Noise prediction method and system for gear transmission system

Abstract

The invention discloses a noise prediction method, system and device of a gear transmission system and a medium, and relates to the technical field of vehicle NVH. The method aims at solving the problems that in the prior art, a gear noise prediction model is tedious in calculation, and it is difficult to quantitatively evaluate the influence of microscopic modification and dislocation amount. The method comprises the following steps: determining helical gear pair meshing stiffness by utilizing straight gear stiffness model integration based on a slicing method; determining the vibration displacement by using a vibration displacement model considering the tooth direction crowning quantity, the tooth shape crowning quantity, the tooth top edge trimming quantity and the dislocation quantity; and finally, calculating a total noise value based on a noise prediction model comprising three parts of macroscopic parameters, power and vibration displacement. According to the method, theoretical derivation is combined with test correction, rapid and accurate prediction of the noise of the helical gear of the electric drive assembly is achieved, and gear modification design can be effectively guided.

Description

Technical Field

[0001] This invention relates to the fields of vehicle engineering and NVH (noise, vibration and harshness), and particularly to a noise prediction method and system for an electric drive assembly gear transmission system. Background Technology

[0002] Electric vehicles represent the mainstream development direction of the automotive industry. Due to the trend towards lightweighting and increasing power density in electric drive systems, the NVH (noise, vibration, and harshness) issues of gears have become particularly important, serving as a key factor restricting the improvement of power density and speed in electric vehicle drive systems. The vibration of gear systems is mainly caused by transmission errors resulting from time-varying stiffness of gear meshing, manufacturing errors, installation errors, and housing deformation. These factors cause impacts during gear engagement and disengagement, as well as periodic fluctuations in speed loads, thus generating excitation vibration.

[0003] If a pair of involute gears with ideal shapes and infinite stiffness mesh, the driven gear's rotation angle will strictly match the driving gear's rotation angle according to their speed ratio, resulting in minimal noise. However, in reality, due to errors in gear manufacturing, assembly, and deformation under load on the reducer system, the driven gear's rotation angle will sometimes lead or lag behind its theoretical rotation angle at different times. This rotation angle error is known as gear transmission error. As an internal excitation source of the gear system, the larger the amplitude of gear transmission error, the greater the vibration and noise.

[0004] In existing technologies, gear noise prediction mainly involves the following types of methods and their shortcomings:

[0005] 1. Finite element method and boundary element method: Although they can solve the vibration and noise of a single-stage spur gearbox and obtain the gearbox sound field, they are mainly limited to spur gears, and the modeling process is very complicated and the amount of calculation is large, making it difficult to quickly apply to the more complex helical gear transmission system.

[0006] 2. Simple empirical formulas: One type of model relates noise intensity only to tooth width and meshing frequency, assuming that vibration amplitude is the dynamic transmission error. Another type establishes a linear regression equation through experiments. These methods struggle to find a simple and accurate predictive model because gear noise is affected by various factors such as tooth profile deviation and transmitted power.

[0007] 3. Genetic Algorithm Optimization Formula: Existing empirical formulas based on genetic algorithms often rely solely on gear machining errors, which are insufficient to cover the noise level of the entire transmission system. The impact of other core gear parameters, such as contact ratio and addendum coefficient, on noise is not fully mentioned, and the influence of misalignment in the gear transmission system is not considered, making the entire noise model unable to reflect the real situation.

[0008] In summary, current research is largely based on traditional gear meshing theory. When analyzing the impact of transmission errors, it treats the error as a complex composite error, without specifically analyzing individual component errors (such as tooth profile, tooth direction modification, and misalignment). Furthermore, the quantitative relationship between transmission errors and gear meshing transmission is not sufficiently explored. Therefore, there is an urgent need for a gear noise prediction method that can combine macroscopic and microscopic parameters, consider the influence of misalignment, and is computationally efficient. Summary of the Invention

[0009] To address the aforementioned shortcomings in the prior art, this invention provides a noise prediction method, system, device, and medium for gear transmission systems, aiming to solve the technical problems of cumbersome calculations, inability to quantitatively assess the impact of micro-modification parameters and misalignment on noise, and insufficient prediction accuracy in existing gear noise prediction models.

