Method and device for determining lining parameters in suspension system

Abstract

The invention discloses a method and device for determining lining parameters in a suspension system. The method comprises the steps that attribute parameters of a target power assembly and suspension parameters of a target suspension system are obtained, and the target suspension system is used for suspending the target power assembly; based on the attribute parameters and the suspension parameters, a plurality of objective functions are constructed for a plurality of optimization objectives of stiffness parameters of a bush in the target suspension system, and each objective function corresponds to one optimization objective; fusing the plurality of objective functions to obtain a comprehensive objective function; and based on a preset algorithm, solving the comprehensive target function, and determining an optimal rigidity parameter of the bushing in the target suspension system. According to the method, the solving process is simplified by fusing the idea of multi-objective optimization, and the efficiency and quality of determining the bushing parameters are improved.

Description

Technical Field

[0001] The present application belongs to the field of vehicle technology, and more particularly to a method and device for determining bushing parameters in a suspension system. Background Art

[0002] As the connecting part between the powertrain and the vehicle body, the suspension system needs to support the weight of the powertrain and limit its displacement and rotation. If it is not designed properly, vibration may be transmitted to the passenger compartment, reducing comfort and even causing resonance. Suspension rubber bushings play a key role in the suspension system due to their excellent vibration and noise reduction performance.

[0003] Existing bushing design methods usually rely on joint optimization of multiple software, which is cumbersome and inefficient. In addition, classic optimization algorithms find it difficult to quickly find the optimal solution that meets multi-dimensional performance requirements.

[0004] Therefore, how to provide an efficient suspension system bushing design method has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a method and device for determining bushing parameters in a suspension system, which can improve the design efficiency of the suspension bushing.

[0006] In a first aspect, an embodiment of the present application provides a method for determining bushing parameters in a suspension system, the method comprising:

[0007] Acquiring property parameters of a target powertrain and suspension parameters of a target suspension system, where the target suspension system is used to suspend the target powertrain;

[0008] Based on the attribute parameters and the suspension parameters, multiple objective functions are respectively constructed for multiple optimization targets of the stiffness parameters of the bushing in the target suspension system, and each objective function corresponds to an optimization target;

[0009] Fusion of multiple objective functions to obtain a comprehensive objective function;

[0010] Based on the preset algorithm, the comprehensive objective function is solved to determine the optimal stiffness parameters of the bushing in the target suspension system.

[0011] In a possible implementation manner of the first aspect, multiple objective functions are integrated to obtain a comprehensive objective function, and the method includes:

[0012] According to the preset weight corresponding to each objective function in the multiple objective functions, the multiple objective functions are merged into a comprehensive objective function.

[0013] In a possible implementation manner of the first aspect, the multiple objective functions include at least two of the first objective function, the second objective function, or the third objective function;

[0014] The optimization goal corresponding to the first objective function is: the interval of each rigid body mode corresponding to the target powertrain is greater than or equal to a first preset value;

[0015] The optimization goal corresponding to the second objective function is: the frequency of each rigid body mode corresponding to the target powertrain meets the preset frequency condition;

[0016] The optimization goal corresponding to the third objective function is: the decoupling rate of each rigid body mode corresponding to the target powertrain is greater than or equal to the second preset value.

[0017] In a possible implementation of the first aspect, based on a preset algorithm, solving the comprehensive objective function to determine the optimal stiffness parameters of each bushing in the target suspension system includes:

[0018] The preset stiffness range of the bushing in the target suspension system is determined as a constraint condition;

[0019] Based on the preset algorithm and constraints, the comprehensive objective function is solved to determine the optimal stiffness parameters of each bushing in the target suspension system.

[0020] In a possible implementation of the first aspect, based on a preset algorithm and constraint conditions, solving a comprehensive objective function to determine an optimal stiffness parameter of each bushing in a target suspension system includes:

[0021] Through iterative updating, the target value of the comprehensive objective function is found, and the target value is the minimum value that meets the constraint conditions and reaches the preset number of iterations;

[0022] The stiffness parameter corresponding to the target value is determined as the optimal stiffness parameter.

