CN115238373A - Vehicle quality optimization method, device, equipment and storage medium - Google Patents
Vehicle quality optimization method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of vehicles, and discloses a vehicle quality optimization method, a device, equipment and a storage medium, wherein the method comprises the following steps: determining a plurality of plate members according to the structural parameters and the subject performance parameters of the vehicle to be optimized; constructing a target performance balance explicit function according to a plurality of plates and preset variable ranges of the plates; when the performance parameters of a plurality of plates meet preset performance conditions, determining target optimization parameters according to a target performance balance explicit function; optimizing the quality of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy; through the method, the target performance balance explicit function is constructed according to the determined number of plates and the preset variable range, the target optimization parameters are determined through the target performance balance explicit function, and finally the optimization of the mass of the vehicle to be optimized is achieved through the method of optimizing the target optimization parameters, so that the accuracy of optimizing the mass of the vehicle can be effectively improved.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a vehicle quality optimization method, device, equipment and storage medium.
Background
With the gradual enhancement of environmental awareness, energy efficiency and energy saving factors are considered when a vehicle is purchased, so that the above conditions are met, for manufacturers, the optimization of the vehicle quality can reduce energy conservation and emission reduction and improve the vehicle energy efficiency, but the vehicle bears the safety mission of passengers in the vehicle, so that the requirements of the whole vehicle rigidity, mode, NVH, collision and the like need to be met on the premise of optimizing the vehicle quality, and in the currently common related technology, after the lightweight design is performed on the whole vehicle structure, the CAE analysis and verification needs to be performed on the rigidity, mode, NVH and collision respectively, but when the verification fails, the whole vehicle structure is returned to be adjusted, then the performance analysis is performed, and the above steps are repeated, so that when the requirements of the whole vehicle rigidity, mode, NVH, collision and the like are met, the quality of the vehicle is not optimized to the minimum, and the accuracy of optimizing the vehicle quality is low.
The above is only for the purpose of assisting understanding of the technical solution of the present invention, and does not represent an admission that the above is the prior art.
Disclosure of Invention
The invention mainly aims to provide a vehicle mass optimization method, a vehicle mass optimization device, vehicle mass optimization equipment and a storage medium, and aims to solve the technical problem that the accuracy of vehicle mass optimization in the prior art is low.
In order to achieve the above object, the present invention provides a vehicle mass optimization method, including the steps of:
determining a plurality of plate members according to the structural parameters and the subject performance parameters of the vehicle to be optimized;
constructing a target performance balance explicit function according to the plate pieces with the number and the preset variable range of the plate pieces;
when the performance parameters of the plates in the number meet preset performance conditions, determining target optimization parameters according to the target performance balance explicit function;
and optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy.
Optionally, the determining a number of panels according to the structural parameters and the subject performance parameters of the vehicle to be optimized includes:
obtaining plates at a plurality of area positions according to the structural parameters of the vehicle to be optimized;
obtaining corresponding structural rigidity performance parameters, vehicle body modal performance parameters, NVH performance parameters and collision performance parameters according to the scientific performance parameters;
respectively screening a rigidity influence plate, a modal influence plate, an NVH influence plate and a collision influence plate from the plates in the plurality of area positions according to the structural rigidity performance parameter, the vehicle body modal performance parameter, the NVH performance parameter and the collision performance parameter;
accumulating the rigidity influencing plate, the modal influencing plate, the NVH influencing plate and the collision influencing plate;
and carrying out duplication elimination operation on the accumulated influence plates to obtain a plurality of plates.
Optionally, the screening, according to the structural rigidity performance parameter, the vehicle body modal performance parameter, the NVH performance parameter, and the collision performance parameter, a rigidity-affected panel, a modal-affected panel, an NVH-affected panel, and a collision-affected panel from the panels at the plurality of region positions respectively includes:
selecting target points on the plates in the plurality of area positions, and acquiring the current positions of the target points;
setting constraint, load force, statics analysis working conditions and torsion and bending control cards on the plate members at the plurality of area positions according to the structural rigidity performance parameters;
after the setting is finished, determining the displacement of the target point according to the current position and the first position;
analyzing the displacement through a CAE intelligent platform to obtain a corresponding rigidity performance value;
screening out rigidity influence plates from the plates at the positions of the plurality of areas according to the rigidity performance values;
setting load steps, analysis conditions and modal control cards on the plates of the plurality of area positions according to the vehicle body modal performance parameters;
after the setting is finished, determining the offset of the target point according to the current position and the second position;
analyzing the offset through a CAE intelligent platform to obtain a corresponding modal performance value;
screening modal influence plates from the plates in the plurality of area positions according to the modal performance values;
screening out NVH (noise, vibration and harshness) influence plates from the plates at the plurality of area positions according to the NVH performance parameters;
and screening out the plate elements with the impact influence from the plate elements at the plurality of area positions according to the impact performance parameters.
