CN117610159A - Robustness optimization method for automobile suspension system - Google Patents
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Abstract
The application provides an automobile suspension system robustness optimization method, which relates to the technical field of computer systems, wherein the method comprises the following steps: acquiring a plurality of robustness optimization directions and system constraint conditions of an automobile suspension system; acquiring a simulation system aiming at an automobile suspension system; configuring a multi-objective optimization model for the simulation system based on a plurality of robustness optimization directions and system constraint conditions; inputting the suspension system data set into a simulation system, so that the simulation system executes a multi-objective optimization model to determine an optimal solution for each suspension parameter type according to the suspension system data set; each suspension system data in the suspension system data set originates from a respective live driving scenario of the vehicle. Therefore, simulation, robustness verification and optimization of the automobile suspension system are realized through simulation running of the multi-objective optimization model, so that the automobile suspension system can be improved and balanced in a plurality of robustness optimization directions.
Description
Technical Field
The application relates to the technical field of computer systems, in particular to an automobile suspension system robustness optimization method.
Background
The automobile suspension system is a crucial component in the dynamics of the vehicle, and has direct influence on the travelling comfort, the stability and the performance of the whole vehicle.
With the development of computer simulation system technology, the simulation of an automobile suspension system has become an important means for design and optimization. However, the existing simulation effect has some unsatisfactory problems, such as insufficient adaptability to complex working conditions, and the vibration and impact of the automobile on the actual road cannot be accurately simulated, so that the performance of the automobile suspension system cannot be effectively evaluated and optimized.
In view of the above problems, currently, no preferred technical solution is proposed.
Disclosure of Invention
The application provides a robustness optimization method of an automobile suspension system, which is used for at least solving the problem that a simulation system in the prior art cannot evaluate and optimize the performance of the automobile suspension system better.
The application provides an automobile suspension system robustness optimization method, which comprises the following steps: acquiring a plurality of robustness optimization directions and system constraint conditions of an automobile suspension system; acquiring a simulation system aiming at the automobile suspension system; the simulation system defines various suspension parameter types of the automobile suspension system; configuring a multi-objective optimization model for the simulation system based on a plurality of the robustness optimization directions and the system constraint conditions; decision variables of the multi-objective optimization model are defined according to the respective suspension parameter types, objective functions of the multi-objective optimization model are defined according to the plurality of robustness optimization directions, and optimization paths of the multi-objective optimization model are defined according to the system constraint conditions; inputting a suspension system dataset into the simulation system such that the simulation system executes the multi-objective optimization model to determine an optimal solution for each of the suspension parameter types from the suspension system dataset; each of the suspension system data in the suspension system data set is respectively derived from a corresponding live running scene of the automobile, the suspension system data comprises a plurality of suspension system parameters, and each suspension system parameter is respectively provided with a corresponding suspension parameter type.
Optionally, the robustness optimization direction includes at least one of: minimizing vibration amplitude, minimizing vehicle roll angle, and maximizing suspension system response frequency; the system constraints include at least one of: the suspension height is not less than a preset minimum suspension height, the suspension system stroke is not less than a preset minimum stroke, and the maximum vertical acceleration of the vehicle during acceleration is less than a preset acceleration threshold.
Optionally, the suspension parameter type includes any one of the following: spring rate, damping coefficient, suspension height, anti-roll bar stiffness, suspension travel, and shock absorber configuration parameters.
Optionally, the multi-objective optimization model adopts a hybrid multi-objective optimization model, and the hybrid multi-objective optimization model comprises a particle swarm optimization model module and an NSGA-II model module which are cascaded.
Optionally, the simulation system executes the multi-objective optimization model to determine an optimal solution for each of the suspension parameter types from the suspension system dataset, comprising: inputting the suspension system dataset into the particle swarm optimization model module to initialize a particle swarm position according to each suspension system parameter in the suspension system dataset; updating the position and the speed of the particle swarm based on the particle swarm optimization model module, and solving the optimal particle swarm corresponding to each suspension parameter type; inputting the optimal particle set and the suspension system dataset to the NSGA-II model module to initialize a population according to the optimal particle set and the suspension system dataset; performing genetic operation processing based on the NSGA-II model module to update the population, and calculating pareto fronts corresponding to the objective functions; the genetic manipulation process comprises: selection, crossover and mutation; and updating at least one particle in the optimal particle group according to the pareto front, and determining an optimal solution for each suspension parameter type based on the updated optimal particle group.
Optionally, the particle velocity is updated by including:
the particle location is updated by including:
wherein, for the i-th particle (i=1, 2,., N), the parameter space is searched in D-dimension (d=1, 2,., D);
wherein,representing the d-th dimensional position of the ith particle at time t; />Representing the d-th dimensional velocity of the ith particle at time t; p (P) id The d-th dimensional coordinates representing the best position encountered by the ith particle so far, i.e. the individual optima; g d D-th dimensional coordinates representing the best positions encountered by all particles so far, i.e. global optima; w represents inertial weight, controlling the influence of the current speed of the particles on the update speed of the particles; c 1 And c 2 Respectively representing individual learning weights and group learning weights; r is (r) 1 And r 2 Respectively the intervals [0,1 ]]The random number on the code is introduced into randomness for each iteration; n represents the total particle number of the particle swarm; d represents the number of objective functions.
