CN116956614A - Method, device, equipment and storage medium for selecting vehicle system - Google Patents
Method, device, equipment and storage medium for selecting vehicle system Download PDFInfo
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Abstract
The application provides a method, a device, equipment and a storage medium for selecting a vehicle system. The method comprises the steps of acquiring a plurality of groups of configuration parameters of each part subsystem associated with an optimization target, carrying out parameter combination processing according to the plurality of groups of configuration parameters to obtain a plurality of groups of combination parameters, respectively inputting the plurality of groups of combination parameters into a pre-built drivability multi-body dynamics model to carry out simulation test to obtain simulation test working condition data corresponding to each group of combination parameters, and finally selecting target combination parameters conforming to the optimization target according to the optimization target and the simulation test working condition data corresponding to each group of combination parameters.
Description
Technical Field
The present application relates to the field of vehicle system model selection technologies, and in particular, to a method, an apparatus, a device, and a storage medium for vehicle system model selection.
Background
The drivability is feedback of driving force in forward or reverse direction by the vehicle to the driver operation when the user drives the vehicle, and is mainly calculated and evaluated as longitudinal acceleration change of the whole vehicle. Drivability is one of the important manifestations of vehicle dynamics perceived by a user, directly affecting the user's confidence and pleasure in driving daily. The factors influencing the drivability of the vehicle are numerous, and the chassis structure, the rigidity of the driving half shaft, the performance of the engine or the motor and the gear shifting control of the gearbox can all influence the drivability. As such, the development of drivability of vehicles is particularly difficult.
The conventional development flow in the prior art generally adjusts the drivability through the calibration of the parameters of the control system of the power assembly after the test sample vehicle is assembled, and the development has the limitation that once the vehicle hardware is improper in model selection or has obvious defects, the optimization space of the drivability of the vehicle is limited, the development period of the drivability of the vehicle can be prolonged, and the system design model selection instruction has high risk.
In view of the foregoing, how to shorten the vehicle drivability development period and reduce the risk of system design and model selection is a challenge in the art.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for selecting a vehicle system, which are used for solving the problems of shortening the development period of vehicle drivability and reducing the risk of selecting the system design.
In a first aspect, the present application provides a method of vehicle system profiling, comprising:
aiming at an optimization target of a preset vehicle, acquiring a plurality of groups of configuration parameters of each part subsystem associated with the optimization target, wherein the optimization target comprises a threshold value of working condition data of at least one vehicle;
carrying out parameter combination processing according to a plurality of groups of configuration parameters of each part subsystem to obtain a plurality of groups of combination parameters;
respectively inputting the multiple groups of combination parameters into a pre-built driving multi-body dynamics model for simulation test to obtain simulation test working condition data corresponding to each group of combination parameters; wherein the driving multi-body dynamics model comprises a simulation calculation model of each part subsystem;
and selecting target combination parameters which accord with the optimization targets according to the optimization targets and simulation test working condition data corresponding to each group of combination parameters, wherein the target combination parameters are used for guiding the model selection of the part subsystems of the preset vehicle.
With reference to the first aspect, in some embodiments, the method further includes:
and outputting a vehicle system type selection guide message according to the configuration parameters of each part subsystem included in the target combination parameters, wherein the vehicle system type selection guide message is used for indicating the configuration parameters of each part subsystem meeting the optimization target.
With reference to the first aspect, in some embodiments, the optimization objective specifically includes: at least one threshold value of working condition data of the vehicle in a sudden accelerator stepping state and/or at least one threshold value of working condition data of the vehicle in a sudden accelerator releasing state;
the working condition data of the vehicle in the state of stepping on the accelerator suddenly comprises: response delay, acceleration peak, shock, bump, and continuous jitter; the working condition data of the vehicle in the state of quick throttle release comprise: impact, jerk, and continuous shake.
With reference to the first aspect, in some embodiments, performing parameter combination processing according to multiple groups of configuration parameters of each part subsystem to obtain multiple groups of combination parameters includes:
and according to the multiple groups of configuration parameters of each part subsystem, arranging and combining the values of the configuration parameters of each part subsystem to obtain the multiple groups of combination parameters.
With reference to the first aspect, in some embodiments, selecting, according to the optimization objective and the simulated test condition data corresponding to each set of combination parameters, an objective combination parameter that meets the optimization objective includes:
selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, as the target combination parameters conforming to the optimization target;
or alternatively, the process may be performed,
and selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, as the target combination parameters conforming to the optimization target.
