CN-121997493-A - Gear parameter determining method, device, equipment, storage medium and program product

CN121997493ACN 121997493 ACN121997493 ACN 121997493ACN-121997493-A

Abstract

The disclosure provides a gear parameter determining method, a gear parameter determining device, gear parameter determining equipment, a storage medium and a program product, and relates to the technical field of electric drive reducers. The method comprises the steps of obtaining design requirements of an electric drive speed reducer, wherein the design requirements comprise a transmission ratio, a minimum tooth number limit and a plurality of engineering constraint conditions, generating a plurality of feasible tooth number combinations of a high-speed gear pair and a low-speed gear pair according to the transmission ratio and the minimum tooth number limit, determining a preliminary feasible scheme based on the plurality of feasible tooth number combinations and at least a part of engineering constraint conditions, enabling the preliminary feasible scheme to comprise the feasible tooth number combinations, a center distance range and a tooth width range, obtaining one or more groups of optimal solutions of macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model under the condition that all engineering constraint conditions are met based on the preliminary feasible scheme, and determining a target gear parameter scheme according to the one or more groups of optimal solutions. The method can improve the optimization efficiency of the gear parameters of the electric drive speed reducer.

Inventors

MIAO GUO
HU ZHIHONG
YAN YIQUAN
WEI YUNPENG
LIU YIKANG

Assignees

小米汽车科技有限公司

Dates

Publication Date: 20260508
Application Date: 20260109

Claims (15)

