A brushless direct current motor rotor shell heat dissipation weight reduction collaborative optimization method

Abstract

The application relates to the technical field of brushless direct-current motors, in particular to a brushless direct-current motor rotor shell heat dissipation and weight reduction synergic optimization method. The method comprises the following steps: determining the minimum allowable value of the thickness of heat dissipation ribs according to the mechanical strength requirement of the rotor shell material and the processing technology feasibility; calculating the maximum number of heat dissipation ribs that can be arranged on the outer surface of the rotor shell according to the total axial length of the rotor shell and the minimum allowable value of the thickness of the heat dissipation ribs; arranging multiple heat dissipation ribs on the outer surface of the rotor shell according to the maximum number of heat dissipation ribs and the minimum allowable thickness, so that the plane where the heat dissipation ribs are located is perpendicular to the axis of the rotor shell; performing blunt processing on the sharp edges of the heat dissipation ribs to obtain the rotor shell with synergic optimization of heat dissipation and weight reduction performance. Through the discovery of the characteristic that the rectangular cross-section heat dissipation rib has a smaller cross-section area under the same heat dissipation area, the weight of the rotor shell is reduced under the premise of not reducing the heat dissipation performance, and the structural strength is ensured.

Description

Technical Field

[0001] This application relates to the field of brushless DC motor technology, and in particular to a method for synergistic optimization of heat dissipation and weight reduction of the rotor housing of a brushless DC motor. Background Technology

[0002] External rotor brushless DC motors offer advantages such as compact structure, high torque density, and smooth operation, making them widely used in industrial drives, new energy vehicles, aerospace, and other fields. With the continuous increase in motor power density, heat generation has become an increasingly prominent issue, directly impacting the motor's operational reliability and lifespan. Currently, the heat dissipation of medium-power external rotor brushless DC motors primarily relies on forced ventilation cooling via radial fans at the end caps. The rotor housing, as the carrier of the magnets, simultaneously undertakes the important functions of magnetic circuit conduction, bearing complex force loads, and auxiliary heat dissipation.

[0003] In existing technologies, auxiliary heat dissipation designs for rotor housings mainly fall into two categories: one is to design the outer surface of the rotor housing as a smooth curved structure. This structure facilitates the affixing of company logos, performance labels, and safety warning labels, but the heat dissipation area is limited, resulting in poor auxiliary heat dissipation and failing to meet the heat dissipation requirements of high-power-density motors. The other is to set trapezoidal cross-section heat dissipation fins on the outer surface of the rotor housing, thereby increasing the heat dissipation area to improve the auxiliary heat dissipation effect. However, this design method has obvious inherent drawbacks: while increasing the heat dissipation area, the trapezoidal cross-section heat dissipation fins significantly increase the overall weight of the rotor housing, leading to an increase in the motor's rotational inertia, a slower response speed, and increased material and transportation costs. Summary of the Invention

[0004] This application provides a method for synergistic optimization of heat dissipation and weight reduction of the rotor housing of a brushless DC motor. It aims to solve the problem that while existing trapezoidal cross-section heat dissipation fins increase the heat dissipation area, they also significantly increase the overall weight of the rotor housing, leading to increased motor rotational inertia, slower response speed, and increased material and transportation costs.

[0005] In a first aspect, embodiments of this application provide a method for the coordinated optimization of heat dissipation and weight reduction of a brushless DC motor rotor housing, applied to the thin-walled metal rotor housing of an external rotor brushless DC motor. The outer surface of the rotor housing is provided with multiple heat dissipation fins, which have a rectangular cross-sectional structure; including:

[0006] Based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology, the minimum allowable value of the heat dissipation fin thickness is determined;

[0007] Based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the aforementioned heat dissipation ribs, the maximum number of heat dissipation ribs that can be arranged on the outer surface of the rotor housing is calculated; according to the maximum number of heat dissipation ribs and the minimum allowable value, multiple heat dissipation ribs are evenly arranged on the outer surface of the rotor housing, so that the plane where the heat dissipation ribs are located is perpendicular to the axis of the rotor housing.

[0008] The sharp edges of the heat dissipation fins are blunted to obtain a rotor housing with optimized heat dissipation and weight reduction performance.

[0009] In some embodiments, determining the minimum allowable value of the heat dissipation fin thickness based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology includes: establishing a finite element mechanical model of the rotor housing and inputting the mechanical performance parameters of the rotor housing material; applying all loads borne by the rotor housing during motor operation and performing static simulation analysis to obtain structural stress distribution data corresponding to different heat dissipation fin thicknesses; establishing a die-casting processing simulation model of the rotor housing, inputting die-casting process parameters, and performing filling simulation and solidification simulation analysis to obtain processing defect distribution data corresponding to different heat dissipation fin thicknesses; and comprehensively considering the structural stress distribution data and processing defect distribution data to select the minimum heat dissipation fin thickness that simultaneously meets the structural strength requirements and processing technology requirements, as the minimum allowable value of the heat dissipation fin thickness.

[0010] In some embodiments, calculating the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the aforementioned heat dissipation fins includes: setting a range of values ​​for the spacing of the heat dissipation fins, and selecting multiple discrete heat dissipation fin spacing values ​​within the range; for each discrete heat dissipation fin spacing value, calculating the corresponding number of heat dissipation fins in combination with the minimum allowable value of the thickness of the heat dissipation fins and the total axial length of the rotor housing; establishing a fluid dynamics simulation model of the motor rotor region, inputting parameters of different numbers of heat dissipation fins and heat dissipation fin spacing, performing forced ventilation heat dissipation simulation analysis, and obtaining heat dissipation efficiency data corresponding to each set of parameters; selecting the number of heat dissipation fins with the highest heat dissipation efficiency and meeting the processing accuracy requirements as the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing.

[0011] In some embodiments, the step of uniformly arranging multiple heat dissipation fins on the outer surface of the rotor housing according to the maximum number and minimum allowable value of heat dissipation fins includes: determining the circumferential center line of the rotor housing; using the circumferential center line as a reference, dividing the outer circumference of the rotor housing into equal parts, the number of equal parts being equal to the maximum number of heat dissipation fins; arranging a heat dissipation fin along the radial direction of the rotor housing at the position corresponding to each division point; and controlling the axial length of each heat dissipation fin to be exactly equal to the total axial length of the rotor housing, so that all heat dissipation fins are uniformly distributed along the circumferential direction of the rotor housing.

[0012] In some embodiments, making the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing includes: establishing a three-dimensional machining coordinate system for the rotor housing and determining the axial direction vector of the rotor housing; during the machining of each heat dissipation fin, acquiring the plane normal vector information of the heat dissipation fin in real time; comparing the acquired plane normal vector information with the axial direction vector of the rotor housing to calculate the perpendicularity deviation value; and adjusting the posture of the machining tool in real time according to the perpendicularity deviation value to keep the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing.

[0013] In some embodiments, the blunting process of the sharp edges of the heat dissipation fins to obtain a rotor housing with optimized heat dissipation and weight reduction performance includes: scanning the edge contours of all heat dissipation fins using a 3D scanning device to identify all sharp edge positions; automatically generating a corresponding CNC blunting machining path for each identified sharp edge position; performing blunting processing on all sharp edges with uniform parameters according to the generated CNC blunting machining path; and after the blunting processing is completed, scanning the edges of all heat dissipation fins again to detect the blunting dimensions of all sharp edges to ensure that all blunting dimensions meet preset standards.

[0014] In some embodiments, the method further includes: establishing a multi-objective optimization function that includes heat dissipation performance indicators, weight reduction performance indicators, and structural strength indicators; employing a multi-objective genetic algorithm, with heat dissipation fin thickness, heat dissipation fin radial height, and heat dissipation fin spacing as optimization variables, and performing a global search within a preset range of variable values; obtaining multiple Pareto optimal solutions, and selecting the optimal combination of design parameters from the multiple Pareto optimal solutions according to preset weight coefficients for each indicator; and adjusting the heat dissipation fin design parameters of the rotor housing according to the selected optimal combination of design parameters.

[0015] In some embodiments, the method further includes: collecting operating data of the target motor under various typical operating conditions, including motor speed, output power and ambient temperature; inputting the collected operating data into a pre-trained heat load prediction machine learning model to predict the rotor housing heat load of the motor under different operating conditions; and dynamically adjusting the design parameters of the heat dissipation fins according to the predicted rotor housing heat load so that the heat dissipation performance of the rotor housing matches the actual heat load of the motor.

[0016] In some embodiments, the method further includes: establishing a fatigue life analysis model of the rotor housing, inputting fatigue performance parameters of the rotor housing material and alternating load parameters during motor operation; using the finite element fatigue analysis method to calculate fatigue life data of the rotor housing under different design parameters; inputting the calculated fatigue life data into a pre-trained reliability prediction machine learning model to predict the long-term operational reliability of the rotor housing; and selecting design parameters that meet the preset operational reliability requirements as the final design parameters of the rotor housing.

[0017] In some embodiments, the method further includes: establishing a digital twin model of the rotor housing, and keeping the digital twin model synchronized with the physical rotor housing in real time; collecting operating temperature data, vibration data, and speed data of the physical rotor housing in real time, and transmitting the collected data to the digital twin model; evaluating the heat dissipation performance and weight reduction performance of the rotor housing in real time through the digital twin model; and optimizing the operating parameters of the motor online based on the evaluation results to improve the overall operating reliability of the motor.

[0018] This application breaks the conventional wisdom that "trapezoidal cross-section heat dissipation fins are optimal" and discovers that rectangular cross-section heat dissipation fins have a smaller cross-sectional area for the same heat dissipation area. Without reducing heat dissipation performance, the weight of the rotor housing is reduced by approximately 10%, while ensuring structural strength.

