CN112131672A - Durable load spectrum simulation method, device, storage medium and device - Google Patents
Durable load spectrum simulation method, device, storage medium and device Download PDFInfo
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Abstract
The invention discloses a durable load spectrum simulation method, a device, a storage medium and a device, wherein the method comprises the steps of obtaining vehicle data of a basic vehicle type with the same chassis as a vehicle to be detected, generating equivalent 3D road information and wheel center five-component driving information of the vehicle to be detected through a virtual iterative model according to the vehicle data, and constructing a multi-body dynamic model corresponding to the vehicle to be detected; the method comprises the steps of carrying out endurance load spectrum simulation according to wheel center five-component force information, equivalent 3D road surface information and a multi-body dynamic model, and obtaining a simulation result.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a durable load spectrum simulation method, durable load spectrum simulation equipment, a durable load spectrum simulation storage medium and a durable load spectrum simulation device.
Background
At present, with the development of the automobile industry, requirements on safety, durability and functionality of an automobile are continuously improved, so that the influence of an automobile durability simulation load spectrum on the durability simulation precision of a whole automobile structure is critical, and for the durability test of the whole automobile structure of a new automobile type, the existing technology is to perform the durability test in a test field by adopting a real automobile, so that the durability test period of the automobile is long, and the labor and cost expenditure are high.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, equipment, a storage medium and a device for simulating an endurance load spectrum, and aims to solve the technical problem of acquiring the endurance load spectrum of a vehicle under the condition that no real vehicle collects road spectrums at the early stage of vehicle project development in the prior art.
In order to achieve the above object, the present invention provides a durable loading spectrum simulation method, which includes the following steps:
acquiring whole vehicle data of a basic vehicle type of a vehicle to be detected and the same chassis;
determining vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type according to the whole vehicle data;
generating equivalent 3D road information and wheel center five-component driving information of the basic vehicle type through a virtual iteration model based on the front and rear suspension shaft head vertical displacement driving data;
acquiring a wheel base change value between the vehicle to be detected and the basic vehicle type, and updating the equivalent 3D road information according to the wheel base change value to acquire target equivalent 3D road information of the vehicle to be detected;
acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type, and determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio;
constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data;
and carrying out durable load spectrum simulation according to the target wheel center five-component driving information, the target equivalent 3D road surface information and the multi-body dynamic model, and obtaining a simulation result.
Preferably, the step of determining the vertical displacement driving data of the front and rear suspension shaft heads of the basic vehicle type according to the whole vehicle data specifically comprises:
extracting road spectrum parameters of the basic vehicle type from the whole vehicle data;
constructing a first multi-body dynamic model corresponding to a basic vehicle model not containing Ftire tires according to the road spectrum parameters;
and acquiring vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type from the road spectrum parameters based on the first multi-body dynamic model.
Preferably, the step of generating equivalent 3D road information and wheel center five-component driving information of the basic vehicle type through a virtual iterative model based on the front and rear suspension spindle nose vertical displacement driving data includes:
obtaining an Ftire tire model, and adding the Ftire tire model into the first multi-body dynamic model to generate a second multi-body dynamic model;
generating initial equivalent 3D road information and initial wheel center five-component driving information of the basic vehicle type according to vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type;
and carrying out iterative operation on the initial equivalent 3D road surface information and the initial wheel center five-component driving information to generate equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type.
Preferably, the step of performing iterative operation on the initial equivalent 3D road surface information and the initial wheel center five-component drive information to generate the equivalent 3D road surface information and the wheel center five-component drive information of the basic vehicle type includes:
taking the initial equivalent 3D road surface information and the initial wheel center five-component force driving information as initial driving excitation of the second multi-body dynamic model, and driving the second multi-body dynamic model;
and generating equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type based on the virtual iterative model.
Preferably, the step of updating the equivalent 3D road information according to the wheel base change value to obtain the target equivalent 3D road information of the vehicle to be detected includes:
acquiring running speeds of the basic vehicle type corresponding to different characteristic road sections;
performing phase translation on time axis data contained in the equivalent 3D road surface information according to the wheel base change value and the driving speed, and obtaining a translation result;
and updating the equivalent 3D road surface information according to the translation result to obtain target equivalent 3D road surface information of the vehicle to be detected.
Preferably, the step of obtaining an axle load ratio between the vehicle to be detected and the basic vehicle type and determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio includes:
acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type, and determining time domain data corresponding to five wheel center components of the vehicle to be detected according to the axle load ratio;
and updating the wheel center five-component driving information of the basic vehicle type according to the translation result and the time domain data result so as to obtain the target wheel center five-component driving information of the vehicle to be detected.
Preferably, the step of updating the wheel center five-component driving information of the basic vehicle type according to the translation result and the time domain data result to obtain the target wheel center five-component driving information of the vehicle to be detected includes:
updating rear suspension wheel center five-component driving information in the wheel center five-component driving information of the basic vehicle type according to the translation result, and obtaining the rear suspension wheel center five-component driving information of the vehicle to be detected;
and determining the wheel center five-component driving information of the vehicle to be detected according to the wheel center five-component driving information of the rear suspension and the wheel center five-component driving information of the basic vehicle type.
Furthermore, to achieve the above object, the present invention further provides an endurance load spectrum simulation apparatus, which includes a memory, a processor, and an endurance load spectrum simulation program stored on the memory and executable on the processor, the endurance load spectrum simulation program being configured to implement the steps of endurance load spectrum simulation as described above.