Optionally, the screening of the NVH influencing panels in the panel of the plurality of zone positions according to the NVH performance parameters includes:
extracting noise performance parameters and vibration performance parameters of the NVH performance parameters;
selecting a first output point set and a second output point set on the plates at the plurality of area positions, wherein the first output point set comprises three key points of a main driving right ear, a middle right left ear and a rear row right ear, and the second output point set comprises two key points of a front direction of a steering wheel and a rear right point of a main driving seat guide rail;
setting an analysis frequency range, a modal frequency range, structural fluid damping, a noise working condition and a noise control card on the plates at the plurality of region positions according to the noise performance parameters;
after setting is completed, collecting a noise data file of the first output point set;
setting an analysis frequency range, a module frequency range, structural damping, a vibration working condition and a vibration control card on the plates at the plurality of region positions according to the vibration performance parameters;
after the setting is finished, collecting a vibration data file of the second output point set;
analyzing the noise data file and the vibration data file respectively through a CAE intelligent platform to obtain a corresponding noise performance value and a corresponding vibration performance value;
screening out NVH influencing plates from the plates at the plurality of zone positions according to the noise performance value and the vibration performance value.
Optionally, the constructing a target performance balance explicit function according to the number of plates and the preset variable range of the plates includes:
determining a total design variable according to the rigidity sensitivity, the modal sensitivity, the NVH contribution and the collision analysis animation;
determining a preset variable range according to the total design variable and a preset discrete continuous range;
sampling the plurality of plate members according to a target sampling algorithm to obtain sample plate members with different thick plates;
and constructing a target performance balance explicit function according to the preset variable range and the sample plates of different thick plates.
Optionally, when the performance parameters of the plates in the number of plates satisfy a preset performance condition, determining a target optimization parameter according to the target performance balance explicit function includes:
when the performance parameters of the plates in the number meet preset performance conditions, constructing a corresponding approximate performance model according to the performance parameters and the target performance balance explicit function;
calculating the approximate performance model through a target loss function to obtain a precision value of the current model;
and when the precision value of the current model is greater than or equal to a preset precision threshold value, analyzing the plates of the plurality of numbers according to the approximate performance model to obtain target optimization parameters.
Optionally, after the optimizing the mass of the vehicle to be optimized according to the target optimization parameter by using a preset optimization strategy, the method further includes:
obtaining the current mass of the optimized vehicle to be optimized;
when the current mass is smaller than the mass of the vehicle to be optimized, acquiring a current structural rigidity performance parameter, a current vehicle body modal performance parameter, a current NVH performance parameter and a current collision performance parameter;
and when the current structural rigidity performance parameter is not less than a preset rigidity performance threshold, the current vehicle body modal performance parameter is not less than a preset modal performance threshold, the current NVH performance parameter is not less than a preset NVH performance threshold and the current collision performance parameter is not less than a preset collision performance threshold, optimizing the quality of other vehicles of the same type through the preset optimization strategy.
Further, to achieve the above object, the present invention also proposes a vehicle mass optimizing apparatus including:
the determining module is used for determining a plurality of plate members according to the structural parameters and the subject performance parameters of the vehicle to be optimized;
the construction module is used for constructing a target performance balance explicit function according to the plate pieces with the number and the preset variable range of the plate pieces;
the judging module is used for determining a target optimization parameter according to the target performance balance explicit function when the performance parameters of the plates in the number meet a preset performance condition;
and the optimization module is used for optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy.
Further, to achieve the above object, the present invention also proposes a vehicle mass optimizing apparatus including: a memory, a processor and a vehicle mass optimization program stored on the memory and executable on the processor, the vehicle mass optimization program configured to implement the vehicle mass optimization method as described above.
Furthermore, to achieve the above object, the present invention also proposes a storage medium having stored thereon a vehicle mass optimization program which, when executed by a processor, implements the vehicle mass optimization method as described above.
The vehicle quality optimization method provided by the invention comprises the steps of determining a plurality of plates according to structural parameters and subject performance parameters of a vehicle to be optimized; constructing a target performance balance explicit function according to the plate pieces with the number and the preset variable range of the plate pieces; when the performance parameters of the plates in the number meet preset performance conditions, determining target optimization parameters according to the target performance balance explicit function; optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy; through the method, the target performance balance explicit function is constructed according to the determined number of plates and the preset variable range, the target optimization parameters are determined through the target performance balance explicit function, and finally the optimization of the mass of the vehicle to be optimized is achieved through the method of optimizing the target optimization parameters, so that the accuracy of optimizing the mass of the vehicle can be effectively improved.
Drawings
FIG. 1 is a schematic diagram of a vehicle quality optimization device for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of a first embodiment of a vehicle mass optimization method of the present invention;
FIG. 3 is a schematic flow chart diagram of a second embodiment of a vehicle mass optimization method of the present invention;
fig. 4 is a functional block diagram of a first embodiment of the vehicle mass optimizing apparatus of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle quality optimization device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the vehicle mass optimizing apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001 described previously.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the vehicle mass optimization device and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a vehicle quality optimization program.