[0019] By using the thickness of the heat dissipation fins as the core optimization variable, and determining the minimum allowable value through comprehensive mechanical strength and processing technology, the maximum number of heat dissipation fins can be derived, thereby achieving quantitative synergistic optimization of heat dissipation and weight reduction, and solving the problem of blindness in existing technology experience-based design.

[0020] The structure of this invention is fully compatible with existing aluminum alloy die-casting processes, requiring no additional processing equipment or procedures, thus improving performance while reducing material and manufacturing costs. By reducing the motor's moment of inertia, the dynamic response speed of the motor is improved, while heat dissipation is enhanced, extending the motor's service life. This provides key technical support for the development of high-power-density external rotor brushless DC motors.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is an axially unfolded view of the rotor housing;

[0024] Figure 2 This is an axial side view of the rotor housing;

[0025] Figure 3 This is an overall view of the end face of the rotor housing;

[0026] Figure 4 This is a partially enlarged sectional view of the end face of the rotor housing;

[0027] Figure 5 A schematic diagram of the circumferential arrangement of the heat dissipation fins on the rotor housing;

[0028] Figure 6 This is a schematic flowchart illustrating the steps of a method for synergistic optimization of heat dissipation and weight reduction of a brushless DC motor rotor housing according to an embodiment of this application;

[0029] Figure 7 This is a schematic block diagram of a brushless DC motor rotor housing heat dissipation and weight reduction synergistic optimization system provided in one embodiment of this application;

[0030] Figure 8 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0031] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0034] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0035] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0036] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0037] External rotor brushless DC motors offer advantages such as compact structure, high torque density, and smooth operation, making them widely used in industrial drives, new energy vehicles, aerospace, and other fields. With the continuous increase in motor power density, heat generation has become an increasingly prominent issue, directly impacting the motor's operational reliability and lifespan. Currently, the heat dissipation of medium-power external rotor brushless DC motors primarily relies on forced ventilation cooling via radial fans at the end caps. The rotor housing, as the carrier of the magnets, simultaneously undertakes the important functions of magnetic circuit conduction, bearing complex force loads, and auxiliary heat dissipation.

[0038] In existing technologies, auxiliary heat dissipation designs for rotor housings mainly fall into two categories: one is to design the outer surface of the rotor housing as a smooth curved structure. This structure facilitates the affixing of company logos, performance labels, and safety warning labels, but the heat dissipation area is limited, resulting in poor auxiliary heat dissipation and failing to meet the heat dissipation requirements of high-power-density motors. The other is to set trapezoidal cross-section heat dissipation fins on the outer surface of the rotor housing, thereby increasing the heat dissipation area to improve the auxiliary heat dissipation effect. However, this design method has obvious inherent drawbacks: while increasing the heat dissipation area, the trapezoidal cross-section heat dissipation fins significantly increase the overall weight of the rotor housing, leading to an increase in the motor's rotational inertia, a slower response speed, and increased material and transportation costs.

[0039] For a long time, those skilled in the art have generally held the technical prejudice that "trapezoidal cross-section cooling fins are the optimal choice for rotor housing heat dissipation structure." This prejudice stems from the belief that trapezoidal cross-sections have better mechanical properties, can better withstand centrifugal force and vibration loads during motor operation, and have relatively mature manufacturing processes. Therefore, all existing improvements focus on increasing the height of the cooling fins, increasing the number of cooling fins, or optimizing the trapezoidal cross-sectional angle of the cooling fins. Essentially, these all increase the heat dissipation area by increasing the cross-sectional area of ​​the cooling fins, inevitably leading to a further increase in the weight of the rotor housing, thus falling into the technical dilemma that "improved heat dissipation inevitably comes with increased weight."

[0040] like Figures 1 to 5 As shown, this application provides a rotor housing for a brushless DC motor, applicable to medium-power external rotor brushless DC motors ranging from 10kW to 30kW. It is integrally die-cast from aluminum alloy material, achieving synergistic optimization of heat dissipation and weight reduction performance.

[0041] The rotor housing 1 is a thin-walled cylindrical metal structure with multiple rectangular cross-section heat dissipation ribs 2 on its outer surface. The planes of all the rectangular cross-section heat dissipation ribs 2 are perpendicular to the axis of the rotor housing. In this embodiment, the outer diameter of the base plate of the rotor housing 1 is 100mm, and the total axial length is 60mm. Considering the mechanical strength requirements of the aluminum alloy material and the feasibility of the die-casting process, the minimum allowable thickness of the heat dissipation ribs is determined to be 1mm; the radial height of the heat dissipation ribs is 2mm; and the spacing between adjacent heat dissipation ribs is 2mm. Based on the above parameters, the maximum number of heat dissipation ribs that can be arranged on the outer surface of the rotor housing 1 is 20, and all the rectangular cross-section heat dissipation ribs 2 are evenly distributed at equal angles along the circumference of the rotor housing 1.

[0042] Figure 1 The axial unfolded view of the rotor housing clearly shows the overall shape of the 20 rectangular cross-section heat dissipation fins 2 arranged laterally at equal intervals along the entire axial length of the rotor housing 1. The position and number of the two rows of circular mounting holes 3 on the upper and lower edges of the rotor housing 1 are also marked.

[0043] Figure 2 This is an axial side view of the rotor housing, showing the total axial height of the rotor housing 1, the radial height of the rectangular cross-section heat dissipation fins 2, and the distribution of the mounting holes 3 on the upper and lower end faces of the rotor housing 1 from an axial side perspective. The end face contours of the rectangular cross-section heat dissipation fins 2 can be seen intuitively from the left and right sides.

[0044] Figure 3 This is an overall view of the rotor housing end face. The view from the front of the circular end face shows the overall thin-walled annular outline of the rotor housing 1, as well as the position and number of the mounting and positioning bosses evenly distributed on the circumference of the housing.

[0045] Figure 4 This is a partially enlarged cross-sectional view of the rotor housing end face, which magnifies the complete rectangular cross-sectional shape of the heat dissipation fin 2 and its integral connection structure with the rotor housing 1 substrate. The position of the sharp edge of the heat dissipation fin is clearly marked, and this part is blunted after processing.

[0046] Figure 5 This is a schematic diagram of the circumferential arrangement of the heat dissipation fins on the rotor housing. The central angle between adjacent heat dissipation fins (e.g., 30°) is marked by radial auxiliary lines, which intuitively shows the design method of the heat dissipation fins 2 of all rectangular sections being evenly distributed at equal angles in the circumferential direction of the rotor housing 1.

[0047] The specific implementation process of this embodiment is as follows: First, it is determined that the heat dissipation fins adopt a rectangular cross-section structure to replace the trapezoidal cross-section heat dissipation fins in the prior art; then, considering the mechanical strength requirements of the rotor shell material and the feasibility of the processing technology, the minimum allowable value of the heat dissipation fin thickness is determined to be 1mm; next, based on the total axial length of the rotor shell of 60mm and the heat dissipation fin thickness of 1mm, the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor shell is calculated to be 20; then, according to the above-mentioned number and thickness parameters, the rectangular cross-section heat dissipation fins are evenly arranged on the outer surface of the rotor shell, so that the plane where the heat dissipation fins are located is perpendicular to the axis of the rotor shell; finally, the sharp edges of all heat dissipation fins are blunted to obtain a rotor shell with optimized heat dissipation and weight reduction performance.

[0048] Compared with the trapezoidal cross-section heat dissipation fins with the same heat dissipation area in the prior art, the rectangular cross-section heat dissipation fins used in this embodiment have a smaller cross-sectional area. Under the same heat dissipation efficiency, the weight of the rotor housing can be reduced by about 15%, while ensuring the structural strength and processing feasibility of the rotor housing, effectively improving the operational reliability of the external rotor brushless DC motor.

[0049] Please refer to Figure 6 This application provides a method for the coordinated optimization of heat dissipation and weight reduction of the rotor housing of a brushless DC motor, which is applied to... Figures 1 to 5 The corresponding rotor housing is designed with multiple heat dissipation fins on its outer surface, and the heat dissipation fins adopt a rectangular cross-section structure.

[0050] The provided method for coordinated optimization of heat dissipation and weight reduction of the rotor housing of a brushless DC motor includes steps S101 to S103. Details are as follows:

[0051] Step S101. Determine the minimum allowable value of the heat dissipation fin thickness based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology.

[0052] Specifically, this step is the core foundation of the entire optimization method. Its technical essence is to compress the thickness of the heat dissipation fins to the physical limit without sacrificing structural reliability and processing feasibility, thus creating the only feasible condition for maximizing the number of heat dissipation fins and increasing the total heat dissipation area.

[0053] This step is to obtain the minimum heat dissipation fin thickness that satisfies all the following constraints. This value will be used as a fixed input parameter for all subsequent design calculations and should not be changed arbitrarily.

[0054] Hard constraints include:

[0055] 1. Mechanical constraints: At the rated speed of the motor and 1.2 times the overload speed, the maximum equivalent stress at the root of the heat dissipation fin is less than the allowable stress of the material, and the safety factor is not less than 1.5; there is no permanent deformation and no risk of breakage;

[0056] 2. Process constraints: Meet the minimum formable rib thickness requirements of aluminum alloy die casting process, and be free from defects such as undercasting, shrinkage cavities, porosity, cracks, and deformation; the mold can be demolded smoothly, and the mold life is not less than 50,000 cycles;

[0057] 3. Machining constraints: Meet the minimum clamping and cutting requirements for subsequent CNC finishing, and prevent the heat dissipation fins from chattering or deforming during machining.

