CN116305572A

CN116305572A - Vehicle optimization method, device, storage medium and electronic equipment

Info

Publication number: CN116305572A
Application number: CN202310272759.4A
Authority: CN
Inventors: 苏永雷
Original assignee: Xiaomi Automobile Technology Co Ltd
Current assignee: Xiaomi Automobile Technology Co Ltd
Priority date: 2023-03-20
Filing date: 2023-03-20
Publication date: 2023-06-23
Anticipated expiration: 2043-03-20
Also published as: CN116305572B

Abstract

The method obtains a first key force transmission path of a vehicle body frame based on a first topological model corresponding to the vehicle body frame of a vehicle to be optimized, optimizes the structure of the vehicle body frame according to the first key force transmission path, obtains an optimized vehicle body frame, obtains an optimization scheme of a first target component based on a second topological model corresponding to a first target component of the vehicle to be optimized, obtains an optimized first vehicle based on the optimized vehicle body frame and the optimized first target component, determines a target material thickness parameter of a second target component in the optimized first vehicle based on the optimized first vehicle, obtains an optimized second vehicle according to the target material thickness parameter, can carry out integrated optimization design on the vehicle body frame, performance and weight of the vehicle, and realizes the maximum light weight of the non-bearing vehicle body of the vehicle through structural optimization.

Description

Vehicle optimization method, device, storage medium and electronic equipment

Technical Field

The disclosure relates to the technical field of vehicles, and in particular relates to a vehicle optimization method, a vehicle optimization device, a storage medium and electronic equipment.

Background

In the vehicle development process, in order to reduce the vehicle production cost and improve the vehicle comfort, a manner of reducing the vehicle quality and/or optimizing the vehicle structure may be adopted to ensure that the production cost is reduced while meeting the vehicle performance requirements. However, the existing vehicle optimization method generally carries out data processing aiming at single optimization performance, and has the problems of low weight reduction amplitude of the vehicle, lack of system optimality, too few related vehicle performance indexes, increased material cost and the like caused by conflict of performance development, light weight and cost control.

Disclosure of Invention

To overcome the problems in the related art, the present disclosure provides a vehicle optimizing method, apparatus, storage medium, and electronic device to optimize a vehicle in an all-round, multi-disciplinary manner.

According to a first aspect of an embodiment of the present disclosure, there is provided a vehicle optimization method including:

obtaining a first key force transmission path of a vehicle body frame based on a first topological model corresponding to the vehicle body frame of a vehicle to be optimized, wherein the first topological model is a topological model simultaneously applicable to collision performance analysis and NVH performance analysis, the first key force transmission path is a force transmission path meeting target collision performance and target NVH performance, and the first key force transmission path is used for optimizing the structure of the vehicle body frame so as to obtain an optimized vehicle body frame;

Obtaining an optimization scheme of a first target component of the vehicle to be optimized based on a second topological model corresponding to the first target component, wherein the optimization scheme of the first target component comprises a second key force transmission path and a material thickness parameter, the first target component is a component with an area larger than a preset area threshold value in the vehicle to be optimized, and the optimization scheme of the first target component is used for optimizing the first target component to obtain an optimized first target component;

obtaining an optimized first vehicle based on the optimized vehicle body frame and the optimized first target component;

and determining a target material thickness parameter of a second target component in the optimized first vehicle based on the optimized first vehicle, so as to obtain the optimized second vehicle according to the target material thickness parameter.

Optionally, the determining, based on the optimized first vehicle, a target thickness parameter of a second target component in the optimized first vehicle includes:

constructing a hybrid proxy model based on the optimized first vehicle, wherein the hybrid proxy model is constructed according to different types of approximate models, and the approximate models are used for representing the relation between the material thickness parameters of the second target component and the vehicle working condition performance;

And determining a target material thickness parameter of a second target component in the optimized first vehicle based on the hybrid proxy model, wherein the hybrid proxy model is constructed according to different types of approximation models.

Optionally, the building a hybrid proxy model based on the optimized first vehicle includes:

determining a target mapping relation based on the optimized finite element model corresponding to the first vehicle aiming at the second target component, wherein the target mapping relation comprises the corresponding relation between different material thickness parameters of the second target component and the vehicle working condition performance;

constructing a plurality of different types of approximate models based on the target mapping relation;

and constructing the mixed agent model based on an approximate model with the precision larger than a preset precision threshold value in the plurality of approximate models and a weight coefficient corresponding to each approximate model, wherein the weight coefficient is related to the precision of the approximate model.

Optionally, the determining, based on the hybrid proxy model, a target thickness parameter of a second target component in the optimized first vehicle includes:

and solving the mixed agent model based on a global optimization algorithm to obtain the target material thickness parameter, wherein the constraint condition of the global optimization algorithm is that the vehicle working condition performance meets a first preset performance index, and the optimization target of the global optimization algorithm is that the sum of the masses of the second target component is minimum.

Optionally, after determining the target charge thickness parameter of the second target component in the optimized first vehicle based on the hybrid proxy model, the method further comprises:

obtaining a material thickness parameter range based on the target material thickness parameter, wherein the upper limit value of the material thickness parameter range is larger than the target material thickness parameter, and the lower limit value of the material thickness parameter range is smaller than the target material thickness parameter;

and based on the material thickness parameter range and the optimized simulation physical model corresponding to the first vehicle, combining a local optimization algorithm to obtain an optimized target material thickness parameter, wherein the constraint condition of the local optimization algorithm is that the vehicle working condition performance meets a second preset performance index, and the optimization target of the local optimization algorithm is that the sum of the masses of the second target components is minimum.