Optionally, the performing genetic manipulation based on the NSGA-II model module to update the population and calculate pareto fronts corresponding to each of the objective functions includes: assigning a dominance level to each individual in the updated population for said individual; determining, for each of the dominance levels, a crowding distance for each individual at the dominance level relative to other individuals in the updated population; and determining the pareto front according to the dominance grade and the congestion grade.
Optionally, the crowding distance is determined by including:
I(i)＝(I(i+1) m -I(i-1) m )(f m,max -f m,min )，
wherein I (I) is the crowding distance of the ith individual, f m Represents the mth objective function, f m,max And f m,min The maximum and minimum of the whole population on the objective function, respectively.
Optionally, the simulation system comprises a plurality of physical simulation models, the physical simulation models including at least one of: particle model, spring model, damping model, road surface excitation model and multi-degree of freedom model.
Optionally, the particle model is determined by including:
where m represents the mass of the particle,indicating mass acceleration, c the damping coefficient of the suspension, k the spring rate of the suspension, y the mass displacement, +.>Indicating particle velocity, z indicating road surface excitation displacement, and +.>Representing the speed of road surface excitation;
the multiple degree of freedom model is determined by including the following ways:
where M represents a mass matrix of the vehicle,representing vectors containing acceleration of parts of the vehicle, C representing the damping matrix, K representing the stiffness matrix,/->Is a vector containing the speed of each part of the vehicle, Y is a vector containing the displacement of each part of the vehicle, and R (t) is a road surface excitation vector to which each part of the vehicle is subjected; the displacement of each part of the vehicle comprises the following steps: vertical displacement of the vehicle body, suspension vibration, wheel displacement, and vehicle body tilting.
The application also provides an automobile suspension system robustness optimization system, comprising: the system comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring a plurality of robustness optimization directions and system constraint conditions of an automobile suspension system; the second acquisition unit is used for acquiring a simulation system aiming at the automobile suspension system; the simulation system defines various suspension parameter types of the automobile suspension system; an optimization model configuration unit, configured to configure a multi-objective optimization model for the simulation system based on a plurality of the robustness optimization directions and the system constraint conditions; decision variables of the multi-objective optimization model are defined according to the respective suspension parameter types, objective functions of the multi-objective optimization model are defined according to the plurality of robustness optimization directions, and optimization paths of the multi-objective optimization model are defined according to the system constraint conditions; a simulation run unit for inputting a suspension system dataset into the simulation system such that the simulation system executes the multi-objective optimization model to determine an optimal solution for each of the suspension parameter types from the suspension system dataset; each of the suspension system data in the suspension system data set is respectively derived from a corresponding live running scene of the automobile, the suspension system data comprises a plurality of suspension system parameters, and each suspension system parameter is respectively provided with a corresponding suspension parameter type.
The application also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for optimizing the robustness of the automotive suspension system according to any one of the above when executing the program.
The present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of optimizing robustness of an automotive suspension system as described in any of the above.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements a method of optimizing the robustness of an automotive suspension system as described in any one of the above.
According to the robustness optimization method, system, electronic equipment and non-transitory computer readable storage medium for the automobile suspension system, provided by the application, the optimization problem and the objective function of the optimization model are more comprehensive and accurate by comprehensively considering a plurality of robustness optimization directions and system constraint conditions. Based on the establishment of a simulation system and the configuration of a multi-objective optimization model, the problem of insufficient simulation effect at present is effectively solved, and particularly, the adaptability under complex working conditions is remarkably improved. Then, by defining decision variables, objective functions and optimization paths, the optimization model reflects the actual characteristics of the automobile suspension system more accurately, and in the aspect of simulation of vibration and impact of an actual road, by using the suspension system data set, the simulation system is ensured to fully consider various live running scenes of the automobile, so that the running condition of the automobile on the actual road is simulated more truly. Furthermore, by simulating and running the multi-objective optimization model, the optimal solution for each suspension parameter type is obtained, and powerful support is provided for performance evaluation and optimization of the automobile suspension system. Therefore, a more reliable optimized simulation system is provided for automobile manufacturers and designers, simulation, robustness verification and optimization of an automobile suspension system are realized, an optimal optimization scheme aiming at suspension system parameters is provided, the automobile suspension system is obviously improved in the aspects of driving comfort, stability and overall vehicle performance, and the comfort and driving stability of a driver are improved.
Drawings
For a clearer description of the present application or of the prior art, the drawings that are used in the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a flowchart of an example of an automotive suspension system robustness optimization method according to an embodiment of the present application;
FIG. 2 illustrates a block diagram of an example of a multi-objective optimization model in accordance with an embodiment of the present application;
FIG. 3 illustrates a flowchart of one example of a process for determining an optimal solution using a hybrid multi-objective optimization model, according to an embodiment of the present application;
FIG. 4 illustrates an example operational flow diagram for calculating a pareto front by NSGA-II according to an embodiment of the present application;
FIG. 5 illustrates a block diagram of an example of an automotive suspension system robustness optimization system according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
FIG. 1 illustrates a flowchart of an example of a method of optimizing robustness of an automotive suspension system according to an embodiment of the present application.
