Determination method and device for design parameters of hub motor reducer, medium and equipment

Abstract

The invention provides a method and device for determining design parameters of a hub motor speed reducer, a medium and equipment, and the method comprises the steps: carrying out the parametric modeling of all key parts of the speed reducer, and obtaining a speed reducer parametric model; the parameterized model is combined with design requirements of all parts of the speed reducer and assembly relation constraints between adjacent parts, and the key point of the lightweight design of the speed reducer is to reduce the weight of the speed reducer on the basis of ensuring the meshing degree; when the geometric parameter model and the assembly parameter model are used as constraint conditions and the overall mass and the gear meshing degree of the speed reducer are used as optimization targets to optimize the design parameters of the speed reducer, the design requirements of the lightweight speed reducer can be well met; when the non-dominated genetic algorithm is used for multi-objective optimization, it can be ensured that the overall quality and the meshing degree are optimal within a certain range, so that the optimal design parameters can be determined without the help of artificial experience or complicated and repeated verification, and the power density of the hub motor is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lightweight design of a hub motor reducer, and in particular to a method, device, medium and equipment for determining design parameters of a hub motor reducer. Background Art

[0002] In the drive system of electric vehicles, the hub motor can be directly arranged on the wheel, which can shorten the transmission chain to the greatest extent, improve the transmission efficiency, and realize the independent controllable performance of the four wheels of the electric vehicle. Therefore, it has broad development prospects.

[0003] There are two types of wheel hub motors: direct drive wheel hub motors and reduction wheel hub motors. Among them, the reduction wheel hub motor consists of two parts: the high-speed motor body and the reducer; reducing the mass of the reducer can greatly improve the power density of the wheel hub motor. In addition, since the wheel hub motor is directly arranged in the unsprung wheel rim, reducing the wheel hub motor reducer is also crucial to reducing the unsprung mass and improving the vehicle's handling.

[0004] At present, the selection of parameters in the process of reducer design mostly depends on the designer's experience and judgment based on other reducer designs. The design process requires repeated verification and frequent modifications, which greatly reduces the design efficiency and makes it difficult to achieve the optimal effect. It is easy to cause problems such as reducer design redundancy and large reducer mass, thus failing to ensure the power density of the hub motor. Summary of the invention

[0005] In response to the problems existing in the prior art, the embodiments of the present invention provide a method, device, medium and equipment for determining the design parameters of a hub motor reducer, so as to solve or partially solve the technical problem that when designing a hub motor reducer in the prior art, the design process relies on manual experience, which is prone to reducer design redundancy and large reducer mass, thereby resulting in the inability to ensure the power density of the hub motor.

[0006] A first aspect of the present invention provides a method for determining design parameters of a hub motor reducer, the method comprising:

[0007] Performing parametric modeling on each key component of the reducer to obtain a parametric model of the reducer; the parametric model of the reducer includes: a geometric parameter model corresponding to the key component itself, an assembly parameter model between each key component, and a meshing model between each key component;

[0008] Determine the key parameters of each key component;

[0009] Based on the geometric parameter model and the assembly parameter model, taking the overall mass of the reducer and the gear meshing degree as optimization targets, the initial values ​​of the key parameters are optimized using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer.

[0010] In the above scheme, the key components include: sun gear, planetary gear and inner ring gear; the key components of the reducer are parametrically modeled to obtain a parametric model of the reducer, including:

[0011] When the reducer parameterized model is a geometric parameter model corresponding to the key component itself, the parameterized model includes: a gear non-undercutting condition model, a module and tooth width constraint model; wherein,

[0012] The gear non-undercutting condition model is:

[0013] The module and tooth width constraint model is: 5m≤b≤17m; where,

[0014] The X a is the displacement coefficient of the sun gear, and the Z a is the number of teeth of the sun gear, and the X g is the displacement coefficient of the planetary gear, and the Z g is the number of teeth of the planetary gear, and the X r is the displacement coefficient of the inner gear ring, and the Z r is the number of teeth of the inner gear ring, m is the gear module, and b is the gear tooth width.

[0015] In the above scheme, the key components include: sun gear, planetary gear and inner ring gear; the key components of the reducer are parametrically modeled to obtain a parametric model of the reducer, including:

[0016] When the reducer parameterized model is an assembly parameter model between various key components, the parameterized model includes: a concentric condition model of the sun gear, the planetary gear and the inner gear ring, an adjacent condition model of the sun gear and the planetary gear, and an assembly condition model of the sun gear, the planetary gear and the inner gear ring; wherein,

[0017] The concentric condition model of the sun gear, the planetary gear and the inner gear ring is:

[0018]

[0019] The adjacent condition model of the sun gear and the planet gear is:

[0020] The assembly condition model of the sun gear and the planet gear is: in,

[0021] The Z a is the number of teeth of the sun gear, and the Z g is the number of teeth of the planetary gear, and Z r is the number of teeth of the inner gear ring, and a ag is the meshing angle between the sun gear and the planet gear, and the a gr is the meshing angle between the planetary gear and the inner gear ring, and d ag is the diameter of the addendum circle of the planetary gear, and a′ ag is the center distance between the sun gear and the planet gear, and n p is the number of the planetary gears, and N is a preset first positive integer.