Optionally, the obtaining an optimization scheme of the first target component based on the second topology model corresponding to the first target component of the vehicle to be optimized includes:

obtaining a second key force transmission path of the first target component based on a second topological model corresponding to the first target component of the vehicle to be optimized, wherein the second key force transmission path is used for optimizing the structure of the first target component so as to obtain an optimized first target component;

Solving an approximate model of the first target component based on a global optimization algorithm to obtain a material thickness parameter corresponding to the first target component, wherein the approximate model is used for representing the relation between the material thickness parameter of the first target component and the target vehicle working condition performance, the constraint condition of the global optimization algorithm is that the target vehicle working condition performance meets a third preset performance index, and the optimization target of the global optimization algorithm is that the sum of the quality of the first target component is minimum;

and obtaining an optimization scheme of the first target component based on the second key force transmission path and the material thickness parameter corresponding to the first target component.

Optionally, the first topology model is constructed by:

carrying out topology on a collision model corresponding to the vehicle to be optimized based on a first boundary condition and a first constraint condition, and obtaining a collision topology model, wherein the first boundary condition is a boundary obtained by loading force corresponding to collision linearization on the collision model, and the first constraint condition is that a flexibility value obtained by calculation according to the force corresponding to collision linearization meets a first flexibility threshold;

Topology is conducted on the white body model corresponding to the vehicle to be optimized based on a second boundary condition and a second constraint condition, so that an NVH topology model is obtained, wherein the second boundary condition is a boundary corresponding to the finite element model corresponding to the vehicle to be optimized, and the second constraint condition is that a flexibility value calculated according to the finite element model corresponding to the vehicle to be optimized meets a second flexibility threshold;

and obtaining the first topology model based on the collision topology model and the NVH topology model.

Optionally, the second target component is a component that satisfies a preset condition, wherein the preset condition includes at least one of:

the method comprises the steps of a component with initial mass being larger than or equal to a preset mass threshold value, a component with direct sensitivity being smaller than a preset direct sensitivity threshold value, and a component with relative sensitivity meeting a preset relative sensitivity threshold value.

According to a second aspect of the embodiments of the present disclosure, there is provided a vehicle optimizing apparatus including:

the system comprises a first optimization module, a first load transfer module and a second optimization module, wherein the first optimization module is configured to obtain a first key load transfer path of a vehicle body frame based on a first topological model corresponding to the vehicle body frame of a vehicle to be optimized, the first topological model is a topological model simultaneously applicable to collision performance analysis and NVH performance analysis, the first key load transfer path is a load transfer path meeting target collision performance and target NVH performance, and the first key load transfer path is used for optimizing the structure of the vehicle body frame to obtain an optimized vehicle body frame;

The second optimization module is configured to obtain an optimization scheme of a first target component of the vehicle to be optimized based on a second topological model corresponding to the first target component, wherein the optimization scheme of the first target component comprises a second key force transmission path and a material thickness parameter, the first target component is a component with an area larger than a preset area threshold value in the vehicle to be optimized, and the optimization scheme of the first target component is used for optimizing the first target component to obtain an optimized first target component;

a third optimization module configured to obtain an optimized first vehicle based on the optimized body frame and the optimized first target component;

and a fourth optimization module configured to determine a target stock thickness parameter of a second target component in the optimized first vehicle based on the optimized first vehicle to obtain an optimized second vehicle according to the target stock thickness parameter.

According to a third aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the vehicle optimisation method provided in the first aspect of the present disclosure.

According to a fourth aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to execute the executable instructions to implement the steps of the vehicle optimization method provided in any one of the first aspects.

The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: the method comprises the steps of firstly obtaining a first key force transmission path of a vehicle body frame based on a first topological model corresponding to the vehicle body frame of a vehicle to be optimized, optimizing the structure of the vehicle body frame according to the first key force transmission path to obtain an optimized vehicle body frame, then obtaining an optimization scheme of a first target component based on a second topological model corresponding to a first target component of the vehicle to be optimized, obtaining an optimized first vehicle based on the optimized vehicle body frame and the optimized first target component, and then determining target material thickness parameters of a second target component in the optimized first vehicle based on the optimized first vehicle to obtain an optimized second vehicle according to the target material thickness parameters.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.

FIG. 1 is a flow chart illustrating a method of vehicle optimization according to an exemplary embodiment.

Fig. 2 is a schematic diagram illustrating a first critical force transfer path according to an exemplary embodiment.

Fig. 3 is a schematic diagram of a second critical force transfer path shown according to an exemplary embodiment.

Fig. 4 is a detailed flow chart of step 140 shown in fig. 1.

Fig. 5 is a detailed flow chart of step 120 shown in fig. 1.

FIG. 6 is a schematic diagram of a collision topology model, according to an example embodiment.

FIG. 7 is a schematic diagram of an NVH topology model shown in accordance with an example embodiment.

Fig. 8 is a block diagram illustrating a vehicle optimizing apparatus according to an exemplary embodiment.

Fig. 9 is a block diagram of an electronic device, according to an example embodiment.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

It should be noted that, all actions for acquiring signals, information or data in the present disclosure are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.

FIG. 1 is a flow chart illustrating a method of vehicle optimization according to an exemplary embodiment. As shown in fig. 1, the vehicle optimization method provided by the embodiment of the present disclosure may be performed by an electronic device, including the following steps.

In step 110, a first key force transmission path of a vehicle body frame is obtained based on a first topology model corresponding to the vehicle body frame of a vehicle to be optimized, wherein the first topology model is a topology model simultaneously applicable to collision performance analysis and NVH performance analysis, the first key force transmission path is a force transmission path meeting target collision performance and target NVH performance, and the first key force transmission path is used for optimizing a structure of the vehicle body frame to obtain the optimized vehicle body frame.