[0010] To address the aforementioned technical problems, in a first aspect, the present invention provides a noise prediction method for a gear transmission system, comprising:

[0011] Using a spur gear meshing stiffness model with unit tooth width, the meshing stiffness of the helical gear pair is determined by integration along the contact line based on the discretization slicing principle.

[0012] Based on the deformation, micro-modification parameters and misalignment of the gear pair at the pitch circle under load, the vibration displacement is determined using a vibration displacement model. The vibration displacement represents the difference between the actual meshing position and the ideal meshing position of the corrected gear pair.

[0013] The vibration displacement, the geometric parameters of the gear pair, and the operating parameters are substituted into the gear noise prediction model to obtain the overall noise value of the gear transmission system. The gear noise prediction model includes a macroscopic parameter contribution, a power contribution, and a vibration displacement contribution.

[0014] The above-described solution of this invention starts with gear meshing stiffness, uses a slicing method to fit a complete helical gear stiffness curve, and introduces a vibration displacement model. It calculates the difference between the actual meshing position and the ideal position using specific parameters such as gear helix modification, meshing misalignment, tooth tip trimming, and bulging. Finally, it predicts noise using a comprehensive noise model that includes macroscopic, microscopic, and power factors. This method is convenient and fast, and fully considers the influence of various modification parameters and misalignment, greatly improving the efficiency and accuracy of gear noise assessment in the early stages of product design.

[0015] As a preferred embodiment of the present invention, the meshing stiffness model of the spur gear per unit tooth width is expressed as follows:

[0016]

[0017] In the formula, Cth Let K be the stiffness value per unit tooth width, c be the reference stiffness value per unit tooth width, and K be the stiffness value per unit tooth width. f X is the end magnification factor. pos These are dimensionless meshing position coordinates, ranging from 0 to 1.

[0018] In a preferred embodiment of the present invention, the meshing stiffness of the helical gear pair is obtained by dividing the gear pair into N equal segments along the tooth width direction, treating each segment as a spur gear, and integrating the contact line between the spur gear meshing stiffness model and the helical gear; the calculation formula for the meshing stiffness of the helical gear pair is:

[0019]

[0020] In the formula, K total L represents the meshing stiffness of the helical gear pair. t Let W be the contact line length of the helical gear pair, W be the gear width, and β be the helix angle. Using the slicing method and the principle of deformation compatibility, the stiffness characteristics of spur gears can be accurately transferred to helical gears.

[0021] In a preferred embodiment of the present invention, the parameters in the vibration displacement model satisfy the following relationship:

[0022]

[0023] In the formula, CP is the comprehensive deformation at the pitch circle; X is the vibration displacement, i.e., the predetermined deformation; C β e represents the tooth-shaped bulge; e represents the misalignment; T represents the tooth-shaped bulge. p C is the amount of trimming at the tooth tip; α The model defines the amount of tooth-shaped bulge. It clarifies the contribution of each microscopic shaping parameter and error to the total deformation.

[0024] As a preferred embodiment of the present invention, the specific formula of the gear noise prediction model is as follows:

[0025]

[0026] In the formula, L is the overall noise level; K is the speed coefficient; β is the helix angle; μ is the speed ratio; ε α X represents the end face overlap; P represents the input power; X represents the end face overlap. max W represents the maximum value of the vibration displacement amplitude distributed along the meshing line. corr This is a correction factor. Thus, the relationship between the macroscopic parameters, microscopic parameters (represented by X), power, and noise of the gear is quantitatively given.

[0027] To address the aforementioned technical problems, in a second aspect, the present invention provides a noise prediction system for a gear transmission system, comprising:

[0028] The stiffness calculation module is used to determine the meshing stiffness of helical gear pairs by integrating along the contact line based on the discretization slicing principle using a spur gear meshing stiffness model with a unit tooth width.