The execution body of the method of the embodiment of the application or the carrier of the system of the embodiment of the application can be any electronic equipment with processing and calculating capabilities, such as a computer, a mobile phone and the like, so as to realize simulation, robustness verification and optimization of the suspension system of the automobile, and provide an optimal optimization scheme for the parameters of the suspension system.
As shown in fig. 1, in step S110, a plurality of robustness optimization directions and system constraints of an automobile suspension system are acquired.
In some embodiments, analysis and modeling of the suspension system is required to determine a plurality of robustness optimization directions involved in the system, such as shock absorption performance, suspension stiffness, etc. Through intensive research on the characteristics of the suspension system, key performance indexes and adjustable parameters of the system are obtained. At the same time, constraints of the system are clarified, including but not limited to process limitations, cost constraints, and performance limitations. Therefore, key performance parameters of the suspension system are accurately captured, and basic conditions are provided for subsequent simulation optimization design.
More specifically, the robustness optimization direction includes at least one of: minimizing vibration amplitude, minimizing vehicle roll angle, and maximizing suspension response frequency. The system constraints include at least one of: the suspension height is not less than a preset minimum suspension height, the suspension system stroke is not less than a preset minimum stroke, and the maximum vertical acceleration of the vehicle during acceleration is less than a preset acceleration threshold. Of course, other types of parameters may also be employed for the robustness optimization direction and system constraints, and the system constraints may also be set, for example, based on material properties, geometric parameters, costs, and safety environmental regulations, and the robustness optimization direction may also be to minimize energy loss of the vehicle suspension system, to minimize vertical acceleration during braking, and so forth.
It should be noted that, the setting of the robustness optimization direction and the system constraint condition is described as follows: 1) Minimizing vibration amplitude: by reducing the vibration amplitude of the suspension system, the driving comfort of the vehicle is improved. 2) Minimizing vehicle roll angle: the roll angle of the vehicle under the conditions of turning and the like is reduced, and the stability of the vehicle is improved. 3) Maximizing the response frequency of the suspension system: by adjusting parameters of the suspension system, the natural frequency of the suspension system is higher, and the adaptability of the system to different road conditions is improved. 4) Minimizing energy loss of a vehicle suspension system: the energy loss of the suspension system in the running process is reduced, and the energy efficiency performance of the vehicle is improved. 5) Minimizing vertical acceleration during braking: by optimizing the suspension system parameters, the vertical acceleration of the vehicle during braking is reduced, and the comfort during braking is improved. 6) Material constraint conditions: ensuring that the spring material used in the suspension system does not exceed its strength limit when subjected to a force. 7) Geometric parameter constraint conditions: the minimum height of the suspension system is limited to ensure a minimum ground clearance for the vehicle under certain conditions. 8) Constraint maximum vertical acceleration: the maximum vertical acceleration of the vehicle during acceleration is limited to ensure safety performance. 9) Suspension system travel constraints: ensuring that the suspension system has sufficient travel to accommodate different road conditions.
In step S120, a simulation system for an automobile suspension system is acquired.
Here, the simulation system defines the respective suspension parameter types of the car suspension system. In particular, the simulation system is capable of simulating the behavioral actions of the suspension system and providing an assessment of system performance. The simulation system should cover various suspension parameter types of the suspension system that may be optimized, such as spring rate, damping coefficient, etc.
In some implementations, the suspension parameter types include any one of the following: spring rate k, damping coefficient c, suspension height h, suspension travel s, anti-roll bar stiffness τ, and shock absorber configuration parameter b.
In addition, the parameter optimization settings, such as ANSYS, ABAQUS, simulink, etc., can be performed on an existing simulation tool platform. Therefore, a reliable simulation environment for highly restoring the characteristics of the actual suspension system is established, and an accurate test platform is provided for subsequent optimization.
In step S130, a multi-objective optimization model is configured for the simulation system based on the plurality of robustness optimization directions and the system constraints.
Here, decision variables of the multi-objective optimization model are defined according to respective suspension parameter types, objective functions of the multi-objective optimization model are defined according to a plurality of robustness optimization directions, and optimization paths of the multi-objective optimization model are defined according to system constraints.
It should be noted that the type of the multi-objective optimization model is not limited herein, and for example, a mixed integer programming model, a genetic algorithm model, a particle swarm optimization model, an ant colony optimization model, a fuzzy clustering model, or a neural network model may be used. In one example of the embodiment of the present application, the architecture of the multi-objective optimization model in the prior art is directly multiplexed, and only the corresponding parameters need to be modified. In another example of an embodiment of the present application, an updated model architecture is presented for an automotive suspension system robustness optimization scenario, with more details being developed below in connection with other examples.
Furthermore, when the multi-objective optimization model employs a genetic algorithm or a particle swarm algorithm, each individual or particle in the algorithm is defined by a set of variables comprising the respective suspension parameter types described above, e.g., by [ k, c, h, s, τ, b ].