Furthermore, to achieve the above object, the present invention further provides a storage medium having an endurance load spectrum simulation program stored thereon, which when executed by a processor implements the steps of the endurance load spectrum simulation method as described above.
In order to achieve the above object, the present invention further provides a durable loading spectrum simulation apparatus, including:
the data acquisition module is used for acquiring the whole vehicle data of a basic vehicle type with the same chassis as the vehicle to be detected;
the data acquisition module is also used for determining vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type according to the whole vehicle data;
the virtual iteration module is used for generating equivalent 3D road surface information and wheel center five-component force driving information of the basic vehicle type through a virtual iteration model based on the vertical displacement driving data of the front and rear suspension shaft heads;
the data acquisition module is also used for acquiring a wheel base change value between the vehicle to be detected and the basic vehicle type;
the data updating module is used for updating the equivalent 3D road surface information according to the wheel base change value so as to obtain target equivalent 3D road surface information of the vehicle to be detected;
the data acquisition module is further used for acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type and determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio;
the model construction module is used for constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data;
the driving simulation module. And the system is used for carrying out endurance load spectrum simulation according to the target wheel center five-component driving information, the target equivalent 3D road surface information and the multi-body dynamic model, and obtaining a simulation result.
According to the method, vehicle data of a basic vehicle type of a vehicle to be detected and the same chassis are obtained, vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type are determined according to the vehicle data, and equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type are generated through a virtual iterative model; acquiring a wheel base change value between a vehicle to be detected and a basic vehicle type, and updating equivalent 3D road information according to the wheel base change value to acquire target equivalent 3D road information of the vehicle to be detected; acquiring an axle load ratio between a vehicle to be detected and a basic vehicle type, and determining target wheel center five-component drive information of the vehicle to be detected based on the axle load ratio; constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data; the method comprises the steps of carrying out endurance load spectrum simulation according to target wheel center five-component driving information, target equivalent 3D road surface information and a multi-body dynamic model, and obtaining a simulation result, wherein vertical displacement driving data of front and rear suspension shaft heads are determined according to whole vehicle data of a basic vehicle type, target wheel center five-component information and target equivalent 3D road surface information of a vehicle to be detected are determined according to a wheel base change value and an axle load ratio value between the basic vehicle type and the vehicle to be detected, and endurance load spectrum simulation is carried out on the multi-body dynamic model corresponding to the vehicle to be detected. And the project development period is shortened.
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FIG. 1 is a schematic structural diagram of a endurance load spectrum simulation apparatus of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a endurance load spectrum simulation method according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a durable loading spectrum simulation method according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a third exemplary embodiment of a endurance load spectrum simulation method according to the present invention;
fig. 5 is a block diagram of a first embodiment of the endurance load spectrum simulation apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained 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 endurance load spectrum simulation device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the endurance load spectrum simulation 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), and the optional user interface 1003 may further include a standard wired interface and a wireless interface, and the wired interface for the user interface 1003 may be a USB interface in the present invention. 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.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of the endurance load spectrum simulation apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in FIG. 1, memory 1005, identified as one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a durable load spectrum emulation program.
In the durable load spectrum simulation device shown in fig. 1, the network interface 1004 is mainly used for connecting a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting user equipment; the endurance load spectrum simulation apparatus calls the endurance load spectrum simulation program stored in the memory 1005 through the processor 1001, and executes the endurance load spectrum simulation method provided by the embodiment of the present invention.
Based on the hardware structure, the embodiment of the endurance load spectrum simulation method is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the endurance load spectrum simulation method of the present invention, and proposes the first embodiment of the endurance load spectrum simulation method of the present invention.
In a first embodiment, the endurance load spectrum simulation method includes the steps of:
step S10: and acquiring the whole vehicle data of the basic vehicle type of the vehicle to be detected and the chassis.
It should be noted that the execution main body of the embodiment may be a vehicle-mounted computer, and the vehicle-mounted computer is a special vehicle information product which is developed specially for the special operating environment of the vehicle and the circuit characteristics of the electric appliance, has the functions of high temperature resistance, dust resistance and shock resistance, and can be fused with the electronic circuit of the vehicle. The vehicle-mounted computer can be equipment containing durable load spectrum simulation software.
It should be understood that the vehicle to be detected may be a vehicle which needs to be subjected to an endurance test, and the basic vehicle type may be a vehicle type which has the same chassis as the vehicle to be detected, or may be a vehicle type which contains endurance test data and has the same chassis as the vehicle to be detected; the vehicle-finished data can refer to vehicle-finished technical data in technical data, and mainly comprises road spectrum parameters, vehicle type parameters, vehicle-finished size parameters, vehicle quality parameters, vehicle-finished performance parameters and the like of a basic vehicle type. .
In the concrete implementation, the vehicle-mounted computer can obtain road spectrum parameters, vehicle type parameters, whole vehicle size parameters, whole vehicle mass parameters, whole vehicle performance parameters and other data of a basic vehicle type with a chassis of a vehicle to be detected from a historical database, for example, the road spectrum parameters can comprise six component force of a shaft head, acceleration of the shaft head, suspension displacement, damper force and other data, the vehicle-mounted computer can extract suspension displacement from the road spectrum parameters so as to determine vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type, and the vertical displacement data of the front and rear suspension shaft heads can be used as vertical driving excitation data of the basic vehicle type on 4 shaft heads of the vehicle through tire force when the basic vehicle type runs on a road surface of a test field.