[0029] The displacement calculation module is used to determine the vibration displacement based on the deformation, micro-modification parameters and misalignment of the gear pair at the pitch circle under load, using a vibration displacement model. The vibration displacement represents the difference between the actual meshing position and the ideal meshing position of the gear pair after correction.

[0030] The noise prediction module is used to substitute the vibration displacement, the geometric parameters of the gear pair, and the operating parameters into the gear noise prediction model to obtain the overall noise value of the gear transmission system; the gear noise prediction model includes a macroscopic parameter contribution part, a power contribution part, and a vibration displacement contribution part.

[0031] To address the aforementioned technical problems, in a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect.

[0032] To address the aforementioned technical problems, in a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. Comprehensive and accurate prediction: The gear noise model described in this invention combines key parameters such as macroscopic gear parameters (helix angle, overlap ratio, etc.), microscopic parameters (modification amount), and gear misalignment amount to comprehensively evaluate gear noise levels under different working conditions. Theoretical derivation combined with experimental data correction ensures that the error in engineering applications is controlled within 5%.

[0035] 2. Strong quantitative analysis capability: Compared with previous models that only used error as a comprehensive influence coefficient, this invention can quantitatively analyze the quantitative relationship between specific sub-errors such as tooth profile bulging, tooth profile bulging, tooth tip trimming, and misalignment and transmission error and noise, and conduct more in-depth research.

[0036] 3. High development efficiency: This method does not require complex finite element modeling, and the calculation process is fast. It is especially suitable for the early stage of product development (such as the design of high-speed gearboxes for electric vehicles). It can conveniently and effectively predict the noise level of helical gears and provide direct guidance for the design of gear tooth profile deformation. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments disclosed in this invention, the accompanying drawings of the embodiments will be briefly described below. These drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0038] Figure 1 This is a schematic diagram of vibration displacement calculation in an embodiment of the present invention.

[0039] Figure 2 This is a flowchart illustrating the calculation process of the noise prediction method in this embodiment of the invention. Detailed Implementation

[0040] The technical solutions (including preferred technical solutions) of the present invention will be further described in detail below with reference to the accompanying drawings and by way of listing some optional embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0041] Example 1

[0042] This embodiment provides a noise prediction method for helical gear pairs in electric drive assemblies, mainly applied to the noise prediction and optimization design of high-speed helical gear reducers in electric drive assemblies of electric vehicles.

[0043] like Figure 2 As shown, this method mainly includes three core stages: stiffness calculation, vibration displacement calculation, and noise calculation. In a specific application scenario, the meshing transmission pair to be analyzed is a pair of helical gears in the gearbox of an electric vehicle.

[0044] Step S1: Data Acquisition

[0045] First, obtain the relevant parameters of the gear pair to be analyzed. These parameters are divided into four categories:

[0046] Geometric parameters include, but are not limited to, module, number of teeth, pressure angle, helix angle β, tooth width W, center distance, and end face overlap ε. α wait.

[0047] Micro-modification parameters: obtained through gear design software or measuring equipment, including the tooth tip trimming amount T. p Toothed drum shape quantity C α Tooth-direction bulging amount C β These parameters define the minute differences between the working surface of the gear teeth and the theoretical involute tooth surface.

[0048] Installation offset error e (misalignment): refers to the axial misalignment of the gears during meshing caused by the non-parallel or non-coplanar nature of the gear axes due to bearing hole machining errors, housing deformation, etc.

[0049] Operating parameters include input speed, input power P (or torque), and speed ratio μ determined by speed and number of teeth.