1. A gear parameter determination method, comprising: acquiring the design requirement of an electric drive speed reducer, wherein the design requirement comprises a transmission ratio, a minimum tooth number limit and a plurality of engineering constraint conditions for representing the performance and reliability of a gear; Generating a plurality of feasible tooth number combinations of the high-speed gear pair and the low-speed gear pair according to the transmission ratio and the minimum tooth number limit; Determining a preliminary feasible solution based on the plurality of feasible tooth number combinations and at least a portion of the engineering constraints, the preliminary feasible solution comprising a feasible tooth number combination, a center distance range, and a tooth width range for a subsequent optimization process; Based on the preliminary feasible scheme, under the condition that all engineering constraint conditions are met, obtaining one or more groups of optimal solutions of macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model, wherein the macroscopic parameters comprise structural parameters used for representing the geometric shape and the size of the gears; And determining a target gear parameter scheme according to the one or more groups of optimal solutions.
2. The method according to claim 1, wherein said obtaining, based on said preliminary feasible solution, one or more sets of optimal solutions for macroscopic parameters of said high-speed gear pair and said low-speed gear pair by means of a multi-objective optimization model under all said engineering constraints comprises: Initializing an initial population based on macroscopic parameter ranges of the high-speed gear pair and the low-speed gear pair defined by the preliminary feasible scheme; performing selection, crossing and mutation operations on the initial population to generate a corresponding offspring population, and combining the initial population and the offspring population to form a combined population; non-dominated sorting is carried out on the combined population, and the crowding degree of individuals in each non-dominated layer is calculated; selecting individuals from the combined population according to non-dominant levels and the crowding degree to form a new generation parent population; And repeatedly executing operations of generating a child population, merging, non-dominant sorting, calculating the crowding degree and selecting on the basis of the new generation parent population until a preset termination condition is met, so as to obtain one or more groups of optimal solutions which meet all engineering constraint conditions and reach equilibrium on a plurality of optimization targets.
3. The method of claim 2, wherein the non-dominant ranking of the combined population comprises: for each individual in the combined population, determining whether the individual is dominated by other individuals in the combined population based on the values of the plurality of optimization objectives; Grouping all individuals in the combined population that are not subject to any other individuals into a first non-subject layer; Among the individuals remaining after the removal of the first non-dominant layer, assigning individuals not being dominant by the remaining individuals to a second non-dominant layer; the above operations of removing stratified individuals and screening new non-dominant layers are repeated until all individuals in the combined population are assigned to corresponding non-dominant layers.
4. A method according to claim 3, wherein said calculating the degree of congestion of individuals in each non-dominant layer comprises: For each non-dominant layer, respectively carrying out ascending arrangement on all individuals in the non-dominant layer according to the value of each optimization target; For each individual, calculating the value difference between the individual and the adjacent individual on the optimization target in each dimension of the optimization target; and summing the value difference values of each individual in all the optimized target dimensions to obtain the crowdedness of the individual.
5. The method of claim 1, wherein the design requirements include a retarder function requirement, a work environment requirement, a gear drive design requirement, and the engineering constraints, the retarder function requirement including the gear ratio and the minimum tooth count limit, the retarder function requirement further including a space envelope limit, a retarder output load spectrum, a static strength check condition requirement, a noise and order avoidance requirement, and/or a transmission efficiency requirement.
6. The method of claim 5, wherein said determining a preliminary feasibility based on said plurality of viable tooth count combinations and at least a portion of said engineering constraints comprises: Calculating a corresponding center distance according to each feasible tooth number combination, and screening out combinations meeting the space envelope limitation; Based on the screened combination, the center distance range and the tooth width range are determined by combining the load spectrum of the output end of the speed reducer and the static strength checking working condition requirement.
7. The method of any one of claims 1-6, wherein the engineering constraints comprise constraints related to one or more of the following parameters: Contact fatigue strength safety factor, bending fatigue strength safety factor, total weight, end face overlap ratio, slip ratio, or manufacturing process requirements.
8. The method of any one of claims 1-6, wherein the macro parameters include one or more of the following: Center distance, tooth width, modulus, tooth number, pressure angle, helix angle, deflection coefficient, tooth top coefficient or top clearance coefficient.
9. A gear parameter determination device, characterized by comprising: The design requirement acquisition module is used for acquiring the design requirement of the electric drive speed reducer, wherein the design requirement comprises a transmission ratio, a minimum tooth number limit and a plurality of engineering constraint conditions for representing the performance and reliability of the gear; The gear number combination generating module is used for generating a plurality of feasible gear number combinations of the high-speed gear pair and the low-speed gear pair according to the transmission ratio and the minimum gear number limit; A preliminary scheme determination module, configured to determine a preliminary feasible scheme based on the plurality of feasible tooth number combinations and at least a portion of the engineering constraint conditions, where the preliminary feasible scheme includes a feasible tooth number combination, a center distance range, and a tooth width range for subsequent optimization; The multi-objective optimization module is used for obtaining one or more groups of optimal solutions of macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model under the condition that all engineering constraint conditions are met based on the preliminary feasible scheme, wherein the macroscopic parameters comprise structural parameters used for representing the geometric shape and the size of the gears; and the target scheme determining module is used for determining a target gear parameter scheme according to the one or more groups of optimal solutions.
10. The apparatus of claim 9, wherein the multi-objective optimization module comprises: An initializing unit, configured to initialize an initial population based on macroscopic parameter ranges of the high-speed gear pair and the low-speed gear pair defined by the preliminary feasible scheme; the operation execution unit is used for executing selection, crossing and mutation operations on the initial population, generating a corresponding child population, and combining the initial population and the child population to form a combined population; The population processing unit is used for carrying out non-dominant sorting on the combined population and calculating the crowding degree of the individuals in each non-dominant layer; the parent population construction unit is used for selecting individuals from the combined population according to the non-dominant level and the crowding degree to form a new-generation parent population; And the iteration execution unit is used for repeatedly executing operations of generating a child population, merging, non-dominant sorting, calculating the crowding degree and selecting on the basis of the new generation parent population until the preset termination condition is met, so as to obtain one or more groups of optimal solutions which meet all the engineering constraint conditions and reach equilibrium on a plurality of optimization targets.
11. The apparatus of claim 10, wherein the population processing unit non-dominated ordering of the combined population comprises determining, for each individual in the combined population, whether the individual is dominated by other individuals in the combined population based on the values of the plurality of optimization objectives, categorizing all individuals in the combined population that are not dominated by any other individual into a first non-dominated layer, categorizing individuals that are not dominated by the remaining individuals into a second non-dominated layer among the individuals remaining after removal of the first non-dominated layer, and repeating the removing of stratified individuals and screening of new non-dominated layers until all individuals in the combined population are assigned to corresponding non-dominated layers.
12. The apparatus of claim 11, wherein the population processing unit calculates the crowdedness of individuals in each non-dominant layer, comprising, for each non-dominant layer, arranging all individuals in the non-dominant layer in ascending order according to the value of each optimization objective, calculating, for each individual, the value differences between the individual and neighboring individuals in each optimization objective dimension, and summing the value differences between each individual in all optimization objective dimensions to obtain the crowdedness of the individual.
13. An electronic device, comprising: A processor; A memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the method of any one of claims 1-8.
14. A computer readable storage medium, which when executed by a processor of a mobile terminal, enables the mobile terminal to perform the steps of the method of any of claims 1-8.
15. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-8.