Therefore, a multi-objective optimization model is configured by utilizing the robustness optimization direction of the suspension system and the constraint condition of the system, and the driving comfort and stability of the automobile suspension system under different road conditions are simulated through the design of decision variables and objective functions. Furthermore, the optimization path of the target optimization model is constrained by the constraint condition of the system, so that the obtained optimization scheme can be ensured to be feasible. By the embodiment of the application, an optimization model which comprehensively considers the demands of multiple aspects and the system constraint is established, and the comprehensive performance of the suspension system is improved.
In step S140, the suspension system dataset is input to the simulation system such that the simulation system executes a multi-objective optimization model to determine an optimal solution for each suspension parameter type from the suspension system dataset.
Here, each suspension system data in the suspension system data set originates from a respective live running scene of the vehicle, the suspension system data comprising a plurality of suspension system parameters, each suspension system parameter having a respective suspension parameter type.
More specifically, suspension system parameters of a plurality of automobiles of different types in various actual road condition driving scenes are collected, and the suspension system parameters can be obtained in the form process through actual testing, vehicle driving record and other modes. Then, after inputting the data into the simulation system, the simulation system may determine an optimal suspension parameter combination according to the multi-objective optimization model. Therefore, through the input of actual data, the change of suspension system parameters when the automobile runs under different actual road conditions is collected, the actual working condition of the suspension system under different road conditions is simulated, the multi-objective optimization model can be adjusted and optimized according to the actual data, and the feasibility and the effectiveness of an optimization scheme are further improved.
In the embodiment of the application, the multi-objective optimization model is operated through simulation, so that the optimal solution for each suspension parameter type is obtained, powerful support is provided for performance evaluation and optimization of the automobile suspension system, simulation, robustness verification and optimization of the automobile suspension system are realized, an optimal optimization scheme for suspension system parameters is provided, the automobile suspension system is obviously improved in the aspects of driving comfort, stability and overall vehicle performance, and the comfort and driving stability of a driver are improved.
FIG. 2 illustrates a block diagram of an example of a multi-objective optimization model in accordance with an embodiment of the present application.
As shown in fig. 2, the multi-objective optimization model 200 employs a hybrid multi-objective optimization model that includes a cascaded particle swarm optimization model module 210 and an NSGA-II model module 220.
According to the embodiment of the application, the particle swarm optimization model and the NSGA-II model are combined by adopting the mixed multi-objective optimization model, so that the advantages of the particle swarm optimization model and the NSGA-II model can be fully exerted in the optimization process, and the model is more comprehensively searched in the whole parameter space for optimizing a plurality of performance indexes (such as driving comfort and stability) related in a suspension system, so that the comprehensive improvement of a plurality of performance targets is realized. In addition, the particle swarm algorithm has higher local searching and convergence speed, and the NSGA-II algorithm can keep the diversity of solutions and avoid sinking into a local optimal solution. The mixed multi-objective optimization model fully utilizes the advantages of the mixed multi-objective optimization model and the mixed multi-objective optimization model through reasonable module design, and realizes the balance of rapid local convergence and global search in the optimization process, thereby improving the overall search efficiency. In addition, in the suspension system optimization, the optimal parameters of different vehicle types and different working conditions may have differences, and different optimization algorithm modules can be selected according to the characteristics of specific problems through the flexibility of the mixed multi-objective optimization model, so that the characteristics of different suspension systems are better adapted, and the universality and the adaptability of the algorithm are improved.
Therefore, by using the hybrid multi-objective optimization model, the advantages of different algorithms are fully exerted, so that the method has comprehensiveness, adaptability and robustness in the robustness optimization of the suspension system, and a better solution can be found in a shorter time than that of a single algorithm.
FIG. 3 illustrates a flowchart of one example of a process for determining an optimal solution using a hybrid multi-objective optimization model, according to an embodiment of the present application.
As shown in fig. 3, in step S310, the suspension system dataset is input to a particle swarm optimization model module to initialize the particle swarm positions according to the individual suspension system parameters in the suspension system dataset.
In particular, the suspension system dataset comprises a combination of different suspension system parameters, which data originate from the respective live driving scenario of the vehicle. By inputting these data into the particle swarm optimization model module, the position of the particle swarm, i.e. the initial value of the suspension system parameter, can be initialized. Therefore, the data of the real scene is utilized to provide an accurate initial point for the optimization algorithm, which is helpful for accelerating the optimization process and improving the accuracy of the optimization result.
In step S320, the position and speed of the particle swarm are updated based on the particle swarm optimization model module, and the optimal particle swarm corresponding to each suspension parameter type is solved.
Therefore, the particle positions are gradually adjusted through the particle swarm optimization algorithm to enable the particle swarm optimization algorithm to be closer to the optimal solution, and therefore searching capacity of the local optimal solution is improved.
In step S330, the optimal particle set and suspension system dataset are input to the NSGA-II model module to initialize the population based on the optimal particle set and suspension system dataset.
It should be noted that, the optimal particle set is a set of optimal solutions found in the particle swarm optimization model module, but there may be a case of trapping in a locally optimal solution. By inputting these optimal solutions and suspension system datasets into the NSGA-II model module, the population of NSGA-II algorithms is initialized, thereby increasing the diversity of global searches, helping to find a more global optimal solution throughout the parameter space.