Step S20: and determining vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type according to the whole vehicle data.
It should be noted that the suspension is a generic term for a force transmission connection device between a frame and a wheel of an automobile, and may be composed of an elastic element, a guide mechanism, a shock absorber, and the like. The front suspension can be the suspension of connecting front wheel and automobile body, and the rear suspension can be the suspension of connecting rear wheel and automobile body, and front and rear suspension spindle nose vertical drive data can indicate the spindle nose vertical displacement drive data that the front suspension corresponds and the spindle nose vertical displacement drive data that the rear suspension corresponds. The spindle heads can be two end parts of a mechanical part which supports a vehicle rotating part and rotates together with the vehicle rotating part to transfer motion, torque or bending moment, and the spindle head vertical displacement can be the displacement of the spindle head in the vertical direction and can be obtained through calculation according to the spindle head vertical acceleration.
In the concrete implementation, the road spectrum parameters are extracted from the whole vehicle data by the vehicle-mounted computer, the road spectrum parameters comprise data such as six component force of a shaft head, acceleration of the shaft head, suspension displacement and shock absorber force, and the vertical displacement of the front suspension shaft head and the vertical displacement of the rear suspension shaft head of the basic vehicle type are determined according to the road spectrum parameters.
Step S30: and generating equivalent 3D road information and wheel center five-component driving information of the basic vehicle type through a virtual iterative model based on the front and rear suspension shaft head vertical displacement driving data.
It should be noted that the virtual iterative model may be a model for accurate numerical values, may be used to calculate a dynamically input vertical displacement when the vehicle is traveling, and the equivalent 3D road information may be virtual road information generated by three-dimensional modeling of a road surface when a basic vehicle type is driving in a test field. The five-component wheel center force is the force which is left in five directions except the vertical force in the six-component wheel center force.
In the concrete implementation, when fatigue analysis is carried out on a vehicle, accurate spindle nose vertical displacement driving data is needed, virtual iteration of accuracy can be carried out on vertical displacement through a virtual iteration model, and equivalent 3D road surface information and wheel center five-component driving information of a basic vehicle type are generated based on front and rear suspension spindle nose vertical displacement driving data.
Step S40: and acquiring a wheel base change value between the vehicle to be detected and the basic vehicle type, and updating the equivalent 3D road information according to the wheel base change value to acquire target equivalent 3D road information of the vehicle to be detected.
It should be noted that the wheel base may be a distance from a center of a front axle to a center of a rear axle of the vehicle, that is, a distance between two perpendicular lines passing through centers of two adjacent wheels on the same side of the vehicle and perpendicular to a longitudinal symmetry plane of the vehicle. The wheel base change value can be a change value of the wheel base of the vehicle to be detected relative to the basic vehicle type.
In the specific implementation, because the vehicle to be detected and the basic vehicle model are the same chassis platform, when the vehicle to be detected and the basic vehicle model run on the same test field characteristic road surface, each wheel runs through the equivalent 3D road surface identically, but because the vehicle to be detected has a change in wheelbase relative to the basic vehicle model, the equivalent 3D road surface information is updated according to the change in wheelbase to obtain the target equivalent 3D road surface information of the vehicle to be detected, for example, a characteristic road section set in the test field may have an asphalt road section, a sand road section and various different sand road sections so as to perform a strengthening and durability test, and the vehicle-mounted computer updates the equivalent 3D road surface information corresponding to the basic vehicle model according to the change in wheelbase when each road section runs so as to obtain the target equivalent 3D road surface information of the vehicle to be detected.
Step S50: and acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type, and determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio.
It should be noted that the axle load may be the maximum weight allowed to be allocated to each axle, the axle load ratio may be a ratio of the maximum vehicle weight allowed to be allocated to each axle of the vehicle to be detected to the maximum vehicle weight allowed to be allocated to each axle of the basic vehicle type, and the target wheel center five-component driving information may be two remaining directional forces Fx ', Fy ', and three remaining moments Tx ', Ty ', and Tz ' on the axle head of the vehicle to be detected except for the vertical force.
In the concrete implementation, when the vehicle is subjected to durability test, the vehicle to be detected and a basic vehicle type are the same chassis platform, and considering that the vehicle to be detected and the basic vehicle type are the same chassis platform, namely the suspension type is the same, the tire model is the same, when the wheel drives the same road at the same speed, the six component forces of the wheel center are influenced by the axle load difference, except the vertical force of the wheel which is influenced most by the axle load and has certain nonlinearity, the magnitude of other five component forces of the wheel center can be approximately in linear direct proportion to the axle load, so when the vehicle to be detected and the basic vehicle type drive on the road surface with the same test field characteristics, the five component force driving information of the target wheel center of the vehicle to be detected can be determined according to the axle load ratio between the vehicle to be detected and the basic.
Step S60: and constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data.
It should be noted that the multi-body dynamic model may be a complete vehicle model including a chassis platform, front and rear suspensions, and motion parameters required for integrated control. The whole vehicle model comprises a 4-wheel Ftire subsystem, a front suspension bracket system, a rear suspension bracket system, a steering subsystem, a power assembly subsystem, a vehicle body subsystem and the like
In specific implementation, the vehicle-mounted computer can extract motion parameters required by a chassis platform, front and rear suspensions and integrated control from the whole vehicle data to construct a multi-body dynamic model.