[0058] For example, the user collects the standard mechanical property parameters of the ADC12 aluminum alloy used for the rotor housing, including density, elastic modulus, Poisson's ratio, yield strength, tensile strength, and fatigue strength; and collects the standard process parameters of the company's existing die-casting equipment, including pouring temperature, mold temperature, injection speed, pressurization pressure, and holding time. A parametric finite element mechanical model of the rotor housing is established, and the mesh is refined in the heat dissipation fin area, with a mesh size no greater than 0.5 mm, and the mesh size in the substrate area no greater than 2 mm. All loads borne by the rotor housing during the entire motor operation process are applied, including: centrifugal load at rated speed, centrifugal load at 1.2 times overload speed, radial magnetic pull of the magnets on the rotor housing, bolt assembly preload, and 10g vibration impact load during operation. Static simulation analysis is performed to obtain stress distribution cloud maps, strain distribution cloud maps, and deformation data corresponding to different heat dissipation fin thicknesses, and a set of thickness values ​​that meet the mechanical strength requirements is selected. A simulation model of the die-casting process for the rotor housing was established. The collected process parameters were input, and simulations of the filling and solidification processes were performed to obtain defect distribution cloud maps corresponding to different heat dissipation fin thicknesses. A set of thickness values ​​meeting the processing requirements was then selected. The intersection of these two thickness value sets was taken, and the thickness value with the smallest value in the intersection was selected as the minimum allowable thickness of the heat dissipation fin. Simultaneously, both mechanical and process simulations must be performed for bidirectional verification; neither is dispensable. Considering only mechanical strength will result in excessive thickness, failing to achieve optimal weight reduction; considering only process feasibility will lead to insufficient structural strength, posing safety hazards.

[0059] Step S102. Based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the aforementioned heat dissipation ribs, calculate the maximum number of heat dissipation ribs that can be arranged on the outer surface of the rotor housing; according to the maximum number of heat dissipation ribs and the minimum allowable value, arrange multiple heat dissipation ribs evenly on the outer surface of the rotor housing, so that the plane where the heat dissipation ribs are located is perpendicular to the axis of the rotor housing.

[0060] Specifically, this step is the core link to achieve synergistic optimization of heat dissipation and weight reduction. Its technical essence is to maximize the total heat dissipation area by maximizing the number of heat dissipation fins, while ensuring uniform stress on the structure and minimal wind resistance, given that the thickness of the heat dissipation fins has reached its limit.

[0061] This step aims to achieve the maximum total heat dissipation area without increasing the outer diameter of the rotor housing or the total mass of the heat dissipation fins; at the same time, it ensures that the heat dissipation fins are subjected to uniform stress, the airflow is smooth, and the heat dissipation efficiency is the highest.

[0062] Key constraints include:

[0063] 1. Dimensional constraints: The outer diameter and total axial length of the rotor housing are fixed values ​​and must not exceed the overall installation dimensions of the motor;

[0064] 2. Process constraints: The minimum spacing between adjacent heat dissipation fins must not be less than the minimum spacing allowed by the die casting process; otherwise, the mold core will be too thin and prone to breakage.

[0065] 3. Airflow constraints: The spacing between the heat dissipation fins must not be too small, otherwise airflow will not be able to pass through smoothly, resulting in increased wind resistance and reduced heat dissipation efficiency.

[0066] For example, based on the processing capacity and lifespan requirements of the company's existing die-casting molds, a reasonable range of values ​​for the spacing of the heat dissipation fins is set. Within this range, multiple discrete heat dissipation fin spacing values ​​are selected with a fixed step size. For each discrete heat dissipation fin spacing value, combined with the minimum allowable value of the heat dissipation fin thickness determined in step S101 and the total axial length of the rotor housing, the corresponding number of heat dissipation fins that can be arranged is calculated. A complete motor rotor region fluid dynamics simulation model including the rotor housing, end cover fan, stator, and air gap is established, and boundary conditions such as ambient temperature, stator copper loss, stator iron loss, fan speed, and air volume are set. Different numbers of heat dissipation fins and heat dissipation fin spacing parameters are input into the simulation model to perform forced ventilation heat dissipation simulation analysis, obtaining the rotor housing surface temperature distribution cloud map, average heat transfer coefficient, and total heat dissipation data corresponding to each set of parameters. By comparing the heat dissipation efficiency data of all parameter groups, the number of heat dissipation fins with the highest heat dissipation efficiency that simultaneously meets the processing requirements and installation clearance requirements is selected as the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing. The circumference centerline of the rotor housing is determined. Using this centerline as a reference, the outer circumference of the rotor housing is divided into equal parts, the number of which is equal to the maximum number of cooling fins, ensuring that the central angles between adjacent cooling fins are exactly equal. At each division point, a cooling fin is arranged radially outward from the rotor housing, ensuring that the axial length of each cooling fin is exactly equal to the total axial length of the rotor housing, thus ensuring that all cooling fins are evenly distributed along the circumference of the rotor housing. During the processing of the cooling fins, the perpendicularity of the plane containing each cooling fin to the axis of the rotor housing is monitored in real time. By adjusting the orientation of the machining tool, the perpendicularity deviation is ensured to be no greater than 0.02 mm. More cooling fins are not necessarily better. When the spacing between the cooling fins is too small, airflow cannot pass smoothly through the channels between the fins, forming vortices, leading to a sharp increase in wind resistance and a decrease in heat dissipation efficiency. Therefore, the optimal combination of number and spacing must be determined through fluid dynamics simulation.

[0067] Step S103. The sharp edges of the heat dissipation fins are blunted to obtain a rotor housing with optimized heat dissipation and weight reduction performance.

[0068] Specifically, this step is the final key process in product molding. Its purpose is to eliminate safety hazards, reduce stress concentration, and optimize airflow, without compromising heat dissipation performance and weight reduction.

[0069] This step involves removing all sharp edges of the heat dissipation fins to prevent scratches to operators during assembly; eliminating stress concentration at sharp edges to improve the fatigue life of the rotor housing; and optimizing the airflow field across the surface of the heat dissipation fins to reduce wind resistance.

[0070] For example, a laser 3D scanning device with an accuracy of no less than 0.01 mm is used to perform a full-surface scan of the processed rotor housing to obtain complete point cloud data of the heat dissipation fin edge contours. The point cloud data is imported into 3D reverse engineering software, and a curvature analysis algorithm automatically identifies the sharp edge positions of all heat dissipation fins, generating sharp edge contour lines. Based on preset blunting dimension parameters, a CNC blunting machining path is automatically generated for each sharp edge, outputting standard G-code. The CNC machining code is imported into a CNC milling machine, and a ball end mill of the corresponding specification is used to blunt all sharp edges according to uniform machining parameters. After blunting, the edges of all heat dissipation fins are scanned again using a laser 3D scanning device, and a coordinate measuring machine is used to measure the dimensions of 20% of randomly selected sharp edges to ensure that the error of all blunting dimensions is within ±0.1 mm. Blunting is only applied to the sharp corners of the heat dissipation fins and must not change the cross-sectional thickness, radial height, or axial length of the heat dissipation fins, must not reduce the heat dissipation area, and must not add extra weight. The blunting dimension should not be too large, otherwise it will significantly reduce the heat dissipation area and reduce the heat dissipation effect.

[0071] In some embodiments, determining the minimum allowable value of the heat dissipation fin thickness based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology includes: establishing a finite element mechanical model of the rotor housing and inputting the mechanical performance parameters of the rotor housing material; applying all loads borne by the rotor housing during motor operation and performing static simulation analysis to obtain structural stress distribution data corresponding to different heat dissipation fin thicknesses; establishing a die-casting processing simulation model of the rotor housing, inputting die-casting process parameters, and performing filling simulation and solidification simulation analysis to obtain processing defect distribution data corresponding to different heat dissipation fin thicknesses; and comprehensively considering the structural stress distribution data and processing defect distribution data to select the minimum heat dissipation fin thickness that simultaneously meets the structural strength requirements and processing technology requirements, as the minimum allowable value of the heat dissipation fin thickness.

[0072] This embodiment details how to determine the minimum allowable value of the heat dissipation fin thickness through dual verification using mechanical simulation and process simulation.

[0073] Mechanical property parameters of ADC12 aluminum alloy: density 2700 kg / m³ 3 The model exhibits an elastic modulus of 70 GPa, Poisson's ratio of 0.33, yield strength of 160 MPa, and tensile strength of 240 MPa. Die-casting process parameters were collected: pouring temperature 680℃, mold temperature 220℃, injection speed 3 m / s, pressurization pressure 80 MPa, and holding time 15 s. A parametric finite element model of the rotor shell was established using finite element software such as ANSYS Workbench. The base plate has an outer diameter of 100 mm, an axial length of 60 mm, and a thickness of 3 mm. The mesh size was refined in the heat dissipation fin area (0.5 mm) and the base plate area (2 mm).

[0074] The applied loads included: a centrifugal load at a rated speed of 1500 rpm, a centrifugal load at 1.2 times the overload speed of 1800 rpm, a radial magnetic pull of 100 N per pole magnet, a preload of 500 N for each of the eight M4 bolts, and a 10g triaxial vibration impact load. Static simulations were performed across heat sink fin thicknesses ranging from 0.5 mm to 2.0 mm in 0.1 mm increments. Results showed that when the thickness was less than 1.0 mm, the maximum equivalent stress at the root of the heat sink fin exceeded 240 MPa, failing to meet strength requirements; when the thickness was greater than or equal to 1.0 mm, the maximum equivalent stress was less than 150 MPa, with a safety factor greater than 1.6, meeting strength requirements. A process model was established in the MAGMA die-casting simulation software, and the above process parameters were input, simulating the same thickness range. Results showed that when the thickness was less than 1.0 mm, undercasting defects appeared at the tip of the heat sink fin, with a shrinkage rate greater than 3%; when the thickness was greater than or equal to 1.0 mm, there were no undercasting, shrinkage, or crack defects, and mold demolding was smooth. Based on the above results, the minimum thickness that satisfies all constraints is 1.0 mm. Therefore, the minimum allowable thickness of the heat dissipation fin is determined to be 1.0 mm.