With reference to the first aspect, in some embodiments, the plurality of sets of configuration parameters of each part subsystem associated with the optimization objective include: two sets of air resistance coefficients, two sets of drive train backlash, and two sets of clutch inertia; correspondingly, the plurality of groups of combination parameters comprise eight groups of combination parameters including air resistance coefficients, transmission chain tooth gaps and clutch inertia.
In a second aspect, the present application provides a vehicle system model selection apparatus, comprising:
The system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of groups of configuration parameters of each part subsystem associated with an optimization target of a preset vehicle, and the optimization target comprises a threshold value of working condition data of at least one vehicle;
the parameter combination module is used for carrying out parameter combination processing according to a plurality of groups of configuration parameters of each part subsystem to obtain a plurality of groups of combination parameters;
the simulation test module is used for respectively inputting the multiple groups of combination parameters into a pre-built driving multi-body dynamics model to perform simulation test to obtain simulation test working condition data corresponding to each group of combination parameters; wherein the driving multi-body dynamics model comprises a simulation calculation model of each part subsystem;
the data selection module is used for selecting target combination parameters which accord with the optimization targets according to the optimization targets and the simulation test working condition data corresponding to each group of combination parameters, and the target combination parameters are used for guiding the model selection of the part subsystems of the preset vehicle.
With reference to the second aspect, in some embodiments, the apparatus further includes:
and the optimization guidance module is used for outputting a vehicle system type selection guidance message according to the configuration parameters of each part subsystem included in the target combination parameters, wherein the vehicle system type selection guidance message is used for indicating the configuration parameters of each part subsystem meeting the optimization target.
With reference to the second aspect, in some embodiments, the optimization objective specifically includes: at least one threshold value of working condition data of the vehicle in a sudden accelerator stepping state and/or at least one threshold value of working condition data of the vehicle in a sudden accelerator releasing state;
the working condition data of the vehicle in the state of stepping on the accelerator suddenly comprises: response delay, acceleration peak, shock, bump, and continuous jitter; the working condition data of the vehicle in the state of quick throttle release comprise: impact, jerk, and continuous shake.
With reference to the second aspect, in some embodiments, the parameter combination module includes:
and the arrangement and combination unit is used for carrying out arrangement and combination aiming at the values of the configuration parameters of each part subsystem according to the plurality of groups of configuration parameters of each part subsystem to obtain the plurality of groups of combination parameters.
With reference to the second aspect, in some embodiments, the data selecting module includes:
the first selecting unit is used for selecting a group of parameters, of which each working condition data in the simulation test working condition data is closest to a corresponding working condition data threshold in the optimization target, from the plurality of groups of combination parameters, and taking the group of parameters as the target combination parameters conforming to the optimization target;
Or alternatively, the process may be performed,
the second selecting unit is used for selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, and the group of parameters are used as the target combination parameters which accord with the optimization target.
With reference to the second aspect, in some embodiments, the plurality of sets of configuration parameters of each part subsystem associated with the optimization objective include: two sets of air resistance coefficients, two sets of drive train backlash, and two sets of clutch inertia; correspondingly, the plurality of groups of combination parameters comprise eight groups of combination parameters including air resistance coefficients, transmission chain tooth gaps and clutch inertia.
In a third aspect, the present application provides an electronic device comprising: the device comprises a memory, a processor, a communication interface and a display;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any of the above aspects.
In a fourth aspect, the present application provides a computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of vehicle system selection according to any one of the above aspects.