In step S340, genetic manipulation processing is performed based on the NSGA-II model module to update the population, and pareto fronts corresponding to the respective objective functions are calculated.
Here, the genetic manipulation process includes: selection, crossover and mutation. Therefore, individuals in the population are optimized through genetic operation processing, and the diversity of solutions is kept, so that the algorithm can simultaneously pursue a plurality of targets in the optimization process, and local and global searches are balanced better.
In step S350, at least one particle in the optimal particle set is updated according to the pareto front, and an optimal solution for each suspension parameter type is determined based on the updated optimal particle set.
Therefore, by comprehensively considering a plurality of objective functions and selecting a proper solution for updating, the final suspension system parameter combination more comprehensively meets the design requirements of each robustness optimization direction.
Note that Pareto front (Pareto front) refers to a set of non-inferior solutions of different objective function values in the multi-objective optimization problem. In multi-objective optimization, multiple conflicting objectives are often involved, and improving one objective may result in the deterioration of another objective. The pareto front shows the optimal other target values that can be achieved without sacrificing one target. The solution on this leading edge is referred to as a "non-inferior solution" or a "pareto optimal solution". Pareto fronts are formed because of the advantage of some solutions on one target, but not absolute advantages on other targets. Thus, the pareto front shows a set of solutions that are weighted, where none of the solutions is superior to the others on all targets.
Thus, solutions in pareto fronts typically give a range of possible solutions to the multi-objective optimization problem for decision makers to trade-off and choose between different objectives. In suspension system optimization, the pareto front provides a designer of a vehicle suspension system with a range of design choices that trade-off between different performance goals of ride comfort, stability, and response frequency.
The multi-objective optimization operation for the hybrid multi-objective optimization model is expanded as follows:
it should be noted that, in the context of a multi-objective optimization algorithm, the data features may consist of parameter values and their corresponding objective function values, which constitute the dimensions of the solution space.
In a Particle Swarm Optimization (PSO) model module, it utilizes a set of candidate solutions (particles) to move in a search space to find an optimal solution. The position of each particle represents a potential solution, the particles guiding their movement by tracking the optimal position found so far.
Specifically, the particle velocity is updated by including:
updating the position of the population of particles by including:
wherein, for the i-th particle (i=1, 2,., N), the parameter space is searched in D-dimension (d=1, 2,., D);
Wherein,representing the d-th dimensional position of the ith particle at time t; />Representing the d-th dimensional velocity of the ith particle at time t; p (P) id The d-th dimensional coordinates representing the best position encountered by the ith particle so far, i.e. the individual optima; g d D-th dimensional coordinates representing the best positions encountered by all particles so far, i.e. global optima; w represents inertial weight, controlling the influence of the current speed of the particles on the update speed of the particles; c 1 And c 2 Respectively representing individual learning weights and group learning weights; r is (r) 1 And r 2 Respectively the intervals [0,1 ]]The random number on the code is introduced into randomness for each iteration; n represents the total particle number of the particle swarm; d represents the number of objective functions.
More specifically, the PSO model module has more specific details of operation:
first, initializing a particle swarm:
-randomly reorganizing from individual suspension system parameters in the suspension system dataset to generate a number of particles as an initial solution.
-for each particle, randomly initializing its positionAnd speed->
Then, in each iteration, the fitness of the particles is evaluated according to the objective function, and then the individual optimum P is evaluated and determined id And global optimum G d 。
Further, the particle swarm is updated by iteration:
for each particle, calculate its new velocity and new position in the dimensions.
-application of updatesTo calculate the new position +.>
-if the function value corresponding to the updated position improves, updating P id The method comprises the steps of carrying out a first treatment on the surface of the If the global optimum is also refreshed by the particle, then update G d 。
Then, checking whether the fixed iteration times are met, otherwise repeating the iteration.
Finally, when the iteration times are satisfied, the elite example in the final particle swarm is output as the solution of the problem, namely, the individual best example particle P best And global optimum G best And forming an optimal particle group.
Further, the PSO model module non-dominated ordering the resulting optimal solution as part of the NSGA-II (second version of non-dominated ordering genetic algorithm) population. Then, NSGA-II selection, crossover and mutation operations are performed to generate a new population. Then, a plurality of particles are selected from the pareto front edge of NSGA-II to update P best And G best 。
Fig. 4 illustrates an operational flow diagram of an example of the calculation of the pareto front by NSGA-II according to an embodiment of the present application.
As shown in fig. 4, in step S410, for each individual in the updated population, the individual is assigned a dominance rank.
Specifically, to determine the dominance level of each solution in a population, the entire population is divided into multiple levels according to the dominance relationship. Each individual is compared to other individuals in the population. If one individual A is not dominated by any other individual (i.e., it does not score worse on all objective functions than the other individuals), then it is considered the non-dominated individual of the current highest rank (rank 1). Then, all the individuals of rank 1 are removed from the population, and the above procedure is repeated to assign rank 2, rank 3, etc. to the remaining individuals. If solution a dominates solution B, then solution B is added to the dominant solution list of solution a, if solution a is dominated by solution B, then the dominant number of solution a is increased by 1, and all solutions whose dominant number is 0 (i.e. not dominated by any other solution) will form part of the first leading edge, i.e. the pareto leading edge. Finally, these non-dominant solutions are removed and the process is repeated until all solutions are assigned a rank.
