High power density rear axle platform design method
By optimizing gear parameters and bearing structure, the problems of weight and noise control in the design of high power density rear axles were solved, achieving lightweighting and noise reduction of the drive axle.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-12-14
- Publication Date
- 2026-03-17
AI Technical Summary
The lack of a high-power-density rear axle platform design method in the existing technology results in a heavy drive axle assembly and poor vibration and noise control.
The gear parameter optimization algorithm is used to optimize the macroscopic parameters of the hyperboloid gear. Combined with the gear system-level tooth surface micro-parameter modification and optimization, the bearing structure parameter matching design and optimization are carried out. The structural parameters of the axle housing, differential housing and reducer housing are optimized. The half-shaft bearing selection analysis and structural optimization are carried out, and finally the optimization of the half-tooth base structure is achieved.
These steps increased the drive axle's load-bearing capacity by 16%, reduced the overall mass by 13%, and lowered vibration and noise levels by 3 dB.
Smart Images
Figure CN114186345B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of automobile manufacturing technology, and in particular to a method for designing a rear axle for automobiles. Background Technology
[0002] In China's long-term development plan for the automotive industry, in-depth research on high-performance transmission systems for automotive chassis has been listed as a priority area, particularly the theoretical and design methods, key manufacturing technologies, and experimental research of gear transmission components. With the global trend towards lightweighting in the automotive industry, stringent emission regulations, and demanding customer requirements for vehicle power stability and vibration and noise control, automotive transmission systems are evolving towards lightweighting, high efficiency, low noise, and high reliability. As a core component of the micro-vehicle transmission system, the drive axle withstands complex coupling forces and torques during normal operation, and its performance directly affects the vehicle's safety, comfort, and power. How to reduce the weight of the drive axle assembly, increase its power density, and effectively control vibration and noise during operation has become a pressing challenge for drive axle manufacturers worldwide and a research hotspot in both domestic and international academic and automotive engineering communities.
[0003] There is currently no high power density rear axle platform design method. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to provide a high power density rear axle platform design method.
[0005] The technical solution adopted by this invention to solve its technical problem is: a high power density rear axle platform design method, including the following steps:
[0006] A. Optimize the macroscopic parameters of the hyperboloid gear using a gear parameter optimization algorithm;
[0007] B. Gear system-level tooth surface micro-parameter modification and optimization;
[0008] C. Bearing structural parameter matching design and optimization, selection analysis and spatial layout optimization;
[0009] D. Optimization of structural parameters for axle housing, differential housing, and reducer housing;
[0010] E. Half-shaft bearing selection analysis and half-shaft structure optimization;
[0011] F. Optimization of the semi-tooth matrix structure.
[0012] Furthermore, for step A, the specific steps of the gear parameter optimization algorithm are as follows:
[0013] After setting the dimension n, the number of vertices k, the termination condition ε, and determining the value range of each design variable, the specific algorithm operation steps are as follows:
[0014] a. Generate the first vertex of the initial complex: take n initial values q with slight differences between 0 and 1. i Substituting into the Logistic model, we obtain the initial chaotic variable p. i,1 And transform it into the optimization variable x i,1 =x imin +p i,1 (x imax -x imin ), x imin x imax The gear design variable x i The lower and upper limits of x's value. i The end face module m d Number of teeth on the driving gear Z a Any one of the following: average helix angle β0, tooth width b, and mounting offset D, X = [m d Z a ,β0,b,D] T =[x1,x2,x3,x4,x5] T ;
[0015] b. The remaining k-1 vertices of the initial complex: Substituting the initial chaotic variable of the previous vertex into the Logistic model yields the initial chaotic variable p of the k-1 vertices. i,j And transform it into the optimization variable x i,j =x i,jmin +p i,j (x i,jmax -x i,jmin );
[0016] c. Calculate the objective function value T(X) for each vertex, sort the vertices according to their values, and distinguish between the best and worst points;
[0017] d. Termination conditions for whether iterative optimization continues If we continue to seek the optimal solution, we calculate and remove...
[0018] Remove bad pixels X H The center of each vertex after Is there a new vertex center available?
[0019] e. If the new vertex center is unusable, redefine the search interval, return to step one of the calculations, and regenerate a vertex of the initial complex. If the new vertex center is usable, calculate the mapping point X. S +α i (X S -X H );
[0020] f. If the mapping point is feasible, use it to replace the worst-case point and continue to the next iteration; otherwise, recalculate the initial value α of the mapping coefficients of the design variables. i =1+z i New perturbation points are generated until a new value point better than the worst point appears;
[0021] g. Convergence of the iterative program: When the objective function value of the optimal point of the composite meets the given convergence condition, or the total number of iterations exceeds the preset value, the program terminates and the current best point is taken as the final value.