According to the method, the device, the equipment and the storage medium for selecting the vehicle system, multiple groups of configuration parameters of each part subsystem associated with the optimization target are acquired aiming at the optimization target of a preset vehicle, then the multiple groups of configuration parameters of each part subsystem are processed by parameter combination to obtain multiple groups of combination parameters, the multiple groups of combination parameters are respectively input into a pre-built drivability multiple-body dynamics model to carry out simulation test to obtain simulation test working condition data corresponding to each group of combination parameters, and finally the target combination parameters conforming to the optimization target are selected according to the optimization target and the simulation test working condition data corresponding to each group of combination parameters.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is an application scenario diagram of a vehicle system model selection method provided by an embodiment of the present application;
FIG. 2 is a schematic flow chart of a first embodiment of a method for selecting a vehicle system according to an embodiment of the present application;
FIG. 3 is a schematic diagram of acceleration response delay according to an embodiment of the present application;
FIG. 4 is a schematic view of an impact of a vehicle according to an embodiment of the present application during acceleration;
FIG. 5 is a schematic diagram of an output result of Adams/insight simulation optimization provided by an embodiment of the present application;
fig. 6 is a schematic flow chart of a second embodiment of a method for selecting a vehicle system according to an embodiment of the present application;
fig. 7 is a schematic flow chart of a third embodiment of a method for selecting a vehicle system according to an embodiment of the present application;
fig. 8 is a schematic flow chart of a fourth embodiment of a method for selecting a vehicle system according to an embodiment of the present application;
FIG. 9 is a schematic diagram of a driving multi-body mechanics Adams simulation model provided by an embodiment of the application;
FIG. 10 is a schematic diagram of a step-shaped driving torque input signal provided by an embodiment of the present application;
FIG. 11 is a schematic diagram of validity verification of a driving simulation model according to an embodiment of the present application;
FIG. 12 is a schematic diagram of a dual mass flywheel configuration parameter according to an embodiment of the present application;
FIG. 13 is a diagram illustrating an analysis of inertial effects of a dual mass flywheel according to an embodiment of the present application;
Fig. 14 is a schematic structural diagram of an embodiment of a vehicle system model selection device according to an embodiment of the present application;
fig. 15 is a schematic structural diagram of a second embodiment of a vehicle system model selection device according to an embodiment of the present application;
fig. 16 is a schematic structural diagram of a third embodiment of a vehicle system selection device according to the present application;
fig. 17 is a schematic structural diagram of a fourth embodiment of a vehicle system selection device according to the present application;
fig. 18 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
Drivability is one of the important manifestations of vehicle dynamics perceived by a user, directly affecting the user's confidence and pleasure in driving daily. The factors influencing the drivability of the vehicle are numerous, and the chassis structure, the rigidity of the driving half shaft, the performance of the engine or the motor and the gear shifting control of the gearbox can all influence the drivability. As such, the development of drivability of vehicles is particularly difficult. The conventional development flow generally adjusts the drivability through the calibration of the parameters of the control system of the power assembly after the test sample vehicle is assembled, and once the vehicle hardware is improper in model selection or has obvious defects, the original design and matching scheme of the whole vehicle need to be readjusted and improved, which leads to the rising of development cost and the increase of period and has great risks in guiding the model selection development of the vehicle system.
Aiming at the problems, the method, the device, the equipment and the storage medium for selecting the vehicle system realize performance simulation of the driving performance of the whole vehicle and the design analysis work of the model selection of related part subsystems when the real vehicle is not delivered, greatly reduce the cost and the design verification period of vehicle hardware development trial-and-error, effectively improve the dynamic driving quality of the vehicle, and particularly, for vehicle system model selection, the conventional development means is to calibrate the driving performance through the calibration of the parameters of the control system of the power assembly after the test sample vehicle is assembled, and then optimize the test sample vehicle. Considering the problems, the inventor researches whether the user language can be converted into engineering language from product definition and user requirements, and the target value output is finally achieved by performing a series of target decomposition on the control requirement of the target value of the whole vehicle, forming the related subsystem model selection/design development target, building the whole vehicle multi-body dynamics model to perform model selection and design optimization of the related system, and performing performance calibration adjustment and road verification based on the test sample vehicle. Based on the above, the technical scheme of the application is provided.
Fig. 1 is an application scenario diagram of a vehicle system model selection method provided by an embodiment of the present application, where, as shown in fig. 1, the vehicle system model selection method provided by the present application is mainly applied to a scenario where a vehicle enterprise develops a product of a vehicle, where the scenario includes at least a vehicle critical component subsystem and a simulation model, where the critical component subsystem may be a suspension, a driving half shaft, a backlash, a suspension, a tire, clutch inertia, etc., and specifically may be freely selected and combined according to a requirement of the vehicle enterprise on the product, and the simulation system is a model capable of implementing driving simulation on normal performance of the vehicle, and the model adopted by the present application is a driving multi-body dynamics simulation model.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a schematic flow chart of a first embodiment of a method for selecting a vehicle system according to an embodiment of the present application, where, as shown in fig. 2, the method specifically includes:
S101: and aiming at an optimization target of a preset vehicle, acquiring a plurality of groups of configuration parameters of each part subsystem associated with the optimization target.
In this step, in order to improve the simulation efficiency, and further greatly reduce the trial-and-error cost and the design verification period of vehicle hardware development, an optimization target of the vehicle is preset, and then multiple groups of configuration parameters of each part subsystem associated with the optimization target are obtained according to the subsystems and key parameters thereof with larger and sensitive drivability association, wherein the optimization target comprises at least one threshold value of the working condition data of the vehicle.
TABLE 1 drivability-impact factor correlation matrix
Specifically, table 1 is a driving impact factor correlation matrix, as shown in table 1, the working condition data includes two working conditions of sudden stepping on the accelerator and sudden releasing the accelerator, the threshold includes response delay, acceleration peak value, impact, continuous shake, and bump under the working condition of sudden releasing the accelerator, the threshold includes impact, continuous shake, bump includes different thresholds respectively, under these two different working conditions, the threshold of the working condition data of the vehicle can be one or more of them, parameters of the subsystem of the component can be selected according to the correlation, and the threshold of the working condition data of the vehicle is not specifically limited.
