the optimization target is selected to minimize torque ripple, 100 individuals are optimized per generation, and the optimization is performed for 50 generations. It should be noted that in other embodiments, other optimization algorithms can also be used to complete the optimization of the control variable.

[0052] The finally optimized built-in permanent magnet motor has an average torque of 127.85 Nm, a torque ripple of 11.78%, and a maximum stress of 335.16 MPa. The performance simulation results are shown in Table 2.

[0053]

[0054] To further verify the performance of the permanent magnet motor provided in this embodiment, a permanent magnet motor based on a conventional air gap side magnetic bridge structure is used as a comparison, and the remaining structural parameters of the motor are identical. The comparison results are shown in Figures 5 to 10 , wherein, Figure 5 is the torque waveform comparison of the hybrid permanent magnet motor under one electrical period before and after the design of the Bessel air gap side magnetic bridge under the rated operating condition; Figure 6 is the torque harmonic comparison of the hybrid permanent magnet motor under one electrical period before and after the design of the Bessel air gap side magnetic bridge under the rated operating condition; Figure 7 is the line voltage waveform comparison of the hybrid permanent magnet motor under one electrical period before and after the design of the Bessel air gap side magnetic bridge under the rated operating condition; Figure 8 is the line voltage harmonic comparison of the hybrid permanent magnet motor under one electrical period before and after the design of the Bessel air gap side magnetic bridge under the rated operating condition; Figure 9 is the stress distribution diagram of the motor designed with a conventional air gap side magnetic bridge under 1.2 times the highest speed (10080 rpm); Figure 10 is the stress distribution diagram of the motor designed with a Bessel air gap side magnetic bridge under 1.2 times the highest speed (10080 rpm).

[0055] According to the experiment comparison, in the case that the remaining structure parameters of the motor are completely same, the average torque of the permanent magnet motor with the conventional air gap side magnetic bridge structure is 130.85 Nm, the torque ripple is 43.54%, and the maximum stress is 345.28 MPa. Compared with the conventional air gap side magnetic bridge structure of the permanent magnet motor, the torque ripple of the embodiment is reduced by 72.94% under the condition that the average torque changes little, the total harmonic distortion (THD) of the voltage is reduced by 58.80%, and the maximum stress is reduced by 3%. Therefore, it can be proved that the built-in permanent magnet motor rotor air gap side magnetic bridge optimization method can significantly suppress the torque ripple and the total harmonic distortion of the voltage while keeping the average torque and the mechanical strength unaffected.

[0056] Embodiment 2 The application also relates to a built-in permanent magnet motor rotor air gap side magnetic bridge design system, which comprises a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the above method when executing the computer program.

[0057] The system can be loaded on an electronic device, and the electronic device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The processor can be a central processing unit (CPU), and can 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 the like. The memory can be used to store computer programs and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory.

[0058] Embodiment 3 The application also relates to a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to realize the steps of the above method.

[0059] Specifically, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device or other volatile solid-state memory device.

[0060] Embodiment 4 The embodiment of the present application provides a computer program product or computer program, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer equipment reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer equipment executes the steps of the method of the above-mentioned embodiment of the present application.

[0061] The technical features of the above embodiments can be combined in any manner. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, it should be considered that it is within the scope of the present application. It should be noted that the present application is intended to illustrate the present application, and is not intended to limit the present application.

[0062] The above embodiments only express several implementation manners of the present application, the description is more specific and detailed, but it should not be understood as the limitation of the patent application scope. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the protection scope of the present application.