[0050] Step S2: Establishment and calculation of meshing stiffness model

[0051] This stage corresponds to Figure 2 The calculation of spur gear stiffness, the slicing method, and the calculation of helical gear stiffness are discussed. In this embodiment, the meshing transmission pair is a helical gear pair, and its meshing stiffness is calculated using the discretization slicing principle. That is to say, Figure 2 In this context, the "spur gear slicing" step represents the process of using the discretization slicing principle to convert a helical gear into multiple thin slices of spur gears.

[0052] (1) Using the spur gear meshing stiffness model with unit tooth width, determine the slice meshing stiffness:

[0053] This embodiment is based on spur gears, and the helical gear is imagined as being composed of N extremely thin spur gear slices stacked together along the tooth width direction. According to finite element analysis, the stiffness variation curve of a single pair of teeth in a complete meshing cycle (from tooth tip engagement to tooth root engagement) can be obtained.

[0054] To facilitate analytical calculations, this embodiment fits the curve to a parabolic stiffness function C. th Its formula is:

[0055]

[0056] In the formula, C th This represents the stiffness value per unit tooth width.

[0057] c is the stiffness reference value per unit tooth width, which is a constant that can be calculated or obtained by looking up material properties and basic tooth profile parameters;

[0058] K f This is the end amplification factor (bending strength inter-tooth load distribution factor), used to characterize the phenomenon of low stiffness of gear teeth near the engagement and disengagement points, and is usually taken as a value greater than 1;

[0059] X pos The dimensionless coordinates from the start position to the end position of the meshing point, with values ​​ranging from 0 to 1.

[0060] (2) Integrate along the contact line to determine the meshing stiffness of the helical gear pair:

[0061] Because the contact line of a helical gear is an inclined straight line, the meshing state at different positions on the contact line is different at any given time. Therefore, the stiffness function C of the single slice obtained above... th Along the instantaneous contact line L t By integrating, the total meshing stiffness K of the entire helical gear pair at that moment can be obtained. total :

[0062]

[0063] In the formula, L t Let W be the contact line length of the helical gear pair, W be the gear width, and β be the helix angle. This integral operation considers the stiffness contribution of all meshing tooth slices, thus accurately establishing the meshing stiffness model of the helical gear.

[0064] Step S3: Calculation of vibration displacement

[0065] This stage corresponds to Figure 2 The calculation of vibration displacement in this stage involves establishing a force balance equation that connects various deformations, modifications, errors, and loads in order to solve for the vibration displacement X, which is the main source of noise excitation.

[0066] (1) Establish the vibration displacement model:

[0067] The overall approach to calculating vibration displacement in this embodiment is as follows: the meshing displacement is calculated by measuring the difference between the corrected actual meshing position of the gear pair and the ideal meshing position of the gear pair.

[0068] like Figure 1 As shown, a coordinate system is established at the gear meshing point P, with the y-axis along the pressure angle direction and the x-axis along the tooth width direction. Under the action of meshing force, the gear teeth will undergo elastic deformation. Simultaneously, micro-deformation during design and errors in actual assembly will also cause the actual contact point to deviate from the theoretical position. This embodiment superimposes the displacements in the pressure angle direction caused by all these factors to establish the following vibration displacement model:

[0069]

[0070] In the formula, CP is the comprehensive deformation at the pitch circle; X is the vibration displacement, i.e., the predetermined deformation; C β e represents the tooth-shaped bulge; e represents the misalignment; T represents the tooth-shaped bulge. p C is the amount of trimming at the tooth tip; α For tooth-shaped drum-shaped quantities.

[0071] The model here is a linear superposition model, which is reasonable under the assumption of small deformation. For more complex nonlinear contact problems, more complex nonlinear functional relationships can also be established, which also fall within the scope of the protection of this invention.

[0072] (2) Determine the vibration displacement X:

[0073] The helical gear is approximated as N spur gear slices with the same end face parameters. By calculating the combined deformation CP of each spur gear slice at the pitch circle, the deformation of the helical gear at the contact position P is obtained.

[0074] Based on the principle of force balance, the combined deformation CP and the total meshing stiffness K of the helical gear are considered. total The product of these two forces should equal the normal load F acting on the gear teeth. That is: .