[0022] The beneficial effects of the present invention are as follows: The method of the present invention has been verified. As can be seen from the accompanying drawings, Table 1 and Table 2, after optimization using this method, the load-bearing capacity of the drive axle on the same platform is increased by 16%, the assembly mass is reduced by 13%, and the vibration and noise level is improved by 3dB. Attached Figure Description
[0023] Figure 1 This is a flowchart of the gear parameter optimization algorithm;
[0024] Figure 2 This is a graph showing the front noise test values for the CV5 product.
[0025] Figure 3 This is a graph showing the noise level test values for product CV5;
[0026] Figure 4 This is a graph showing the rear seat noise test values for the CV5 product.
[0027] Figure 5 This is a graph showing the noise test values for the N350P product.
[0028] Figure 6 This is a graph showing the noise test values for product P3011;
[0029] Figure 7 This is a graph showing the R111 fourth-gear acceleration noise test values for the product;
[0030] Figure 8 This is a graph showing the R111 fifth-gear acceleration noise test values for the product;
[0031] Figure 9 This is a graph showing the test values for the R111 fourth-gear coasting noise of the product;
[0032] Figure 10 This is a graph showing the test values for the R111 product's fifth-gear coasting noise. Detailed Implementation
[0033] The invention will now be further described with reference to the accompanying drawings.
[0034] The technical approach of this design method is as follows:
[0035] a) Macroscopic parameter optimization of quasi-hyperboloid gears to improve the load-bearing capacity of gears on the same platform. The macroscopic parameters mainly include module, number of teeth, tooth width, offset, helix angle, and pressure angle. A gear parameter optimization algorithm based on the coupling of chaos theory and the complex method is proposed to solve the target model, avoiding local minima.
[0036] b. Gear system-level tooth surface micro-parameter modification and optimization, controlling gear transmission error, adjusting gear meshing imprint and meshing misalignment, and ensuring the correct spatial theoretical meshing area of meshing gears;
[0037] c. Bearing structural parameter matching design and optimization, selection analysis and spatial layout optimization, determine bearing model and optimal installation position, and ensure that the gear-bearing system has sufficient rigidity and service life;
[0038] d. Optimize the structural parameters of the axle housing, differential housing, and reducer housing; enhance the rigidity of the bearing mounting positions of the differential and reducer housings; reduce the weight of the casting mid-section structure; reduce the vibration response on the transmission path; and enhance the overall axle support rigidity.
[0039] e. Half-shaft bearing selection analysis and half-shaft structure optimization: chamfer size and rod slimming;
[0040] f. Optimize the structure of the tooth matrix to improve the stress distribution on the tooth surface and root.
[0041] 1. Determine the main design technical specifications:
[0042] The dimensions are defined by the outer diameter of the tooth, such as 180mm, 200mm, 220mm, etc.
[0043] Select a commonly used speed ratio range, such as 3.5 to 6;
[0044] Based on the load-bearing capacity technical requirements, the maximum torque that it can withstand is set;
[0045] We strive for smaller assembly weight and better NVH performance on the same platform.
[0046] 2. Determine the macroscopic parameters of the quasi-hyperboloid gear.
[0047] 1) Based on the determined speed ratio range and the constraint of the tooth outer diameter, calculate the helix angle of the reducer gears with different speed ratios. The calculation requirements for the helix angle are as follows:
[0048] The helix angle is selected based on theoretical calculations. If the theoretical value exceeds 50 degrees, all calculations are based on 50 degrees. The tooth surface width is calculated as 1 / 3 of the outer cone distance of the driven tooth. The pressure angle of the master and driven teeth is taken as 40 degrees. The strength calculations are all based on equal life and the maximum torque setting.
[0049] 2) Then, based on the value of the helix angle, determine the range of the offset distance. For example, when designing a hypoid gear with a tooth diameter of 180mm, the offset range can be 25 to 35.
[0050] 3) Then check the bending strength and contact strength of each gear of the reducer with different speed ratios and different offset distances, and mark each calculated value in the chart.
[0051] 4) Analyze the changing trend of the strength verification curve: as the offset distance increases, the bending strength and contact strength values decrease, and the gear load-bearing capacity increases. Then, based on the set output load-bearing capacity index, further narrow down the range of offset distance values determined in the previous step.
[0052] 5) Further analyze the robustness of the gear transmission belt, mainly considering the rate of change of transmission efficiency with the offset distance and the speed of change of gear strength.
[0053] Calculations of transmission efficiency at different speed ratios show that the efficiency decreases as the offset increases; however, a larger offset is beneficial for improving load-bearing capacity. A comprehensive analysis considering load-bearing capacity, efficiency, and the rate of change of strength and efficiency is necessary. The aforementioned rate of change reflects how quickly the gear's load-bearing capacity changes under different offsets; the larger the offset, the slower the increase in load-bearing capacity. In other words, for the same loss of transmission efficiency, the improvement in gear strength is limited.