In the vehicle quality optimizing apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with the network-integrated platform workstation; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the vehicle mass optimization apparatus of the present invention may be provided in the vehicle mass optimization apparatus that calls the vehicle mass optimization program stored in the memory 1005 through the processor 1001 and executes the vehicle mass optimization method provided by the embodiment of the present invention.
Based on the hardware structure, the embodiment of the vehicle quality optimization method is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a vehicle mass optimization method according to a first embodiment of the present invention.
In a first embodiment, the vehicle mass optimization method comprises the steps of:
and S10, determining a plurality of plate members according to the structural parameters and the subject performance parameters of the vehicle to be optimized.
It should be noted that the execution subject of the present embodiment is a vehicle quality optimization device, and may also be other devices that can achieve the same or similar functions, such as a vehicle controller, for example.
It should be understood that the structural parameters refer to the structural parameters of each position of the vehicle to be optimized, the structural parameters include but are not limited to the structural parameters of an engine, a chassis, a vehicle body and electrical equipment, the structural parameters of the vehicle to be optimized can be obtained through design, manufacturing and production data, the subject performance parameters refer to parameters for researching the performance of the vehicle to be optimized as a subject, the subject performance parameters include structural rigidity performance parameters, vehicle body modal performance parameters, NVH performance parameters and crash performance parameters, and the number of plates refers to the plates which have the greatest influence on the subject performance.
And S20, constructing a target performance balance explicit function according to the plate pieces with the number and the preset variable range of the plate pieces.
It is understood that the preset variable range refers to a range in which the plate is elastically changed in the design process, the sample spaces with different thicknesses are automatically generated according to the preset variable range, and the target performance balance explicit function refers to an explicit mathematical function between the plate and each performance, and the performance of the plate is in a balanced state through the target performance balance explicit function, so as to determine the target optimization parameter.
Further, step S20 includes: determining a total design variable according to the rigidity sensitivity, the modal sensitivity, the NVH contribution and the collision analysis animation; determining a preset variable range according to the total design variable and a preset discrete continuous range; sampling the plates in the number according to a target sampling algorithm to obtain sample plates of different thick plates; and constructing a target performance balance explicit function according to the preset variable range and the sample plates of different thick plates.
It should be understood that the total design variables are determined by the respective related design variables, specifically, the stiffness and the mode determine the related design variables with the sensitivity, the NVH determines the related design variables with the contribution, the collision determines the related design variables with the analysis animation, the preset discrete continuous range refers to the set discrete or continuous range, the preset variable range is determined by the total design variables and the preset discrete continuous range, and then the sampling is performed according to the target sampling algorithm, the target sampling algorithm includes but is not limited to a full factor algorithm, a partial factor algorithm, a central composite algorithm and a latin hypercube algorithm, the sampling frequency is 2 times of the design variables by the stiffness and the mode sampling number, the NVH and the collision sampling number are 3 times of the variables, and then the target performance balance explicit function is constructed according to the preset variable range and the sample plates of different slabs.
And S30, when the performance parameters of the plates meet preset performance conditions, determining target optimization parameters according to the target performance balance explicit function.
It should be understood that the preset performance condition refers to a condition for vehicle pre-factory qualification detection, the preset performance condition includes, but is not limited to, that the overall stiffness is greater than or equal to a preset stiffness value, the overall mode is greater than or equal to a preset mode value, the NVH related performance is greater than or equal to a preset NVH threshold value, the collision safety performance is greater than or equal to a preset collision threshold value, the target optimization parameter refers to a parameter for reducing the vehicle mass, that is, the vehicle mass can be reduced by the target optimization parameter, when it is determined that the performance parameters of a plurality of plates meet the preset performance condition, it indicates that the plates meet factory requirements, and at this time, the target optimization parameter is determined according to the target performance balance explicit function.
Further, step S30 includes: when the performance parameters of the plates in the number meet preset performance conditions, constructing a corresponding approximate performance model according to the performance parameters and the target performance balance explicit function; calculating the approximate performance model through a target loss function to obtain a precision value of the current model; and when the precision value of the current model is greater than or equal to a preset precision threshold value, analyzing the plates in the number according to the approximate performance model to obtain target optimization parameters.
It can be understood that the approximate performance model refers to an approximate model of each performance of the vehicle, the approximate performance model is determined by a target performance balance explicit function and qualified performance parameters of the sampled plates, the target loss function refers to a function for calculating a precision value of the model, the target loss function can be a softmax function or other loss functions capable of realizing the same or similar functions, then a current model precision value of the similar performance model is calculated through the target loss function, and then whether the current model precision value is greater than or equal to a preset precision threshold value or not is judged, if yes, a plurality of plates are analyzed in sequence through the approximate performance model, and if not, the approximate performance model is iterated continuously until the calculated model precision value is greater than or equal to the preset precision threshold value.
And S40, optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy.