S102: and carrying out parameter combination processing according to a plurality of groups of configuration parameters of each part subsystem to obtain a plurality of groups of combination parameters.
In this step, for multiple groups of configuration parameters, in order to enable the simulation result to be closer to the optimization, parameters of different part subsystems need to be subjected to parameter combination processing, so as to obtain multiple groups of combination parameters.
Specifically, according to the multiple groups of configuration parameters of each part subsystem, the values of the configuration parameters of each part subsystem are arranged and combined to obtain multiple groups of combination parameters.
S103: and respectively inputting a plurality of groups of combination parameters into a pre-built driving multi-body dynamics model for simulation test to obtain simulation test working condition data corresponding to each group of combination parameters.
In the step, in order to improve the development efficiency of the vehicle product and shorten the development period, the vehicle drivability is simulated by means of a model, so that the guidance on the development of the vehicle product is realized, the multiple groups of combination parameters obtained in the step are respectively input into a pre-built drivability multi-body dynamics model, and simulation test working condition data corresponding to each group of combination parameters are obtained through simulation calculation of the pre-built drivability multi-body dynamics model.
Specifically, the simulation process of the driving multi-body dynamics model is described by taking Aero (air resistance coefficient), backlash (transmission chain backlash) and Clutch insert (Clutch inertia) 3 part subsystems as an example, 8 groups of combined data, the optimization target is set to be response delay and impact under the condition of sudden accelerator stepping, as shown in FIG. 3, the response delay is set to be that the opening degree change is more than or equal to 15%, the change rate is more than or equal to 40%/s and the vehicle acceleration change exceeds 1m/s from the condition of sudden accelerator stepping 2 As shown in fig. 4, the impact is mainly represented by the rapid fluctuation rate of the longitudinal acceleration when the vehicle is loaded, and the impact and the response delay belong to transient performance in the motion process of the vehicle, which directly affects the smoothness and the comfort in the driving process. According to a model building element Parameter tree, 3 part subsystems of Aero (air resistance coefficient), backlash (transmission chain tooth gap) and Clutch inertia (Clutch inertia) are selected, and Parameter scheme setting is respectively carried out on the selected part system configuration parameters to be optimized, as Set3-Front differential in fig. 3: the backlash is provided with 2 steps (different tooth gaps rad) which are respectively 1 and 0.0104; 2. 0.0124. 2 steps are respectively Set by combining the Set1-Aero and the Set2-Clutch, so that 8 groups of combination parameters are generated in total, and the 8 groups of combination parameters are respectively calculated by a driving multi-body dynamics model call solver to obtain simulation test working condition data corresponding to each group of combination parameters.
Optionally, the driving multi-body dynamics model provided by the embodiment of the application may further perform simulation optimization on a subsystem of a single component, taking the driving half shaft as an example, where the optimization objective is the same as the multi-system simulation setting described above, and is not described herein, the configuration parameters of the driving half shaft are obtained to be the rigidity of the left and right half shafts, and the boundary conditions of the left and right half shafts are set to be [120, 240] and [100, 200], respectively, and the driving multi-body dynamics model calls a solver Adams/insight to perform four iterative computations on the configuration parameters and output simulation test condition data, where the solver may also call the outside through an API interface.
S104: and selecting target combination parameters which accord with the optimization targets according to the optimization targets and the simulation test working condition data corresponding to each group of combination parameters.
In this step, in order to obtain a desired product, an optimization target is set before the drivability simulation, and after the simulation is completed, a target combination parameter conforming to the optimization target is selected from simulation test condition data corresponding to each set of combination parameters output by the optimization target.
Specifically, a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, of each working condition data in the simulation test working condition data is selected from a plurality of groups of combination parameters to be used as target combination parameters conforming to the optimization target, or a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, of most working condition data in the simulation test working condition data is selected from a plurality of groups of combination parameters to be used as target combination parameters conforming to the optimization target or with optimal performance balance.
Alternatively, for single-system drivability simulation, for example, the optimized simulation of the driving half shaft in the above step, a set of output data is obtained, and then the output result of the optimized simulation of the driving half shaft in the step is shown in fig. 5, and the comparison between the output result in fig. 5 and the data before optimization is shown in table 2.