[0023] In a possible implementation manner of the first aspect, finding a target value of the comprehensive objective function through iterative updating includes:

[0024] Initializing algorithm parameters and population solutions of the optimization algorithm, the population solution including multiple sets of candidate stiffness parameters of each bushing in the target suspension system, and the optimization algorithm including multiple optimization search strategies;

[0025] Through a variety of optimization search strategies, the population solution is iteratively updated to find the target value.

[0026] In a possible implementation manner of the first aspect, the stiffness parameters of each bushing in the target suspension system include front-to-rear static stiffness, left-to-right static stiffness, and top-to-bottom static stiffness.

[0027] In a second aspect, an embodiment of the present application provides a device for determining bushing parameters in a suspension system, the device comprising:

[0028] An acquisition module, used for acquiring property parameters of a target powertrain and suspension parameters of a target suspension system, wherein the target suspension system is used for suspending the target powertrain;

[0029] A model building module, for building a plurality of objective functions for a plurality of optimization objectives of a stiffness parameter of a bushing in a target suspension system based on the attribute parameters and the suspension parameters, each objective function corresponding to an optimization objective;

[0030] A fusion module is used to fuse multiple objective functions to obtain a comprehensive objective function;

[0031] The optimization module is used to solve the comprehensive objective function based on a preset algorithm and determine the optimal stiffness parameters of the bushing in the target suspension system.

[0032] In a third aspect, an embodiment of the present application provides a computer device, comprising: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement any one of the methods in the first aspect above.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the method of any one of the above-mentioned first aspects is implemented.

[0034] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements any one of the methods in the above-mentioned first aspect.

[0035] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0036] The method for determining bushing parameters in the suspension system provided in the embodiment of the present application is based on the attribute parameters of the powertrain and the suspension parameters of the suspension system, takes the bushing stiffness as the optimization variable, constructs objective functions for multiple optimization objectives respectively, and integrates them to form a comprehensive objective function. The comprehensive objective function is solved using a preset algorithm, and the bushing stiffness is gradually iterated and optimized to finally determine the optimal stiffness parameters that meet the comprehensive performance requirements. This method, by integrating the idea of ​​multi-objective optimization, transforms a complex multi-objective optimization problem into a single comprehensive objective optimization problem, simplifies the solution process, and avoids the complexity and inefficiency caused by the joint operation of multiple software in traditional solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application.

[0038] Figure 1is a flow chart of a method for determining bushing parameters in a suspension system provided by an embodiment of the present application;

[0039] Figure 2 is a flow chart of a method for determining bushing parameters in a suspension system provided by an embodiment of the present application;

[0040] Figure 3 is a flow chart of a method for determining bushing parameters in a suspension system provided by an embodiment of the present application;

[0041] Figure 4 is a structural schematic diagram of a device for determining bushing parameters in a suspension system provided in an embodiment of the present application;

[0042] Figure 5 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make those of ordinary skill in the art better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0044] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples consistent with some aspects of the present application as detailed in the attached claims.

[0045] As described in the background technology section, the prior art has not yet provided a method for optimizing the bushing of a suspension system that can efficiently and accurately meet the requirements of multi-objective optimization while simplifying the design process and reducing the complexity of multi-software collaboration. In order to solve this technical problem, the embodiments of the present application provide a method, device, equipment and computer storage medium for determining bushing parameters in a suspension system, which can accurately and efficiently design bushing parameters in a suspension system.

[0046] Figure 1 A schematic flow chart of a method for determining bushing parameters in a suspension system provided by an embodiment of the present application is shown.

[0047] like Figure 1 As shown, the method may include the following steps.

[0048] S110 : Acquire property parameters of a target powertrain and suspension parameters of a target suspension system.

[0049] The powertrain refers to the power transmission system consisting of the engine, transmission, drive shaft and other related components, which is responsible for driving the vehicle.

[0050] The suspension system refers to the device that connects the powertrain to the vehicle body or frame. It is usually composed of suspension, rubber bushings and related components. Its main function is to support the weight of the powertrain, limit its displacement and rotation, and effectively isolate vibration and noise.

[0051] In the early stage of suspension system development, the suspension system needs to be optimized according to the NVH performance requirements of the vehicle to improve the comfort and vibration isolation performance of the vehicle.