[0022] In the above scheme, the key components include: sun gear, planetary gear and inner ring gear; the key components of the reducer are parametrically modeled to obtain a parametric model of the reducer, including:

[0023] When the reducer parameterized model is a meshing model between the key components, the parameterized model includes: a transmission ratio constraint model, a non-interference constraint model for gear meshing, a geometric constraint model for backlash-free meshing, a gear surface contact fatigue strength constraint model, and a tooth root bending strength constraint model; wherein,

[0024] The transmission ratio constraint model is:

[0025] The non-interference constraint model for gear meshing is:

[0026]

[0027] The geometric constraint model of the backlash-free meshing is:

[0028] The gear surface contact fatigue strength constraint model is:

[0029] The tooth root bending strength constraint model is:

[0030] Wherein, i is the transmission ratio, N1 is a preset second positive integer, and Z a is the number of teeth of the sun gear, and the Z g is the number of teeth of the planetary gear, and Z r is the number of teeth of the inner gear ring, and a ag is the meshing angle between the sun gear and the planet gear, and the a gis the tooth top pressure angle of the planetary gear, is the tooth top height coefficient, a is the standard gear pressure angle, and X a is the displacement coefficient of the sun gear, and the Z a is the number of teeth of the sun gear, and the X g is the displacement coefficient of the planetary gear, inva ag is the involute function value of the meshing angle between the sun gear and the planet gear; inva is the involute function value of the pressure angle of the standard gear; the σ H is the surface contact stress between the sun gear and the planet gear; K is the correction coefficient, and F t is the nominal tangential force on the end face pitch circle of the sun gear and the planet gear, u is the gear ratio of the sun gear and the planet gear; b is the gear width of the sun gear and the planet gear, m is the gear module of the sun gear and the planet gear, and σ F The tooth root stress of the sun gear and the planet gear, the Y Fa is the tooth shape coefficient when the load acts on the sun gear tooth top, the Y Sa It is the stress correction factor when the load acts on the sun gear tooth top.

[0031] In the above solution, the step of determining the key parameters of each key component includes:

[0032] For each parameter of the key components of the reducer, use the formula Determine the sensitivity index G(x) of the jth parameter on the optimization target j ), and then obtain multiple impact sensitivity indexes;

[0033] Arrange the multiple sensitivity indexes in descending order to form an impact sensitivity index sequence;

[0034] The parameters corresponding to the sensitivity index sequences ranked first S in the sensitivity index sequence are determined as the key parameters; wherein,

[0035] The x j is the current value of the jth parameter, the Δx j is the value change of the jth parameter; the y j For x j As a result of determining the optimization goal, the Δy j The change in the optimization result corresponding to the change in the value of the jth parameter.

[0036] In the above scheme, the key parameters include: the number of sun gear teeth, the number of planet gear teeth, the number of inner ring teeth, the gear tooth width of the sun gear and the planet gear, the gear module of the sun gear and the planet gear, the sun gear modification coefficient, the planet gear modification coefficient, the inner ring modification coefficient, the meshing angle between the sun gear and the planet gear, and the meshing angle between the planet gear and the inner ring gear.

[0037] In the above scheme, based on the geometric parameter model and the assembly parameter model, taking the overall mass of the reducer and the gear meshing degree as the optimization target, the initial values ​​of the key parameters are optimized using a non-dominated genetic algorithm to obtain the target values ​​of the key parameters, including:

[0038] Taking the geometric parameter model and the assembly parameter model as constraint conditions, taking the overall mass of the reducer and the gear meshing degree as optimization targets, a non-dominated genetic algorithm is used to perform non-dominated sorting and crowding calculation on the initial values ​​of the key parameters to obtain a Pareto solution set; the Pareto solution set includes multiple Pareto solutions; each Pareto solution represents a set of values ​​of key parameters;

[0039] Obtaining the normalized reducer mass and the normalized gear meshing degree corresponding to each Pareto solution; and determining the optimal solution index according to the normalized reducer mass and the normalized gear meshing degree;

[0040] The Pareto solution corresponding to the minimum optimal solution index is determined as the optimal Pareto solution, and the value of the key parameter corresponding to the optimal Pareto solution is determined as the target value.

[0041] In the above solution, determining the optimal solution index according to the normalized reducer mass and the normalized gear meshing degree includes:

[0042] Determine a first weight coefficient corresponding to the normalized reducer mass and determine a second weight coefficient corresponding to the normalized gear meshing degree;

[0043] Determine a first index according to the first weight coefficient and the normalized reducer mass; determine a second index according to the second weight coefficient and the normalized gear meshing degree;

[0044] The optimal solution index is determined according to the first index and the second index.

[0045] A second aspect of the present invention provides a device for determining design parameters of a hub motor reducer, the device comprising:

[0046] A creation unit is used to perform parametric modeling on each key component of the reducer to obtain a parametric model of the reducer; the parametric model of the reducer includes: a geometric parameter model corresponding to the key component itself, an assembly parameter model between each key component, and a meshing model between each key component;

[0047] A determination unit, used to determine key parameters of each key component;

[0048] The optimization unit is used to optimize the initial values ​​of the key parameters based on the geometric parameter model and the assembly parameter model, with the overall mass of the reducer and the gear meshing degree as optimization targets, using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer.