It can be understood that the preset optimization strategy refers to an optimization strategy for reducing the vehicle mass, the preset optimization strategy is determined by a sequential quadratic programming algorithm, a multi-objective genetic algorithm and a hybrid algorithm, after a target optimization parameter is determined, the target optimization parameter is calculated by the preset optimization strategy to obtain a quality optimization parameter of an optimal solution, and specifically, the target optimization parameter is adjusted to reduce the mass of the vehicle to be optimized.
Further, after step S40, the method further includes: obtaining the current mass of the optimized vehicle to be optimized; when the current mass is smaller than the mass of the vehicle to be optimized, acquiring a current structural rigidity performance parameter, a current vehicle body modal performance parameter, a current NVH performance parameter and a current collision performance parameter; and when the current structural rigidity performance parameter is not less than a preset rigidity performance threshold, the current vehicle body modal performance parameter is not less than a preset modal performance threshold, the current NVH performance parameter is not less than a preset NVH performance threshold and the current collision performance parameter is not less than a preset collision performance threshold, optimizing the quality of other vehicles of the same type through the preset optimization strategy.
It should be understood that the current mass refers to the mass of the vehicle to be optimized after being reduced by the target optimization parameter, and after the mass of the vehicle to be optimized is optimized, whether a lightweight requirement is further required to be met, specifically, whether the current mass is smaller than the mass of the vehicle to be optimized is judged, if yes, whether a current structural rigidity performance parameter is not smaller than a preset rigidity performance threshold, whether a current vehicle body modal performance parameter is not smaller than a preset modal performance threshold, whether a current NVH performance parameter is not smaller than a preset NVH performance threshold, and whether a current collision performance parameter is not smaller than a preset collision performance threshold are continuously judged, and if both, it is indicated that the lightweight requirement is met by using the mass optimization method of the present embodiment, that is, the mass of other vehicles of the same type is optimized by using the mass optimization method, so as to reduce energy consumption.
The method comprises the steps that a plurality of plate pieces are determined according to structural parameters and subject performance parameters of a vehicle to be optimized; constructing a target performance balance explicit function according to the plates with the number and the preset variable range of the plates; when the performance parameters of the plates in the number meet preset performance conditions, determining target optimization parameters according to the target performance balance explicit function; optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy; according to the method, the target performance balance explicit function is constructed according to the determined number of plates and the preset variable range, the target optimization parameters are determined through the target performance balance explicit function, and finally the optimization of the mass of the vehicle to be optimized is achieved by means of optimizing the target optimization parameters, so that the accuracy of optimizing the mass of the vehicle can be effectively improved.
In an embodiment, as shown in fig. 3, a second embodiment of the vehicle mass optimizing method of the present invention is proposed based on the first embodiment, and the step S10 includes:
and S101, obtaining plates at a plurality of area positions according to the structural parameters of the vehicle to be optimized.
It should be understood that the several-zone-position panel refers to a panel at each zone position of the vehicle to be optimized, for example, a panel of a vehicle body is located at a side of the vehicle to be optimized, and a panel of a chassis is located at a bottom of the vehicle to be optimized, specifically, the several-zone-position panel is obtained according to structural parameters.
And S102, obtaining corresponding structural rigidity performance parameters, vehicle body modal performance parameters, NVH performance parameters and collision performance parameters according to the scientific performance parameters.
It can be understood that the structural rigidity performance parameter refers to a performance parameter of the plate resisting deformation caused by external force, the vehicle body modal performance parameter includes but is not limited to a body-in-white modal performance parameter, an interior trim vehicle body modal performance parameter and a whole vehicle modal performance parameter, the NVH performance parameter refers to a performance parameter of noise, vibration and sound vibration roughness, the collision performance parameter refers to a performance parameter of deformation when the plate collides with other objects, and after the subject performance parameters are obtained, the structural rigidity performance parameter, the vehicle body modal performance parameter, the NVH performance parameter and the collision performance parameter are extracted from the subject performance parameters.
Step S103, respectively screening out rigidity influence plates, modal influence plates, NVH influence plates and collision influence plates from the plates in the plurality of region positions according to the structural rigidity performance parameters, the vehicle body modal performance parameters, the NVH performance parameters and the collision performance parameters.
It should be understood that the rigidity-affecting plate means a plate that most affects rigidity, for example, the rigidity-affecting plate may be the members 1, 3, 15, 18, 22, 26, 31, 39, 45, 48, 57, and 66, the modal-affecting plate means a plate that most affects modal, for example, the modal-affecting plate may be the members 2, 3, 15, 17, 22, 24, 31, 36, 43, 48, 52, and 61, the NVH-affecting plate means a plate that most affects NVH, for example, the NVH-affecting plate may be the members 1, 2, 15, 17, 23, 33, 36, 49, 51, and 57, the impact-affecting plate means a plate that most affects impact, for example, the impact-affecting plate may be the members 2, 18, 22, 33, 39, 45, 48, 51, and 66.