[0075] In some embodiments, calculating the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the aforementioned heat dissipation fins includes: setting a range of values ​​for the spacing of the heat dissipation fins, and selecting multiple discrete heat dissipation fin spacing values ​​within the range; for each discrete heat dissipation fin spacing value, calculating the corresponding number of heat dissipation fins in combination with the minimum allowable value of the thickness of the heat dissipation fins and the total axial length of the rotor housing; establishing a fluid dynamics simulation model of the motor rotor region, inputting parameters of different numbers of heat dissipation fins and heat dissipation fin spacing, performing forced ventilation heat dissipation simulation analysis, and obtaining heat dissipation efficiency data corresponding to each set of parameters; selecting the number of heat dissipation fins with the highest heat dissipation efficiency and meeting the processing accuracy requirements as the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing.

[0076] This embodiment details how to determine the maximum number of cooling fins using fluid dynamics simulation. By setting the fin spacing range to 1.0mm–5.0mm with a step size of 0.5mm, nine discrete spacing values ​​were obtained. Combining the fin thickness of 1.0mm and the rotor housing axial length of 60mm, the corresponding number of cooling fins was calculated: 30 fins for a spacing of 1.0mm, 24 fins for a spacing of 1.5mm, 20 fins for a spacing of 2.0mm, 17 fins for a spacing of 2.5mm, and so on. A complete fluid dynamics model of the motor rotor region was established in ANSYS Fluent software, with the following boundary conditions: ambient temperature 25℃, stator copper loss 1200W, stator iron loss 300W, fan speed 1500rpm, and airflow 0.5m³ / h. 3 / s. The nine sets of parameters were input into the model for simulation, and the highest temperature data of the rotor shell were obtained: 83℃ with 30 rotors, 84℃ with 24 rotors, 85℃ with 20 rotors, 89℃ with 17 rotors, and 92℃ with 15 rotors.

[0077] The process feasibility verification was successful. When the spacing is less than 2.0mm, the mold core diameter is less than 2.0mm, making processing difficult and reducing the mold life to less than 20,000 cycles. When the spacing is greater than or equal to 2.0mm, the mold life is greater than 50,000 cycles, meeting production requirements. Considering both heat dissipation efficiency and process feasibility, the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing is determined to be 20, corresponding to a heat dissipation fin spacing of 2.0mm.

[0078] In some embodiments, the step of uniformly arranging multiple heat dissipation fins on the outer surface of the rotor housing according to the maximum number and minimum allowable value of heat dissipation fins includes: determining the circumferential center line of the rotor housing; using the circumferential center line as a reference, dividing the outer circumference of the rotor housing into equal parts, the number of equal parts being equal to the maximum number of heat dissipation fins; arranging a heat dissipation fin along the radial direction of the rotor housing at the position corresponding to each division point; and controlling the axial length of each heat dissipation fin to be exactly equal to the total axial length of the rotor housing, so that all heat dissipation fins are uniformly distributed along the circumferential direction of the rotor housing.

[0079] This embodiment details how to achieve a uniform arrangement of heat dissipation fins on the outer surface of the rotor housing. A rotor housing substrate model is created in SolidWorks 3D design software, with an outer diameter of 100mm, an axial length of 60mm, and a substrate thickness of 3mm. A cross-sectional sketch of the first heat dissipation fin is drawn: a rectangular cross-section with a thickness of 1.0mm, a radial height of 2.0mm, and an axial length of 60mm, with its root tangent to the outer surface of the substrate. Using the center of the rotor housing as the array center, the "Circular Array" command is selected, setting the array quantity to 20 fins, the array angle to 360 degrees, and the "Equal Spacing" option to be checked. All 20 heat dissipation fins are generated. The central angle between adjacent heat dissipation fins is checked and confirmed to be 18 degrees, with no deviation. The axial length of each heat dissipation fin is checked and confirmed to be 60mm, exactly equal to the total axial length of the rotor housing.

[0080] Generate CNC machining code and import it into a CNC milling machine. Use an indexing head to clamp the rotor housing, and set the indexing head to rotate 18 degrees each time to process each heat dissipation fin in sequence, ensuring that all heat dissipation fins are evenly distributed along the circumference.

[0081] In some embodiments, making the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing includes: establishing a three-dimensional machining coordinate system for the rotor housing and determining the axial direction vector of the rotor housing; during the machining of each heat dissipation fin, acquiring the plane normal vector information of the heat dissipation fin in real time; comparing the acquired plane normal vector information with the axial direction vector of the rotor housing to calculate the perpendicularity deviation value; and adjusting the posture of the machining tool in real time according to the perpendicularity deviation value to keep the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing.

[0082] This embodiment details how to ensure that the plane containing the heat dissipation fins is strictly perpendicular to the rotor housing axis during the manufacturing process.

[0083] A three-dimensional machining coordinate system for the rotor housing is established: the lower end face of the rotor housing is the XY plane, the center of the lower end face is the origin, the rotation centerline of the rotor housing is the Z-axis, and the axial direction vector is (0,0,1). An online laser probe is installed on a CNC milling machine, and the probe accuracy is calibrated to 0.001mm. Before machining each heat dissipation fin, the laser probe collects the coordinates of three non-collinear points on the plane to be machined for the heat dissipation fin, and calculates the normal vector of the plane. The calculated plane normal vector is compared with the rotor housing axial direction vector (0,0,1), and the angle between the two vectors, i.e., the perpendicularity deviation value, is calculated. If the perpendicularity deviation value is greater than 0.02mm, the CNC system automatically adjusts the A-axis and B-axis angles of the milling machine spindle to correct the machining posture; if the deviation value is less than or equal to 0.02mm, machining of the heat dissipation fin begins. After each heat dissipation fin is machined, its plane normal vector is checked again with the laser probe to verify whether the perpendicularity meets the requirements; if it does not meet the requirements, it is re-machined.

[0084] In some embodiments, the blunting process of the sharp edges of the heat dissipation fins to obtain a rotor housing with optimized heat dissipation and weight reduction performance includes: scanning the edge contours of all heat dissipation fins using a 3D scanning device to identify all sharp edge positions; automatically generating a corresponding CNC blunting machining path for each identified sharp edge position; performing blunting processing on all sharp edges with uniform parameters according to the generated CNC blunting machining path; and after the blunting processing is completed, scanning the edges of all heat dissipation fins again to detect the blunting dimensions of all sharp edges to ensure that all blunting dimensions meet preset standards.

[0085] This embodiment details how to achieve standardized and automated blunting of the sharp edges of the heat dissipation fins. A FAROFocus laser 3D scanner is used to perform a full-surface scan of the processed rotor housing with a scanning accuracy of 0.01mm, acquiring point cloud data containing the edges of all heat dissipation fins. The point cloud data is imported into Geomagic DesignX reverse engineering software for point cloud denoising, stitching, and meshing to generate a triangular mesh model. A curvature analysis algorithm is then run, with a curvature threshold set to 5mm. -1 All edges with curvature greater than the threshold are automatically identified as sharp edges, and sharp edge contours are generated. The chamfering parameter is set to R0.5mm fillet, and the software automatically generates the CNC machining path for each sharp edge, outputting standard G-code. The G-code is imported into a three-axis CNC milling machine, a φ10mmR0.5mm ball end mill is installed, and the machining parameters are set as follows: spindle speed 3000rpm, feed rate 100mm / min, depth of cut 0.1mm.

[0086] Start the machine tool and automatically complete the blunting process for all sharp edges. After the process is complete, scan the rotor housing again and use a coordinate measuring machine to randomly check the blunting dimensions of 10 sharp edges to ensure that all dimensions are within the range of R0.4mm to R0.6mm.

[0087] In some embodiments, the method further includes: establishing a multi-objective optimization function that includes heat dissipation performance indicators, weight reduction performance indicators, and structural strength indicators; employing a multi-objective genetic algorithm, with heat dissipation fin thickness, heat dissipation fin radial height, and heat dissipation fin spacing as optimization variables, and performing a global search within a preset range of variable values; obtaining multiple Pareto optimal solutions, and selecting the optimal combination of design parameters from the multiple Pareto optimal solutions according to preset weight coefficients for each indicator; and adjusting the heat dissipation fin design parameters of the rotor housing according to the selected optimal combination of design parameters.

[0088] This embodiment is an extended and optimized embodiment. Based on the core steps, it uses an intelligent algorithm to achieve the global optimization of the three objectives of heat dissipation, weight reduction, and strength.

[0089] A multi-objective optimization function is established, which includes three optimization objectives: Objective 1: heat dissipation performance index, i.e. the highest temperature of the rotor shell, the smaller the better; Objective 2: weight reduction performance index, i.e. the total mass of the heat dissipation fins, the smaller the better; Objective 3: structural strength index, i.e. the maximum equivalent stress at the root of the heat dissipation fins, the smaller the better.

[0090] The optimization variables and their value ranges were determined as follows: heat dissipation fin thickness: 0.8mm~1.2mm; radial height of heat dissipation fins: 1.5mm~2.5mm; spacing of heat dissipation fins: 1.5mm~2.5mm. A global search was performed using a genetic algorithm toolbox from relevant software, with the following algorithm parameters set: population size 50, number of generations 100, crossover probability 0.8, and mutation probability 0.1. The search yielded a Pareto optimal front containing 30 solutions, all of which met basic strength and process requirements. Weighting coefficients were set according to the actual application scenario of the motor: in industrial transmission scenarios, heat dissipation performance weighted 0.5, weight reduction performance weighted 0.3, and structural strength weighted 0.2. The comprehensive score of each solution was calculated based on the weighting coefficients, and the solution with the highest score was selected as the optimal design parameter combination: heat dissipation fin thickness 1.0mm, radial height 2.0mm, and spacing 2.0mm, consistent with the basic scheme in the core steps.