Step S70: and carrying out durable load spectrum simulation according to the target wheel center five-component driving information, the target equivalent 3D road surface information and the multi-body dynamic model, and obtaining a simulation result.
It should be noted that the durable load spectrum simulation may be implemented by taking the target wheel center five-component driving information and the target equivalent 3D road surface information of the vehicle to be detected as inputs of a multi-body dynamic model, and performing virtual simulation analysis to obtain the durable load spectrum of the vehicle to be detected.
In the specific implementation, the vehicle-mounted computer uses the target wheel center five-component driving information and the target equivalent 3D road surface information of the vehicle to be detected as the input of a multi-body dynamic model through the durable load spectrum simulation software to perform virtual simulation analysis, so as to obtain the durable load spectrum of the vehicle to be detected.
According to the embodiment, the whole vehicle data of a basic vehicle type of a vehicle to be detected and the same chassis are obtained, the vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type are determined according to the whole vehicle data, and equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type are generated through a virtual iterative model; acquiring a wheel base change value between a vehicle to be detected and a basic vehicle type, and updating equivalent 3D road information according to the wheel base change value to acquire target equivalent 3D road information of the vehicle to be detected; acquiring an axle load ratio between a vehicle to be detected and a basic vehicle type, and determining target wheel center five-component drive information of the vehicle to be detected based on the axle load ratio; constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data; the method comprises the steps of carrying out endurance load spectrum simulation according to target wheel center five-component driving information, target equivalent 3D road surface information and a multi-body dynamic model, and obtaining a simulation result, wherein vertical displacement driving data of front and rear suspension shaft heads are determined according to whole vehicle data of a basic vehicle type, target wheel center five-component driving information and target equivalent 3D road surface information of a vehicle to be detected are determined according to an axle distance change value and an axle load ratio value between the basic vehicle type and the vehicle to be detected, and endurance load spectrum simulation is carried out on the multi-body dynamic model corresponding to the vehicle to be detected, compared with the prior art, the embodiment has the advantages that the vehicle to be detected is developed on a same chassis platform, real vehicle road spectrum collection is not needed, an endurance simulation load spectrum can be obtained at the early stage of development of the vehicle to be detected, and the simulation evaluation is carried out on the durability of, and the project development period is shortened.
Referring to fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the endurance load spectrum simulation method according to the present invention, and the second embodiment of the endurance load spectrum simulation method according to the present invention is proposed based on the first embodiment shown in fig. 2.
In the second embodiment, the step S20 includes:
step S201: and extracting road spectrum parameters of the basic vehicle type from the whole vehicle data.
It should be noted that the road spectrum may refer to a road surface spectrum, and the road spectrum parameters may be data such as acceleration parameters, displacement parameters, force load parameters, and the like of a basic vehicle type extracted from the entire vehicle data.
In the concrete implementation, the vehicle-mounted computer can extract the acceleration parameter, the displacement parameter, the force load parameter and other data of the basic vehicle type from the whole vehicle data to determine the required road spectrum parameter.
Step S202: and constructing a first multi-body dynamic model corresponding to a basic vehicle model without the Ftire tire model according to the road spectrum parameters.
It should be noted that the Ftie Tire Model (Flexible Structure Tire Model) may be a completely three-dimensional nonlinear in-plane and out-of-plane simulation Model. The Ftire tire model can be used for simulating the working conditions of rolling delay, sidewall contact, tire misuse and the like.
It is understood that the first multi-body kinetic model may be a multi-body kinetic model that does not include the Ftire tire model corresponding to the base vehicle model.
In the concrete implementation, the vehicle-mounted computer constructs a multi-body dynamic model without the Ftire tire model through a virtual iteration model according to data such as six component forces of the spindle head, acceleration of the spindle head, suspension displacement, force of a shock absorber and the like corresponding to a basic vehicle model in road spectrum parameters.
Step S203: and acquiring vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type from the road spectrum parameters based on the first multi-body dynamic model.
In the concrete implementation, the vehicle-mounted computer accurately calculates the data of six force components of the axle head, the acceleration of the axle head, the suspension displacement, the force of a shock absorber and the like contained in road spectrum parameters through virtual iteration based on the constructed first multi-body dynamic model without the Ftire tire model, and obtains the driving data of the vertical displacement of the axle head of the front suspension and the driving data of the vertical displacement of the axle head of the rear suspension of the basic vehicle model.
Further, in order to make the simulation result more accurate, carry out iterative operation to the vertical displacement drive data of front and back suspension spindle nose, specifically include: obtaining an Ftire tire model, and adding the Ftire tire model into the first multi-body dynamic model to generate a second multi-body dynamic model; generating initial equivalent 3D road information and initial wheel center five-component driving information of the basic vehicle type according to vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type; and carrying out iterative operation on the initial equivalent 3D road surface information and the initial wheel center five-component driving information to generate equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type.
It should be noted that the second multi-body dynamic model may be a complete vehicle multi-body dynamic model which includes an Ftire tire model and can run on a virtual 3D road surface, the initial equivalent 3D road surface information may be road surface initial information generated according to vertical displacement driving data of front and rear suspension shaft heads of a basic vehicle type, the initial wheel center five-component driving information may be data information generated by establishing initial wheel center five-component driving at a wheel center in an iterative process according to initial equivalent 3D road surface information corresponding to the basic vehicle type, and the initial wheel center five-component driving information includes forces Fx and Fy in two directions and three moments Tx, Ty and Tz.