Claims

1. A method for optimizing the air gap side magnetic bridge of an interior permanent magnet motor rotor, characterized in that, The motor rotor contains one or more magnetic barriers, and a permanent magnet is arranged in the magnetic barrier. Two edge points P1 and P9 near the air gap side of the permanent magnet are selected as the starting point and the ending point of the air gap side magnetic bridge boundary. Reference points P4 and P6 are determined, the line P1P4 and the line P6P9 are perpendicular to the line P1P9, the distance between P4 and the air gap boundary and the distance between P6 and the air gap boundary are equal to the preset magnetic bridge thickness. The positions of control points P2, P3, P5, P7 and P8 are determined to control the constraints, wherein P2 is located on the line P1P4, P3, P5 and P7 are located on the line P4P6 and its extension line, and P3 and P7 are located on the two sides of P5, and P8 is located on the line P6P9. The motor performance is improved, including increasing the motor output torque and reducing the motor torque ripple, the positions of P2, P3, P5, P7 and P8 are used as optimization variables, an optimization algorithm is executed, two Bézier curves tangent to point P5 are formed, and the two Bézier curves are tangent to the side surface of the permanent magnet at point P1 and point P9 respectively, and the two Bézier curves are spliced to form a complete air gap side magnetic bridge boundary contour.

2. The optimization method of claim 1, wherein, The Bézier curve is a third-order Bézier curve.

3. The optimization method of claim 1, wherein, Taking positions P2, P3, P5, P7, P8 as optimization variables, specifically taking L P1P2 / L P1P4 , L P8P9 / L P6P9 , L P5P6 / L P4P6 , L P5P7 / L P4P6 , L P3P5 / L P4P6 as optimization variables, L P1P2 , L P1P4 , L P8P9 , L P6P9 , L P5P6 , L P4P6 , L P5P7 , L P3P5 are lengths of lines P1P2, P1P4, P8P9, P6P9, P5P6, P4P6, P5P7, P3P5 respectively.

4. The optimization method of claim 1, wherein, The optimization algorithm includes: S41, an initial population is randomly generated, the population includes N individuals, the i-th individual Vi of the population represents the i-th set of optimization variables, i=1, 2, 3, …, N, and N is the number of individuals in the population; S42, for each individual Vi in the current population, a series of individuals are randomly collected within the preset manufacturing tolerance range to form an evaluation individual set of the individual Vi, the motor performance corresponding to each individual in the evaluation individual set is predicted based on a motor performance prediction model, and statistical analysis is performed to obtain the average output torque, the average torque ripple and the variation coefficient of the output torque and the torque ripple of the individual Vi considering the manufacturing tolerance; S43, the maximum average output torque, the minimum average torque ripple and the minimum variation coefficient of the output torque and the torque ripple are used as optimization objectives, a multi-objective evolutionary algorithm considering the Pareto front is executed to obtain a new generation of population; S44, whether the preset number of generations is reached is judged; if not, S45 is executed; if yes, S46 is jumped to; S45, the motor performance corresponding to each individual in the new population is obtained by a finite element analysis algorithm to form new training data to continue training the motor performance prediction model and then jump to S42; S46, the optimal scheme of the optimization variables in the Pareto front is selected.

5. The optimization method of claim 4, wherein, Before S41, the following steps are further included: Different proxy models are trained using training samples, and the proxy model with the best performance is selected as the motor performance prediction model; The proxy model includes a BP neural network model, a Kriging model, a polynomial model and a k-nearest neighbor model.

6. The optimization method of claim 4, wherein, The multi-objective evolutionary algorithm considering the Pareto front is executed to obtain a new generation of population, including: Evolutionary operations are performed on N parent individuals in the current population to generate N offspring individuals, and the N parent individuals and the N offspring individuals form a set of individuals to be selected; The Pareto front technology is adopted, and firstly, individuals in the set of individuals to be selected are divided into a plurality of non-dominated fronts with different priorities based on the non-dominated degree sorting principle, and N individuals in the non-dominated degree fronts are selected according to the priority to form a new generation population; when it is required to select part of individuals from the jth priority non-dominated degree front to make up N individuals with individuals in the first j-1 non-dominated degree fronts, then further based on the crowding degree calculation, part of individuals with the highest crowding degree are selected from the jth priority non-dominated degree front, wherein the crowding degree of an individual is the perimeter of the largest rectangle generated by the individual without touching other individuals.

7. The optimization method of claim 4, wherein, The multi-objective evolutionary algorithm is a differential evolution algorithm.

8. An interior permanent magnet motor rotor air gap side magnetic bridge optimization system comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. A computer program product comprising computer programs or instructions, characterized in that, The processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program or the instructions.

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