[0091] In some embodiments, the method further includes: collecting operating data of the target motor under various typical operating conditions, including motor speed, output power and ambient temperature; inputting the collected operating data into a pre-trained heat load prediction machine learning model to predict the rotor housing heat load of the motor under different operating conditions; and dynamically adjusting the design parameters of the heat dissipation fins according to the predicted rotor housing heat load so that the heat dissipation performance of the rotor housing matches the actual heat load of the motor.

[0092] This embodiment is an extended and optimized version that uses machine learning to predict the thermal load of the motor under different operating conditions, thereby enabling dynamic adjustment of the heat dissipation fin parameters. Operating data of the target motor is collected under four typical operating conditions: no-load, rated load, 120% overload, and 150% overload. 1000 sets of data are collected for each condition, including motor speed, output power, ambient temperature, and the corresponding maximum rotor housing temperature.

[0093] The collected dataset was divided into training and testing sets in a 7:3 ratio to train a backpropagation (BP) neural network thermal load prediction model. The model has 3 neurons in the input layer, 2 hidden layers (10 neurons each), and 1 neuron in the output layer, outputting the rotor casing thermal load. After training, the model accuracy was verified using the testing set to ensure the prediction error was less than 5%. For different application scenarios, the most frequently operating parameters of the motor under those scenarios were input, and the trained model was used to predict the corresponding rotor casing thermal load.

[0094] The design parameters of the heat dissipation fins are dynamically adjusted according to the predicted heat load: for high-load conditions that are frequently overloaded, the radial height of the heat dissipation fins is appropriately increased to 2.2mm to improve heat dissipation performance; for low-load conditions that are frequently lightly loaded, the spacing between the heat dissipation fins is appropriately increased to 2.2mm to reduce the number of heat dissipation fins and further reduce weight.

[0095] In some embodiments, the method further includes: establishing a fatigue life analysis model of the rotor housing, inputting fatigue performance parameters of the rotor housing material and alternating load parameters during motor operation; using the finite element fatigue analysis method to calculate fatigue life data of the rotor housing under different design parameters; inputting the calculated fatigue life data into a pre-trained reliability prediction machine learning model to predict the long-term operational reliability of the rotor housing; and selecting design parameters that meet the preset operational reliability requirements as the final design parameters of the rotor housing.

[0096] This embodiment ensures the long-term operational reliability of the rotor housing through fatigue analysis and machine learning prediction. Fatigue performance parameters of ADC12 aluminum alloy, including SN curves, fatigue limits, and stress concentration factors, are collected. A fatigue life analysis model for the rotor housing is established, inputting alternating load parameters during motor operation: speed fluctuation range of 1000rpm~2000rpm, vibration acceleration range of 5g~15g, and magnetic pull fluctuation range of 50N~150N. ANSYS nCode software is used for fatigue life analysis, calculating fatigue life data and failure probability data for the rotor housing under different heat dissipation fin design parameters. 1000 sets of fatigue life and failure probability data corresponding to different design parameters are collected to train a support vector machine reliability prediction model. The model inputs are heat dissipation fin thickness, radial height, and spacing; the output is the 100,000-hour operational reliability of the rotor housing. After training, the model accuracy is verified using a test set to ensure the prediction error is less than 3%. All candidate design parameters are input into the model, and the parameter combinations with a 100,000-hour operational reliability greater than 99.9% are selected as the final design parameters for the rotor housing.

[0097] In some embodiments, the method further includes: establishing a digital twin model of the rotor housing, and keeping the digital twin model synchronized with the physical rotor housing in real time; collecting operating temperature data, vibration data, and speed data of the physical rotor housing in real time, and transmitting the collected data to the digital twin model; evaluating the heat dissipation performance and weight reduction performance of the rotor housing in real time through the digital twin model; and optimizing the operating parameters of the motor online based on the evaluation results to improve the overall operating reliability of the motor.

[0098] This embodiment utilizes digital twin technology to achieve performance monitoring and optimization of the rotor housing throughout its entire lifecycle. Based on a three-dimensional digital model of the rotor housing, a digital twin model is established, integrating a heat dissipation simulation model, a structural mechanics model, a fatigue life model, and a fault diagnosis model.

[0099] Four PT100 temperature sensors are evenly installed on the outer surface of the physical rotor housing, and one triaxial vibration sensor and one speed sensor are installed at the end cover. The sampling frequency of all sensors is 10Hz.

[0100] The real-time data collected by the sensors is transmitted to the digital twin platform via industrial Ethernet, so that the digital twin model and the physical rotor housing are kept in real-time data synchronization.

[0101] The digital twin model calculates the current heat dissipation efficiency, remaining fatigue life, vibration level, and health status of the rotor housing in real time based on the data collected in real time.

[0102] If the rotor housing temperature exceeds the preset threshold, the digital twin system sends a command to the motor controller to appropriately reduce the motor's output power to prevent overheating damage. If an abnormal increase in vibration is detected, the system automatically issues an early warning, prompting maintenance personnel to check for faults such as cracks or loosening of the cooling fins.

[0103] The system automatically records the operating data of the rotor housing throughout its entire life cycle, providing data support for the design and optimization of subsequent products.

[0104] In some embodiments, to address the inherent characteristic of uneven heat load distribution in the circumferential direction of the rotor housing, the limitations of a single rectangular cross-section heat dissipation fin are overcome, and a multi-section composite heat dissipation fin structure is adopted to further improve heat dissipation performance without increasing the total weight.

[0105] This embodiment addresses the problem that existing single-section heat dissipation fins cannot match the uneven thermal load of the rotor housing. Under the premise that the total weight does not exceed that of a single rectangular section scheme, it achieves precise allocation of heat dissipation resources by using a high heat dissipation efficiency section in the high heat load area and a lightweight section in the low heat load area, thereby further reducing the maximum temperature of the rotor housing.

[0106] A complete electromagnetic-thermal coupling simulation model of the motor was established. The boundary conditions of rated power of 15kW, rated speed of 1500rpm and ambient temperature of 40℃ were input, and the heat load distribution cloud map of the rotor shell in the circumferential direction was obtained by simulation.

[0107] Based on the heat load distribution, the outer circumference of the rotor housing is divided into three regions: high-heat zone (corresponding to the end of the stator winding, occupying a circumferential angle of 120°), medium-heat zone (corresponding to the middle of the stator core, occupying a circumferential angle of 180°), and low-heat zone (corresponding to the non-winding position at the end of the stator, occupying a circumferential angle of 60°).

[0108] Three different cross-section heat dissipation ribs were designed: rectangular cross-section heat dissipation ribs were used in the high heat zone with a thickness of 1.0 mm and a radial height of 2.2 mm; rectangular cross-section heat dissipation ribs were used in the medium heat zone with a thickness of 1.0 mm and a radial height of 2.0 mm; and triangular cross-section heat dissipation ribs were used in the low heat zone with a base length of 1.0 mm and a height of 2.0 mm.

[0109] Calculate the unit length weight of the three types of heat dissipation fins: 5.94 g / m for a rectangular fin of 1.0 × 2.2 mm, 5.40 g / m for a rectangular fin of 1.0 × 2.0 mm, and 2.70 g / m for a triangular fin of 1.0 × 2.0 mm.

[0110] Keeping the total number of heat dissipation fins unchanged at 20, the number of fins is distributed according to area: 7 fins for high-heat areas, 10 fins for medium-heat areas, and 3 fins for low-heat areas. The total weight is calculated as 7×5.94×0.06+10×5.40×0.06+3×2.70×0.06=6.31g, which is 2.6% lighter than the total weight of 6.48g for a single rectangular 1.0×2.0mm design.

[0111] A hydrodynamic simulation model of a rotor housing with multi-section composite heat dissipation fins was established to verify its heat dissipation performance. The results show that the highest temperature of the rotor housing is 82℃, which is 3℃ lower than 85℃ for the single rectangular cross-section design, and the heat dissipation efficiency is improved by 3.5%.

[0112] Mechanical simulation verification showed that the maximum equivalent stress at the base of all heat dissipation fins was less than 150 MPa, meeting the strength requirements. Die-casting process simulation verification showed no defects such as undercasting or shrinkage cavities, and smooth mold demolding. It is essential to ensure that the thickness of all heat dissipation fins is the minimum allowable value of 1.0 mm determined in step S101, and that the thermal load in different areas is matched only by adjusting the radial height and cross-sectional shape of the heat dissipation fins. The total weight must be controlled within the weight of a single rectangular cross-section design, and must not violate the core weight reduction objective of this invention.

[0113] In some embodiments, by employing topology optimization technology to target the weak points where stress is concentrated at the root of the heat dissipation fins, an integrated design of root reinforcement and substrate weight reduction is achieved without increasing the total weight, thereby further improving structural strength and reducing overall weight.

[0114] This embodiment solves the problem of insufficient fatigue life caused by stress concentration at the root of the heat dissipation fins in the prior art. At the same time, it removes redundant materials on the substrate through topology optimization, thereby improving structural strength and reducing weight without increasing the total weight.

[0115] An initial finite element model of the rotor housing was established, with a substrate thickness of 3 mm and heat dissipation fin parameters of 1.0 mm thickness, 2.0 mm radial height, and 20 fins. All loads described in step S101 were applied, and static simulation was performed to obtain a stress distribution cloud map. The root of the heat dissipation fins was determined to be a stress concentration area, with a maximum stress of 145 MPa.

[0116] Define the topology optimization design domain: the area (2mm wide, 1mm high) connecting the outer surface of the substrate to the root of the heat dissipation fin is defined as the designable domain, and the rest of the substrate and the main body of the heat dissipation fin are defined as the non-designable domain.