Further, step S103 includes: selecting a target point on the plates in the plurality of area positions, and acquiring the current position of the target point; setting constraint, load force, statics analysis working conditions and torsion and bending control cards on the plate members at the plurality of area positions according to the structural rigidity performance parameters; after the setting is finished, determining the displacement of the target point according to the current position and the first position; analyzing the displacement through a CAE intelligent platform to obtain a corresponding rigidity performance value; screening out rigidity influence plates from the plates at the positions of the plurality of areas according to the rigidity performance value; setting a load step, an analysis working condition and a modal control card on the plates of the plurality of area positions according to the vehicle body modal performance parameters; after the setting is finished, determining the offset of the target point according to the current position and the second position; analyzing the offset through a CAE intelligent platform to obtain a corresponding modal performance value; screening modal influence plates from the plates at the positions of the plurality of areas according to the modal performance values; screening out NVH (noise, vibration and harshness) influence plates from the plates at the plurality of area positions according to the NVH performance parameters; and screening out the plate elements with the impact influence from the plate elements at the plurality of area positions according to the impact performance parameters.
It can be understood that the target point refers to a selected key point on the plate at a plurality of regional positions, the target point can be a key measuring point of bending stiffness and is divided into a, B, C, D, E and F, for structural stiffness performance, the imported relevant model is BIP, then constraints, load force, static analysis conditions and torsion and bending control cards are respectively set on the plate at the plurality of regional positions, optionally, constraint direction of constraint SPC, load force FROCE, static analysis conditions Load Steps are set, four points of torsional stiffness and six points of bending stiffness are selected as the constraint direction, the torsion and bending control cards are set as the constraint direction, the Load force FROCE, the static analysis conditions Load Steps, then a stiffness performance value is analyzed according to displacement determined by the current position and the first position through a CAE intelligent platform, and finally, a stiffness-affected plate is screened from the plate at the plurality of regional positions according to the stiffness performance value.
It should be understood that, for modal performance, the imported relevant model is BIW, then Load Steps, analysis conditions and modal control cards are set on the plates of a plurality of area positions, optionally, the setting of Load Steps selects an "EIGRA" algorithm, and sets an analysis frequency range, the setting of the Load Steps selects a "Normal modes" analysis type, and the modal control cards include but are not limited to selecting a solving type, setting analysis parameters, and setting output parameters, after the setting is completed, modal performance values are analyzed for offsets determined by the CAE intelligent platform for the current position and the second position, and finally, modal influence plates are screened from the plates of the plurality of area positions according to the modal performance values.
Further, screening out NVH influence plates from the plates at the plurality of area positions according to the NVH performance parameters, wherein the screening comprises the following steps: extracting noise performance parameters and vibration performance parameters of the NVH performance parameters; selecting a first output point set and a second output point set on the plates at the plurality of area positions, wherein the first output point set comprises three key points of a main driving right ear, a middle right left ear and a rear row right ear, and the second output point set comprises two key points of a right front direction of a steering wheel and a rear right point of a main seat driving guide rail; setting an analysis frequency range, a modal frequency range, structural fluid damping, a noise working condition and a noise control card on the plates at the plurality of region positions according to the noise performance parameters; after setting is completed, collecting a noise data file of the first output point set; setting an analysis frequency range, a module frequency range, structural damping, a vibration working condition and a vibration control card on the plates at the plurality of region positions according to the vibration performance parameters; after the setting is finished, collecting a vibration data file of the second output point set; analyzing the noise data file and the vibration data file respectively through a CAE intelligent platform to obtain a corresponding noise performance value and a corresponding vibration performance value; screening out NVH influencing plates from the plates at the plurality of zone positions according to the noise performance value and the vibration performance value.
It should be understood that for noise of NVH performance, the imported relevant models are a TB model and a sound cavity model, the first output point set is a point set analyzed by a noise transfer function, and comprises three key points of a main driving right ear, a middle right left ear and a back row right ear, the set content comprises an analysis frequency range, a modal frequency range, structural fluid damping, a noise condition and a noise control card, the noise data file refers to a data file of noise transfer under a set condition, and the format of the noise data file can be bdf or pch, and then the noise data file is analyzed by a CAE intelligent platform to obtain a noise performance value, and further, for vibration of NVH performance, the imported model is a TB model, the second output point set is a point set analyzed by a vibration transfer function, and comprises two key points of a front direction of a steering wheel, a right back point of a main driving seat guide rail, optionally, the front direction can be a 12 o' clock direction of the steering wheel, then the analysis frequency range, the module frequency range, the structural damping card, the vibration control card, and the vibration control file can be selected by a plurality of analysis of the analysis frequency range, and the vibration control card can be used for obtaining a final vibration control card.
And step S104, accumulating the rigidity influence plate, the modal influence plate, the NVH influence plate and the collision influence plate.