TABLE 2Adams/insight simulation optimization output results
According to the method for selecting the model of the vehicle system, multiple groups of configuration parameters of each part subsystem associated with the optimization target are acquired aiming at the optimization target of the preset vehicle, then the multiple groups of configuration parameters of each part subsystem are processed through parameter combination according to the multiple groups of configuration parameters of each part subsystem to obtain multiple groups of combination parameters, the multiple groups of combination parameters are respectively input into a pre-built drivability multiple-body dynamics model to carry out simulation test, simulation test working condition data corresponding to each group of combination parameters are obtained, and finally target combination parameters conforming to the optimization target are selected according to the optimization target and the simulation test working condition data corresponding to each group of combination parameters.
Fig. 6 is a schematic flow chart of a second embodiment of a method for selecting a vehicle system according to an embodiment of the present application, as shown in fig. 6, on the basis of the foregoing embodiment, the method for selecting a vehicle system according to the present application further includes:
s105: and outputting a vehicle system type selection guide message according to the configuration parameters of each part subsystem included in the target combination parameters.
In this step, in order to enable the dynamic driving quality of the vehicle to be higher, before the product development, driving simulation is performed on the vehicle part subsystems, a vehicle system type selection guide message is output according to the configuration parameters of each part subsystem included in the target combination parameters, for example, driving simulation is performed on the driving half shafts, response delay and impact are used as optimization targets, finally, the driving half shafts with the target parameters of 120 (Nm/deg) of left half shaft rigidity and 119.95 (Nm/deg) of right half shaft rigidity are obtained through model simulation, and according to the target parameters, in the product development process, a suggestion of selecting the driving half shafts close to the target parameters as the product parts can be output.
According to the vehicle system type selection method provided by the embodiment, the vehicle system type selection guide message is output according to the configuration parameters of each part subsystem included in the target combination parameters, so that performance simulation of the whole vehicle drivability and design analysis work of the selection of related part subsystems are started when the real vehicle is not delivered, the whole vehicle drivability development work can be greatly advanced, the vehicle hardware development trial-and-error cost and the design verification period are greatly reduced, and the vehicle dynamic driving quality is effectively improved.
Fig. 7 is a schematic flow chart of a third embodiment of a method for selecting a vehicle system according to an embodiment of the present application, as shown in fig. 7, on the basis of the foregoing embodiment, step S102 specifically includes:
s1021: and according to the multiple groups of configuration parameters of each part subsystem, arranging and combining the values of the configuration parameters of each part subsystem to obtain multiple groups of combination parameters.
In this step, in order to make the simulation result more objective and accurate, multiple groups of configuration parameters need to be set, different component subsystems are selected, multiple groups of configuration parameters of each component subsystem are arranged and combined for the values of the configuration parameters of each component subsystem, and multiple groups of combination parameters are further obtained.
Specifically, n component subsystems are set, values of m groups of configuration parameters are set, one component subsystem is selected to be combined with other component subsystems each time from the values of m groups of configuration parameters of each component subsystem, and finally all the values are respectively combined with the values of the configuration parameters of the other component subsystems. For example, 3 component subsystems are selected, 2 parameter values are respectively configured, and finally 8 groups of combination parameters are obtained through permutation and combination.
According to the vehicle system type selection method provided by the embodiment, the values of the configuration parameters of each part subsystem are arranged and combined according to the multiple groups of the configuration parameters of each part subsystem, multiple groups of the combination parameters are obtained, and the multiple groups of the parameters are configured for each part subsystem, so that the simulation result is more accurate, the development period is shortened, and the risk is reduced.
Fig. 8 is a flow chart of a fourth embodiment of a method for selecting a vehicle system according to an embodiment of the present application, as shown in fig. 8, on the basis of the foregoing embodiment, step S104 specifically includes:
s1041: and selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, as target combination parameters which accord with the optimization target.
In this step, according to the development requirements of actual products, the requirements on the vehicle part subsystems are different, and then the optimal configuration parameters of the part subsystems are different, and for the corresponding working condition data threshold values in the optimization target, the parameter closest to the threshold value corresponding to each working condition data in the simulation test working condition data can be selected as the target combination parameter according with the optimization target.
Or alternatively, the process may be performed,
s1042: and selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, as target combination parameters conforming to the optimization target.
In this step, the product development requirements mentioned in the above steps are different, and for the corresponding working condition data threshold in the optimization target, in order to meet the overall performance optimization of the vehicle, the fitting degree between a few of the simulation test working condition data and the corresponding working condition data threshold in the optimization target is lower, and in order to meet the overall performance of the vehicle, a group of parameters, in which most of the simulation test working condition data and the corresponding working condition data threshold in the optimization target are closest, can be selected as the target combination parameters meeting the optimization target.