[0052] The NVH performance of the whole vehicle refers to the comprehensive performance of the noise (Noise), vibration (Vibration) and sound roughness (Harshness) generated by the car during driving.

[0053] It should be noted that the optimization design of the suspension system usually includes multiple design variables, such as the static stiffness, installation position and installation angle of the suspension. However, since the spatial layout factors such as the installation position and angle are subject to many restrictions (such as the body structure, process requirements, etc.), the variable cost of adjusting these parameters is high. Therefore, in this application, the stiffness parameter of the bushing is used as the key variable for the optimization design, focusing on optimizing the performance of the suspension system by adjusting the bushing stiffness.

[0054] The target suspension system refers to the suspension system to be optimized. Specifically, the target suspension system is the suspension system whose bushing stiffness parameters are to be determined.

[0055] The target powertrain refers to the powertrain connected to the target suspension system. Specifically, the target suspension system is used to suspend the target powertrain, that is, to support and isolate the vibration and noise of the powertrain.

[0056] The property parameters of the target powertrain and the suspension parameters of the target suspension system are obtained.

[0057] The target powertrain attribute parameters refer to the parameters that affect the NVH performance.

[0058] Exemplarily, the attribute parameters may include, for example, mass, vibration characteristics, etc.

[0059] The suspension parameters of the target suspension system refer to the parameters that describe the performance and working characteristics of the suspension system.

[0060] Exemplarily, the suspension parameters may include, for example, a suspension mode, a dynamic-static ratio, a damping value, and the like.

[0061] In one implementation, obtaining the property parameters of the target powertrain and the suspension parameters of the target suspension system may include: responding to basic information of the target powertrain input by a user, querying the property parameters of the target powertrain in a database according to the basic information, and performing dynamic modeling using simulation software to obtain the suspension parameters of the target suspension system.

[0062] S120. Based on the attribute parameters and the suspension parameters, a plurality of objective functions are respectively constructed for a plurality of optimization objectives of the stiffness parameters of the bushing in the target suspension system.

[0063] A plurality of optimization targets for improving the NVH performance of the vehicle are preset and constructed, and a plurality of corresponding objective functions are constructed based on the plurality of optimization targets. In other words, each objective function corresponds to one optimization target.

[0064] For each objective function, the stiffness parameters of the bushing in the target suspension system are taken as optimization variables, and are constructed by combining the attribute parameters of the target powertrain, the suspension parameters of the target suspension system and the corresponding optimization objectives.

[0065] For example, a plurality of bushings may be included in the target suspension system.

[0066] Exemplarily, the stiffness parameter of each bushing may refer to three-dimensional static stiffness, namely, horizontal static stiffness, vertical static stiffness and vertical static stiffness.

[0067] In other words, the optimization variables may be: the three-dimensional static stiffness values ​​of each bushing in the target suspension system.

[0068] In one implementation, an objective function related to each optimization target is constructed based on existing design experience or standards. For example, corresponding objective functions are constructed for parameters such as rigid body modal interval, modal frequency range, and decoupling rate. The input of each objective function is the static stiffness of the bushing of the suspension system, and the output is the value of the optimization target. By comprehensively optimizing these objective functions, the optimal bushing stiffness design is obtained.

[0069] In one implementation, a corresponding objective function is constructed for each optimization target of the target suspension system by combining the simulation model with the experimental data. First, based on the NVH performance requirements of the whole vehicle, the vibration response of the powertrain and suspension system is analyzed using simulation software (such as finite element analysis or multi-body dynamics simulation) to obtain the numerical relationship of each optimization target. Then, combined with the actual test data (such as experimental results of the vibration frequency and decoupling rate of the suspension system), a specific mathematical expression is set for each target to construct multiple objective functions. Each objective function corresponds to a different optimization target, and through iterative optimization, the optimal stiffness parameters that meet all design requirements are obtained.

[0070] S130, integrating multiple objective functions to obtain a comprehensive objective function.

[0071] It is understandable that traditional multi-objective optimization methods usually need to process each objective function separately, and usually rely on multiple optimization software for joint optimization, which is complicated and inefficient. When each objective function is optimized separately, there may be duplication and redundancy in the calculation process, resulting in a long optimization time and less than expected results.