It will be appreciated that the stiffness-affecting, modal-affecting, NVH-affecting, and impact-affecting panels are summed, for example, with the stiffness-affecting panels being members 1, 3, 15, 18, 22, 26, 31, 39, 45, 48, 57, and 66, the modal-affecting panels being members 2, 3, 15, 17, 22, 24, 31, 36, 43, 48, 52, and 61, the NVH-affecting panels being members 1, 2, 15, 17, 23, 33, 36, 49, 51, and 57, the impact-affecting panel members may be members 2, 18, 22, 33, 39, 45, 48, 51, and 66, with the final, accumulated panel members being members 1, 3, 15, 18, 22, 26, 31, 39, 45, 48, 57, and 66, members 2, 3, 15, 17, 22, 24, 31, 36, 43, 48, 52, and 61, members 1, 2, 15, 17, 23, 33, 36, 49, 51, and 57, members 2, 18, 22, 33, 39, 45, 48, 51, and 66.
And step S105, carrying out duplication elimination operation on the accumulated influence plates to obtain a plurality of plate elements.
It should be understood that after the accumulated affected panel members are obtained, the repeated panel members are removed, i.e., a number of panel members are obtained, such as member 1, member 2, member 3, member 15, member 17, member 18, member 22, member 23, member 24, member 26, member 31, member 33, member 36, member 39, member 43, member 45, member 48, member 49, member 51, member 52, member 57, member 61, and member 66.
According to the method, plates of a plurality of area positions are obtained according to the structural parameters of the vehicle to be optimized; obtaining corresponding structural rigidity performance parameters, vehicle body modal performance parameters, NVH performance parameters and collision performance parameters according to the scientific performance parameters; screening out a rigidity influence plate, a modal influence plate, an NVH influence plate and a collision influence plate from the plates in the plurality of region positions according to the structural rigidity performance parameters, the vehicle body modal performance parameters, the NVH performance parameters and the collision performance parameters; accumulating the rigidity influence plate, the modal influence plate, the NVH influence plate and the collision influence plate; carrying out duplicate removal operation on the accumulated influence plates to obtain a plurality of plate members; through the mode, the corresponding plates are selected according to the structural rigidity performance parameters, the vehicle body modal performance parameters, the NVH performance parameters and the collision performance parameters obtained according to the subject performance parameters, then the plates are accumulated, and finally the repeated operation is carried out on the accumulated influence plates, so that the accuracy of obtaining a plurality of numbers of plates can be effectively improved.
Furthermore, an embodiment of the present invention also proposes a storage medium having a vehicle mass optimization program stored thereon, which when executed by a processor implements the steps of the vehicle mass optimization method as described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
In addition, referring to fig. 4, an embodiment of the present invention further provides a vehicle mass optimization apparatus, including:
the determination module 10 is used for determining a plurality of plate members according to the structural parameters and the subject performance parameters of the vehicle to be optimized.
And the building module 20 is used for building a target performance balance explicit function according to the plate pieces with the number and the preset variable range of the plate pieces.
And the judging module 30 is configured to determine a target optimization parameter according to the target performance balancing explicit function when the performance parameters of the plurality of plates meet a preset performance condition.
And the optimization module 40 is used for optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy.
In the embodiment, the material parameters of a plurality of parts are obtained according to the vehicle body attribute parameters by obtaining the vehicle body attribute parameters of the vehicle to be lightened; performing performance analysis on the material parameters through a target CAE intelligent platform to obtain target thickness and target strength parameters of the parts; determining a target lightweight parameter according to the target thickness and the target strength parameter; carrying out weight reduction treatment on the body of the vehicle to be subjected to weight reduction according to a preset material optimization processing strategy and the target weight reduction parameters; by the mode, performance analysis is carried out on the basis of material parameters of a plurality of parts of the target CAE intelligent platform, then the target lightweight parameter is determined according to the target thickness and the target strength parameter, and finally the vehicle body is subjected to lightweight processing according to the preset material optimization processing strategy and the target lightweight parameter, so that the weight of the vehicle body can be effectively reduced on the basis of meeting the requirements of thickness and strength performance.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not elaborated in this embodiment can be referred to the vehicle quality optimization method provided by any embodiment of the present invention, and are not described herein again.
In an embodiment, the determining module 10 is further configured to obtain panel members of a plurality of area positions according to the structural parameters of the vehicle to be optimized; obtaining corresponding structural rigidity performance parameters, vehicle body modal performance parameters, NVH performance parameters and collision performance parameters according to the scientific performance parameters; respectively screening a rigidity influence plate, a modal influence plate, an NVH influence plate and a collision influence plate from the plates in the plurality of area positions according to the structural rigidity performance parameter, the vehicle body modal performance parameter, the NVH performance parameter and the collision performance parameter; accumulating the rigidity influencing plate, the modal influencing plate, the NVH influencing plate and the collision influencing plate; and carrying out duplication removing operation on the accumulated influence plates to obtain a plurality of plate members.