According to the vehicle system type selection method provided by the embodiment, one group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, of each working condition data in the simulation test working condition data is selected from multiple groups of combination parameters to serve as target combination parameters conforming to the optimization target, or one group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, of most working condition data in the simulation test working condition data is selected from multiple groups of combination parameters to serve as target combination parameters conforming to the optimization target, a simulation result is preferentially selected according to the working condition corresponding to the optimization target, subsystem parameters are more optimized, and the threshold value corresponding to the optimization target can be changed according to requirements, so that the vehicle system type selection is more accurate and flexible, the cost of vehicle hardware development test error and the design verification period are greatly reduced, and the vehicle dynamic driving quality is effectively improved.
According to the method for selecting the model of the vehicle system, which is provided by the embodiment of the application, before the driving simulation is carried out on the subsystem of the key parts of the vehicle, a simulation model is also required to be built in advance, and the specific processes of building the driving multi-body dynamics model and debugging and checking the model are as follows:
the problems of pain points such as impact and response of vehicle drivability are greatly related to the hardware performance of the vehicle. In order to accurately reflect the influence of interaction of vehicle power in the transmission path on drivability, a multi-body dynamics model capable of accurately reflecting interaction forces among different systems must be built; in order to simplify the model construction task and reduce the model calculation amount, the key part subsystem which influences the vehicle drivability needs to be identified for modeling (such as suspension, half axle and the like), the constructed simulation model is shown in fig. 9, and reasonable equivalent abstraction is carried out on the key part subsystem.
And secondly, debugging and checking the built driving multi-body dynamics simulation model. The built model performs simulation pre-running, and normal running complete part program can be supported without reporting a suspension fault. In view of the complexity of the vehicle not yet equipped with the off-line and hardware-in-the-loop simulation system, verification of the model may be performed by software-in-the-loop simulation. The preliminary model verification can be carried out by giving a fixed driving torque input signal, obtaining a vehicle speed and acceleration curve through model operation simulation, comparing the result with vehicle speed and acceleration curves calculated by other calculation tools (for example, AVL Cruise), wherein data calculated by other calculation tools are a large amount of collected real vehicle data, further verification can be carried out by simulating driving sudden accelerator stepping operation, a step-shaped driving torque input signal (shown in figure 10) is given to a model, the vehicle speed and acceleration curve is obtained through model operation simulation, the result is compared with other data of the same type measured by the previous real vehicle, so as to judge whether the model is reasonable in construction and abstraction, a specific effectiveness verification schematic diagram is shown in figure 11, and the rationality of the model is judged according to the graph fitting degree of the real vehicle test data and the simulation result.
Furthermore, different parameters of some common key component subsystems can be simulated, and the simulation is used as a test for the model to judge the reliability of the model. The critical component subsystem model used for modeling test must be well-defined, the parameter variations must be apparent, and the impact on drivability must be either consensus-formed or real vehicle proven. Taking the dual-mass flywheel parameters as an example, the specific output signals are shown in fig. 12, the confidence index of the built drivability simulation model accuracy can be increased through simulation test, the specific output results are shown in fig. 13, simulation is carried out through the dual-mass flywheel parameters of 2mm, 6mm and 10mm respectively, and the model accuracy is judged through the fitting degree of the output result graph.
Fig. 14 is a schematic structural diagram of a first embodiment of a vehicle system type selection device provided in an embodiment of the present application, and as shown in fig. 14, a vehicle system type selection device 200 includes:
the obtaining module 201 is configured to obtain, for an optimization target of a preset vehicle, multiple sets of configuration parameters of each part subsystem associated with the optimization target, where the optimization target includes a threshold value of working condition data of at least one vehicle.
The parameter combination module 202 is configured to perform parameter combination processing according to multiple groups of configuration parameters of each component subsystem, so as to obtain multiple groups of combination parameters.
The simulation test module 203 is configured to input multiple groups of combination parameters into a pre-built driving multi-body dynamics model respectively for performing a simulation test, so as to obtain simulation test working condition data corresponding to each group of combination parameters; the driving multi-body dynamics model comprises a simulation calculation model of each part subsystem.
The data selection module 204 is configured to select, according to the optimization objective and the simulated test condition data corresponding to each set of combination parameters, an objective combination parameter that meets the optimization objective, where the objective combination parameter is used to guide the model selection of the part subsystem of the preset vehicle.
Fig. 15 is a schematic structural diagram of a second embodiment of a vehicle system type selection device according to an embodiment of the present application, and as shown in fig. 15, the vehicle system type selection device 200 further includes:
the optimization guidance module 205 is configured to output a vehicle system type selection guidance message according to the configuration parameters of each component subsystem included in the target combination parameters, where the vehicle system type selection guidance message is used to indicate each component subsystem configuration parameter that meets the optimization target.