Here, the collision performance is a performance index for measuring structural crashworthiness of the vehicle, wherein the collision performance can be expressed by a change in the vehicle structure when the vehicle faces a frontal collision, a side collision, and a top collision. NVH (Noise, vibration, harshness, noise, vibration and harshness) performance is a comprehensive indicator of vehicle manufacturing quality.

The first topology model is a corresponding topology model of a body frame of the vehicle to be optimized, and is a topology model of the body frame suitable for collision performance analysis and NVH performance analysis at the same time. In general, a complete vehicle model including a vehicle body frame is used for collision performance analysis, and a white vehicle model is used for NVH performance analysis, which results in that the collision performance analysis and the NVH performance analysis cannot be performed simultaneously, and thus, optimization of the vehicle body frame cannot meet the requirements of various vehicle performances. Through the first topology model, collision performance analysis and NVH performance analysis can be performed simultaneously, so that the structure of the vehicle body frame is optimized from multiple dimensions. It should be understood that the construction process regarding the first topology model will be described in the subsequent embodiments.

Through the first topology model, the target collision performance and the target NVH performance can be combined, the vehicle body frame is optimized, and a first key force transmission path meeting the requirements of the target collision performance and the target NVH performance is obtained. For example, the target NVH performance may be a torsional compliance value of 7857.0 or less for the body frame during torsional conditions and 1796.3 or less for the body frame during bending conditions. The target collision performance may be a compliance value of the vehicle body frame under the roof pressure condition of 2.7E6 or less, a compliance value of the vehicle body frame under the side collision condition of 4.6E6 or less, and a compliance value of the vehicle body frame under the frontal collision condition of 1.2E6 or less. The first critical force transfer path is a force transfer path for the body frame that meets both the target crash performance and the target NVH performance.

It is worth to say that, based on the first topology model corresponding to the body frame of the vehicle to be optimized, the first key force transmission path of the body frame is obtained, and the target collision performance and the target NVH performance are taken as performance indexes, the first topology model is subjected to topology optimization, and the first key force transmission path is obtained. Wherein the topology optimization is a mathematical method that optimizes the distribution of materials within the first topology model based on given loading conditions, constraints and performance metrics. It should be understood that the key force transmission paths meeting the target collision performance and the target NVH performance obtained by performing topology optimization on the first topology model may include a plurality of key force transmission paths, and in practical application, the key force transmission paths with clear load transmission paths may be regarded as first key force transmission paths.

Fig. 2 is a schematic diagram illustrating a first critical force transfer path according to an exemplary embodiment. As shown in fig. 2, the first topology model 201 is topologically optimized to obtain a first critical force transfer path 202 that meets the target crash performance as well as the target NVH performance. The first critical force transfer path 202 has a clear load transfer path and the retained body frame material is capable of forming body-in-white like features.

The first critical force transmission path can be used for optimizing the structure of the body frame of the vehicle to be optimized so as to obtain the optimized body frame. For example, structural reinforcement members or the like are added to the weakened areas of the body frame according to the first critical force transmission path.

In step 120, an optimization scheme of the first target component is obtained based on a second topology model corresponding to the first target component of the vehicle to be optimized, where the optimization scheme of the first target component includes a second critical force transmission path and a thickness parameter, the first target component is a component of the vehicle to be optimized, the area of the component is greater than a preset area threshold, and the optimization scheme of the first target component is used for optimizing the first target component to obtain an optimized first target component.

Here, the first target component is a component in which the area in the vehicle to be optimized is greater than a preset area threshold. It should be understood that the first target component may refer to a large panel component in the vehicle to be optimized, such as a back panel component, a floor component, a roof component, etc. of the vehicle. Therefore, the preset area threshold value may be set according to actual conditions.

The region of the first target component in the vehicle to be optimized can be set as a topological domain, other regions remain unchanged, and a second topological model is constructed. And then, performing topological optimization on the second topological model to obtain a second key force transmission path. The constraint condition for performing topological optimization on the second topological model may be that the volume fraction is smaller than or equal to a preset volume fraction threshold, and the optimization target may be to meet a preset vehicle performance requirement. For example, in order to meet the avoidance requirement, the back panel mode of the back panel part is required to be larger than the acoustic cavity mode, mode coupling is avoided, the risk degree of bombing is reduced, in order to meet the collision performance, the deformation of the safety belt retractor bracket area of the back panel part cannot be too large and cannot be pulled to collapse, and then the preset vehicle performance requirement can be that the weighted second-order mode and the weighted rigidity are maximized, and the volume fraction is less than or equal to 25%. Of course, constraints for topology optimization and preset vehicle performance requirements may be set according to the type of first target component.

The second critical force transfer path is used to optimize the structure of the first target component to obtain an optimized first target component. For example, adding structural reinforcement members to the weakened areas of the first target member, etc. according to the second critical force transfer path.

Fig. 3 is a schematic diagram of a second critical force transfer path shown according to an exemplary embodiment. As shown in fig. 3, a second topological model 301 of the back panel component is topologically optimized to obtain a second critical force transfer path 302. Compared with the initial structure 303 of the back wall plate component, the structure represented by the second key force transmission path 302 is improved by 4Hz in the first-order mode of NVH and is improved by nearly 10Hz in the second-order mode and the third-order mode, so that good frequency avoidance of the acoustic cavity mode can be realized, in addition, in the aspect of collision performance, the structural deformation is 70mm, and the structural deformation is 74mm due to the standard deformation.

Wherein the stock thickness parameter of the first target component refers to the stock thickness of the part that makes up the second target component. It will be appreciated that by virtue of the effect of the thickness of the material on performance, it is possible to dig up space in which the weight of the vehicle to be optimised can be reduced, at a reduced cost.

The second topology model may be subjected to physical simulation to obtain vehicle working condition performances corresponding to the first target component under different material thickness parameters, and an approximation model of the first target component is solved based on an optimization algorithm to obtain the material thickness parameters corresponding to the first target component, where the approximation model is a model for representing a relationship between the material thickness parameters of the first target component and the target vehicle working condition performances.