[0049] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0050] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any one of the methods described in the first aspect are implemented.

[0051] The present invention provides a method, device, medium and equipment for determining design parameters of a hub motor reducer. The method comprises performing parameterized modeling on key components of the reducer to obtain a parameterized model of the reducer; the parameterized model of the reducer comprises: a geometric parameter model corresponding to the key components themselves, an assembly parameter model between the key components and a meshing model between the key components; determining key parameters of the key components; based on the geometric parameter model and the assembly parameter model, taking the overall mass of the reducer and the gear meshing degree as optimization targets, optimizing the initial values ​​of the key parameters using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer ; In this way, since the parametric model combines the design requirements of each component of the reducer and the assembly relationship constraints between adjacent components, and the focus of the lightweight design of the reducer is to reduce the weight of the reducer while ensuring the degree of meshing; then when the geometric parameter model and the assembly parameter model are used as constraints, the overall mass of the reducer and the degree of gear meshing are used as optimization targets to optimize the design parameters of the reducer, the design requirements of the lightweight reducer can be well met; and the non-dominated genetic algorithm can ensure that the overall mass and degree of meshing are optimal within a certain range when performing multi-objective optimization. Therefore, there is no need to rely on manual experience or perform complicated repeated verifications to ensure that the design parameters of the reducer are optimal, thereby improving the power density of the hub motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0053] Figure 1 A schematic flow chart of a method for determining design parameters of a hub motor reducer according to an embodiment of the present invention is shown;

[0054] Figure 2 A schematic structural diagram of a NGW planetary gear reducer according to an embodiment of the present invention is shown;

[0055] Figure 3 A schematic diagram of a Pareto front determined according to an embodiment of the present invention is shown;

[0056] Figure 4 A schematic structural diagram of a device for determining design parameters of a hub motor reducer according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] The present invention provides a method for determining design parameters of a hub motor reducer, such as Figure 1 As shown, the method comprises the following steps:

[0059] S110, parametric modeling is performed on each key component of the reducer to obtain a parametric model of the reducer; the parametric model of the reducer includes: a geometric parameter model corresponding to the key component itself, an assembly parameter model between each key component, and a meshing model between each key component.

[0060] like Figure 2 As shown, the hub motor-specific reducer used in the present invention adopts an NGW planetary gear reducer and is integrally nested in the hub motor housing. Among them, the inner gear ring 21 is fixed, and the sun gear 22 is located on the high-speed motor shaft and meshes with the planetary gear 23 for transmission. The sun gear 22 serves as input, and the planet carrier connected to the planetary gear 23 serves as output. It can be seen that the key components of the reducer include: sun gear, planetary gear and inner gear ring.

[0061] It is understandable that when designing a reducer, not only the geometric parameters of the reducer itself should be considered, but also the assembly relationship between different components. Therefore, the present invention performs parametric modeling on the key components of the reducer according to the layout form and functional requirements of the reducer for the hub motor to obtain a parametric model of the reducer. The parametric model of the reducer includes: a geometric parameter model corresponding to the key components themselves, an assembly parameter model between each key component, and a meshing model between each key component.

[0062] In one embodiment, the parametric modeling of the key components of the reducer to obtain the parametric model of the reducer includes:

[0063] When the reducer parameterized model is a geometric parameter model corresponding to the key components themselves, the parameterized model includes: a gear non-root cutting condition model, a module and tooth width constraint model; wherein,

[0064] The gear non-undercutting condition model is:

[0065] The constraint model of module and tooth width is: 5m≤b≤17m; among them,

[0066] X ais the displacement coefficient of the sun gear, Z a is the number of teeth of the sun gear, X g is the displacement coefficient of the planetary gear, Z g is the number of teeth of the planetary gear, X r is the displacement coefficient of the inner gear ring, Z r is the number of teeth on the inner ring gear.

[0067] Specifically, the sun gear and the planetary gear are both gears. When the intersection of the gear tooth top and the meshing line exceeds the limit meshing point of the cut gear, a part of the involute tooth profile of the cut gear tooth root is cut off. This phenomenon is called root cutting. When the gear tooth is root cut, the root thickness is thinned, the bending resistance of the gear tooth is reduced, and the overlap is reduced, which is not conducive to transmission. Therefore, it is necessary to avoid the gear root cutting phenomenon, so the present invention sets a gear non-root cutting condition model.

[0068] Similarly, the gear module and tooth width are two important parameters of the gear. The larger the gear module, the higher and wider each tooth of the gear is, and the bearing capacity and torque transmission capacity are also stronger. The tooth width directly affects the gear transmission ratio and the accuracy of meshing. Therefore, the gear module and tooth width cannot be too large or too small, and a constraint model needs to be set. In the present invention, the sun gear and the planetary gear have the same module and tooth width, and the constraint models of the sun gear and the planetary gear module and tooth width can also be the same.