The device for selecting the vehicle system provided by the embodiment of the application specifically comprises the following optimization targets: at least one threshold value of working condition data of the vehicle in a sudden accelerator stepping state and/or at least one threshold value of working condition data of the vehicle in a sudden accelerator releasing state;
the working condition data of the vehicle in the state of stepping on the accelerator suddenly comprises: response delay, acceleration peak, shock, bump, and continuous jitter; the working condition data of the vehicle in the state of quick throttle release comprise: impact, jerk, and continuous shake.
Fig. 16 is a schematic structural diagram of a third embodiment of a vehicle system selection device according to an embodiment of the present application, where, as shown in fig. 16, a parameter combination module 202 includes:
the permutation and combination unit 2021 is configured to permutation and combine values of the configuration parameters of each component subsystem according to multiple groups of configuration parameters of each component subsystem, so as to obtain multiple groups of combination parameters.
Fig. 17 is a schematic structural diagram of a fourth embodiment of a vehicle system type selection device according to an embodiment of the present application, where, as shown in fig. 17, a data selection module 204 includes:
the first selecting unit 2041 is configured to select, from the multiple sets of combination parameters, a set of parameters that is closest to a threshold value of the corresponding working condition data in the optimization target, where each working condition data in the simulation test working condition data is used as a target combination parameter that meets the optimization target.
Or alternatively, the process may be performed,
the second selecting unit 2042 is configured to select, from the multiple sets of combination parameters, a set of parameters that most of the working condition data in the simulation test working condition data is closest to the corresponding working condition data threshold in the optimization target, as a target combination parameter that meets the optimization target.
The device for selecting the vehicle system provided by the embodiment of the application comprises the following components and parts, wherein the components and parts are related to the optimization target, and the configuration parameters comprise: two sets of air resistance coefficients, two sets of drive train backlash, and two sets of clutch inertia; correspondingly, the multiple groups of combination parameters comprise eight groups of combination parameters including air resistance coefficients, transmission chain tooth gaps and clutch inertia.
The device for selecting the vehicle system provided in this embodiment is used for executing the method for selecting the vehicle system in any of the foregoing method embodiments, and its implementation principle and technical effects are similar, and are not described herein again.
Fig. 18 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 18, the electronic device 300 includes: a memory 301, a processor 302, a communication interface 303, a display 304;
the memory 301 stores computer-executable instructions.
The processor 302 executes computer-executable instructions stored in the memory 301 to implement the technical solution in any of the above embodiments.
The communication interface 303 is used to implement the call to the external solver algorithm model.
The display 304 is used to display simulation results.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
All or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The aforementioned program may be stored in a readable memory. The program, when executed, performs steps including the method embodiments described above; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid state disk, magnetic tape, floppy disk, optical disk (optical disc), and any combination thereof.
The electronic device provided by the embodiment of the present application is configured to execute the technical scheme in any of the foregoing method embodiments, and its implementation principle and technical effects are similar and are not described herein again.
Embodiments of the present application also provide a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are configured to implement the method of any of the embodiments.
The computer readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as static random access memory, electrically erasable programmable read-only memory, magnetic memory, flash memory, magnetic disk or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
In the alternative, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC). The processor and the readable storage medium may reside as discrete components in a device.
The embodiment of the application also provides a computer program product, which comprises a computer program, the computer program is stored in a computer readable storage medium, at least one processor can read the computer program from the computer readable storage medium, and the technical scheme provided by any one of the method embodiments can be realized when the at least one processor executes the computer program.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (14)
1. A method of vehicle system selection, comprising:
aiming at an optimization target of a preset vehicle, acquiring a plurality of groups of configuration parameters of each part subsystem associated with the optimization target, wherein the optimization target comprises a threshold value of working condition data of at least one vehicle;
carrying out parameter combination processing according to a plurality of groups of configuration parameters of each part subsystem to obtain a plurality of groups of combination parameters;
respectively inputting the multiple groups of combination parameters into a pre-built driving multi-body dynamics model for simulation test to obtain simulation test working condition data corresponding to each group of combination parameters; wherein the driving multi-body dynamics model comprises a simulation calculation model of each part subsystem;
and selecting target combination parameters which accord with the optimization targets according to the optimization targets and simulation test working condition data corresponding to each group of combination parameters, wherein the target combination parameters are used for guiding the model selection of the part subsystems of the preset vehicle.
2. The method according to claim 1, wherein the method further comprises:
and outputting a vehicle system type selection guide message according to the configuration parameters of each part subsystem included in the target combination parameters, wherein the vehicle system type selection guide message is used for indicating the configuration parameters of each part subsystem meeting the optimization target.