Description

Gear parameter determining method, device, equipment, storage medium and program product Technical Field The present disclosure relates to the technical field of electrically driven reducers, and in particular, to a method, an apparatus, a device, a storage medium, and a program product for determining gear parameters. Background In electric vehicles and hybrid vehicle drive systems, an electric drive reducer is used as a key transmission component, and the parameter design of a gear pair directly influences the transmission efficiency, noise Vibration (NVH) performance, structural weight and reliability of the whole vehicle. Along with the improvement of multi-dimensional performance requirements of high efficiency, low noise, light weight and the like of an electric drive system, how to better determine the gear parameter scheme of the electric drive speed reducer is called as a problem to be solved. It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art. Disclosure of Invention The present disclosure aims to provide a gear parameter determination method, a gear parameter determination device, a gear parameter determination apparatus, a storage medium and a program product. According to a first aspect of the embodiment of the present disclosure, a gear parameter determining method is provided, which includes obtaining a design requirement of an electrically driven reducer, wherein the design requirement includes a transmission ratio, a minimum number of teeth limit and a plurality of engineering constraint conditions for characterizing gear performance and reliability, generating a plurality of feasible tooth number combinations of a high-speed gear pair and a low-speed gear pair according to the transmission ratio and the minimum number of teeth limit, determining a preliminary feasible scheme based on the plurality of feasible tooth number combinations and at least a part of engineering constraint conditions, the preliminary feasible scheme includes the feasible tooth number combinations, a center distance range and a tooth width range for a subsequent optimization process, obtaining one or more sets of optimal solutions for macroscopic parameters of the high-speed gear pair and the low-speed gear pair through a multi-objective optimization model based on the preliminary feasible scheme under all engineering constraint conditions, wherein the macroscopic parameters include structural parameters for characterizing gear geometry and size, and determining a target gear parameter scheme according to the one or more sets of optimal solutions. In some embodiments, based on a preliminary feasible scheme, one or more groups of optimal solutions of macroscopic parameters of a high-speed gear pair and a low-speed gear pair are obtained through a multi-objective optimization model under the condition that all engineering constraint conditions are met, the method comprises the steps of initializing an initial population based on macroscopic parameter ranges of the high-speed gear pair and the low-speed gear pair defined by the preliminary feasible scheme, performing selection, crossover and mutation operations on the initial population, generating corresponding child populations, merging the initial population and the child populations to form a combined population, performing non-dominant sorting on the combined population, calculating the crowding degree of individuals in each non-dominant layer, selecting individuals from the combined population according to the non-dominant level and the crowding degree to form a new generation parent population, and repeatedly performing operations of generating the child populations, merging, non-dominant sorting, crowding degree calculation and selection until the preset termination conditions are met, and one or more groups of optimal solutions which meet all engineering constraint conditions and reach equilibrium on a plurality of optimization targets are obtained. In some embodiments, non-dominated sorting is performed on the combined population, including determining, for each individual in the combined population, whether the individual is dominated by other individuals in the combined population based on values of a plurality of optimization objectives, classifying all individuals in the combined population that are not dominated by any other individual into a first non-dominated layer, classifying individuals that are not dominated by other individuals out of the individuals remaining after removal of the first non-dominated layer into a second non-dominated layer, and repeating the operations of removing the layered individuals and screening for new non-dominated layers until all the individuals in the combined population are assigned to corresponding