In step S420, for each dominant rank, a crowding distance is determined for each individual at the dominant rank relative to other individuals in the updated population.
It should be noted that the crowding distance is a measure of the density in its neighborhood, which is used to support the diversity of pareto fronts.
Specifically, for each objective function value, the individuals in the rank are ordered by the value of the objective function.
For each individual in the rank, the crowding distance is the sum of the differences of the objective function values of its left and right individuals. The corresponding calculation formula is as follows:
I(i)＝(I(i+1) m -I(i-1) m )(f m,max -f m,min )，
wherein I (I) is the crowding distance of the ith individual, f m Represents the mth objective function, f m,max And f m,min The maximum and minimum of the whole population on the mth objective function, respectively.
Further, an optimal solution is selected from the superpopulation that is formed by combining the current population and the offspring population according to the non-dominant ranking and the crowding distance.
Thus, when NSGA-II processes multiple objective functions, a set of solutions (pareto fronts) can be found, where different solutions may represent different performance tradeoffs optimized for each objective.
In step S430, a pareto front is determined according to the dominant level and the congestion level.
In connection with the design example of robustness optimization of an automotive suspension system, it is assumed that the direction of optimization is to reduce vertical vibration acceleration (increase ride comfort) while desiring to maintain a certain road feel. By initializing a set of random suspension system configurations, i.e. the initial population in NSGA-II and the initial particle positions in PSO. Through crossover and mutation operations, NSGA-II can develop a new suspension system configuration, and updating of individual positions in PSO is guided by an individual optimal solution and a global optimal solution.
Over multiple iterations, the hybrid multi-objective optimization model can gradually lock onto suspension system configurations that provide higher ride comfort and ideal road feel, which are considered optimal parameters. Illustratively, if a particular spring rate and damping coefficient configuration is found to give the best fitness, the hybrid multi-objective optimization model will tend to search around these configurations while preserving non-dominant solutions that are inferior in fitness to the optimal solution but may be better at other objectives (e.g., cost or durability).
Therefore, the optimal solution of PSO can be updated by using the non-dominant solution set of NSGA-II to obtain a group of optimal suspension system parameters, and specific requirements and performance targets of the vehicle are met.
In a further optimized embodiment, in the simulation system, a suitable visualization analysis tool (e.g., pareto drawing tool) may also be used to analyze and visualize the Pareto optimal solution set, and select the most suitable design from the Pareto optimal solution set according to different priorities and requirements.
In some examples of embodiments of the present application, the simulation system includes a plurality of physical simulation models including at least one of: particle model, spring model, damping model, road surface excitation model and multi-degree of freedom model.
The basic effect of suspension stiffness and damping ratio on ride comfort and road surface response can be rapidly assessed by the particle model. Through the spring model and the damping model, the working condition of the suspension assembly can be more accurately revealed, and the design of more optimized spring and damper configuration and performance under specific road conditions can be facilitated. Road surface irregularities of various types and frequencies are simulated by a road surface excitation model. Through the multi-degree-of-freedom model, complex dynamic phenomena such as driving comfort and driving stability at different speeds can be simulated.
More specifically, the particle model is determined by including:
Where m represents the mass of the particle,indicating mass acceleration, c the damping coefficient of the suspension, k the spring rate of the suspension, y the mass displacement, +.>Indicating particle velocity, z indicating road surface excitation displacement, and +.>Representing the speed of road surface excitation;
the multiple degree of freedom model is determined by including the following ways:
where M represents a mass matrix of the vehicle,representing vectors containing acceleration of parts of the vehicle, C representing the damping matrix, K representing the stiffness matrix,/->Is a vector containing the speed of each part of the vehicle, Y is a vector containing the displacement of each part of the vehicle, and R (t) is a road surface excitation vector to which each part of the vehicle is subjected; the displacement of each part of the vehicle comprises the following steps: vertical displacement of the vehicle body, suspension vibration, wheel displacement, and vehicle body tilting.
The spring damping model is determined by including the following:
wherein the spring force F spring = -kx and damping forcex represents displacement of mass point, t represents time, k represents stiffness coefficient of spring, c represents damping coefficient, F ext Indicating the road surface excitation force.
The road surface excitation model is determined by a method comprising:
z(t)＝A sin(2πft+φ)
wherein z (t) represents the vertical displacement of the tire at the point of contact with the road surface at time t, A represents the amplitude of the road surface fluctuation, representing the height of the protrusion or depression; f represents the fluctuation frequency, which is related to the vehicle speed and the road surface ripple spacing; phi is the phase difference, which is related to the starting position of the car.