[0069] In one implementation, each key component of the reducer is parameterized and modeled to obtain a parameterized model of the reducer, including:

[0070] When the reducer parameterized model is an assembly parameter model between various key components, the parameterized model includes: a concentric condition model of the sun gear, planetary gears and inner gear ring, an adjacent condition model of the sun gear and planetary gears, and an assembly condition model of the sun gear, planetary gears and inner gear ring; wherein,

[0071] The concentric condition model of the sun gear, planetary gear and inner ring gear is:

[0072] The adjacent condition model of the sun gear and the planet gear is:

[0073] The assembly condition model of the sun gear, planetary gear and internal gear ring is: in,

[0074] Z a is the number of teeth of the sun gear, Z g is the number of teeth of the planetary gear, Z r is the number of teeth on the inner ring gear, a ag is the meshing angle between the sun gear and the planet gear, a gr is the meshing angle between the planetary gear and the internal gear ring, d agis the diameter of the planetary gear tip circle, a′ ag is the center distance between the sun gear and the planet gear, n p is the number of planetary gears, and N is a preset first positive integer.

[0075] Specifically, when assembling the sun gear and the planetary gear, it is necessary to ensure that the sun gear, the planetary gear and the inner ring are properly meshed and the tooth tops of adjacent gears do not collide, so it is necessary to set a concentric condition model and an adjacent condition model. In order to ensure that the sun gear and the planetary gear can be smoothly installed in the planet carrier, the present embodiment also needs to create an assembly condition model of the sun gear and the planetary gear.

[0076] In one implementation, each key component of the reducer is parameterized and modeled to obtain a parameterized model of the reducer, including:

[0077] When the reducer parameterized model is a meshing model between key components, the parameterized model includes: transmission ratio constraint model, non-interference constraint model when gears are meshing, geometric constraint model of meshing without backlash, gear surface contact fatigue strength constraint model and tooth root bending strength constraint model; among them,

[0078] The transmission ratio constraint model is:

[0079] The non-interference constraint model when gears mesh is:

[0080] The geometric constraint model of backlash-free meshing is:

[0081] The gear surface contact fatigue strength constraint model is:

[0082] The tooth root bending strength constraint model is:

[0083] Where i is the transmission ratio, N1 is the preset second positive integer, and Z a is the number of teeth of the sun gear, Z g is the number of teeth of the planetary gear, Z r is the number of teeth on the inner ring gear, a ag is the meshing angle between the sun gear and the planet gear, a g is the planetary gear tooth tip pressure angle, is the tooth top height coefficient, a is the standard gear pressure angle, X a is the displacement coefficient of the sun gear, Z a is the number of teeth of the sun gear, X g is the displacement coefficient of the planetary gear, inva ag is the involute function value of the meshing angle between the sun gear and the planetary gear; inva is the involute function value of the pressure angle of the standard gear; σ His the surface contact stress between the sun gear and the planetary gear; K is the correction factor, F t is the nominal tangential force on the end face pitch circle of the sun gear and the planet gear, u is the gear ratio of the sun gear and the planet gear; b is the gear width of the sun gear and the planet gear, m is the gear module of the sun gear and the planet gear, σ F is the tooth root stress of the sun gear and planet gear, Y Fa Y is the tooth shape coefficient when the load acts on the sun gear tooth top, Sa It is the stress correction factor when the load acts on the sun gear tooth top.

[0084] Specifically, in order to achieve the preset transmission ratio, a transmission ratio constraint model needs to be set; generally speaking, the transmission ratio i should be greater than or equal to N1, and the value of N1 can be determined based on actual conditions, such as 5.

[0085] In actual work, it is required that there should be no interference between the two gear geometric bodies of a pair of gear pairs, otherwise it will cause problems such as unstable meshing of the gear pairs and large impact loads. Therefore, the present invention creates a non-interference constraint model when the gears (sun gear and planetary gear) are meshed. In order to effectively reduce the noise and vibration in the gear transmission and improve the transmission efficiency and accuracy. The present invention also sets a geometric constraint model for backlash-free meshing for the sun gear, planetary gear and inner ring gear. In order to increase the service life of the reducer, the present invention also sets a gear surface contact fatigue strength constraint model and a tooth root bending strength constraint model.

[0086] In this way, by parametrically modeling the key components of the reducer, the parameter design boundary of the reducer can be standardized, providing boundary constraints for each design parameter for subsequent optimization using a non-dominated genetic algorithm.

[0087] S111, determining key parameters of each key component.

[0088] Each key component includes many parameters. For example, for the sun gear, the parameters include: number of teeth, module, tooth thickness and displacement coefficient, etc. For the planetary gear, the parameters may also include the number of teeth, module, tooth thickness and displacement coefficient, etc. For the inner ring gear, the parameters may include: number of teeth, tooth thickness, displacement coefficient, etc. However, the change of some parameters has little effect on the optimization target of the reducer, while the change of some parameters has a great impact on the optimization target of the reducer.