[0072] In the present application, the above problem can be solved by fusing multiple objective functions to obtain a comprehensive objective function.

[0073] In one implementation, multiple objective functions are weighted according to the preset weight of each objective function in the multiple objective functions, and are integrated into a comprehensive objective function. By weighting, the optimization objectives of each objective are integrated into a comprehensive function, so that only one solution is required during the optimization process, which greatly simplifies the optimization process. Through the comprehensive objective function, not only can each optimization objective be considered at the same time, but also the importance of each objective can be flexibly adjusted according to actual needs (through weight setting). This method not only improves the optimization efficiency, but also effectively avoids conflicts between objective functions, thereby achieving multiple optimization objectives in one optimization and reducing resource waste during the optimization process.

[0074] S140. Based on a preset algorithm, the comprehensive objective function is solved to determine the optimal stiffness parameters of the bushing in the target suspension system.

[0075] The preset algorithm refers to the algorithm that has been determined and set before the optimization process begins and is used to solve the comprehensive objective function.

[0076] After integrating multiple optimization objectives into a comprehensive objective function, the general implementation method for solving the comprehensive objective function based on the preset algorithm includes: first, initialize the parameters of the optimization algorithm (such as population size, maximum number of iterations, etc.), and determine the initial values ​​and constraints of the optimization variables. Then, using the preset optimization algorithm, the comprehensive objective function of the target suspension system is gradually solved by iteratively updating the optimization variables (i.e., the stiffness parameters of the bushing). In each iteration, the fitness of the current solution is evaluated and the optimal solution is updated until the preset termination conditions are met (such as satisfying the constraints, reaching the maximum number of iterations, or the minimum target value). Finally, the optimal bushing stiffness parameters that meet the comprehensive performance requirements are output. These parameters can simultaneously meet multiple optimization objectives and optimize the NVH performance of the vehicle.

[0077] As an example but not limitation, the preset algorithm may be: whale optimization algorithm, ant colony optimization algorithm, genetic algorithm, grey wolf algorithm, etc.

[0078] This application scheme is based on the attribute parameters of the powertrain and the suspension parameters of the suspension system, takes the bushing stiffness as the optimization variable, constructs objective functions for multiple optimization objectives respectively, and integrates them to form a comprehensive objective function. The preset algorithm is used to solve the comprehensive objective function, and the bushing stiffness is gradually iterated and optimized to finally determine the optimal stiffness parameters that meet the comprehensive performance requirements. This method, by integrating the idea of ​​multi-objective optimization, transforms the complex multi-objective optimization problem into a single comprehensive objective optimization problem, simplifies the solution process, and avoids the complexity and inefficiency caused by the joint operation of multiple software in traditional solutions. In addition, the use of intelligent optimization algorithms for global optimization not only improves the calculation efficiency, but also effectively avoids the problem of local optimal solutions, thereby ensuring that the optimal bushing stiffness parameters are found quickly and accurately while meeting multiple optimization objectives, significantly improving the design efficiency and optimization quality.

[0079] Figure 2 A flow chart of a method for determining bushing parameters in a suspension system provided by an embodiment of the present application is shown.

[0080] Understandably, Figure 2 The embodiment introduces the solution of the present application from the perspective of simulation optimization and describes the entire optimization process. Figure 2 As shown, the method may include the following steps.

[0081] S210: Determine the stiffness parameters of each bushing in the target suspension system as optimization variables.

[0082] For example, when developing a vehicle model with a three-point powertrain suspension, the static stiffness of each suspension bushing in three directions are used as optimization variables, that is, a total of 9 variables.

[0083] S220. Determine the stiffness value range of each bushing in the target suspension system as a constraint condition.

[0084] Exemplarily, the optimized static stiffness of each suspension bushing in the target suspension system ranges from 50 to 400 N / mm.

[0085] That is to say, within this range, the bushing stiffness value should not exceed the upper limit or be lower than the lower limit, otherwise the suspension system will fail to meet the performance requirements or structural problems will occur.

[0086] It should be understood that the specific constraints can be further adjusted according to actual needs, and this application does not limit this.