By the simulation system, a large number of virtual tests can be carried out before actual automobile manufacture or delivery, so that the expensive actual test cost is saved, and the simulation system is very important for optimizing and evaluating the suspension system in an early stage. In addition, the performance of the suspension system under different parameter configurations can be rapidly evaluated, and compared with an actual test, the suspension system is rapid, so that the development period is shortened. More importantly, the simulation system provided by the embodiment of the application can simulate the response of each component in various road conditions and driving scenes based on the diversified physical simulation model module, including different road conditions, driving styles and environmental conditions, so as to more comprehensively evaluate the performance of the suspension system. By means of the simulation system integrated with the multi-objective optimization model, real-time feedback can be provided for an input suspension system data set in real time, so that engineers can know the response of the suspension system under different conditions in real time, and design problems can be found and adjusted quickly.
The following describes a robustness optimization system of an automotive suspension system provided by the application, and the robustness optimization system of the automotive suspension system described below and the robustness optimization method of the automotive suspension system described above can be referred to correspondingly.
Fig. 5 shows a block diagram of an example of a robustness optimization system for an automotive suspension system according to an embodiment of the present application.
As shown in fig. 5, the robustness optimization system 500 of the automobile suspension system includes a first acquisition unit 510, a second acquisition unit 520, an optimization model configuration unit 530, and a simulation run unit 540.
The first obtaining unit 510 is configured to obtain a plurality of robustness optimization directions and system constraints of the suspension system of the automobile.
The second acquisition unit 520 is configured to acquire a simulation system for the vehicle suspension system; the simulation system defines individual suspension parameter types for the automotive suspension system.
The optimization model configuration unit 530 is configured to configure a multi-objective optimization model for the simulation system based on a plurality of the robustness optimization directions and the system constraint conditions; decision variables of the multi-objective optimization model are defined according to the respective suspension parameter types, objective functions of the multi-objective optimization model are defined according to the plurality of robustness optimization directions, and optimization paths of the multi-objective optimization model are defined according to the system constraint conditions.
A simulation run unit 540 is used to input suspension system datasets into the simulation system, such that the simulation system executes the multi-objective optimization model to determine optimal solutions for each of the suspension parameter types from the suspension system datasets; each of the suspension system data in the suspension system data set is respectively derived from a corresponding live running scene of the automobile, the suspension system data comprises a plurality of suspension system parameters, and each suspension system parameter is respectively provided with a corresponding suspension parameter type.
In some embodiments, embodiments of the present application provide a non-transitory computer readable storage medium having stored therein one or more programs including execution instructions that are readable and executable by an electronic device (including, but not limited to, a computer, a server, or a network device, etc.) for performing the above-described method of vehicle suspension robustness optimization.
In some embodiments, embodiments of the present application also provide a computer program product comprising a computer program stored on a non-volatile computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the above-described method of optimizing robustness of an automotive suspension system.
In some embodiments, embodiments of the present application further provide an electronic device, including: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform an automotive suspension system robustness optimization method.
Fig. 6 is a schematic hardware structure of an electronic device for performing a method for optimizing robustness of an automotive suspension system according to another embodiment of the present application, as shown in fig. 6, where the device includes:
one or more processors 610, and a memory 620, one processor 610 being illustrated in fig. 6.
The apparatus for performing the method for optimizing robustness of the automotive suspension system may further include: an input device 630 and an output device 640.
The processor 610, memory 620, input devices 630, and output devices 640 may be connected by a bus or other means, for example in fig. 6.
The memory 620, which is a non-volatile computer readable storage medium, may be used to store non-volatile software programs, non-volatile computer executable programs, and modules, such as program instructions/modules corresponding to the method for optimizing the robustness of the suspension system of the vehicle in the embodiments of the present application. The processor 610 executes various functional applications of the server and data processing by running non-volatile software programs, instructions and modules stored in the memory 620, i.e., implements the method of optimizing the robustness of the automotive suspension system of the method embodiment described above.
Memory 620 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the electronic device, etc. In addition, memory 620 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 620 optionally includes memory remotely located relative to processor 610, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 630 may receive input digital or character information and generate signals related to user settings and function control of the electronic device. The output device 640 may include a display device such as a display screen.
The one or more modules are stored in the memory 620, which when executed by the one or more processors 610, perform the method of optimizing the robustness of the automotive suspension system in any of the method embodiments described above.
The product can execute the method for optimizing the robustness of the automobile suspension system, which is provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present application.
The electronic device of the embodiments of the present application exist in a variety of forms including, but not limited to:
(1) Mobile communication devices, which are characterized by mobile communication functionality and are aimed at providing voice, data communication. Such terminals include smart phones, multimedia phones, functional phones, low-end phones, and the like.
(2) Ultra mobile personal computer equipment, which belongs to the category of personal computers, has the functions of calculation and processing and generally has the characteristic of mobile internet surfing. Such terminals include PDA, MID, and UMPC devices, etc.
(3) Portable entertainment devices such devices can display and play multimedia content. The device comprises an audio player, a video player, a palm game machine, an electronic book, an intelligent toy and a portable vehicle navigation device.