In step 130, an optimized first vehicle is obtained based on the optimized body frame and the optimized first target component.

Here, the optimized first vehicle may be obtained by replacing the body frame of the vehicle to be optimized with the optimized body frame and replacing the first target component of the vehicle to be optimized with the optimized first target component.

In step 140, a target stock thickness parameter of a second target component in the optimized first vehicle is determined based on the optimized first vehicle to obtain an optimized second vehicle according to the target stock thickness parameter.

Here, after the body frame of the vehicle to be optimized and the second target component are optimized to obtain an optimized first vehicle, the material thickness parameter of the second target component in the optimized first vehicle is optimized according to the optimized first vehicle to obtain a target material thickness parameter of the second target component, so that the optimized second vehicle is obtained according to the target material thickness parameter.

Wherein the second target component may be a component other than the first target component. Of course, the second target component may also refer to a component that is sensitive to performance and has a mass greater than a threshold. The target stock thickness parameter may refer to a thickness parameter of a component of the vehicle, such as a thickness parameter of a sheet metal part.

The method comprises the steps of obtaining a first key force transmission path of a vehicle body frame based on a first topological model corresponding to the vehicle body frame of a vehicle to be optimized, optimizing the structure of the vehicle body frame according to the first key force transmission path to obtain an optimized vehicle body frame, obtaining an optimization scheme of a first target component based on a second topological model corresponding to a first target component of the vehicle to be optimized, obtaining an optimized first vehicle based on the optimized vehicle body frame and the optimized first target component, and then determining target material thickness parameters of a second target component in the optimized first vehicle based on the optimized first vehicle to obtain an optimized second vehicle according to the target material thickness parameters.

Fig. 4 is a detailed flow chart of step 140 shown in fig. 1. As shown in fig. 4, in some implementations that may be implemented, step 140 may include the following steps.

In step 141, a hybrid proxy model is constructed based on the optimized first vehicle, wherein the hybrid proxy model is constructed from different types of approximation models that are models that characterize the relationship between the gauge parameters of the second target component and vehicle operating performance.

Here, the physical simulation may be performed on the optimized simulated physical model of the first vehicle based on different material thickness parameters, to obtain a mapping relationship between the material thickness parameters and the vehicle working condition performance, and construct different types of approximate models based on the mapping relationship, and construct the hybrid proxy model based on the different types of approximate models.

It should be noted that the approximation model may also be represented by a proxy model in some scenarios, all of which are used to describe the relationship between the variables and the output responses.

The vehicle operating condition properties may include at least one of modal, stiffness, equivalent static stiffness, collision performance, and fatigue durability. The modes can comprise a typical breathing mode, a first-order bending mode and a torsional mode, the rigidity can comprise bending rigidity and torsional rigidity, the equivalent static rigidity can be a rigidity value when a dynamic rigidity analysis result is 1Hz, and the collision performance can comprise the performance of a vehicle under side collision, front collision, offset collision and top pressure.

The different types of approximation models may be least squares approximation models, difference approximation models, machine learning response surface models, and the like. The least squares approximation model may be a taylor polynomial model, a red pool information criterion model, an optimized response surface model, and the like, the difference approximation model may be a kriging model, a radial basis function model, a vector regression model, and the like, and the machine learning response surface model may be a neural network model, a random forest regression model, and the like.

In some embodiments, for the second target component, a target mapping relationship is determined based on the finite element model corresponding to the optimized first vehicle, a plurality of different types of approximate models are constructed based on the target mapping relationship, and a hybrid proxy model is constructed based on an approximate model with the accuracy greater than a preset accuracy threshold value and a weight coefficient corresponding to each approximate model in the plurality of approximate models.

Here, the target map includes a correspondence between different material thickness parameters of the second target component and vehicle operating condition performance. For example, the second target part has a gauge parameter of 5mm and a compliance value (used to characterize crash performance) under side impact conditions of 4.6E6.

For each second target component, the range of the material thickness parameter of the second target component can be 0.65mm and 5.0mm, and the material thickness parameter set can be generated by selecting the material thickness parameter corresponding to each second target component with the target number in the range of the material thickness of 0.65mm and 5.0mm through a Latin hypercube algorithm. For example, the target number may be 120, and the set of gauge parameters includes 120 sets of gauge parameters.

And then, respectively carrying out physical simulation based on the optimized finite element model corresponding to the first vehicle and 120 groups of material thickness parameters in the material thickness parameter set to obtain a target mapping relation. The target mapping relation comprises each group of material thickness parameters and corresponding vehicle working condition performances. The optimized finite element model corresponding to the first vehicle may be a detailed finite element model of the battery pack.

Then, a plurality of different types of approximate models can be constructed based on the target mapping relation, and a mixed agent model is constructed based on the plurality of different types of approximate models and weighting coefficients corresponding to the approximate models. Wherein the weight coefficient is related to the accuracy of the approximation model, and the higher the accuracy of the approximation model is, the larger the weight coefficient is. For example, the hybrid proxy model may be represented as h=ax+by+cz, where H is the vehicle operating performance corresponding to the hybrid proxy model, X is the vehicle operating performance calculated bY the first type of approximation model, Y is the vehicle operating performance calculated bY the second type of approximation model, Z is the vehicle operating performance calculated bY the third type of approximation model, and a, b, and c are weighting coefficients.

It is worth to say that the accuracy of a plurality of different types of approximate models can be calculated, and a mixed proxy model can be constructed based on the approximate models with the accuracy greater than a preset accuracy threshold. For example, the preset accuracy threshold may be 85%, and an approximation model with an accuracy greater than 85% of the constructed plurality of different types of approximation models may be used to construct the hybrid proxy model. By constructing the hybrid proxy model based on an approximation model with an accuracy greater than a preset accuracy threshold, errors can be reduced.