3. The method according to claim 1 or 2, wherein the optimization objective specifically comprises: at least one threshold value of working condition data of the vehicle in a sudden accelerator stepping state and/or at least one threshold value of working condition data of the vehicle in a sudden accelerator releasing state;
the working condition data of the vehicle in the state of stepping on the accelerator suddenly comprises: response delay, acceleration peak, shock, bump, and continuous jitter; the working condition data of the vehicle in the state of quick throttle release comprise: impact, jerk, and continuous shake.
4. The method according to claim 1 or 2, wherein the performing parameter combination processing according to the plurality of sets of configuration parameters of each part subsystem to obtain a plurality of sets of combination parameters includes:
and according to the multiple groups of configuration parameters of each part subsystem, arranging and combining the values of the configuration parameters of each part subsystem to obtain the multiple groups of combination parameters.
5. The method according to claim 1 or 2, wherein selecting the target combination parameters according to the optimization target and the simulation test condition data corresponding to each set of combination parameters includes:
Selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, as the target combination parameters conforming to the optimization target;
or alternatively, the process may be performed,
and selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, as the target combination parameters conforming to the optimization target.
6. The method of claim 1, wherein the plurality of sets of configuration parameters for each part subsystem associated with the optimization objective comprises: two sets of air resistance coefficients, two sets of drive train backlash, and two sets of clutch inertia; correspondingly, the plurality of groups of combination parameters comprise eight groups of combination parameters including air resistance coefficients, transmission chain tooth gaps and clutch inertia.
7. A vehicle system-type selection apparatus, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of groups of configuration parameters of each part subsystem associated with an optimization target of a preset vehicle, and the optimization target comprises a threshold value of working condition data of at least one vehicle;
The parameter combination module is used for carrying out parameter combination processing according to a plurality of groups of configuration parameters of each part subsystem to obtain a plurality of groups of combination parameters;
the simulation test module is used for respectively inputting the multiple groups of combination parameters into a pre-built driving multi-body dynamics model to perform simulation test to obtain simulation test working condition data corresponding to each group of combination parameters; wherein the driving multi-body dynamics model comprises a simulation calculation model of each part subsystem;
the data selection module is used for selecting target combination parameters which accord with the optimization targets according to the optimization targets and the simulation test working condition data corresponding to each group of combination parameters, and the target combination parameters are used for guiding the model selection of the part subsystems of the preset vehicle.
8. The apparatus of claim 7, wherein the apparatus further comprises:
and the optimization guidance module is used for outputting a vehicle system type selection guidance message according to the configuration parameters of each part subsystem included in the target combination parameters, wherein the vehicle system type selection guidance message is used for indicating the configuration parameters of each part subsystem meeting the optimization target.
9. The device according to claim 7 or 8, wherein the optimization objective specifically comprises: at least one threshold value of working condition data of the vehicle in a sudden accelerator stepping state and/or at least one threshold value of working condition data of the vehicle in a sudden accelerator releasing state;
the working condition data of the vehicle in the state of stepping on the accelerator suddenly comprises: response delay, acceleration peak, shock, bump, and continuous jitter; the working condition data of the vehicle in the state of quick throttle release comprise: impact, jerk, and continuous shake.
10. The apparatus of claim 7 or 8, wherein the parameter combination module comprises:
and the arrangement and combination unit is used for carrying out arrangement and combination aiming at the values of the configuration parameters of each part subsystem according to the plurality of groups of configuration parameters of each part subsystem to obtain the plurality of groups of combination parameters.
11. The apparatus according to claim 7 or 8, wherein the data selection module comprises:
the first selecting unit is used for selecting a group of parameters, of which each working condition data in the simulation test working condition data is closest to a corresponding working condition data threshold in the optimization target, from the plurality of groups of combination parameters, and taking the group of parameters as the target combination parameters conforming to the optimization target;
Or alternatively, the process may be performed,
the second selecting unit is used for selecting a group of parameters, which are closest to the corresponding working condition data threshold value in the optimization target, from the plurality of groups of combination parameters, and the group of parameters are used as the target combination parameters which accord with the optimization target.
12. The apparatus of claim 7, wherein the plurality of sets of configuration parameters for each part subsystem associated with the optimization objective comprises: two sets of air resistance coefficients, two sets of drive train backlash, and two sets of clutch inertia; correspondingly, the plurality of groups of combination parameters comprise eight groups of combination parameters including air resistance coefficients, transmission chain tooth gaps and clutch inertia.
13. An electronic device, comprising: the device comprises a memory, a processor, a communication interface and a display;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 6.
14. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out a method of vehicle system selection according to any one of claims 1 to 6.
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