It can be understood that the equivalent 3D road information of the basic vehicle type may be road information generated after virtual iteration is performed according to the initial equivalent 3D road information, and the wheel center five-component driving information of the basic vehicle type may be driving information generated by virtual iteration performed according to the initial wheel center five-component driving information.
In the specific implementation, the vehicle-mounted computer adds the Ftire tire model according to the first multi-body dynamic model to construct a second multi-body dynamic model capable of driving on a virtual 3D road surface, and performs virtual iteration according to the second multi-body dynamic model and the spindle head vertical displacement driving data to generate equivalent 3D road surface information and wheel center five-component driving information corresponding to a basic vehicle type.
Further, in order to accurately obtain equivalent 3D road information and wheel center five-component drive information, the step of generating the equivalent 3D road information and wheel center five-component drive information of the basic vehicle type by performing iterative operation on the initial equivalent 3D road information and the initial wheel center five-component drive information includes: taking the initial equivalent 3D road surface information and the initial wheel center five-component force driving information as initial driving excitation of the second multi-body dynamic model; and generating equivalent 3D road information and wheel center five-component driving information of a basic vehicle type based on a virtual iteration model contained in the second multi-body dynamic model.
It should be noted that the initial driving excitation may be a force acting on a mechanical component, the second multi-body dynamic model is driven according to acceleration excitation and force excitation included in the initial wheel center five-component driving information, the virtual iterative model may be a model that is iterated when a multi-body simulation is determined for a time domain model excitation value, and a load acting on the structure externally may be adjusted in a manner of reproducing internal measurement with required accuracy when a durability test is performed.
In a specific implementation, the vehicle-mounted computer may utilize the virtual iteration model to simplify steps in the endurance test, for example, a time-consuming structural force measurement or a process of measuring a wheel load with a load cell, and the vehicle-mounted computer may obtain required parameters through virtual iteration to simplify the process.
In the embodiment, the method comprises the steps of obtaining whole vehicle data of a basic vehicle type with the same chassis as a vehicle to be detected, extracting road spectrum parameters of the basic vehicle type from the whole vehicle data, constructing a first multi-body dynamic model corresponding to the basic vehicle type without an Ftire tire according to the road spectrum parameters, obtaining vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type from the road spectrum parameters based on the first multi-body dynamic model, and generating equivalent 3D road information and wheel center five-component driving information of the basic vehicle type through a virtual iteration model based on the vertical displacement driving data of the front and rear suspension shaft heads; acquiring a wheel base change value between the vehicle to be detected and the basic vehicle type; updating the equivalent 3D road surface information according to the wheel base change value to obtain target equivalent 3D road surface information of the vehicle to be detected; acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type, and determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio; constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data; and carrying out durable load spectrum simulation according to the target wheel center five-component driving information, the target equivalent 3D road surface information and the multi-body dynamic model, and obtaining a simulation result. Because the target equivalent 3D road surface information corresponding to the vehicle to be detected is determined according to the equivalent 3D road surface information corresponding to the basic vehicle type, compared with the prior art, the method does not need to perform test field 3D road surface scanning, obtains the equivalent 3D road surface with higher precision by a virtual iteration method, and saves the test field 3D road surface scanning cost. .
Referring to fig. 4, fig. 4 is a schematic flow chart of a third embodiment of the endurance load spectrum simulation of the present invention, and the third embodiment of the endurance load spectrum simulation of the present invention is proposed based on the first embodiment shown in fig. 2.
In the third embodiment, the step S40 includes:
step S401: and acquiring the corresponding running speeds of the basic vehicle type at different characteristic road sections.
It should be noted that the characteristic road section may be a high-speed loop track, a straight track, a twist test road, a durability test road, etc., and the driving speed may be a speed of the basic vehicle type driving on different characteristic road sections, for example, when the vehicle drives on the twist test road, the body, the frame, the front and rear axles, the suspension, and the vehicle drive train are all twisted, so as to test the performance of these components, and it may also be monitored whether the performance of the components changes when the speed of the vehicle changes on different driving road sections.
In specific implementation, the vehicle-mounted computer can acquire the running speeds of the basic vehicle type in different characteristic road sections, and can also obtain the running speeds of the basic vehicle type passing through different road sections during historical tests.
Step S402: and performing phase translation on time axis data contained in the equivalent 3D road surface information according to the wheel base change value and the driving speed, and obtaining a translation result.
It should be noted that the time axis data may be data corresponding to a time axis of a signal corresponding to the equivalent 3D road surface information of the base vehicle type in a time domain. The translation result can be a time domain graph generated after phase translation is carried out on data corresponding to a time axis of a signal corresponding to the equivalent 3D road surface information of the basic vehicle type in the time domain.
In the specific implementation, the vehicle-mounted computer translates the phase corresponding to the time domain signal in the equivalent 3D road information of the basic vehicle type according to the wheel base change value and the driving speed, and generates a time domain graph corresponding to the equivalent 3D road information of the vehicle to be detected.
Step S403: and updating the equivalent 3D road surface information according to the translation result to obtain target equivalent 3D road surface information of the vehicle to be detected.