[0089] Therefore, the present invention needs to determine the key parameters from the numerous parameters of the key components. In one embodiment, determining the key parameters of each key component includes:

[0090] For each parameter of the key components of the reducer, use the formula Determine the sensitivity index G(x) of the jth parameter on the optimization target j), and then obtain multiple impact sensitivity indexes;

[0091] Arrange multiple sensitivity indexes in descending order to form an impact sensitivity index sequence;

[0092] The parameters corresponding to the sensitivity index sequences ranked first S in the sensitivity index sequence are determined as key parameters; wherein,

[0093] x j is the current value of the jth parameter, Δx j is the change in the value of the jth parameter; y j For x j The result of the optimization goal determined under j The change in the optimization result corresponding to the change in the value of the jth parameter.

[0094] For example, assuming the parameter is the number of sun gear teeth, when the number of sun gear teeth is 20 and the change in the number of teeth is 1 (the difference between the last value and the current value is 1), then x j =20,Δx j is 1; assuming that the optimization target is the reducer quality, for x j =20, to calculate the corresponding y j ; Similarly, for x j If y is 19, the corresponding y can be calculated. j-1 , so that Δy can be determined j =y j -y j-1 .

[0095] It should be noted that since the optimization objectives of the present invention include the reducer quality and gear meshing degree, generally speaking, the effects of parameter changes of key components on the two optimization objectives show the same trend. Therefore, when determining the effect of parameters on the optimization objectives, it is possible to determine only the effect of parameters on the reducer quality, or only the effect of parameters on the gear meshing degree.

[0096] In this way, for each parameter, an impact sensitivity index can be determined accordingly, and finally the parameters corresponding to the first S largest impact sensitivity indexes are determined as key parameters. The value of S can be determined according to actual conditions, such as 10, which is not limited here.

[0097] According to the above method, the key parameters determined by the present invention include: the number of sun gear teeth, the number of planet gear teeth, the number of inner ring teeth, the gear tooth width of the sun gear and the planet gear, the gear module of the sun gear and the planet gear, the sun gear modification coefficient, the planet gear modification coefficient, the inner ring modification coefficient, the meshing angle between the sun gear and the planet gear, and the meshing angle between the planet gear and the inner ring gear.

[0098] S112, based on the geometric parameter model and the assembly parameter model, taking the overall mass of the reducer and the gear meshing degree as optimization targets, optimizing the initial values ​​of the key parameters using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer.

[0099] After the above key parameters are determined, each parameter has a value range. An initial value can be determined for each parameter in the value range. Then, based on the geometric parameter model and the assembly parameter model, the initial values ​​of the key parameters are optimized using the non-dominated genetic algorithm with the overall mass of the reducer and the gear meshing degree as the optimization target to obtain the target values ​​of the key parameters; the target values ​​are the design parameters of the reducer, specifically including:

[0100] Taking the geometric parameter model and the assembly parameter model as constraints, taking the overall mass of the reducer and the gear meshing degree as optimization targets, a non-dominated genetic algorithm is used to perform non-dominated sorting and crowding calculation on the initial values ​​of key parameters to obtain a Pareto solution set; the Pareto solution set includes multiple Pareto solutions; each Pareto solution represents a set of key parameter values;

[0101] Obtaining the normalized reducer mass and the normalized gear meshing degree corresponding to each Pareto solution; and determining the optimal solution index according to the normalized reducer mass and the normalized gear meshing degree;

[0102] The Pareto solution corresponding to the minimum optimal solution index is determined as the optimal Pareto solution, and the value of the key parameter corresponding to the optimal Pareto solution is determined as the target value.

[0103] Specifically, firstly, random values ​​are taken within the range of values ​​of each key parameter to obtain the initial values ​​of each key parameter and form an initialized population. The initialized population is regarded as the previous generation population, and then the previous generation population is subjected to non-dominated sorting and crowding calculation, and individuals with high non-dominated sorting level and high crowding degree are selected for crossover mutation to generate the current generation population; then the previous generation population and the current generation population are merged, and non-dominated sorting and crowding calculation are performed again; the next generation population is obtained by selecting the elite strategy, and the elite strategy can obtain individuals with excellent performance and meet the constraints as much as possible. Iterate continuously in the above manner (the previous generation population of the current iteration is the next generation population generated by the previous iteration), and judge whether the number of iterations exceeds the set value. If it does not exceed, repeat the above process to perform a new round of genetic operations until the iteration ends, and output the Pareto frontier. The Pareto front contains a Pareto solution set, which includes multiple Pareto solutions (target population); each Pareto solution represents a set of key parameter values.

[0104] Based on each Pareto solution, the reducer mass and gear meshing degree can be determined by the corresponding formula. Specifically, the reducer mass M can be determined according to formula (1):

[0105] M=V a ρ1+n p V g ρ2+V r ρ3(1)

[0106] In formula (1), V a is the volume of the sun gear, ρ1 is the material density of the sun gear, n p is the number of planetary gears, V g is the material density of the planetary gear, V r is the material density of the inner gear ring, and ρ3 is the material density of the inner gear ring. The volume of the sun gear can be determined according to formula (2), the volume of the planetary gear can be determined according to formula (3), and the volume of the inner gear ring can be determined according to formula (4):

[0107]

[0108] (Among them, Z a is the number of teeth of the sun gear, Z g is the number of teeth of the planetary gear, Z r is the number of teeth of the inner gear ring, m is the gear module, b is the gear tooth width, h r is the thickness of the inner gear ring.