[0087] S230: Setting multiple optimization goals.

[0088] In order to ensure that the optimized bushing stiffness parameters can meet the NVH (noise, vibration, and harshness) performance requirements of the entire vehicle, multiple optimization targets need to be set.

[0089] Optimization objectives may include: rigid body modal spacing, frequency range, and decoupling ratio of the powertrain mount system.

[0090] The first optimization goal is that the interval between the rigid body modes corresponding to the target powertrain is greater than or equal to a first preset value.

[0091] It should be understood that the first preset value refers to the minimum interval between rigid body modes, and this value is set to avoid the occurrence of resonance, thereby ensuring the vibration isolation effect of the suspension system. According to different design requirements, the first preset value can be set according to the operating environment of the vehicle, the required vibration isolation effect and the design goal. This application does not limit this.

[0092] Exemplarily, the first preset value may be set to 1 Hz.

[0093] The second optimization goal is that the frequencies of each rigid body mode corresponding to the target powertrain meet preset frequency conditions.

[0094] It should be understood that the preset frequency condition may include a limited range of maximum and minimum rigid body modal frequencies. This condition ensures that the vibration characteristics of the powertrain will not resonate with the natural frequency of the vehicle, avoiding unnecessary vibration and noise. In practical applications, the preset frequency condition can be further adjusted according to the design characteristics of different powertrains and suspension systems, and this application does not limit this.

[0095] Exemplarily, the preset frequency conditions are: the maximum rigid body mode does not exceed 70 Hz, and the minimum mode is not less than 20 Hz.

[0096] The third optimization goal is that the decoupling rate of each rigid body mode corresponding to the target powertrain is greater than or equal to a second preset value.

[0097] It should be understood that the decoupling rate refers to the degree of isolation between rigid body modal vibrations in different directions. A higher decoupling rate means that the vibrations of each mode are more independent, reducing mutual interference, thereby improving vibration isolation performance. In practical applications, the value of the decoupling rate can be determined according to the requirements of the NVH performance of the entire vehicle, and this application does not limit this.

[0098] Exemplarily, the second preset value may be set to 80%, which indicates that the vibrations of each mode should have a better isolation effect to ensure that the vibrations of the powertrain are not excessively transmitted to the vehicle body.

[0099] S240, constructing multiple objective functions based on multiple optimization objectives, and performing linear weighted processing on the multiple objective functions to obtain a comprehensive objective function.

[0100] Each optimization goal corresponds to a separate objective function, which are:

[0101] The first objective function is the rigid body mode interval function, which corresponds to the first optimization goal and reflects whether the interval between rigid body modes meets the requirements.

[0102] The second objective function is a rigid body modal frequency range function, which corresponds to the second optimization goal and ensures that the rigid body modal frequency is within a preset range.

[0103] The third objective function is a decoupling rate function, which corresponds to the third optimization goal and ensures that the decoupling rate meets the design standard.

[0104] These objective functions are linearly weighted by weight coefficients to obtain a comprehensive objective function.

[0105] The expression of the comprehensive objective function is: R=aX+bY+cZ.

[0106] Wherein, R is the weighted comprehensive objective function, X represents the first objective function, Y represents the second objective function, Z represents the third objective function, a is the weight value of the first objective function, b is the weight value of the second objective function, and c is the weight value of the third objective function.

[0107] It should be understood that the setting of the weight coefficient can be adjusted according to design requirements.

[0108] Exemplarily, R=0.55*(X+Y)+0.45*(Z), and this weight assignment indicates that the interval and frequency range of the rigid body mode are more important than the decoupling rate.

[0109] The constructed comprehensive objective function is determined as the optimization objective function.

[0110] S250, optimizing the comprehensive objective function under constraint conditions through a preset optimization algorithm, and finally determining the optimal stiffness parameters of each bushing in the target suspension system through a series of iterative steps.

[0111] Using a preset optimization algorithm (such as the whale optimization algorithm, the ant colony optimization algorithm, etc.), an iterative solution is performed within the range of bushing stiffness and other constraints, and the bushing stiffness parameters are gradually adjusted to find the target value of the comprehensive objective function. The target value is the minimum / maximum comprehensive objective function value that satisfies the constraints and reaches the preset number of iterations. The stiffness parameter corresponding to the target value is determined as the optimal stiffness parameter of each bushing in the target suspension system.