Therefore, the advantages of a plurality of different types of approximate models can be comprehensively considered by constructing the mixed agent model based on the plurality of approximate models and the weight coefficient corresponding to each approximate model, so that the calculated target material thickness parameter is more accurate.

In step 142, a target charge thickness parameter of a second target component in the optimized first vehicle is determined based on the hybrid proxy model, wherein the hybrid proxy model is constructed from different types of approximation models.

Here, the hybrid proxy model actually reflects the relationship between the material thickness parameter of the second target component and the vehicle operating condition performance, and by the hybrid proxy model, the optimal material thickness parameter under the condition that the vehicle operating condition performance satisfies the preset condition can be determined, thereby determining the target material thickness parameter. The target thickness parameter may be a thickness parameter that minimizes a total weight of the vehicle when the vehicle operating performance meets a preset condition. The vehicle working condition performance meeting the preset condition means that the vehicle working condition performance meets the design requirement, such as a torsion flexibility value of the vehicle body frame under a torsion working condition is less than or equal to 7857.0, a torsion flexibility value of the vehicle body frame under a bending working condition is less than or equal to 1796.3, a flexibility value of the vehicle body frame under a top pressure working condition is less than or equal to 2.7E6, a flexibility value of the vehicle body frame under a side collision working condition is less than or equal to 4.6E6, a flexibility value of the vehicle body frame under a front collision working condition is less than or equal to 1.2E6 and the like.

In some embodiments, the hybrid proxy model is solved based on a global optimization algorithm to obtain the target stock thickness parameter.

Illustratively, the global optimization algorithm may be (Efficient Global Optimization, effective global optimization algorithm).

The constraint condition of the global optimization algorithm is that the vehicle working condition performance meets a first preset performance index, and the optimization target of the global optimization algorithm is that the sum of the masses of the second target components is minimum.

The global optimization algorithm takes the vehicle working condition performance as constraint conditions, namely that the material thickness parameter of the second target component is required to be valued so that the vehicle working condition performance of the vehicle meets the first preset performance index. If the material thickness parameter of the second target part cannot enable the vehicle working condition performance of the vehicle to meet the first preset performance index, the material thickness parameter is not an effective value. The first preset performance index may be set according to design requirements. For example, the first preset performance index may refer to a torsional compliance value of the vehicle body frame under a torsional condition of 7857.0 or less, a torsional compliance value of the vehicle body frame under a bending condition of 1796.3 or less, a compliance value of the vehicle body frame under a pressing condition of 2.7E6 or less, a compliance value of the vehicle body frame under a side impact condition of 4.6E6 or less, a compliance value of the vehicle body frame under a frontal impact condition of 1.2E6 or less, and so on.

The optimization target of the global optimization algorithm is that the sum of the masses of the second target components is minimum, namely that the vehicle working condition performance meets the sum of the masses of the material thickness parameters of the second target components corresponding to the first preset performance index is minimum, so that the optimized second vehicle can have the optimal lightweight design under the condition that the vehicle working condition performance meets the condition.

The global optimization algorithm iteratively optimizes the mixed agent model by taking the constraint conditions and the optimization targets as references, and the obtained target material thickness parameters can enable the optimized second vehicle to have an optimal lightweight design under the condition that the vehicle working condition performance meets the conditions.

Therefore, the advantages of a plurality of different types of approximate models can be comprehensively considered through the mixed agent model, the calculated target material thickness parameter is more accurate, and the calculated target material thickness parameter can be enabled to have the optimal lightweight design under the condition that the vehicle working condition performance of the optimized second vehicle meets the condition through the global optimization algorithm.

In some implementations, after the target thickness parameter is obtained, a thickness parameter range may be obtained based on the target thickness parameter, and the optimized target thickness parameter may be obtained based on the thickness parameter range and a simulated physical model corresponding to the optimized first vehicle in combination with a local optimization algorithm.

Here, the upper limit value of the material thickness parameter range is larger than the target material thickness parameter, and the lower limit value of the material thickness parameter range is smaller than the target material thickness parameter. For example, the thickness parameter may be in the range of [ T ] ₁ ，T ₂ ]. Wherein the lower limit value T ₁ Can be one-stage implementable design value smaller than the target material thickness parameter, and the upper limit value T ₂ May be an implementable design value that is one level greater than the target material thickness parameter.

After the material thickness parameter range is constructed, performing physical simulation on the simulation physical model corresponding to the optimized first vehicle through a local optimization algorithm, so as to solve the material thickness parameter meeting the preset optimization target in the material thickness parameter range, and determining the material thickness parameter as the target material thickness parameter.

The constraint condition of the local optimization algorithm is that the vehicle working condition performance meets a second preset performance index, and the optimization target of the local optimization algorithm is that the sum of the masses of the second target components is minimum. It should be understood that the second preset performance index is consistent with the first preset performance index, and will not be described herein. Of course, in the practical application process, the second preset performance index may not include fatigue performance.

By way of example, iterative optimization can be performed on a simulation physical model corresponding to the first vehicle through a nonlinear quadratic programming local optimization algorithm, and an optimized target material thickness parameter is obtained. For example, the maximum iteration step number may be set to be 30, the vehicle working condition performance meets the second preset performance index as a constraint condition, the sum of the masses of the second target components is minimum as an optimization target, and optimization iteration is performed through a nonlinear quadratic programming local optimization algorithm to obtain the optimized target material thickness parameter.