[0109] Determine the gear meshing degree ε according to formula (5):

[0110]

[0111] In formula (2), Z a is the number of teeth of the sun gear, Z g is the number of teeth of the planetary gear, a ag is the meshing angle between the sun gear and the planet gear, a g is the planetary gear tooth top pressure angle, and a is the standard gear pressure angle.

[0112] In this way, for each Pareto solution, a corresponding reducer mass and gear meshing degree can be determined, and then multiple reducer masses and multiple gear meshing degrees can be obtained; then the multiple reducer masses and multiple gear meshing pairs are normalized respectively to obtain the corresponding normalized reducer mass and the corresponding normalized gear meshing degree.

[0113] Then in one implementation, the optimal solution index is determined according to the normalized reducer mass and the normalized gear meshing degree, including:

[0114] Determine a first weight coefficient corresponding to the normalized reducer mass and determine a second weight coefficient corresponding to the normalized gear meshing degree;

[0115] Determine a first index according to the first weight coefficient and the normalized reducer mass; determine a second index according to the second weight coefficient and the normalized gear meshing degree;

[0116] The optimal solution index is determined according to the first index and the second index, where the optimal solution index is the sum of the first index and the second index.

[0117] That is, according to the formula f = ω1M n +ω2ε n Determine the optimal solution index f; where ω1 is the weight coefficient corresponding to the reducer mass, ω2 is the weight coefficient corresponding to the gear meshing degree, M n is the normalized reducer mass, ε n The normalized gear meshing degrees, ω1 and ω2, can be determined empirically.

[0118] It can be seen that for each Pareto solution, an optimal solution index can be determined, and then the Pareto solution corresponding to the minimum optimal solution index is determined as the optimal Pareto solution, and the value of the key parameter corresponding to the optimal Pareto solution is determined as the target value.

[0119] For example, suppose you need to design an NGW type reducer, the radial dimension does not exceed 400mm, the axial dimension does not exceed 40mm, the reduction ratio i≥5, and the value range of each key parameter is shown in Table 1:

[0120] Table 1

[0121] Key parameter name Value range Sun gear teeth number 20～40 Number of planetary gear teeth 20～40 Number of teeth on inner ring gear 80～160 Gear tooth width 5~40mm Gear module 0.5~4mm Sun gear displacement coefficient -0.5～0.95 Planetary gear displacement coefficient -0.5～0.95 Internal gear ring displacement coefficient -0.5～0.95 Meshing angle between sun gear and planet gear 10°～40° Meshing angle between planetary gear and internal gear ring 10°～40°

[0122] Taking the geometric parameter model and the assembly parameter model as constraints, the overall mass of the reducer and the gear meshing degree as the optimization objectives, the non-dominated genetic algorithm is used to perform non-dominated sorting and crowding calculation on the initial values ​​of the key parameters. The Pareto frontier is as follows: Figure 3 shown.

[0123] exist Figure 3 In the figure, the Pareto front includes four Pareto solutions A to D. For each Pareto solution, a corresponding reducer mass and gear meshing degree can be determined, and then multiple reducer masses and multiple gear meshing degrees are obtained; then the multiple reducer masses and multiple gear meshing pairs are normalized respectively to obtain the corresponding normalized reducer masses and the corresponding normalized gear meshing degrees, as shown in Table 2.

[0124] Table 2

[0125] Mn ε f A 0.891 0.966 0.334 B 0.925 0.993 0.350 C 0.961 0.999 0.373 D 1 1 0.4

[0126] Since the meshing degrees corresponding to the four solutions are relatively small and all are greater than a specified threshold (eg, 1.2), the weight coefficients ω1=0.7 and ω2=-0.3 are selected based on experience.

[0127] As can be seen from Table 2, f is 0.334, which is the smallest. Therefore, Pareto solution A is determined as the optimal Pareto solution, and the values ​​of the key parameters corresponding to the optimal Pareto solution are determined as the target values. The values ​​of the key parameters corresponding to the optimal Pareto solution are shown in Table 3.

[0128] Table 3

[0129] Parameter name Value Sun gear teeth number 25 Number of planetary gear teeth 20 Number of teeth on inner ring gear 100 Gear tooth width 38.9mm Gear module 2.6mm Sun gear displacement coefficient -0.34 Planetary gear displacement coefficient -0.14 Internal gear ring displacement coefficient -0.17 Meshing angle between sun gear and planet gear 15.6° Meshing angle between planetary gear and internal gear ring 17.1°

[0130] In this way, the optimal design parameters of the reducer are determined.