[0112] This embodiment simplifies the optimization process by integrating multiple optimization objectives into a comprehensive objective function, avoids the complexity of using multiple software for joint optimization in traditional methods, and improves optimization efficiency. The intelligent optimization algorithm can quickly and accurately find the optimal solution that meets multiple optimization objectives, reducing computing time and resource consumption, while improving the accuracy and reliability of the design.

[0113] Figure 3 A flow chart of a method for determining bushing parameters in a suspension system provided by an embodiment of the present application is shown.

[0114] It should be understood that Figure 3 The illustrated embodiment can be viewed as an example of step S140, which introduces the process of optimizing the stiffness parameters of the suspension bushing using the whale optimization algorithm.

[0115] Whale Optimization Algorithm (WOA) is a meta-heuristic intelligent optimization algorithm that imitates the bubble net predation strategy of humpback whales and adopts three optimization strategies, namely spiral attack, encirclement of prey and random search, to update the bushing stiffness parameters, thereby achieving the optimization goal.

[0116] like Figure 3 As shown, the method may include the following steps.

[0117] S300, start the optimization process.

[0118] The optimization process starts and the optimization algorithm begins to execute.

[0119] S310: Initialize various parameters of the WOA algorithm.

[0120] Exemplarily, the initial parameters of the WOA algorithm are set, such as the number of populations, the maximum number of iterations, etc.

[0121] Exemplarily, an initial population solution of the WOA algorithm is set, wherein the population solution includes multiple groups of candidate stiffness parameters of each bushing in the target suspension system.

[0122] S320. Calculate the value of the comprehensive objective function (fitness) according to the initial conditions.

[0123] According to the initial bushing stiffness parameters, the results of each objective function are calculated, and the fitness value is calculated according to the given comprehensive objective function expression.

[0124] S330, selecting different optimization strategies to update the fitness value.

[0125] The WOA algorithm has three optimization strategies: spiral attack, encirclement of prey, and random search.

[0126] In this step, the fitness value of the bushing stiffness is updated by selecting the corresponding optimization strategy by generating a random number between 0 and 1. By continuously applying these strategies, the optimal stiffness value that satisfies the constraints and minimizes the comprehensive objective function is updated.

[0127] S340: Determine whether the updated fitness value is less than the current minimum value.

[0128] If yes, execute step S350; if no, execute step S360.

[0129] S350: Update the current minimum value and record the rigidity parameters of each bushing in the target suspension system.

[0130] If the fitness value is updated successfully, the current bushing stiffness parameter is recorded and used as the current optimal solution.

[0131] S360: Determine whether the current number of iterations exceeds a preset maximum number of iterations.

[0132] If yes, execute step S370; if no, return to execute step S310 and continue optimization.

[0133] S370, end this optimization process.

[0134] The optimization process ends and the current optimal bushing stiffness parameters are output.

[0135] This embodiment utilizes the global search capability and fast convergence characteristics of the WOA algorithm to find the optimal solution in a relatively short time, thus avoiding the local optimal problem of traditional optimization methods and improving optimization efficiency and accuracy.

[0136] The following is an example of the stiffness optimization result of the suspension bushing obtained by applying the method of the present application.

[0137] It should be noted that this example is applied to a three-point mounted powertrain.

[0138] The following is Table 1:

[0139]

[0140] After optimizing the bushing stiffness in the three-point suspension system using the method of the present application, the static stiffness values ​​of each bushing in different directions (u, v, w) were obtained (Table 1).

[0141] In Table 1, u, v, and w represent the static stiffness values ​​of the bushing in three directions (horizontal, vertical, and vertical), and the stiffness value of each mount is the result of optimization in that direction. These optimized stiffness values ​​not only improve the vibration characteristics of the mount system, but also provide an important basis for further powertrain modal matching.