[0075] By substituting the expression of the vibration displacement model above, an equation about X can be established. By continuously adjusting the deformation to achieve equilibrium, the value of the vibration displacement X can be solved.

[0076] Due to meshing stiffness K total Since the various shaping amounts change during meshing, X is also a time-varying quantity. This embodiment extracts its maximum value X within one meshing cycle. max This is used for subsequent noise prediction.

[0077] Step S4: Calculation of overall noise value

[0078] This stage corresponds to Figure 2 The overall noise value is calculated. This stage, based on a large amount of experimental data and theoretical analysis, establishes a semi-empirical noise prediction model that can comprehensively reflect various factors.

[0079] This embodiment decomposes the predicted total noise value L (unit: dB) into a linear superposition of three main contribution terms and one correction term, corresponding to the contribution of macroscopic parameters, power, and vibration displacement, respectively. The specific model is as follows:

[0080]

[0081] In the formula, L is the overall noise level; K is the speed coefficient; β is the helix angle; μ is the speed ratio; ε α X represents the end face overlap; P represents the input power; X represents the end face overlap. max W represents the maximum value of the vibration displacement amplitude distributed along the meshing line. corr These are correction coefficients. It should be noted that, since this model is a semi-empirical model, all the above parameters must be substituted into the calculation using a consistent unit of measurement.

[0082] The physical meanings of each term in the model are as follows:

[0083] Macroscopic parameter contribution part: i.e., in the formula This section reflects the fundamental influence of gear macroscopic geometry parameters on noise. It indicates that increasing the helix angle β and the end face overlap ε... α It helps reduce noise.

[0084] Power contribution component: This refers to the 20logP part in the formula. This term reflects the impact of the input power P on noise; the greater the power, the greater the load, and the greater the noise.

[0085] The vibration displacement contribution: i.e., 20logX in the formula max Partial. This is one of the core components of the present invention; it calculates the vibration displacement X in step S3. max Directly associated with noise. X max It integrates the effects of micro-shaping and installation errors, serving as a bridge between micro-geometry and macro-noise.

[0086] Coefficient terms: K is the speed coefficient related to rotational speed, W corr These are correction factors. These two factors typically need to be calibrated through bench testing on a specific type of gearbox. Once calibrated, they can be used for noise prediction of products in the same series. In this embodiment, W... corr The determination method is as follows: Select a benchmark product of this series of gearboxes for bench noise testing, and measure its actual noise value L under rated operating conditions. test Using the model of this invention, the following can be calculated: (W not included) corr Theoretical initial noise value L calc Then W corr =L test -L calc For other gear pairs in the same series, due to their similar structural characteristics, the calibrated W can be directly adopted. corr The speed coefficient K is obtained by fitting a frequency sweep test at different speeds (e.g., from 1000 rpm to 10000 rpm).

[0087] The gear noise model described in this embodiment starts with gear meshing stiffness, then solves for vibration displacement, and finally obtains a complete gear noise model. The theoretical derivation is combined with experimental data correction, and the error in engineering applications is within 5%.

[0088] Although this embodiment uses helical gears as an example, those skilled in the art should understand that the above formula is equally applicable to noise prediction of spur gears when the helix angle β=0. Furthermore, this method is not only applicable to reducers in pure electric vehicles, but also to noise prediction of transmissions in hybrid electric vehicles and other industrial gearboxes.

[0089] Example 2

[0090] This embodiment provides a noise prediction system for a gear transmission system, used to implement the method described in Embodiment 1. The system can be a software program running on a general-purpose computer, server, or dedicated engineering workstation. Logically, the system includes the following functional modules:

[0091] Data acquisition module: Provides a user interface that allows users to input or import geometric parameters (such as helix angle β, tooth width W, and contact ratio ε) of the meshing transmission pair to be analyzed from external files (such as CAD model parameters and measurement data files). α ), micro-shaping parameters (Tp, C) α C β The module checks the installation offset error e and operating parameters (input power P, speed / speed ratio μ). It also performs validity checks and preprocesses the input data.