In an embodiment, the determining module 10 is further configured to select a target point on the plate at the plurality of area positions, and obtain a current position of the target point; setting constraint, load force, statics analysis working conditions and torsion and bending control cards on the plates at the plurality of region positions according to the structural rigidity performance parameters; after the setting is finished, determining the displacement of the target point according to the current position and the first position; analyzing the displacement through a CAE intelligent platform to obtain a corresponding rigidity performance value; screening out rigidity influence plates from the plates at the positions of the plurality of areas according to the rigidity performance values; setting load steps, analysis conditions and modal control cards on the plates of the plurality of area positions according to the vehicle body modal performance parameters; after the setting is finished, determining the offset of the target point according to the current position and the second position; analyzing the offset through a CAE intelligent platform to obtain a corresponding modal performance value; screening modal influence plates from the plates at the positions of the plurality of areas according to the modal performance values; screening out NVH (noise, vibration and harshness) influence plates from the plates at the plurality of area positions according to the NVH performance parameters; and screening out the plate elements with the impact influence from the plate elements at the plurality of area positions according to the impact performance parameters.
In an embodiment, the determining module 10 is further configured to extract a noise performance parameter and a vibration performance parameter of the NVH performance parameters; selecting a first output point set and a second output point set on the plates at the plurality of area positions, wherein the first output point set comprises three key points of a main driving right ear, a middle right left ear and a rear row right ear, and the second output point set comprises two key points of a right front direction of a steering wheel and a rear right point of a main seat driving guide rail; setting an analysis frequency range, a modal frequency range, structural fluid damping, a noise working condition and a noise control card on the plates at the plurality of region positions according to the noise performance parameters; after setting is completed, collecting a noise data file of the first output point set; setting an analysis frequency range, a module frequency range, structural damping, a vibration working condition and a vibration control card on the plates at the plurality of region positions according to the vibration performance parameters; after the setting is finished, acquiring a vibration data file of the second output point set; analyzing the noise data file and the vibration data file respectively through a CAE intelligent platform to obtain a corresponding noise performance value and a corresponding vibration performance value; screening out NVH influencing plates from the plates at the plurality of zone positions according to the noise performance value and the vibration performance value.
In one embodiment, the building module 20 is further configured to determine a total design variable according to the stiffness sensitivity, the modal sensitivity, the NVH contribution, and the collision analysis animation; determining a preset variable range according to the total design variable and a preset discrete continuous range; sampling the plurality of plate members according to a target sampling algorithm to obtain sample plate members with different thick plates; and constructing a target performance balance explicit function according to the preset variable range and the sample plates of the different thick plates.
In an embodiment, the determining module 30 is further configured to construct a corresponding approximate performance model according to the performance parameters and the target performance balance explicit function when the performance parameters of the plurality of plates meet a preset performance condition; calculating the approximate performance model through a target loss function to obtain a current model precision value; and when the precision value of the current model is greater than or equal to a preset precision threshold value, analyzing the plates in the number according to the approximate performance model to obtain target optimization parameters.
In an embodiment, the optimization module 40 is further configured to obtain the current mass of the optimized vehicle to be optimized; when the current mass is smaller than the mass of the vehicle to be optimized, obtaining a current structural rigidity performance parameter, a current vehicle body modal performance parameter, a current NVH performance parameter and a current collision performance parameter; and when the current structural rigidity performance parameter is not less than a preset rigidity performance threshold, the current vehicle body modal performance parameter is not less than a preset modal performance threshold, the current NVH performance parameter is not less than a preset NVH performance threshold and the current collision performance parameter is not less than a preset collision performance threshold, optimizing the quality of other vehicles of the same type through the preset optimization strategy.
Other embodiments or methods of implementing the vehicle mass optimization device of the present invention are described with reference to the method embodiments described above and are not exhaustive.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, an integrated platform workstation, or a network device, etc.) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (10)
1. A vehicle mass optimization method, characterized by comprising the steps of:
determining a plurality of plate members according to the structural parameters and the subject performance parameters of the vehicle to be optimized;
constructing a target performance balance explicit function according to the plate pieces with the number and the preset variable range of the plate pieces;
when the performance parameters of the plates in the number meet preset performance conditions, determining target optimization parameters according to the target performance balance explicit function;
and optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy.
2. The vehicle mass optimization method of claim 1, wherein determining the number of panels based on the structural parameters and the subject performance parameters of the vehicle to be optimized comprises:
obtaining plates of a plurality of area positions according to the structural parameters of the vehicle to be optimized;
obtaining corresponding structural rigidity performance parameters, vehicle body modal performance parameters, NVH performance parameters and collision performance parameters according to the scientific performance parameters;
respectively screening a rigidity influence plate, a modal influence plate, an NVH influence plate and a collision influence plate from the plates in the plurality of area positions according to the structural rigidity performance parameter, the vehicle body modal performance parameter, the NVH performance parameter and the collision performance parameter;
accumulating the rigidity influencing plate, the modal influencing plate, the NVH influencing plate and the collision influencing plate;
and carrying out duplication removing operation on the accumulated influence plates to obtain a plurality of plate members.