[0142] The following is Table 2:

[0143] Modal Order First level Second order Third level Fourth level Level 5 Level 6 Unit / Hz 14.4 19.5 22.0 29.5 31.3 51.9 Mode direction Y X Z RX R RY Decoupling rate 93.1％ 95.0％ 95.0％ 81.6％ 94.0％ 84.2％

[0144] Table 2 shows the calculation results of the rigid body mode and decoupling rate of the target powertrain under the optimized static stiffness value.

[0145] It can be seen from Table 2 that the frequencies (unit: Hz) of each order mode after optimization meet the preset frequency range requirements. Specifically, the optimized modal frequencies are evenly distributed from the first order 14.4 Hz to the sixth order 51.9 Hz and do not exceed 70 Hz, which meets the ideal modal distribution requirements.

[0146] In addition, Table 2 also lists the decoupling rate corresponding to each mode, which represents the degree of coupling between modes. According to the data in the table, the decoupling rate of all modes exceeds 80%, among which the decoupling rate of the first-order mode is 93.1%, the second-order mode is 95.0%, and the third-order mode is 95.0%, which are far higher than the requirement of 80%, indicating that the optimized bushing stiffness design greatly improves the decoupling effect of the powertrain, thereby effectively reducing vibration transmission and improving the NVH performance of the vehicle.

[0147] In summary, the bushing stiffness optimized by the method of the present application not only meets the requirements of the powertrain mode, but also effectively improves the decoupling rate, significantly improving the comfort and stability of the entire vehicle.

[0148] The above mainly introduces a method for determining bushing parameters in a suspension system in accordance with an embodiment of the present application in conjunction with the accompanying drawings. At the same time, it should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence, these steps are not necessarily executed in sequence in the order shown in the figure. Unless otherwise specified in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps. The following is an introduction to a device for determining bushing parameters in a suspension system in accordance with an embodiment of the present application in conjunction with the accompanying drawings. For the sake of brevity, appropriate omissions will be made when introducing the device below, and the relevant content can refer to the relevant description in the above method, and will not be repeated.

[0149] Corresponding to the method described in the above embodiment, Figure 4 A structural block diagram of an apparatus 1000 for determining bushing parameters in a suspension system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0150] like Figure 4 As shown, the device 1000 may include:

[0151] The acquisition module 1001 is used to acquire the property parameters of the target powertrain and the suspension parameters of the target suspension system.

[0152] The target mount system is used to suspend the target powertrain.

[0153] The model building module 1002 is used to build multiple objective functions respectively for multiple optimization objectives of the stiffness parameters of the bushing in the target suspension system based on the attribute parameters and the suspension parameters.

[0154] Each objective function corresponds to an optimization goal;

[0155] The fusion module 1003 is used to fuse multiple objective functions to obtain a comprehensive objective function.

[0156] The optimization module 1004 is used to solve the comprehensive objective function based on a preset algorithm to determine the optimal stiffness parameters of the bushing in the target suspension system.

[0157] Figure 5 A schematic diagram of the hardware structure of a computer device provided in an embodiment of the present application is shown.

[0158] The computer device may include a processor 7001 and a memory 7002 storing computer program instructions.

[0159] Specifically, the processor 7001 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0160] The memory 7002 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 7002 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In one example, the memory 7002 may include a removable or non-removable (or fixed) medium, or the memory 7002 is a non-volatile solid-state memory. The memory 7002 may be inside or outside the integrated gateway disaster recovery device.

[0161] In one example, the memory 7002 may be a read-only memory (ROM). In one example, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0162] The memory 7002 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0163] The processor 7001 reads and executes the computer program instructions stored in the memory 7002 to implement Figure 1 A method of determining bushing parameters in a suspension system in an illustrated embodiment.

[0164] In one example, the computer device may further include a communication interface 7003 and a bus 7004. Figure 5 As shown, the processor 7001, the memory 7002, and the communication interface 7003 are connected via a bus 7004 and communicate with each other.

[0165] The communication interface 7003 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0166] Bus 7004 includes hardware, software or both, and the components of online data flow billing equipment are coupled to each other. For example, but not limitation, the bus may include Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), Hyper Transport (HT) interconnection, Industry Standard Architecture (ISA) bus, InfiniBand interconnection, Low Pin Count (LPC) bus, Memory bus, Micro Channel Architecture (MCA) bus, Peripheral Component Interconnect (PCI) bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 7004 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0167] In addition, in combination with the method for determining bushing parameters in the suspension system in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any method for determining bushing parameters in the suspension system in the above embodiment is implemented.