It should be noted that the target material thickness parameter calculated by the global optimization algorithm is an optimal solution considered in the global scope. The optimized target material thickness parameter is carried out in a material thickness parameter range corresponding to the target material thickness parameter obtained through global optimization, the optimized target material thickness parameter is locally optimized after global optimization, the optimized data size is greatly reduced after the target material thickness parameter is obtained through global optimization, and the optimized target material thickness parameter can be more optimal after all, so that an optimized second vehicle obtained based on the optimized target material thickness parameter can meet the vehicle working condition performance requirement, and the lightest weight of the vehicle can be realized.

Therefore, the vehicle optimization method provided by the embodiment of the disclosure can perform global optimization on the material thickness parameter of the second target component to reduce the optimized data amount, and perform local optimization on the target material thickness parameter under the condition of global optimization, so that the optimized target material thickness parameter can further reduce the weight of the vehicle, and the light weight benefit can be maximized.

In some implementations, the second target component may be a component that satisfies a preset condition, wherein the preset condition includes at least one of: the method comprises the steps of a component with initial mass being larger than or equal to a preset mass threshold value, a component with direct sensitivity being smaller than a preset direct sensitivity threshold value, and a component with relative sensitivity meeting a preset relative sensitivity threshold value.

Here, the second target member may be a beam-based member, a sheet metal-based member, or the like of the vehicle, and be a member that satisfies a preset condition among the beam-based member, the sheet metal-based member, or the like.

The component with the initial mass greater than or equal to the preset mass threshold value refers to the optimized component with the initial mass greater than or equal to the preset mass threshold value in the first vehicle, and the optimized component is a second target component. For example, a part having an initial mass of greater than or equal to 0.2kg is the second target part. It should be noted that, the weight reduction effect of the components with initial mass smaller than the preset mass threshold is extremely limited, and the complexity of the data is increased dramatically. Therefore, by defining the component whose initial mass is greater than or equal to the preset mass threshold as the second target component, the number of components that need to be optimized can be reduced, thereby achieving direct data dimension reduction.

The component having the direct sensitivity smaller than the preset direct sensitivity threshold means that the component having the direct sensitivity smaller than the preset direct sensitivity threshold in the optimized first vehicle is defined as the second target component. The direct sensitivity refers to a rate of change of the structural response index caused by a change of the input variable. The performance is basically consistent with the weight sensitivity, namely, the lifting capacity of the part with small performance sensitivity is also small, even if the weight is reduced, the effect is limited, and the part with the direct sensitivity larger than or equal to the preset direct sensitivity threshold value is directly excluded from the weight reduction range, but the performance degradation can be accepted. Therefore, by defining the component whose direct sensitivity is smaller than the preset direct sensitivity threshold as the second target component, the number of components that need to be optimized can be reduced, thereby achieving direct data dimension reduction.

The component whose relative sensitivity satisfies the preset relative sensitivity threshold may be a component whose relative sensitivity is 20% after the relative sensitivity is ranked in the optimized first vehicle, or may be a component whose relative sensitivity is less than the preset relative sensitivity threshold. The relative sensitivity is the sensitivity obtained by dividing the direct sensitivity of each vehicle working condition performance by the other direct sensitivity, and the relative sensitivity is a variable generated in the form of a direct sensitivity ratio value and reflects the relative efficiency of plate thickness modification. By defining the component whose relative sensitivity satisfies the preset relative sensitivity threshold as the second target component, the number of components that need to be optimized can be reduced, thereby achieving direct data dimension reduction.

Therefore, the components meeting the preset conditions are defined as the second target components, so that the number of the components needing to be optimized can be reduced, direct data dimension reduction is realized, and the weight reduction of the second target components can realize the maximization of light weight income and data dimension reduction.

Fig. 5 is a detailed flow chart of step 120 shown in fig. 1. As shown in fig. 5, in some implementations that may be implemented, step 120 may include the following steps.

In step 121, a second critical force transmission path of the first target component is obtained based on a second topology model corresponding to the first target component of the vehicle to be optimized, where the second critical force transmission path is used to optimize the structure of the first target component, so as to obtain an optimized first target component.

Here, the region of the first target component in the vehicle to be optimized may be set as a topology domain, and the other regions remain unchanged, constructing the second topology model. And performing topological optimization on the second topological model to obtain a second key force transmission path so as to optimize the structure of the first target component based on the second key force transmission path to obtain an optimized first target component.

The first target component is a component of which the area in the vehicle to be optimized is larger than a preset area threshold value. It should be understood that the first target component may refer to a large panel component in the vehicle to be optimized, such as a back panel component, a floor component, a roof component, etc. of the vehicle. Therefore, the preset area threshold value may be set according to actual conditions.

The constraint condition for performing topological optimization on the second topological model may be that the volume fraction is smaller than or equal to a preset volume fraction threshold, and the optimization target may be to meet a preset vehicle performance requirement. For example, in order to meet the avoidance requirement, the back panel mode of the back panel part is required to be larger than the acoustic cavity mode, mode coupling is avoided, the risk degree of bombing is reduced, in order to meet the collision performance, the deformation of the safety belt retractor bracket area of the back panel part cannot be too large and cannot be pulled to collapse, and then the preset vehicle performance requirement can be that the weighted second-order mode and the weighted rigidity are maximized, and the volume fraction is less than or equal to 25%. Of course, constraints for topology optimization and preset vehicle performance requirements may be set according to the type of first target component.

In step 122, an approximation model of the first target component is solved based on a global optimization algorithm, so as to obtain a material thickness parameter corresponding to the first target component, wherein the approximation model is a model for representing a relationship between the material thickness parameter of the first target component and target vehicle working condition performance, a constraint condition of the global optimization algorithm is that the target vehicle working condition performance meets a third preset performance index, and an optimization target of the global optimization algorithm is that a sum of the qualities of the first target component is minimum.