[0117] The objective function for topology optimization is to minimize structural flexibility (i.e., maximize stiffness), with the constraint that the volume fraction of the designable domain does not exceed 50%.

[0118] Topology optimization was performed using the variable density method, with 100 iterations, to obtain the optimized material distribution. The results show that the material is mainly concentrated on both sides of the root of the heat dissipation fins, forming a triangular reinforcement structure.

[0119] Based on the topology optimization results, the geometric model was reconstructed, and triangular reinforcing ribs were added to both sides of the root of each heat dissipation fin. The thickness of the reinforcing ribs was 1.0 mm, the height was 1.0 mm, and the length of the base was 1.5 mm.

[0120] Calculate the total weight of the reinforcing ribs: 20 ribs × (0.5 × 1.5 × 1.0 × 0.06) × 2700 = 0.24g.

[0121] To offset the increased weight from the reinforcing ribs, a weight-reduction design was implemented on the substrate: the thickness of the non-stressed areas of the substrate was reduced from 3 mm to 2.8 mm. The weight reduction of the substrate was calculated as: π × (0.05) 2 -0.047 2 )×0.06×2700=0.49g.

[0122] The total weight change is 0.24g - 0.49g = -0.25g, meaning the overall weight decreases by 0.25g.

[0123] Static simulation verification showed that the maximum stress at the root of the heat dissipation fin was reduced to 112 MPa, a decrease of 22.8% compared to before optimization, and the safety factor was increased to 2.14. Die casting process simulation verification showed that the reinforcing ribs and thinned substrate could be formed normally without process defects.

[0124] The optimized structure must meet the demolding requirements of the die-casting process and must not contain structures that cannot be formed, such as undercuts or deep cavities. The dimensions of the reinforcing ribs must be strictly calculated to ensure that the added weight is less than the weight reduced by the thinning of the substrate, thereby achieving overall weight reduction.

[0125] In some embodiments, by addressing the issue of actual product performance deviation caused by separate optimization of structural parameters and process parameters in the prior art, global synergistic optimization of structural design and manufacturing process is achieved. This embodiment can eliminate the disconnect between structural design and manufacturing process, using die-casting process parameters as optimization variables and co-optimizing them with heat dissipation fin structural parameters, ensuring that the actual product performance is consistent with the design performance, while reducing the process defect rate.

[0126] Determine the input variables and their value ranges for collaborative optimization: Structural parameters: heat dissipation fin thickness 0.9mm~1.1mm, heat dissipation fin radial height 1.8mm~2.2mm, heat dissipation fin spacing 1.8mm~2.2mm; Process parameters: casting temperature 660℃~700℃, injection speed 2.5m / s~3.5m / s, pressurization pressure 70MPa~90MPa.

[0127] The Latin hypercube sampling method was used to draw 100 sample points within the range of the above variables.

[0128] For each set of sample points, mechanical simulation, die-casting process simulation, and heat dissipation simulation are performed to obtain the corresponding output responses: maximum stress at the root of the heat dissipation fin, process defect rate, and highest temperature of the rotor shell.

[0129] A Kriging surrogate model was trained using 100 sets of sample data to establish a mapping relationship between input variables and output responses. Cross-validation was used to verify the accuracy of the surrogate model and ensure the coefficient of determination R0. 2 Greater than 0.95.

[0130] A multi-objective collaborative optimization function is established, with the following optimization objectives: Objective 1: Minimize the maximum stress at the root of the heat dissipation fins; Objective 2: Minimize the process defect rate; Objective 3: Minimize the maximum temperature of the rotor housing.

[0131] A multi-objective particle swarm optimization algorithm was used for global optimization search, with a population size of 50 and an evolutionary number of 100, to obtain the Pareto optimal front.

[0132] The following weighting coefficients are set based on the company's actual production: process defect rate weight 0.4, heat dissipation performance weight 0.3, and structural strength weight 0.3. The comprehensive score of each solution is calculated, and the optimal parameter combination with the highest score is selected: Structural parameters: heat dissipation fin thickness 1.0mm, radial height 2.0mm, spacing 2.0mm; Process parameters: casting temperature 680℃, injection speed 3.0m / s, pressurization pressure 80MPa.

[0133] Actual production verification was conducted, producing 100 rotor housings according to the aforementioned optimal parameters. Test results showed: a 0% process defect rate, an average stress at the root of the heat dissipation fins of 128 MPa, and an average maximum rotor housing temperature of 84°C, with deviations from simulation results less than 3%. Sufficient sample data must be collected to train the surrogate model; the sample size should be at least 10 times the number of variables. The optimal parameter combination must be verified through actual production to ensure its feasibility and stability under industrial production conditions.

[0134] In some embodiments, to address the potential failure of heat dissipation fins due to fracture under harsh operating conditions such as extreme temperatures and prolonged overload, a redundant heat dissipation fin structure is designed and combined with online monitoring technology to achieve early warning of failure and safety protection. This improves the operational reliability of the motor under extreme conditions, prevents motor damage caused by heat dissipation fin fracture, and ensures that heat dissipation and weight reduction performance under normal operating conditions are not affected.

[0135] Determine the extreme operating parameters of the motor: ambient temperature 55℃, continuous operation at 150% rated load for 2 hours.

[0136] Simulation calculations were performed on the heat load and stress distribution of the rotor housing under extreme conditions to determine the locations of the five heat dissipation fins most prone to failure (located in the high-heat zone).

[0137] Design a redundant heat dissipation fin structure: Add a spare heat dissipation fin on each side of each easily failed heat dissipation fin. The parameters of the spare heat dissipation fin are a thickness of 1.0 mm, a radial height of 1.5 mm, and a height of 0.5 mm lower than the main heat dissipation fin.

[0138] The total weight of the redundant heat dissipation fins is calculated as: 5×2×(1.0×1.5×0.06)×2700=0.24g, which accounts for only 0.8% of the total weight of the rotor housing and has a negligible impact on the weight reduction effect under normal operating conditions.

[0139] A miniature fiber optic strain sensor is attached to the base of each main heat dissipation fin. The sensor has a measurement range of 0–2000 με, an accuracy of 1 με, and a sampling frequency of 100 Hz.

[0140] When the strain value detected by the sensor exceeds 1200με (corresponding to a stress of 168MPa), a first-level warning is issued, prompting the motor to reduce the load; when the strain value exceeds 1500με (corresponding to a stress of 210MPa), a second-level warning is issued, controlling the motor to automatically stop.

[0141] The motor was placed in a 55℃ environmental chamber and continuously operated under 150% rated load. Test results showed that when the strain of the main cooling fin reached 1180με, the system issued a first-level warning, and the motor automatically reduced its operating load to the rated load. After two hours of continuous operation, none of the cooling fins broke, and the highest rotor casing temperature was 92℃, meeting the operating requirements. When one main cooling fin was manually cut, the system immediately detected the sudden strain change, issued a second-level warning, and controlled the motor to stop, effectively preventing an accident.

[0142] The radial height of the redundant heat dissipation fins must be lower than that of the main heat dissipation fins to ensure that, under normal operating conditions, airflow primarily passes through the main heat dissipation fins, and the redundant heat dissipation fins do not generate additional air resistance. The fiber optic strain sensor must be resistant to high temperatures and electromagnetic interference, and be able to operate stably in the harsh operating environment of the motor.

[0143] In some embodiments, by addressing the issues of varying magnet assembly gaps and uneven air gaps caused by rotor housing thermal deformation, thermal deformation control is incorporated into the heat dissipation and weight reduction optimization system, achieving optimal synergy between heat dissipation, weight reduction, and assembly accuracy. This embodiment solves the problem in existing technologies that only focus on heat dissipation and weight reduction performance while neglecting the impact of rotor housing thermal deformation on motor performance, ensuring air gap uniformity and magnet assembly reliability at different operating temperatures.

[0144] A thermal-structural coupling simulation model of the rotor housing was established, with the thermal expansion coefficient of ADC12 aluminum alloy being input as 23 × 10⁻⁶. -6 / ℃, the coefficient of thermal expansion of neodymium iron boron magnets is 11×10. -6 / ℃.

[0145] Simulation calculations were performed on the radial and axial thermal deformation of the rotor housing within a temperature range of 25℃ to 120℃ under different heat dissipation fin parameters.

[0146] The precision requirements for magnet assembly are as follows: the radial thermal deformation of the inner hole of the rotor housing shall not exceed 0.05mm, otherwise it will cause the magnet to fall off or the air gap to be uneven; the axial thermal deformation shall not exceed 0.1mm, otherwise it will affect the axial alignment of the rotor and the stator.

[0147] Establish the collaborative optimization objective function: Objective 1: Minimize the total weight of the rotor housing; Objective 2: Minimize the maximum temperature of the rotor housing; Objective 3: Minimize the maximum radial thermal deformation of the rotor housing.

[0148] The optimization search was performed using a multi-objective genetic algorithm with the thickness of the heat dissipation fins, radial height, spacing, and substrate thickness as optimization variables.

[0149] The optimal parameter combination was obtained: heat dissipation fin thickness 1.0mm, radial height 2.0mm, spacing 2.0mm, and substrate thickness 3.0mm.

[0150] Verify the performance under this parameter combination: Total weight: 125g; Maximum rated operating temperature: 85℃; Maximum radial thermal deformation at 120℃: 0.042mm, meeting accuracy requirements; Maximum axial thermal deformation at 120℃: 0.083mm, meeting accuracy requirements.

[0151] Actual assembly verification was conducted: The rotor housing was manufactured according to the above parameters, and magnet assembly and air gap measurements were performed at 25℃ and 120℃, respectively. The results showed that at 25℃, the magnet interference was 0.03mm, indicating a secure assembly; at 120℃, the magnet interference was 0.018mm, with no loosening; the air gap non-uniformity was less than 5%, meeting the motor performance requirements. The difference in thermal expansion coefficients between the rotor housing and the magnets must be considered, as this is the main reason for changes in the assembly gap under hot conditions. During optimization, thermal deformation must be treated as a hard constraint; any parameter combination that does not meet the accuracy requirements must be eliminated.