[0168] An embodiment of the present application further provides a computer program product, including a computer program, which, when executed, implements any one of the methods for determining bushing parameters in a suspension system in the above embodiments.

[0169] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0170] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), appropriate firmware, plug-in, function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or communication link by a data signal carried in a carrier. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (Read-Only Memory, ROM), flash memory, erasable read-only memory (Erasable Read Only Memory, EROM), floppy disks, compact disc read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical discs, hard disks, optical fiber media, radio frequency (Radio Frequency, RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0171] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0172] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for determining bushing parameters in a suspension system, characterized in that: include: Acquiring property parameters of a target powertrain and suspension parameters of a target suspension system, wherein the target suspension system is used to suspend the target powertrain; Based on the attribute parameters and the suspension parameters, for multiple optimization targets of the stiffness parameters of the bushing in the target suspension system, a plurality of objective functions are respectively constructed, each objective function corresponding to an optimization target; fusing the multiple objective functions to obtain a comprehensive objective function; Based on a preset algorithm, the comprehensive objective function is solved to determine the optimal stiffness parameters of the bushing in the target suspension system.

2. The method according to claim 1, characterized in that The fusing of the multiple objective functions to obtain a comprehensive objective function includes: According to the preset weight corresponding to each objective function in the multiple objective functions, the multiple objective functions are merged into the comprehensive objective function.

3. The method according to claim 1, characterized in that The plurality of objective functions include at least two of a first objective function, a second objective function, or a third objective function; The optimization goal corresponding to the first objective function is: the interval between the rigid body modes corresponding to the target powertrain is greater than or equal to a first preset value; The optimization goal corresponding to the second objective function is: the frequency of each rigid body mode corresponding to the target powertrain satisfies a preset frequency condition; The optimization target corresponding to the third objective function is: the decoupling rate of each rigid body mode corresponding to the target powertrain is greater than or equal to a second preset value.

4. The method according to any one of claims 1 to 3, characterized in that The method of solving the comprehensive objective function based on a preset algorithm to determine the optimal stiffness parameters of each bushing in the target suspension system includes: Determining a preset stiffness range of a bushing in the target suspension system as a constraint condition; Based on the preset algorithm and the constraint conditions, the comprehensive objective function is solved to determine the optimal stiffness parameters of the bushing in the target suspension system.

5. The method according to claim 4, characterized in that The step of solving the comprehensive objective function based on the preset algorithm and the constraint conditions to determine the optimal stiffness parameters of each bushing in the target suspension system includes: By iterative updating, a target value of the comprehensive objective function is found, wherein the target value is a maximum value or a minimum value that satisfies the constraint condition and reaches a preset number of iterations; The stiffness parameter corresponding to the target value is determined as the optimal stiffness parameter.

6. The method according to claim 5, characterized in that The step of finding the target value of the comprehensive objective function through iterative updating includes: Initializing algorithm parameters and population solutions of an optimization algorithm, wherein the population solutions include multiple sets of candidate stiffness parameters of each bushing in the target suspension system, and the optimization algorithm includes multiple optimization search strategies; The target value is found by iteratively updating the population solution through the multiple optimization search strategies.

7. The method according to any one of claims 1 to 3, characterized in that The stiffness parameters of each bushing in the target suspension system include horizontal static stiffness, vertical static stiffness and vertical static stiffness.

8. A device for determining bushing parameters in a suspension system, characterized in that: include: An acquisition module, used for acquiring property parameters of a target powertrain and suspension parameters of a target suspension system, wherein the target suspension system is used for suspending the target powertrain; A model building module, for building a plurality of objective functions for a plurality of optimization objectives of a stiffness parameter of a bushing in the target suspension system based on the attribute parameters and the suspension parameters, each objective function corresponding to an optimization objective; A fusion module, used for fusing the multiple objective functions to obtain a comprehensive objective function; The optimization module is used to solve the comprehensive objective function based on a preset algorithm to determine the optimal stiffness parameters of the bushing in the target suspension system.

9. A computer device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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