In the specific implementation, the vehicle to be detected and the basic vehicle type are the same chassis platform, and the vehicle-mounted computer can generate target equivalent 3D road information according to a time domain graph generated after phase translation is carried out on time axis data corresponding to signals corresponding to the basic vehicle type equivalent 3D road information in a time domain.
Further, in order to more efficiently determine the wheel center five-component driving information of the vehicle to be detected, the axle load ratio between the vehicle to be detected and the basic vehicle type can be obtained, and time domain data corresponding to the wheel center five-component driving information of the vehicle to be detected is determined according to the axle load ratio; and updating the wheel center five-component driving information of the basic vehicle type according to the translation result and the time domain data so as to obtain the target wheel center five-component driving information of the vehicle to be detected.
It should be noted that the time domain data may be data corresponding to a time domain curve determined by the axle-to-load ratio and driven by the five-component wheel center of the vehicle to be detected.
In the specific implementation, because the vehicle to be detected and the basic vehicle type are the same chassis platform, the vehicle-mounted computer can determine the time domain curve corresponding to the vehicle to be detected through the axle load ratio between the vehicle to be detected and the basic vehicle type and the time domain curves corresponding to Fx, Fy, Tx, Ty and Tz in the wheel center five-component driving information of the basic vehicle type, and determine the target wheel center five-component driving information of the vehicle to be detected according to the time domain curve corresponding to the vehicle to be detected.
Further, in order to more accurately obtain wheel center five-component driving information corresponding to front and rear suspensions of a vehicle to be detected, the step of updating the wheel center five-component driving information of the basic vehicle type according to the translation result and the time domain data to obtain target wheel center five-component driving information of the vehicle to be detected includes: updating rear suspension wheel center five-component driving information in the wheel center five-component driving information of the basic vehicle type according to the translation result, and obtaining the rear suspension wheel center five-component driving information of the vehicle to be detected; and determining the wheel center five-component driving information of the vehicle to be detected according to the wheel center five-component driving information of the rear suspension and the wheel center five-component driving information of the basic vehicle type.
It should be noted that the rear suspension wheel center five-component drive information may be drive information obtained by performing phase translation on the time axis based on the wheel base change value and the running vehicle speed according to the rear suspension wheel center five-component drive information corresponding to the base vehicle type. The target wheel center five-component driving information can be wheel center five-component driving information corresponding to front and rear suspensions of the vehicle to be detected.
In the concrete implementation, because the vehicle to be detected and the basic vehicle type are the same chassis platform, the five-component driving information of the wheel center of the front suspension corresponding to the vehicle to be detected can be determined according to the axle load ratio between the vehicle to be detected and the basic vehicle type, but because the five-component driving information of the wheel center of the rear suspension can carry out phase translation on a time axis according to the change of the axle distance and the driving speed, the vehicle-mounted computer can determine the five-component driving of the wheel center of the rear suspension corresponding to the vehicle to be detected according to the translated result, and determine the five-component driving information of the target wheel center of the vehicle to be detected according to the five-component driving information of the wheel center of the front suspension.
In the embodiment, vehicle data of a basic vehicle type with a chassis of a vehicle to be detected is obtained, vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type is determined according to the vehicle data, equivalent 3D road information and wheel center five-component driving information of the basic vehicle type are generated through a virtual iteration model based on the vertical displacement driving data of the front and rear suspension shaft heads, driving speeds of the basic vehicle type corresponding to different characteristic road sections are obtained, phase translation is performed on time axis data contained in the equivalent 3D road information according to a wheel base change value and the driving speeds, and a translation result is obtained; updating the equivalent 3D road information according to the translation result to obtain target equivalent 3D road information of the vehicle to be detected, obtaining an axle load ratio between the vehicle to be detected and the basic vehicle type, determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio, constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data, performing endurance load spectrum simulation according to the target wheel center five-component force driving information, the target equivalent 3D road information and the multi-body dynamic model, and obtaining a simulation result, wherein the multi-body dynamic model comprising the Ftie tire model is constructed based on the basic vehicle type, and the multi-body dynamic model is driven to perform the endurance load spectrum simulation according to the target wheel center five-component force information and the target equivalent 3D road information to obtain the simulation result, compared with the prior art, the method has the advantages that the model containing the equivalent 3D road surface and the Ftire tire is built, and the influence of the adjustment of the parameters of the whole vehicle on the endurance load spectrum can be simulated and predicted.
Furthermore, an embodiment of the present invention further provides a storage medium, where the storage medium stores an endurance load spectrum simulation program, and the endurance load spectrum simulation program, when executed by a processor, implements the steps of the endurance load spectrum simulation method as described above.
Referring to fig. 5, fig. 5 is a block diagram of a first embodiment of the endurance load spectrum simulator according to the present invention.
As shown in fig. 5, the endurance load spectrum simulation apparatus according to the embodiment of the present invention includes:
the data acquisition module 10 is used for acquiring the whole vehicle data of a basic vehicle type of a vehicle to be detected and the same chassis;
the data acquisition module 10 is further configured to determine vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type according to the vehicle data;
the virtual iteration module 20 is used for generating equivalent 3D road surface information and wheel center five-component force driving information of the basic vehicle type through a virtual iteration model based on the vertical displacement driving data of the front and rear suspension shaft heads;
the data updating module 30 is configured to acquire a wheel base change value between the vehicle to be detected and the basic vehicle type, and update the equivalent 3D road information according to the wheel base change value to acquire target equivalent 3D road information of the vehicle to be detected;
the data updating module 30 is further configured to obtain an axle load ratio between the vehicle to be detected and the basic vehicle type, and determine target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio;
and the model construction module 40 is used for constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data.