Here, the approximation model is a model for characterizing a relationship between the charge thickness parameter of the first target component and the target vehicle operating condition performance. The target vehicle condition performance may be at least one of collision performance, NVH performance, and modality.

For example, for the first target component, the range of values of the material thickness parameters of the first target component may be [0.65mm,5.0mm ], and the material thickness parameters corresponding to each second target component of the target number may be selected from the range of values of [0.65mm,5.0mm ] through a Latin hypercube algorithm, so as to generate a material thickness parameter set. For example, the target number may be 50, and the set of stock thickness parameters includes 50 sets of stock thickness parameters.

And then, respectively carrying out physical simulation based on 50 groups of material thickness parameters in the material thickness parameter set to obtain the corresponding relation between the material thickness parameter of the first target part and the working condition performance of the target vehicle. And then, constructing an approximate model according to the corresponding relation between the material thickness parameter of the first target component and the working condition performance of the target vehicle.

The global optimization algorithm takes the target vehicle working condition performance meeting the third preset performance index as a constraint condition, which means that the material thickness parameter of the first target component calculated by the global optimization algorithm needs to enable the target vehicle working condition performance of the vehicle to meet the third preset performance index. The third preset performance index may be set according to design requirements. For example, the third preset performance index may refer to a torsional compliance value of the vehicle body frame under a torsional condition of 7857.0 or less, a torsional compliance value of the vehicle body frame under a bending condition of 1796.3 or less, a compliance value of the vehicle body frame under a pressing condition of 2.7E6 or less, a compliance value of the vehicle body frame under a side impact condition of 4.6E6 or less, a compliance value of the vehicle body frame under a frontal impact condition of 1.2E6 or less, and so on.

The global optimization algorithm takes the minimum sum of the masses of the first target components as an optimization target, namely the minimum sum of the masses of the material thickness parameters of the first target components corresponding to the target vehicle working condition performance meeting the third preset performance index, so that the optimized vehicle can have an optimal lightweight design under the condition that the target vehicle working condition performance meets the conditions.

And the global optimization algorithm takes the target vehicle working condition performance meeting a third preset performance index as a constraint condition and takes the sum of the masses of the first target components as an optimization target, and solves the approximate model to obtain the material thickness parameters corresponding to the first target components. The thickness parameter corresponding to the first target component can enable the vehicle to meet the working condition performance of the target vehicle, and the weight is the lightest.

In step 123, an optimization scheme of the first target component is obtained based on the second critical force transmission path and the material thickness parameter corresponding to the first target component.

Here, the second critical force transfer path and the corresponding gauge parameters of the first target component may be determined as an optimization scheme for the first target component.

Therefore, the second key force transmission path of the first target component is obtained based on the second topological model corresponding to the first target component of the vehicle to be optimized, and the approximate model of the first target component is solved through the global optimization algorithm to obtain the material thickness parameter corresponding to the first target component, so that the optimized first target component can keep the rationality of the structure, and the weight is the lightest under the condition that the vehicle meets the working condition performance of the target vehicle.

It should be noted that the foregoing steps 121 to 123 are exemplified by the back panel member of the vehicle, and are not meant to be applicable only to the optimization of the back panel member of the vehicle, but also to the optimization of the floor member, the ceiling member, and the like. In practical applications, the floor components, ceiling components and other components are optimized, and the models, constraints and optimization targets used may be different, but the essence of the optimization can be realized based on the methods from step 121 to step 123.

In some implementations, the first topology model is constructed by:

Here, the first topology model is a corresponding topology model of a body frame of the vehicle to be optimized, which is a topology model of a body frame suitable for both collision performance analysis and NVH performance analysis.

The collision model corresponding to the vehicle to be optimized can be a frame model comprising a battery pack and a model comprising a closing part, a container, a chassis, a cross beam, a seat and other counterweights.

The first boundary condition and the first constraint condition are boundaries of the collision topology model and the constraint condition. The first boundary condition is a boundary obtained by loading a force corresponding to collision linearization on a collision model, namely, the boundary is solved by using inertial release according to the force obtained by collision linearization and loaded in a position of the corresponding collision model without constraint points. The first constraint condition is that a flexibility value calculated according to a force corresponding to collision linearization meets a first flexibility threshold. For example, the first constraint may be a compliance value of the body frame under the top pressure condition of 2.7E6 or less, a compliance value of the body frame under the side impact condition of 4.6E6 or less, and a compliance value of the body frame under the front impact condition of 1.2E6 or less.

FIG. 6 is a schematic diagram of a collision topology model, according to an example embodiment. As shown in fig. 6, a collision topology model 601 obtained based on the first boundary condition and the first constraint condition is shown.

The second boundary condition and the second constraint condition are boundaries of the NVH topology model and the constraint condition. The second boundary condition is a boundary corresponding to a finite element model corresponding to the vehicle to be optimized, namely, a boundary of the NVH topological model is set according to the boundary corresponding to the finite element model. The second constraint condition is that the flexibility value calculated according to the finite element model corresponding to the vehicle to be optimized meets a second flexibility threshold. For example, the second constraint may be a torsional compliance value of the body frame under torsional conditions of 7857.0 or less and a torsional compliance value of the body frame under bending conditions of 1796.3 or less.

FIG. 7 is a schematic diagram of an NVH topology model shown in accordance with an example embodiment. As shown in FIG. 7, NVH topology model 701 obtained based on the second boundary condition and the second constraint condition is shown.

For example, the first topology model may be obtained based on an intersection of the collision topology model and the NVH topology model.

Therefore, through the first boundary condition, the first constraint condition, the second boundary condition and the second constraint condition, a first topological model of the vehicle body frame which is simultaneously suitable for carrying out collision performance analysis and NVH performance analysis can be constructed, so that the multi-performance analysis of the vehicle body frame is realized.