3. The vehicle mass optimization method of claim 2, wherein the screening of the stiffness-influencing, modal-influencing, NVH-influencing, and crash-influencing panels from the panels at the plurality of zone locations according to the structural stiffness performance parameter, the body modal performance parameter, the NVH performance parameter, and the crash performance parameter, respectively, comprises:
selecting target points on the plates in the plurality of area positions, and acquiring the current positions of the target points;
setting constraint, load force, statics analysis working conditions and torsion and bending control cards on the plate members at the plurality of area positions according to the structural rigidity performance parameters;
after the setting is finished, determining the displacement of the target point according to the current position and the first position;
analyzing the displacement through a CAE intelligent platform to obtain a corresponding rigidity performance value;
screening out rigidity influence plates from the plates at the positions of the plurality of areas according to the rigidity performance value;
setting a load step, an analysis working condition and a modal control card on the plates of the plurality of area positions according to the vehicle body modal performance parameters;
after the setting is finished, determining the offset of the target point according to the current position and the second position;
analyzing the offset through a CAE intelligent platform to obtain a corresponding modal performance value;
screening modal influence plates from the plates at the positions of the plurality of areas according to the modal performance values;
screening out NVH (noise, vibration and harshness) influence plates from the plates at the plurality of area positions according to the NVH performance parameters;
and screening out collision influence plates from the plates at the plurality of region positions according to the collision performance parameters.
4. The vehicle mass optimization method of claim 3, wherein the screening of the panel of the plurality of zone locations for NVH affecting panels based on the NVH performance parameters comprises:
extracting noise performance parameters and vibration performance parameters of the NVH performance parameters;
selecting a first output point set and a second output point set on the plates at the plurality of area positions, wherein the first output point set comprises three key points of a main driving right ear, a middle right left ear and a rear row right ear, and the second output point set comprises two key points of a right front direction of a steering wheel and a rear right point of a main seat driving guide rail;
setting an analysis frequency range, a modal frequency range, structural fluid damping, a noise working condition and a noise control card on the plates at the plurality of region positions according to the noise performance parameters;
after setting is completed, collecting a noise data file of the first output point set;
setting an analysis frequency range, a module frequency range, structural damping, a vibration working condition and a vibration control card on the plates at the plurality of region positions according to the vibration performance parameters;
after the setting is finished, acquiring a vibration data file of the second output point set;
analyzing the noise data file and the vibration data file respectively through a CAE intelligent platform to obtain a corresponding noise performance value and a corresponding vibration performance value;
screening out NVH influencing plates from the plates at the plurality of zone positions according to the noise performance value and the vibration performance value.
5. The vehicle mass optimization method of claim 1, wherein said constructing a target performance balance explicit function from said number of plates, said preset variable range of plates, comprises:
determining a total design variable according to the rigidity sensitivity, the modal sensitivity, the NVH contribution and the collision analysis animation;
determining a preset variable range according to the total design variable and a preset discrete continuous range;
sampling the plates in the number according to a target sampling algorithm to obtain sample plates of different thick plates;
and constructing a target performance balance explicit function according to the preset variable range and the sample plates of the different thick plates.
6. The vehicle mass optimization method of claim 1, wherein determining the target optimization parameter according to the target performance balancing explicit function when the performance parameters of the number of panels satisfy a preset performance condition comprises:
when the performance parameters of the plates in the number meet preset performance conditions, constructing a corresponding approximate performance model according to the performance parameters and the target performance balance explicit function;
calculating the approximate performance model through a target loss function to obtain a current model precision value;
and when the precision value of the current model is greater than or equal to a preset precision threshold value, analyzing the plates of the plurality of numbers according to the approximate performance model to obtain target optimization parameters.
7. The vehicle mass optimization method according to any one of claims 1 to 6, wherein after optimizing the mass of the vehicle to be optimized according to the target optimization parameter by a preset optimization strategy, further comprising:
obtaining the current mass of the optimized vehicle to be optimized;
when the current mass is smaller than the mass of the vehicle to be optimized, obtaining a current structural rigidity performance parameter, a current vehicle body modal performance parameter, a current NVH performance parameter and a current collision performance parameter;
and when the current structural rigidity performance parameter is not less than a preset rigidity performance threshold, the current vehicle body modal performance parameter is not less than a preset modal performance threshold, the current NVH performance parameter is not less than a preset NVH performance threshold and the current collision performance parameter is not less than a preset collision performance threshold, optimizing the quality of other vehicles of the same type through the preset optimization strategy.
8. A vehicle mass optimizing apparatus characterized by comprising:
the determining module is used for determining a plurality of plate members according to the structural parameters and the subject performance parameters of the vehicle to be optimized;
the construction module is used for constructing a target performance balance explicit function according to the plate pieces with the number and the preset variable range of the plate pieces;
the judging module is used for determining a target optimization parameter according to the target performance balance explicit function when the performance parameters of the plates in the number meet a preset performance condition;
and the optimization module is used for optimizing the mass of the vehicle to be optimized according to the target optimization parameters through a preset optimization strategy.
9. A vehicle mass optimizing apparatus characterized by comprising: a memory, a processor, and a vehicle mass optimization program stored on the memory and executable on the processor, the vehicle mass optimization program configured to implement the vehicle mass optimization method of any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a vehicle mass optimization program that, when executed by a processor, implements a vehicle mass optimization method according to any one of claims 1 to 7.
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