And the driving simulation module 50 is configured to perform endurance load spectrum simulation according to the target wheel center five-component driving information, the target equivalent 3D road surface information, and the multi-body dynamic model, and obtain a simulation result.
According to the embodiment, the whole vehicle data of a basic vehicle type of a vehicle to be detected and the same chassis are obtained, the vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type are determined according to the whole vehicle data, and equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type are generated through a virtual iterative model; acquiring a wheel base change value between a vehicle to be detected and a basic vehicle type, and updating equivalent 3D road information according to the wheel base change value to acquire target equivalent 3D road information of the vehicle to be detected; acquiring an axle load ratio between a vehicle to be detected and a basic vehicle type, and determining target wheel center five-component drive information of the vehicle to be detected based on the axle load ratio; constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data; the method comprises the steps of carrying out endurance load spectrum simulation according to target wheel center five-component driving information, target equivalent 3D road surface information and a multi-body dynamic model, and obtaining a simulation result, wherein vertical displacement driving data of front and rear suspension shaft heads are determined according to whole vehicle data of a basic vehicle type, target wheel center five-component driving information and target equivalent 3D road surface information of a vehicle to be detected are determined according to an axle distance change value and an axle load ratio value between the basic vehicle type and the vehicle to be detected, and endurance load spectrum simulation is carried out on the multi-body dynamic model corresponding to the vehicle to be detected, compared with the prior art, the embodiment has the advantages that the vehicle to be detected is developed on a same chassis platform, real vehicle road spectrum collection is not needed, an endurance simulation load spectrum can be obtained at the early stage of development of the vehicle to be detected, and the simulation evaluation is carried out on the durability of, and the project development period is shortened.
Further, the data acquisition module 10 is further configured to extract road spectrum parameters of the basic vehicle type from the vehicle-finished data; constructing a first multi-body dynamic model corresponding to a basic vehicle model not containing Ftire tires according to the road spectrum parameters; and acquiring vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type from the road spectrum parameters based on the first multi-body dynamic model.
Further, the data obtaining module 10 is further configured to obtain an Ftire tire model, and add the Ftire tire model to the first multi-body dynamic model to generate a second multi-body dynamic model; generating initial equivalent 3D road information and initial wheel center five-component driving information of the basic vehicle type according to vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type; and carrying out iterative operation on the initial equivalent 3D road surface information and the initial wheel center five-component driving information to generate equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type.
Further, the data acquisition module 10 is further configured to use the initial equivalent 3D road surface information and the initial wheel center five-component force driving information as an initial driving excitation of the second multi-body dynamic model, and drive the second multi-body dynamic model according to the initial driving excitation; and generating equivalent 3D road information and wheel center five-component driving information of a basic vehicle type based on a virtual iteration model contained in the second multi-body dynamic model.
Further, the data updating module 30 is further configured to obtain driving speeds of the basic vehicle type corresponding to different characteristic road sections; performing phase translation on time axis data contained in the equivalent 3D road surface information according to the wheel base change value and the driving speed, and obtaining a translation result; and updating the equivalent 3D road surface information according to the translation result to obtain target equivalent 3D road surface information of the vehicle to be detected.
Further, the data updating module 30 is further configured to obtain an axle load ratio between the vehicle to be detected and the basic vehicle type, and determine time domain data corresponding to five-component force of a wheel center of the vehicle to be detected according to the axle load ratio; and updating the wheel center five-component driving information of the basic vehicle type according to the translation result and the time domain data result so as to obtain the target wheel center five-component driving information of the vehicle to be detected.
Further, the data updating module 30 is further configured to update the rear suspension wheel center five-component driving information in the wheel center five-component driving information of the basic vehicle type according to the translation result, and obtain the rear suspension wheel center five-component driving information of the vehicle to be detected; and determining the wheel center five-component driving information of the vehicle to be detected according to the wheel center five-component driving information of the rear suspension and the wheel center five-component driving information of the basic vehicle type.
Furthermore, an embodiment of the present invention further provides a storage medium, where the storage medium stores an endurance load spectrum simulation program, and the endurance load spectrum simulation program, when executed by a processor, implements the steps of the endurance load spectrum simulation method as described above.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
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 described in detail in this embodiment may refer to the endurance load spectrum simulation method provided in any embodiment of the present invention, and are not described herein again.
It should 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 phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises 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. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order, but rather the words first, second, third, etc. are to be interpreted as names.
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., a Read Only Memory (ROM)/Random Access Memory (RAM), a magnetic disk, an optical disk), and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) 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 not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A durable load spectrum simulation method is characterized by comprising the following steps:
acquiring whole vehicle data of a basic vehicle type of a vehicle to be detected and the same chassis;
determining vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type according to the whole vehicle data;
generating equivalent 3D road information and wheel center five-component driving information of the basic vehicle type through a virtual iteration model based on the front and rear suspension shaft head vertical displacement driving data;
acquiring a wheel base change value between the vehicle to be detected and the basic vehicle type;
updating the equivalent 3D road surface information according to the wheel base change value to obtain target equivalent 3D road surface information of the vehicle to be detected;
acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type, and determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio;
constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data;
and carrying out durable load spectrum simulation according to the target wheel center five-component driving information, the target equivalent 3D road surface information and the multi-body dynamic model, and obtaining a simulation result.