[0054] Within the selected offset range, the bending stress of the driven tooth first decreases and then increases, with a minimum value corresponding to a certain range. Considering the gradual decrease in transmission efficiency, the final offset value is determined.
[0055] 3. After initially determining the macroscopic geometric parameters and relative installation positions of the master and slave teeth, a detailed design and optimization analysis of the tooth surface parameters is carried out.
[0056] By combining the load-bearing capacity, mass, and theoretical meshing noise of the gear, a multi-objective model is established. For the target value of this theoretical study, an improved optimization algorithm based on the coupling of chaos theory and the complex method is proposed to solve the target model, avoiding local minima. In this way, the optimized gear parameters, such as module, average helix angle, pressure angle, and tooth surface width, are finally determined, realizing the optimal combination of the load-bearing capacity, gear mass, and theoretical meshing noise of the gear.
[0057] 4. Due to tooth profile errors arising from actual gear machining and heat treatment processes, and based on years of experience in gear product design and experimental testing, it is necessary to appropriately modify and adjust the micro-parameters of the theoretically designed tooth surface. Specifically, this includes: adjusting the tool radius, tooth profile modification amount, helix angle compensation amount, tool inclination angle correction amount, tooth length modification amount, and diagonal modification correction. The optimal values for these six parameters can be determined through orthogonal experimental design. Through micro-modification of the tooth surface, gear transmission errors can be controlled, gear meshing imprints and meshing misalignment can be adjusted, ensuring the correct theoretical spatial meshing area of the meshing gears.
[0058] 5. Bearing structural parameter matching design and optimization, selection analysis and spatial layout optimization, determine bearing model and optimal installation position, and ensure that the gear-bearing system has sufficient rigidity and service life.
[0059] Bearing parameter optimization mainly includes: roller pitch circle diameter, roller diameter, roller offset center amount, total roller length, large end of roller chamfer radius, small end of roller chamfer radius, roller cone angle, and large flange edge chamfer, etc. Optimizing these detailed parameters can reduce bearing stress damage and extend bearing service life.
[0060] Based on the aforementioned gear design parameters, a flexible system model of gear meshing and bearings is established. For this operating system, the simulation conditions are input as follows: rear axle oil temperature: 90°C; operating condition simulation: completion of pre-run-in; 400 working load cycles.
[0061] According to technical specifications, specific operating conditions can be set, such as speed, torque, and running time. The simulation of the cyclic road spectrum can be obtained by compressing data collected from actual vehicle road tests into composite variable operating conditions. Simulation operating condition tables for product development can be customized according to design needs, including low-speed high torque, high-speed low torque, and medium-speed medium torque.
[0062] By simulating system operation under cyclic conditions, the bearing fatigue damage value, the magnitude of the midpoint lift of the main tooth surface, and the magnitude of gear meshing misalignment are compared to determine the bearing model and specific geometric parameters. Furthermore, by changing the bearing installation position, the optimal bearing spatial layout is analyzed.
[0063] As the main gear bearing span changes, observe the trend of the upward lift of the midpoint of the main gear tooth surface, as well as the trends of the misalignment (Δα(mrad), ΔE(μm), ΔP(μm), ΔG(μm)) and its rate of change. Similarly, by changing the distance between the bearings at both ends of the driven gear, after analyzing multiple sets of parameters, it is found that the smaller the distance between the two support points, the better the rigidity. At the same time, the strength of the half-tooth and differential assembly must be considered, and the distance cannot be reduced indefinitely. This distance value must be greater than the sum of the minimum spherical diameter of the planetary gear and twice the minimum differential housing wall thickness.
[0064] The spatial installation position of the master and slave gear support bearings is determined using the above method.
[0065] 6. Next, we will complete the structural parameter design and optimization of the axle housing, differential housing, and reduction housing. The focus is on strengthening the rigidity of the bearing installation positions of the differential housing and reduction housing, lightweighting the casting middle section structure, reducing the vibration response on the transmission path, and enhancing the overall axle support rigidity.
[0066] Based on the maximum output torque requirement of the rear axle, the spherical radius of the differential was calculated, and the structural parameters of the semi-gear were designed. It is worth noting that the conventional semi-gear base uses a through-type design, which limits its strength. Through improvements made by the construction company and actual performance testing, ribs were added to the base, which improves the stress distribution on the tooth surface, increases the gear's bending strength, and theoretically improves performance by 15%. With this solution, the size of the semi-gear can be reduced to withstand the same torque.