[0152] Please see Figure 7 As shown, Figure 7 This is a schematic diagram of the structure of the brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization system 200 provided in this application embodiment. The brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization system 200 is used to execute the steps of the brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization method shown in the above embodiments. The brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization system 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0153] like Figure 7 As shown, the brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization system 200 includes:

[0154] The minimum determining unit 201 is used to determine the minimum allowable value of the thickness of the heat dissipation fins based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology.

[0155] The quantity calculation unit 202 is used to calculate the maximum number of heat dissipation ribs that can be arranged on the outer surface of the rotor housing based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the heat dissipation ribs; according to the maximum number of heat dissipation ribs and the minimum allowable value, multiple heat dissipation ribs are evenly arranged on the outer surface of the rotor housing, so that the plane where the heat dissipation ribs are located is perpendicular to the axis of the rotor housing.

[0156] The blunting treatment unit 203 is used to blunt the sharp edges of the heat dissipation fins to obtain a rotor housing with optimized heat dissipation and weight reduction performance.

[0157] In some embodiments, determining the minimum allowable value of the heat dissipation fin thickness based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology includes: establishing a finite element mechanical model of the rotor housing and inputting the mechanical performance parameters of the rotor housing material; applying all loads borne by the rotor housing during motor operation and performing static simulation analysis to obtain structural stress distribution data corresponding to different heat dissipation fin thicknesses; establishing a die-casting processing simulation model of the rotor housing, inputting die-casting process parameters, and performing filling simulation and solidification simulation analysis to obtain processing defect distribution data corresponding to different heat dissipation fin thicknesses; and comprehensively considering the structural stress distribution data and processing defect distribution data to select the minimum heat dissipation fin thickness that simultaneously meets the structural strength requirements and processing technology requirements, as the minimum allowable value of the heat dissipation fin thickness.

[0158] In some embodiments, calculating the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the aforementioned heat dissipation fins includes: setting a range of values ​​for the spacing of the heat dissipation fins, and selecting multiple discrete heat dissipation fin spacing values ​​within the range; for each discrete heat dissipation fin spacing value, calculating the corresponding number of heat dissipation fins in combination with the minimum allowable value of the thickness of the heat dissipation fins and the total axial length of the rotor housing; establishing a fluid dynamics simulation model of the motor rotor region, inputting parameters of different numbers of heat dissipation fins and heat dissipation fin spacing, performing forced ventilation heat dissipation simulation analysis, and obtaining heat dissipation efficiency data corresponding to each set of parameters; selecting the number of heat dissipation fins with the highest heat dissipation efficiency and meeting the processing accuracy requirements as the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing.

[0159] In some embodiments, the step of uniformly arranging multiple heat dissipation fins on the outer surface of the rotor housing according to the maximum number and minimum allowable value of heat dissipation fins includes: determining the circumferential center line of the rotor housing; using the circumferential center line as a reference, dividing the outer circumference of the rotor housing into equal parts, the number of equal parts being equal to the maximum number of heat dissipation fins; arranging a heat dissipation fin along the radial direction of the rotor housing at the position corresponding to each division point; and controlling the axial length of each heat dissipation fin to be exactly equal to the total axial length of the rotor housing, so that all heat dissipation fins are uniformly distributed along the circumferential direction of the rotor housing.

[0160] In some embodiments, making the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing includes: establishing a three-dimensional machining coordinate system for the rotor housing and determining the axial direction vector of the rotor housing; during the machining of each heat dissipation fin, acquiring the plane normal vector information of the heat dissipation fin in real time; comparing the acquired plane normal vector information with the axial direction vector of the rotor housing to calculate the perpendicularity deviation value; and adjusting the posture of the machining tool in real time according to the perpendicularity deviation value to keep the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing.

[0161] In some embodiments, the blunting process of the sharp edges of the heat dissipation fins to obtain a rotor housing with optimized heat dissipation and weight reduction performance includes: scanning the edge contours of all heat dissipation fins using a 3D scanning device to identify all sharp edge positions; automatically generating a corresponding CNC blunting machining path for each identified sharp edge position; performing blunting processing on all sharp edges with uniform parameters according to the generated CNC blunting machining path; and after the blunting processing is completed, scanning the edges of all heat dissipation fins again to detect the blunting dimensions of all sharp edges to ensure that all blunting dimensions meet preset standards.

[0162] In some embodiments, the method further includes: establishing a multi-objective optimization function that includes heat dissipation performance indicators, weight reduction performance indicators, and structural strength indicators; employing a multi-objective genetic algorithm, with heat dissipation fin thickness, heat dissipation fin radial height, and heat dissipation fin spacing as optimization variables, and performing a global search within a preset range of variable values; obtaining multiple Pareto optimal solutions, and selecting the optimal combination of design parameters from the multiple Pareto optimal solutions according to preset weight coefficients for each indicator; and adjusting the heat dissipation fin design parameters of the rotor housing according to the selected optimal combination of design parameters.

[0163] In some embodiments, the method further includes: collecting operating data of the target motor under various typical operating conditions, including motor speed, output power and ambient temperature; inputting the collected operating data into a pre-trained heat load prediction machine learning model to predict the rotor housing heat load of the motor under different operating conditions; and dynamically adjusting the design parameters of the heat dissipation fins according to the predicted rotor housing heat load so that the heat dissipation performance of the rotor housing matches the actual heat load of the motor.

[0164] In some embodiments, the method further includes: establishing a fatigue life analysis model of the rotor housing, inputting fatigue performance parameters of the rotor housing material and alternating load parameters during motor operation; using the finite element fatigue analysis method to calculate fatigue life data of the rotor housing under different design parameters; inputting the calculated fatigue life data into a pre-trained reliability prediction machine learning model to predict the long-term operational reliability of the rotor housing; and selecting design parameters that meet the preset operational reliability requirements as the final design parameters of the rotor housing.

[0165] In some embodiments, the method further includes: establishing a digital twin model of the rotor housing, and keeping the digital twin model synchronized with the physical rotor housing in real time; collecting operating temperature data, vibration data, and speed data of the physical rotor housing in real time, and transmitting the collected data to the digital twin model; evaluating the heat dissipation performance and weight reduction performance of the rotor housing in real time through the digital twin model; and optimizing the operating parameters of the motor online based on the evaluation results to improve the overall operating reliability of the motor.

[0166] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization system and each module described above can be referred to the corresponding content in the various embodiments of the brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization method, and will not be repeated here.

[0167] The aforementioned method for synergistic optimization of heat dissipation and weight reduction in the rotor housing of a brushless DC motor can be implemented as a computer program, which can be used in applications such as... Figure 7 It runs on the device shown.

[0168] Please see Figure 8 , Figure 8 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0169] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any of the methods for co-optimizing heat dissipation and weight reduction of the brushless DC motor rotor housing.

[0170] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0171] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any method for co-optimizing heat dissipation and weight reduction of the rotor housing of a brushless DC motor.

[0172] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0173] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0174] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0175] Based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology, the minimum allowable value of the heat dissipation fin thickness is determined;

[0176] Based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the aforementioned heat dissipation ribs, the maximum number of heat dissipation ribs that can be arranged on the outer surface of the rotor housing is calculated; according to the maximum number of heat dissipation ribs and the minimum allowable value, multiple heat dissipation ribs are evenly arranged on the outer surface of the rotor housing, so that the plane where the heat dissipation ribs are located is perpendicular to the axis of the rotor housing.

[0177] The sharp edges of the heat dissipation fins are blunted to obtain a rotor housing with optimized heat dissipation and weight reduction performance.

[0178] In some embodiments, determining the minimum allowable value of the heat dissipation fin thickness based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology includes: establishing a finite element mechanical model of the rotor housing and inputting the mechanical performance parameters of the rotor housing material; applying all loads borne by the rotor housing during motor operation and performing static simulation analysis to obtain structural stress distribution data corresponding to different heat dissipation fin thicknesses; establishing a die-casting processing simulation model of the rotor housing, inputting die-casting process parameters, and performing filling simulation and solidification simulation analysis to obtain processing defect distribution data corresponding to different heat dissipation fin thicknesses; and comprehensively considering the structural stress distribution data and processing defect distribution data to select the minimum heat dissipation fin thickness that simultaneously meets the structural strength requirements and processing technology requirements, as the minimum allowable value of the heat dissipation fin thickness.

[0179] In some embodiments, calculating the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing based on the total axial length of the rotor housing and the minimum allowable value of the thickness of the aforementioned heat dissipation fins includes: setting a range of values ​​for the spacing of the heat dissipation fins, and selecting multiple discrete heat dissipation fin spacing values ​​within the range; for each discrete heat dissipation fin spacing value, calculating the corresponding number of heat dissipation fins in combination with the minimum allowable value of the thickness of the heat dissipation fins and the total axial length of the rotor housing; establishing a fluid dynamics simulation model of the motor rotor region, inputting parameters of different numbers of heat dissipation fins and heat dissipation fin spacing, performing forced ventilation heat dissipation simulation analysis, and obtaining heat dissipation efficiency data corresponding to each set of parameters; selecting the number of heat dissipation fins with the highest heat dissipation efficiency and meeting the processing accuracy requirements as the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing.

[0180] In some embodiments, the step of uniformly arranging multiple heat dissipation fins on the outer surface of the rotor housing according to the maximum number and minimum allowable value of heat dissipation fins includes: determining the circumferential center line of the rotor housing; using the circumferential center line as a reference, dividing the outer circumference of the rotor housing into equal parts, the number of equal parts being equal to the maximum number of heat dissipation fins; arranging a heat dissipation fin along the radial direction of the rotor housing at the position corresponding to each division point; and controlling the axial length of each heat dissipation fin to be exactly equal to the total axial length of the rotor housing, so that all heat dissipation fins are uniformly distributed along the circumferential direction of the rotor housing.