[0092] Stiffness Calculation Module: This module encapsulates the calculation logic of "Step S2" in Example 1. It receives geometric parameters and basic material / tooth profile parameters from the data acquisition module and automatically utilizes the spur gear meshing stiffness model (Formula C). th ) and the discretization slicing principle to perform integral operations (formula K) total The time-varying meshing stiffness curve of the helical gear during the entire meshing cycle is determined, and the result is output to the displacement calculation module.

[0093] Displacement Calculation Module: This module encapsulates the calculation logic of "Step S3" in Example 1. It receives the stiffness curve output by the stiffness calculation module, as well as the micro-modification parameters, installation offset errors, and operating condition parameters transmitted by the data acquisition module. This module automatically constructs a vibration displacement model ( The time-varying vibration displacement is determined by solving the force balance equation, and its maximum amplitude X is extracted. max Then X max The output is sent to the noise prediction module.

[0094] Noise prediction module: This module incorporates the calculation logic of "step S4" in Example 1. It receives the vibration displacement X output by the displacement calculation module. max The module also includes geometric and operating parameters transmitted by the data acquisition module. It also incorporates a pre-calibrated velocity coefficient K and correction coefficient W. corr Finally, the module substitutes all parameters into the noise prediction model, calculates the final overall noise value L, and displays it on the user interface or outputs it as a report.

[0095] In one specific implementation, the system can be integrated with 3D design software (such as CATIA, UG, ProE, etc.). After the designer completes the gear design or modifies a parameter (for example, changing the tooth tip trimming amount from 10μm to 12μm), they can call up this noise prediction system with one click. The system automatically extracts the required parameters from the design model and quickly returns the predicted noise value changes within seconds or minutes, providing the designer with instant NVH performance feedback and greatly improving design optimization efficiency.

[0096] Example 3

[0097] This embodiment provides a noise optimization method for a gear transmission system based on Embodiment 1. Based on the overall noise value obtained in Embodiment 1, the micro-modification parameters of the gear pair are adjusted until the overall noise value meets the preset NVH performance index.

[0098] This embodiment, based on embodiment 2, also provides a noise optimization system for a gear transmission system. In addition to the modules in embodiment 2, this optimization system further includes:

[0099] The optimization design module is used to output adjustment suggestions for the micro-shaping parameters based on the difference between the overall noise value and the preset target value.

[0100] Example 4

[0101] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the program, it implements the calculation process described in Embodiment 1. This electronic device can be a computer equipped with Computer-Aided Engineering (CAE) software. After the designer inputs design parameters, the software automatically evaluates the noise level of the design, thereby assisting the designer in adjusting the tooth tip trimming amount or the crowning amount to optimize NVH performance.

[0102] Example 5

[0103] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, provides a noise prediction method for a gear transmission system as described in Example 1.

[0104] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0105] It will be readily understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, combinations, substitutions, improvements, etc., made under the spirit and principles of the present invention are included within the protection scope of the present invention.

Claims

1. A noise prediction method for a gear transmission system, characterized in that, include: Using a spur gear meshing stiffness model with unit tooth width, the meshing stiffness of the helical gear pair is determined by integration along the contact line based on the discretization slicing principle. Based on the deformation, micro-modification parameters and misalignment of the gear pair at the pitch circle under load, the vibration displacement is determined using a vibration displacement model. The vibration displacement represents the difference between the actual meshing position and the ideal meshing position of the corrected gear pair. The vibration displacement, the geometric parameters of the gear pair, and the operating parameters are substituted into the gear noise prediction model to obtain the overall noise value of the gear transmission system. The gear noise prediction model includes a macroscopic parameter contribution, a power contribution, and a vibration displacement contribution.