[0131] In addition, the method for determining the design parameters of the reducer provided by the present invention combines the design requirements of the various components of the reducer and the assembly relationship constraints between adjacent components in a parameterized model, and the focus of the design of the lightweight reducer is to reduce the weight of the reducer while ensuring the degree of meshing; when the geometric parameter model and the assembly parameter model are used as constraints, the design parameters of the reducer are optimized with the overall mass of the reducer and the degree of meshing of the gears as optimization targets, which can well meet the design requirements of the lightweight reducer; and when the non-dominated genetic algorithm performs multi-objective optimization, it can ensure that the overall mass and the degree of meshing are optimal within a certain range, so the optimal design parameters can be determined without resorting to manual experience or complicated repeated verifications, thereby improving the power density of the hub motor.

[0132] Based on the same inventive concept as in the above-mentioned embodiment, this embodiment also provides a device for determining design parameters of a hub motor reducer, such as Figure 4 As shown, the device comprises:

[0133] The creation unit 41 is used to perform parameterized modeling on each key component of the reducer to obtain a parameterized model of the reducer; the parameterized model of the reducer includes: a geometric parameter model corresponding to the key component itself, an assembly parameter model between each key component, and a meshing model between each key component;

[0134] A determination unit 42, used to determine key parameters of each key component;

[0135] The optimization unit 43 is used to optimize the initial values ​​of the key parameters based on the geometric parameter model and the assembly parameter model, with the overall mass of the reducer and the gear meshing degree as optimization targets, using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer.

[0136] Since the device introduced in the embodiment of the present invention is a device used to implement the method for determining the design parameters of the hub motor reducer of the embodiment of the present invention, based on the method introduced in the embodiment of the present invention, the person skilled in the art can understand the specific structure and deformation of the device, so it is not repeated here. All devices used in the method of the embodiment of the present invention belong to the scope of protection of the present invention.

[0137] Based on the same inventive concept, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any step of the method described above when executing the computer program.

[0138] Based on the same inventive concept, this embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0139] Through one or more embodiments of the present invention, the present invention has the following beneficial effects or advantages:

[0140] The present invention provides a method, device, medium and equipment for determining design parameters of a hub motor reducer. The method comprises performing parameterized modeling on key components of the reducer to obtain a parameterized model of the reducer; the parameterized model of the reducer comprises: a geometric parameter model corresponding to the key components themselves, an assembly parameter model between the key components and a meshing model between the key components; determining key parameters of the key components; based on the geometric parameter model and the assembly parameter model, taking the overall mass of the reducer and the gear meshing degree as optimization targets, optimizing the initial values ​​of the key parameters using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer ; In this way, since the parametric model combines the design requirements of each component of the reducer and the assembly relationship constraints between adjacent components, and the focus of the lightweight design of the reducer is to reduce the weight of the reducer while ensuring the degree of meshing; then when the geometric parameter model and the assembly parameter model are used as constraints, the overall mass of the reducer and the degree of gear meshing are used as optimization targets to optimize the design parameters of the reducer, the design requirements of the lightweight reducer can be well met; and the non-dominated genetic algorithm can ensure that the overall mass and degree of meshing are optimal within a certain range when performing multi-objective optimization. Therefore, there is no need to rely on manual experience or perform complicated repeated verifications to ensure that the design parameters of the reducer are optimal, thereby improving the power density of the hub motor.

[0141] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0142] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining design parameters of a hub motor reducer. It is characterized in that The method comprises: Performing parametric modeling on each key component of the reducer to obtain a parametric model of the reducer; the parametric model of the reducer includes: a geometric parameter model corresponding to the key component itself, an assembly parameter model between each key component, and a meshing model between each key component; Determine the key parameters of each key component; Based on the geometric parameter model and the assembly parameter model, taking the overall mass of the reducer and the gear meshing degree as optimization targets, the initial values ​​of the key parameters are optimized using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer.

2. The method according to claim 1, It is characterized in that The key components include: sun gear, planetary gear and inner ring gear; the key components of the reducer are parametrically modeled to obtain a parametric model of the reducer, including: When the reducer parameterized model is a geometric parameter model corresponding to the key component itself, the parameterized model includes: a gear non-undercutting condition model, a module and tooth width constraint model; wherein, The gear non-undercutting condition model is: The module and tooth width constraint model is: 5m≤b≤17m; where, The X a is the displacement coefficient of the sun gear, and the Z a is the number of teeth of the sun gear, and the X g is the displacement coefficient of the planetary gear, and the Z g is the number of teeth of the planetary gear, and the X r is the displacement coefficient of the inner gear ring, and the Z r is the number of teeth of the inner gear ring.

3. The method according to claim 1, It is characterized in that The key components include: sun gear, planetary gear and inner ring gear; the key components of the reducer are parametrically modeled to obtain a parametric model of the reducer, including: When the reducer parameterized model is an assembly parameter model between various key components, the parameterized model includes: a concentric condition model of the sun gear, the planetary gear and the inner gear ring, an adjacent condition model of the sun gear and the planetary gear, and an assembly condition model of the sun gear, the planetary gear and the inner gear ring; wherein, The concentric condition model of the sun gear, the planetary gear and the inner gear ring is: The adjacent condition model of the sun gear and the planet gear is: The assembly condition model of the sun gear and the planet gear is: in, The Z a is the number of teeth of the sun gear, and the Z g is the number of teeth of the planetary gear, and Z r is the number of teeth of the inner gear ring, and a ag is the meshing angle between the sun gear and the planet gear, and the a gr is the meshing angle between the planetary gear and the inner gear ring, and d ag is the diameter of the addendum circle of the planetary gear, and a′ ag is the center distance between the sun gear and the planet gear, and n p is the number of the planetary gears, and N is a preset first positive integer.