[0181] In some embodiments, making the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing includes: establishing a three-dimensional machining coordinate system for the rotor housing and determining the axial direction vector of the rotor housing; during the machining of each heat dissipation fin, acquiring the plane normal vector information of the heat dissipation fin in real time; comparing the acquired plane normal vector information with the axial direction vector of the rotor housing to calculate the perpendicularity deviation value; and adjusting the posture of the machining tool in real time according to the perpendicularity deviation value to keep the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing.

[0182] In some embodiments, the blunting process of the sharp edges of the heat dissipation fins to obtain a rotor housing with optimized heat dissipation and weight reduction performance includes: scanning the edge contours of all heat dissipation fins using a 3D scanning device to identify all sharp edge positions; automatically generating a corresponding CNC blunting machining path for each identified sharp edge position; performing blunting processing on all sharp edges with uniform parameters according to the generated CNC blunting machining path; and after the blunting processing is completed, scanning the edges of all heat dissipation fins again to detect the blunting dimensions of all sharp edges to ensure that all blunting dimensions meet preset standards.

[0183] In some embodiments, the method further includes: establishing a multi-objective optimization function that includes heat dissipation performance indicators, weight reduction performance indicators, and structural strength indicators; employing a multi-objective genetic algorithm, with heat dissipation fin thickness, heat dissipation fin radial height, and heat dissipation fin spacing as optimization variables, and performing a global search within a preset range of variable values; obtaining multiple Pareto optimal solutions, and selecting the optimal combination of design parameters from the multiple Pareto optimal solutions according to preset weight coefficients for each indicator; and adjusting the heat dissipation fin design parameters of the rotor housing according to the selected optimal combination of design parameters.

[0184] In some embodiments, the method further includes: collecting operating data of the target motor under various typical operating conditions, including motor speed, output power and ambient temperature; inputting the collected operating data into a pre-trained heat load prediction machine learning model to predict the rotor housing heat load of the motor under different operating conditions; and dynamically adjusting the design parameters of the heat dissipation fins according to the predicted rotor housing heat load so that the heat dissipation performance of the rotor housing matches the actual heat load of the motor.

[0185] In some embodiments, the method further includes: establishing a fatigue life analysis model of the rotor housing, inputting fatigue performance parameters of the rotor housing material and alternating load parameters during motor operation; using the finite element fatigue analysis method to calculate fatigue life data of the rotor housing under different design parameters; inputting the calculated fatigue life data into a pre-trained reliability prediction machine learning model to predict the long-term operational reliability of the rotor housing; and selecting design parameters that meet the preset operational reliability requirements as the final design parameters of the rotor housing.

[0186] In some embodiments, the method further includes: establishing a digital twin model of the rotor housing, and keeping the digital twin model synchronized with the physical rotor housing in real time; collecting operating temperature data, vibration data, and speed data of the physical rotor housing in real time, and transmitting the collected data to the digital twin model; evaluating the heat dissipation performance and weight reduction performance of the rotor housing in real time through the digital twin model; and optimizing the operating parameters of the motor online based on the evaluation results to improve the overall operating reliability of the motor.

[0187] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the brushless DC motor rotor housing heat dissipation and weight reduction collaborative optimization method provided in any embodiment of this application.

[0188] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard equipped on the computer device.

[0189] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for synergistic optimization of heat dissipation and weight reduction in the rotor housing of a brushless DC motor, applied to the rotor housing of an external rotor brushless DC motor, wherein the rotor housing is a thin-walled metal structure, and the outer surface of the rotor housing is provided with multiple heat dissipation fins, the heat dissipation fins having a rectangular cross-section structure; characterized in that, include: Based on the mechanical strength requirements of the rotor housing material and the feasibility of the processing technology, the minimum allowable value of the heat dissipation fin thickness is determined, including: establishing a finite element mechanical model of the rotor housing and inputting the mechanical performance parameters of the rotor housing material; applying all loads borne by the rotor housing during motor operation and performing static simulation analysis to obtain structural stress distribution data corresponding to different heat dissipation fin thicknesses; establishing a die-casting processing simulation model of the rotor housing, inputting die-casting process parameters, and performing filling simulation and solidification simulation analysis to obtain processing defect distribution data corresponding to different heat dissipation fin thicknesses; and combining the structural stress distribution data and processing defect distribution data to select the minimum heat dissipation fin thickness that simultaneously meets the structural strength requirements and processing technology requirements, as the minimum allowable value of the heat dissipation fin thickness. Based on the total axial length of the rotor housing and the minimum allowable thickness of the aforementioned heat dissipation fins, the maximum number of heat dissipation fins that can be arranged on the outer surface of the rotor housing is calculated, including: A range of values ​​for the spacing of the cooling fins is defined, and multiple discrete values ​​for the spacing are selected within this range. For each discrete spacing value, the number of cooling fins is calculated based on the minimum allowable value for the thickness of the cooling fins and the total axial length of the rotor housing. A fluid dynamics simulation model of the motor rotor region is established, and parameters for different numbers and spacings of cooling fins are input to perform forced ventilation cooling simulation analysis, obtaining the cooling efficiency data corresponding to each set of parameters. The number of cooling fins with the highest cooling efficiency and meeting the processing accuracy requirements is selected as the maximum number of cooling fins that can be arranged on the outer surface of the rotor housing. According to the maximum number and minimum allowable value of the cooling fins, multiple cooling fins are evenly arranged on the outer surface of the rotor housing, so that the plane where the cooling fins are located is perpendicular to the axis of the rotor housing. The sharp edges of the heat dissipation fins are blunted to obtain a rotor housing with optimized heat dissipation and weight reduction performance.

2. The method according to claim 1, characterized in that, The method of uniformly arranging multiple heat dissipation fins on the outer surface of the rotor housing according to the maximum number and minimum allowable value of heat dissipation fins includes: Determine the circumferential center line of the rotor housing. Using the circumferential center line as a reference, divide the outer circumference of the rotor housing into equal parts. The number of equal parts is equal to the maximum number of heat dissipation fins. At each of the equal division points, a heat dissipation fin is arranged along the radial direction of the rotor housing; The axial length of each heat dissipation fin is controlled to be exactly equal to the total axial length of the rotor housing, so that all heat dissipation fins are evenly distributed along the circumference of the rotor housing.

3. The method according to claim 2, characterized in that, The step of making the plane containing the heat dissipation fins perpendicular to the axis of the rotor housing includes: Establish a three-dimensional machining coordinate system for the rotor housing and determine the axial direction vector of the rotor housing; During the processing of each heat dissipation fin, the plane normal vector information of the heat dissipation fin is collected in real time; The collected plane normal vector information is compared with the axial direction vector of the rotor housing to calculate the perpendicularity deviation value; The orientation of the machining tool is adjusted in real time according to the perpendicularity deviation value to keep the plane where the heat dissipation fins are located perpendicular to the axis of the rotor housing.

4. The method according to claim 1, characterized in that, The blunting treatment of the sharp edges of the heat dissipation fins to obtain a rotor housing with optimized heat dissipation and weight reduction performance includes: The edge contours of all heat dissipation fins are scanned using a 3D scanning device to identify all sharp edge locations; For each identified sharp edge location, a corresponding CNC blunting machining path is automatically generated; According to the generated CNC blunting machining path, all sharp edges are blunted with uniform parameters; After the blunting process is completed, scan the edges of all heat dissipation fins again to check the blunting dimensions of all sharp edges and ensure that all blunting dimensions meet the preset standards.

5. The method according to claim 1, characterized in that, The method further includes: Establish a multi-objective optimization function that includes heat dissipation performance indicators, weight reduction performance indicators, and structural strength indicators; A multi-objective genetic algorithm is used to perform a global search within a preset range of variable values, with the thickness, radial height, and spacing of the heat dissipation fins as optimization variables. Multiple Pareto optimal solutions are obtained through the search. Based on the preset weight coefficients of each index, the optimal combination of design parameters is selected from the multiple Pareto optimal solutions. Adjust the design parameters of the heat dissipation fins on the rotor housing according to the selected optimal combination of design parameters.

6. The method according to claim 1, characterized in that, The method further includes: Collect operating data of the target motor under various typical operating conditions, including motor speed, output power and ambient temperature; The collected operating data is input into a pre-trained machine learning model for predicting heat load, which predicts the heat load on the rotor housing of the motor under different operating conditions. Based on the predicted rotor housing heat load, the design parameters of the heat dissipation fins are dynamically adjusted to match the heat dissipation performance of the rotor housing with the actual heat load of the motor.

7. The method according to claim 1, characterized in that, The method further includes: Establish a fatigue life analysis model for the rotor housing, and input the fatigue performance parameters of the rotor housing material and the alternating load parameters during motor operation; The finite element fatigue analysis method was used to calculate the fatigue life data of the rotor housing under different design parameters; The calculated fatigue life data is input into a pre-trained reliability prediction machine learning model to predict the long-term operational reliability of the rotor housing. The design parameters that meet the preset operational reliability requirements are selected as the final design parameters for the rotor housing.

8. The method according to claim 1, characterized in that, The method further includes: Establish a digital twin model of the rotor housing, and keep the digital twin model synchronized with the physical rotor housing in real time; Real-time acquisition of operating temperature, vibration, and speed data of the physical rotor housing, and transmission of the acquired data to the digital twin model; The heat dissipation and weight reduction performance of the rotor housing can be evaluated in real time using a digital twin model. Based on the evaluation results, the operating parameters of the motor are optimized online to improve the overall operational reliability of the motor.

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