Fig. 8 is a block diagram illustrating a vehicle optimizing apparatus according to an exemplary embodiment. Referring to fig. 8, the apparatus 800 includes:

a first optimizing module 801, configured to obtain a first critical force transfer path of a vehicle body frame based on a first topology model corresponding to the vehicle body frame of a vehicle to be optimized, where the first topology model is a topology model applicable to both collision performance analysis and NVH performance analysis, the first critical force transfer path is a force transfer path that meets target collision performance and target NVH performance, and the first critical force transfer path is used for optimizing a structure of the vehicle body frame to obtain an optimized vehicle body frame;

a second optimizing module 802, configured to obtain an optimizing solution of a first target component of the vehicle to be optimized based on a second topology model corresponding to the first target component, where the optimizing solution of the first target component includes a second critical force transmission path and a material thickness parameter, the first target component is a component of the vehicle to be optimized, the area of the first target component is greater than a preset area threshold, and the optimizing solution of the first target component is used for optimizing the first target component to obtain an optimized first target component;

A third optimization module 803 configured to obtain an optimized first vehicle based on the optimized body frame and the optimized first target component;

a fourth optimization module 804 is configured to determine a target stock thickness parameter of a second target component in the optimized first vehicle based on the optimized first vehicle to obtain an optimized second vehicle according to the target stock thickness parameter.

Optionally, the fourth optimization module 804 includes:

a building unit configured to build a hybrid proxy model based on the optimized first vehicle, wherein the hybrid proxy model is built according to different types of approximation models, the approximation models being models for characterizing a relationship between a material thickness parameter of the second target component and vehicle operating performance;

and a determining unit configured to determine a target material thickness parameter of a second target component in the optimized first vehicle based on the hybrid proxy model, wherein the hybrid proxy model is constructed from different types of approximation models.

Optionally, the building unit is specifically configured to:

Optionally, the determining unit is specifically configured to:

Optionally, the apparatus further comprises a fifth optimization module configured to:

Optionally, the second optimization module 802 is specifically configured to:

Optionally, the first optimization module 801 is specifically configured to:

With respect to the apparatus 800 in the above-described embodiment, the specific manner in which the respective modules perform the operations has been described in detail in relation to the embodiment of the method, and will not be described in detail herein.

The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the vehicle optimization method provided by the present disclosure.

Fig. 9 is a block diagram of an electronic device, according to an example embodiment. For example, the electronic device 900 may be a mobile phone, a computer, a digital broadcast terminal, a tablet device, a personal digital assistant, a server, or the like.

Referring to fig. 9, an electronic device 900 may include one or more of the following components: a processing component 902, a memory 904, a power component 906, a multimedia component 908, an audio component 910, an input/output interface 912, a sensor component 914, and a communication component 916.

The processing component 902 generally controls overall operation of the electronic device 900, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 902 may include one or more processors 920 to execute instructions to perform all or part of the steps of the vehicle optimization method described above. Further, the processing component 902 can include one or more modules that facilitate interaction between the processing component 902 and other components. For example, the processing component 902 can include a multimedia module to facilitate interaction between the multimedia component 908 and the processing component 902.

The memory 904 is configured to store various types of data to support operations at the electronic device 900. Examples of such data include instructions for any application or method operating on the electronic device 900, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 904 may be implemented by any type of volatile or nonvolatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The power supply component 906 provides power to the various components of the electronic device 900. Power supply components 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for electronic device 900.

The multimedia component 908 comprises a screen between the electronic device 900 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 908 includes a front-facing camera and/or a rear-facing camera. When the electronic device 900 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 910 is configured to output and/or input audio signals. For example, the audio component 910 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 900 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 904 or transmitted via the communication component 916. In some embodiments, the audio component 910 further includes a speaker for outputting audio signals.

The input/output interface 912 provides an interface between the processing component 902 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 914 includes one or more sensors for providing status assessment of various aspects of the electronic device 900. For example, the sensor assembly 914 may detect an on/off state of the electronic device 900, a relative positioning of the components, such as a display and keypad of the electronic device 900, the sensor assembly 914 may also detect a change in position of the electronic device 900 or a component of the electronic device 900, the presence or absence of a user's contact with the electronic device 900, an orientation or acceleration/deceleration of the electronic device 900, and a change in temperature of the electronic device 900. The sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 914 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 916 is configured to facilitate communication between the electronic device 900 and other devices, either wired or wireless. The electronic device 900 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication component 916 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 916 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 900 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above-described vehicle optimization methods.

In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as a memory 904 including instructions executable by the processor 920 of the electronic device 900 to perform the above-described vehicle optimization method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-described vehicle optimization method when executed by the programmable apparatus.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A vehicle optimization method, comprising:

2. The method of claim 1, wherein the determining, based on the optimized first vehicle, a target stock thickness parameter for a second target component in the optimized first vehicle comprises:

3. The method of claim 2, wherein the constructing a hybrid proxy model based on the optimized first vehicle comprises:

4. The method of claim 2, wherein the determining, based on the hybrid proxy model, a target charge thickness parameter for a second target component in the optimized first vehicle comprises:

5. The method according to any one of claims 1 to 4, wherein after determining the target charge thickness parameter of the second target component in the optimized first vehicle based on a hybrid proxy model, the method further comprises:

6. The method according to claim 1, wherein the obtaining an optimization solution of the first target component based on the second topology model corresponding to the first target component of the vehicle to be optimized includes:

7. The method of claim 1, wherein the first topology model is constructed by:

8. The method of claim 1, wherein the second target component is a component that meets a preset condition, wherein the preset condition comprises at least one of:

9. A vehicle optimizing apparatus, characterized by comprising:

10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the method of any of claims 1 to 8.

11. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to execute the executable instructions to implement the steps of the method of any one of claims 1 to 8.