4. The method according to claim 1, It is characterized in that The key components include: sun gear, planetary gear and inner ring gear; the key components of the reducer are parametrically modeled to obtain a parametric model of the reducer, including: When the reducer parameterized model is a meshing model between the key components, the parameterized model includes: a transmission ratio constraint model, a non-interference constraint model for gear meshing, a geometric constraint model for backlash-free meshing, a gear surface contact fatigue strength constraint model, and a tooth root bending strength constraint model; wherein, The transmission ratio constraint model is: The non-interference constraint model for gear meshing is: The geometric constraint model of the backlash-free meshing is: The gear surface contact fatigue strength constraint model is: The tooth root bending strength constraint model is: Wherein, i is the transmission ratio, N1 is a preset second positive integer, and Z a is the number of teeth of the sun gear, and the Z g is the number of teeth of the planetary gear, and Z r is the number of teeth of the inner gear ring, and a ag is the meshing angle between the sun gear and the planet gear, and the a g is the tooth top pressure angle of the planetary gear, is the tooth top height coefficient, a is the standard gear pressure angle, and X a is the displacement coefficient of the sun gear, and the Z a is the number of teeth of the sun gear, and the X g is the displacement coefficient of the planetary gear, inva ag is the involute function value of the meshing angle between the sun gear and the planet gear; inv a is the involute function value of the standard gear pressure angle; H is the surface contact stress between the sun gear and the planet gear; K is the correction coefficient, and F t is the nominal tangential force on the end face pitch circle of the sun gear and the planet gear, u is the gear ratio of the sun gear and the planet gear; b is the gear width of the sun gear and the planet gear, m is the gear module of the sun gear and the planet gear, and σ F The tooth root stress of the sun gear and the planet gear, the Y Fa is the tooth shape coefficient when the load acts on the sun gear tooth top, the Y Sa It is the stress correction factor when the load acts on the sun gear tooth top.

5. The method according to claim 1, It is characterized in that Determining the key parameters of each key component includes: For each parameter of the key components of the reducer, use the formula Determine the sensitivity index G(x) of the jth parameter on the optimization target j ), and then obtain multiple impact sensitivity indexes; Arrange the multiple sensitivity indexes in descending order to form an impact sensitivity index sequence; The parameters corresponding to the sensitivity index sequences ranked first S in the sensitivity index sequence are determined as the key parameters; wherein, The x j is the current value of the jth parameter, the Δx j is the value change of the jth parameter; the y j For x j As a result of determining the optimization goal, the Δy j The change in the optimization result corresponding to the change in the value of the jth parameter.

6. The method according to claim 1, It is characterized in that Based on the geometric parameter model and the assembly parameter model, taking the overall mass of the reducer and the gear meshing degree as optimization targets, optimizing the initial values ​​of the key parameters using a non-dominated genetic algorithm to obtain target values ​​of the key parameters includes: Taking the geometric parameter model and the assembly parameter model as constraint conditions, taking the overall mass of the reducer and the gear meshing degree as optimization targets, a non-dominated genetic algorithm is used to perform non-dominated sorting and crowding calculation on the initial values ​​of the key parameters to obtain a Pareto solution set; the Pareto solution set includes multiple Pareto solutions; each Pareto solution represents a set of values ​​of key parameters; Obtaining the normalized reducer mass and the normalized gear meshing degree corresponding to each Pareto solution; and determining the optimal solution index according to the normalized reducer mass and the normalized gear meshing degree; The Pareto solution corresponding to the minimum optimal solution index is determined as the optimal Pareto solution, and the value of the key parameter corresponding to the optimal Pareto solution is determined as the target value.

7. The method according to claim 6, It is characterized in that The determining of the optimal solution index according to the normalized reducer mass and the normalized gear meshing degree includes: Determine a first weight coefficient corresponding to the normalized reducer mass and determine a second weight coefficient corresponding to the normalized gear meshing degree; Determine a first index according to the first weight coefficient and the normalized reducer mass; determine a second index according to the second weight coefficient and the normalized gear meshing degree; The optimal solution index is determined according to the first index and the second index.

8. A device for determining design parameters of a hub motor reducer, It is characterized in that The device comprises: A creation unit is used to perform parametric modeling on each key component of the reducer to obtain a parametric model of the reducer; the parametric model of the reducer includes: a geometric parameter model corresponding to the key component itself, an assembly parameter model between each key component, and a meshing model between each key component; A determination unit, used to determine key parameters of each key component; The optimization unit is used to optimize the initial values ​​of the key parameters based on the geometric parameter model and the assembly parameter model, with the overall mass of the reducer and the gear meshing degree as optimization targets, using a non-dominated genetic algorithm to obtain target values ​​of the key parameters; the target values ​​are the design parameters of the reducer.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

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