2. The noise prediction method for a gear transmission system according to claim 1, characterized in that, The meshing stiffness model of the spur gear per unit tooth width is expressed as: In the formula, C th Let K be the stiffness value per unit tooth width, c be the reference stiffness value per unit tooth width, and K be the stiffness value per unit tooth width. f X is the end magnification factor. pos These are dimensionless meshing position coordinates, ranging from 0 to 1.

3. The noise prediction method for a gear transmission system according to claim 2, characterized in that, The meshing stiffness of the helical gear pair is obtained by dividing the gear pair into N thin plates along the tooth width direction, treating each thin plate as a spur gear, and integrating the contact line between the spur gear meshing stiffness model and the helical gear. The formula for calculating the meshing stiffness of the helical gear pair is: In the formula, K total L represents the meshing stiffness of the helical gear pair. t denoted as the contact line length of the helical gear pair, W as the gear width, and β as the helix angle.

4. The noise prediction method for a gear transmission system according to claim 1, characterized in that, The parameters in the vibration displacement model satisfy the following relationship: In the formula, CP is the comprehensive deformation at the pitch circle; X is the vibration displacement, i.e., the predetermined deformation; C β e represents the tooth-shaped bulge; e represents the misalignment; T represents the tooth-shaped bulge. p C is the amount of trimming at the tooth tip; α For tooth-shaped drum-shaped quantities.

5. The noise prediction method for a gear transmission system according to claim 4, characterized in that, The process of determining the vibration displacement specifically includes: Establish a coordinate system, where the X-axis is along the axial direction and the Y-axis is along the pressure angle direction; The helical gear is approximated as N spur gear slices with the same end face parameters; Calculate the combined deformation CP of each spur gear slice at the pitch circle, and then obtain the deformation of the helical gear at the contact position; By comparing the sum of all deformations with the product of the stiffness of the helical gear and the load, the deformations are corrected until they are equal, thereby determining the vibration displacement X.

6. The noise prediction method for a gear transmission system according to claim 1, characterized in that, The specific formula for the gear noise prediction model is as follows: In the formula, L is the overall noise level; K is the speed coefficient; β is the helix angle; μ is the speed ratio; ε α X represents the end face overlap; P represents the input power; X represents the end face overlap. max W represents the maximum value of the vibration displacement amplitude distributed along the meshing line. corr This is a correction factor.

7. The noise prediction method for a gear transmission system according to claim 6, characterized in that, The correction coefficient W corr The constant value is determined experimentally based on the inherent characteristics of different gearbox structures; the speed coefficient K is a coefficient related to the rotational speed derived experimentally.

8. The noise prediction method for a gear transmission system according to any one of claims 1 to 7, characterized in that, The gear pair is a helical gear pair in the reducer of an electric vehicle's electric drive assembly.

9. A noise prediction system for a gear transmission system, characterized in that, include: The stiffness calculation module is used to determine the meshing stiffness of helical gear pairs by integrating along the contact line based on the discretization slicing principle using a spur gear meshing stiffness model with a unit tooth width. The displacement calculation module is used to determine the vibration displacement based on the deformation, micro-modification parameters and misalignment of the gear pair at the pitch circle under load, using a vibration displacement model. The vibration displacement represents the difference between the actual meshing position and the ideal meshing position of the gear pair after correction. The noise prediction module is used to substitute the vibration displacement, the geometric parameters of the gear pair, and the operating parameters into the gear noise prediction model to obtain the overall noise value of the gear transmission system; the gear noise prediction model includes a macroscopic parameter contribution part, a power contribution part, and a vibration displacement contribution part.

10. The noise prediction system for a gear transmission system according to claim 9, characterized in that, The noise prediction model formula used by the noise prediction module is as follows: In the formula, L is the overall noise level; K is the speed coefficient; β is the helix angle; μ is the speed ratio; ε α X represents the end face overlap; P represents the input power; X represents the end face overlap. max W represents the maximum value of the vibration displacement amplitude distributed along the meshing line. corr This is a correction factor.

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