[0067] After determining the structural parameters of the semi-tooth structure, corresponding differential and reduced shells are designed according to the spatial dimensions. The focus is on strengthening the stiffness of the bearing mounting positions in the differential and reduced shells, and appropriately increasing the number of structural ribs. After modeling using 3D software, finite element stress analysis is performed. The differential shell is subjected to maximum transmitted force at six different locations, and the forward and reverse rotation conditions are checked. For weak structural areas, materials are thickened or their properties are improved. The reduced shell also needs to have its stress distribution checked for the six operating conditions analyzed for the differential shell. After completing the strength analysis of the differential and reduced shells, modal analysis of the irregular shell is also required based on equal strength. Then, lightweight structural design and modifications are carried out to ensure shell strength while improving the first three modal values and avoiding resonance in the system.
[0068] 7. Half-shaft bearing selection analysis and half-shaft structure optimization: chamfer size and rod slimming.
[0069] The design and selection analysis of the half-shaft bearing is similar to that of the main reducer bearing. A flexible simulation model from the main reducer to the half-shaft is built, the bearing model is selected, and the bearing life of the system is checked according to the composite operating conditions of the main reducer.
[0070] The design of the half-shaft mainly considers the maximum torque value it can bear. A three-dimensional model is established, and then the torsional strength and torsional stiffness are analyzed to see if they meet the requirements.
[0071] 8. The next step is to construct the rear axle housing and the casting middle section. This also involves first designing a three-dimensional digital model, and then performing finite element analysis, specifically including axle housing strength analysis, stiffness analysis, fatigue analysis and vibration response analysis. Based on the analysis results, it is determined whether the housing needs to be locally reinforced to eliminate abnormal response points on the transmission path.
[0072] Table 1 Comparison of noise levels of the drive axle before and after optimization using this method.
[0073]
[0074] Table 2 Comparison of drive axle technical level before and after optimization using this method.
[0075]
Claims
1. A high power density rear axle platforming design method, characterized in that, The method comprises the following steps: A. Optimize the macro parameters of the double helical gear using a gear parameter optimization algorithm; According to the speed ratio range and the limit condition of the tooth outer diameter, the spiral angle of the gear of the speed reducer at different speed ratios is calculated, the theoretical calculation is selected as the criterion, if the theoretical value exceeds 50 degrees, all are calculated according to 50 degrees; the tooth surface width is calculated according to 1 / 3 of the tooth outer taper distance; the pressure angle of the main and slave teeth is 40 degrees; the strength calculation is calculated according to the equal life and the set maximum torque; then the offset distance value range is determined according to the value of the spiral angle; then the bending strength and the contact strength of the gear of the speed reducer at each speed ratio and different offset distances are checked one by one, and each calculation value is marked in the chart; Analyze the change trend of the strength checking curve: the bending strength and the contact strength decrease with the increase of the offset distance, the gear carrying capacity is improved, the offset distance value range determined in the previous step is reduced according to the set output carrying capacity index; the gear transmission robustness is analyzed according to the change rate of the transmission efficiency with the change of the offset distance and the change speed of the gear strength; The specific steps of the gear parameter optimization algorithm are: Set dimension , vertex number , termination condition , the specific algorithm operation steps after determining the value range of each design variable: a. The first vertex of the initial complex shape is generated: 0~1 is taken The initial value with a small difference , the initial chaotic variable is obtained by substituting the Logistic model , and is converted into an optimization variable , , is the lower limit and upper limit of the value of the gear design variable , is any one of the face modulus , the number of driving teeth , the average spiral angle , the tooth surface width and the installation offset distance D ; b, initial composite shape remainder One vertex: initial chaotic variable of previous vertex substituted into Logistic model to get One vertex initial chaotic variable and converted to optimization variable ; c. Calculate the objective function value of each vertex Sort the vertices by their objective function values, distinguishing between the best and worst points. d. termination condition for whether to continue iteration optimization If continue optimization, calculate new vertex center after removing bad points whether to have available new vertex center; e. If the new vertex center is not available, redefine the search interval, return to the first step, and regenerate a vertex of the initial complex; if the new vertex center is available, find the mapping point ; f. If the mapping point is feasible, replace the worst point and continue the next iteration; otherwise, recalculate the initial value of the mapping coefficient of the design variable , generate a new disturbance point until a new value point better than the worst point appears; g. Convergence of the iteration program: the optimal objective function value of the complex optimal point meets the given convergence condition, or the total number of iterations exceeds the preset value, the program is terminated, and the current best point is taken as the final value; B. Gear system level gear surface micro parameter modification and optimization; C. Bearing structure parameter matching design and optimization, type selection analysis and space layout optimization; D. Optimization of the structure parameters of the axle housing, differential housing and speed reducer housing; E. Half shaft bearing type selection analysis, half shaft structure optimization; F. Row half tooth base structure optimization.