(4) Other on-board electronic devices with data interaction functions, such as on-board devices mounted on vehicles.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the related art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A method of optimizing robustness of an automotive suspension system, comprising:
acquiring a plurality of robustness optimization directions and system constraint conditions of an automobile suspension system;
acquiring a simulation system aiming at the automobile suspension system; the simulation system defines various suspension parameter types of the automobile suspension system;
configuring a multi-objective optimization model for the simulation system based on a plurality of the robustness optimization directions and the system constraint conditions; decision variables of the multi-objective optimization model are defined according to the respective suspension parameter types, objective functions of the multi-objective optimization model are defined according to the plurality of robustness optimization directions, and optimization paths of the multi-objective optimization model are defined according to the system constraint conditions;
Inputting a suspension system dataset into the simulation system such that the simulation system executes the multi-objective optimization model to determine an optimal solution for each of the suspension parameter types from the suspension system dataset; each of the suspension system data in the suspension system data set is respectively derived from a corresponding live running scene of the automobile, the suspension system data comprises a plurality of suspension system parameters, and each suspension system parameter is respectively provided with a corresponding suspension parameter type.
2. The method of claim 1, wherein the robustness optimization direction includes at least one of: minimizing vibration amplitude, minimizing vehicle roll angle, and maximizing suspension system response frequency; the system constraints include at least one of: the suspension height is not less than a preset minimum suspension height, the suspension system stroke is not less than a preset minimum stroke, and the maximum vertical acceleration of the vehicle during acceleration is less than a preset acceleration threshold.
3. The method of claim 1, wherein the suspension parameter type includes any one of: spring rate, damping coefficient, suspension height, anti-roll bar stiffness, suspension travel, and shock absorber configuration parameters.
4. The method of claim 1, wherein the multi-objective optimization model employs a hybrid multi-objective optimization model comprising a cascaded particle swarm optimization model module and an NSGA-II model module.
5. The method of claim 4, wherein the simulation system executing the multi-objective optimization model to determine an optimal solution for each of the suspension parameter types from the suspension system dataset comprises:
inputting the suspension system dataset into the particle swarm optimization model module to initialize a particle swarm position according to each suspension system parameter in the suspension system dataset;
updating the position and the speed of the particle swarm based on the particle swarm optimization model module, and solving the optimal particle swarm corresponding to each suspension parameter type;
inputting the optimal particle set and the suspension system dataset to the NSGA-II model module to initialize a population according to the optimal particle set and the suspension system dataset;
performing genetic operation processing based on the NSGA-II model module to update the population, and calculating pareto fronts corresponding to the objective functions; the genetic manipulation process comprises: selection, crossover and mutation;
And updating at least one particle in the optimal particle group according to the pareto front, and determining an optimal solution for each suspension parameter type based on the updated optimal particle group.
6. The method of claim 5, wherein the particle velocity is updated by comprising:
the particle location is updated by including:
wherein, for the i-th particle (i=1, 2,., N), the parameter space is searched in D-dimension (d=1, 2,., D);
wherein,representing the d-th dimensional position of the ith particle at time t; />Representing the d-th dimensional velocity of the ith particle at time t; p (P) id The d-th dimensional coordinates representing the best position encountered by the ith particle so far, i.e. the individual optima; g d D-th dimensional coordinates representing the best positions encountered by all particles so far, i.e. global optima; w represents inertial weight, controlling the influence of the current speed of the particles on the update speed of the particles; c 1 And c 2 Respectively representing individual learning weights and group learning weights; r is (r) 1 And r 2 Respectively the intervals [0,1 ]]The random number on the code is introduced into randomness for each iteration; n represents the total particle number of the particle swarm; d represents the number of objective functions.
7. The method of claim 5, wherein the performing genetic manipulation based on the NSGA-II model module to update the population and calculate pareto fronts for each of the objective functions comprises:
Assigning a dominance level to each individual in the updated population for said individual;
determining, for each of the dominance levels, a crowding distance for each individual at the dominance level relative to other individuals in the updated population;
and determining the pareto front according to the dominance grade and the congestion grade.
8. The method of claim 7, wherein the crowding distance is determined by including:
I(i)＝(I(i+1) m -I(i-1) m )(f m,max -f m,min )，
wherein I (I) is the crowding distance of the ith individual, f m Represents the mth objective function, f m,max And f m,min The maximum and minimum of the whole population on the mth objective function, respectively.
9. The method of claim 1, wherein the simulation system comprises a plurality of physical simulation models, the physical simulation models including at least one of: particle model, spring model, damping model, road surface excitation model and multi-degree of freedom model.
10. The method of claim 9, wherein the particle model is determined by including:
where m represents the mass of the particle,indicating mass acceleration, c the damping coefficient of the suspension, k the spring rate of the suspension, y the mass displacement, +. >Indicating particle velocity, z indicating road surface excitation displacement, and +.>Representing the speed of road surface excitation;
the multiple degree of freedom model is determined by including the following ways:
where M represents a mass matrix of the vehicle,representing vectors containing acceleration of parts of the vehicle, C representing the damping matrix, K representing the stiffness matrix,/->Is a vector containing the speed of each part of the vehicle, Y is a vector containing the displacement of each part of the vehicle, and R (t) is a road surface excitation vector to which each part of the vehicle is subjected; the displacement of each part of the vehicle comprises the following steps: vertical displacement of the vehicle body, suspension vibration, wheel displacement, and vehicle body tilting.
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