2. The endurance load spectrum simulation method of claim 1, wherein said step of determining vertical displacement driving data of front and rear suspension axle heads of said base vehicle type according to said full vehicle data specifically comprises:
extracting road spectrum parameters of the basic vehicle type from the whole vehicle data;
constructing a first multi-body dynamic model corresponding to a basic vehicle model not containing Ftire tires according to the road spectrum parameters;
and acquiring vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type from the road spectrum parameters based on the first multi-body dynamic model.
3. The endurance load spectrum simulation method of claim 2, wherein said step of generating equivalent 3D road information and wheel center five-component force driving information of the base vehicle model through a virtual iterative model based on the front and rear suspension axle head vertical displacement driving data comprises:
obtaining an Ftire tire model, and adding the Ftire tire model into the first multi-body dynamic model to generate a second multi-body dynamic model;
generating initial equivalent 3D road information and initial wheel center five-component driving information of the basic vehicle type according to vertical displacement driving data of front and rear suspension shaft heads of the basic vehicle type;
and carrying out iterative operation on the initial equivalent 3D road surface information and the initial wheel center five-component driving information to generate equivalent 3D road surface information and wheel center five-component driving information of the basic vehicle type.
4. The endurance load spectrum simulation method of claim 3, wherein the step of iteratively operating the initial equivalent 3D road surface information and the initial wheel center five-component drive information to generate the equivalent 3D road surface information and the wheel center five-component drive information of the base vehicle type comprises:
taking the initial equivalent 3D road surface information and the initial wheel center five-component force driving information as initial driving excitation of the second multi-body dynamic model, and driving the second multi-body dynamic model according to the initial driving excitation;
and generating equivalent 3D road information and wheel center five-component driving information of a basic vehicle type based on a virtual iteration model contained in the second multi-body dynamic model.
5. The endurance load spectrum simulation method according to claim 1, wherein the step of updating the equivalent 3D road surface information according to the wheel base change value to obtain the target equivalent 3D road surface information of the vehicle to be detected includes:
acquiring running speeds of the basic vehicle type corresponding to different characteristic road sections;
performing phase translation on time axis data contained in the equivalent 3D road surface information according to the wheel base change value and the driving speed, and obtaining a translation result;
and updating the equivalent 3D road surface information according to the translation result to obtain target equivalent 3D road surface information of the vehicle to be detected.
6. The method of claim 5, wherein the step of obtaining an axle load ratio value between the vehicle to be tested and the base vehicle type and determining the target wheel center five-component force driving information of the vehicle to be tested based on the axle load ratio value comprises:
acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type, and determining time domain data corresponding to five wheel center components of the vehicle to be detected according to the axle load ratio;
and updating the wheel center five-component driving information of the basic vehicle type according to the translation result and the time domain data result so as to obtain the target wheel center five-component driving information of the vehicle to be detected.
7. The method of claim 6, wherein the step of updating the wheel center five-component driving information of the base vehicle type according to the translation result and the time domain data result to obtain the target wheel center five-component driving information of the vehicle to be detected comprises:
updating rear suspension wheel center five-component driving information in the wheel center five-component driving information of the basic vehicle type according to the translation result, and obtaining the rear suspension wheel center five-component driving information of the vehicle to be detected;
and determining the wheel center five-component driving information of the vehicle to be detected according to the wheel center five-component driving information of the rear suspension and the wheel center five-component driving information of the basic vehicle type.
8. An endurance load spectrum simulation apparatus, comprising: a memory, a processor and an endurance load spectrum simulation program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the endurance load spectrum simulation method of any of claims 1 to 7.
9. A storage medium having stored thereon an endurance load spectrum simulation program which, when executed by a processor, implements the steps of the endurance load spectrum simulation method of any one of claims 1 to 7.
10. An endurance load spectrum simulation apparatus, comprising:
the data acquisition module is used for acquiring the whole vehicle data of a basic vehicle type of a vehicle to be detected and the same chassis;
the data acquisition module is also used for determining the vertical displacement driving data of the front and rear suspension shaft heads of the basic vehicle type according to the whole vehicle data;
the virtual iteration module is used for generating equivalent 3D road surface information and wheel center five-component force driving information of the basic vehicle type through a virtual iteration model based on the vertical displacement driving data of the front and rear suspension shaft heads;
the data acquisition module is also used for acquiring a wheel base change value between the vehicle to be detected and the basic vehicle type;
the data updating module is used for updating the equivalent 3D road surface information according to the wheel base change value so as to obtain target equivalent 3D road surface information of the vehicle to be detected;
the data acquisition module is further used for acquiring an axle load ratio between the vehicle to be detected and the basic vehicle type and determining target wheel center five-component force driving information of the vehicle to be detected based on the axle load ratio;
the model construction module is used for constructing a multi-body dynamic model corresponding to the vehicle to be detected according to the whole vehicle data;
and driving the simulation module. And the system is used for carrying out endurance load spectrum simulation according to the target wheel center five-component driving information, the target equivalent 3D road surface information and the multi-body dynamic model, and obtaining a simulation result.
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