CN117290965B - Vehicle model simulation test simulation method, equipment and medium of vehicle simulation software - Google Patents
Vehicle model simulation test simulation method, equipment and medium of vehicle simulation software Download PDFInfo
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Abstract
The invention relates to the technical field of simulation, and discloses a vehicle model simulation test simulation method, equipment and medium of vehicle simulation software, wherein the method comprises the following steps: s1, building a vehicle model in vehicle simulation software, wherein the vehicle model comprises a plurality of components; s2, the vehicle simulation software generates a corresponding nonlinear equation set according to the coupling relation of components of the vehicle model and the actual problem of the simulation test; s3, acquiring an initial variable of a nonlinear equation set by vehicle simulation software; and S4, the vehicle simulation software calculates according to the initial variable and the nonlinear equation set to obtain a simulation result corresponding to the coupling relation of the components of the vehicle model. When the iteration initial value is not given, the iteration initial value can be automatically estimated according to the initial value range, abnormal mathematical calculation is avoided, and the robustness of the vehicle simulation software is improved; when the vehicle model has the multi-solution problem, all simulation results can be obtained at one time, so that a user can select according to simulation working conditions, and the applicability of software and the use experience of the user are improved.
Description
Technical Field
The invention relates to the technical field of simulation, in particular to a vehicle model simulation test simulation method, device and medium of vehicle simulation software.
Background
In the simulation process of the vehicle simulation software, in order to obtain accurate vehicle simulation parameters, an iteration method is generally adopted to solve, and a given iteration initial value, namely an initial value of the vehicle parameters, is needed. In the vehicle simulation software, it is difficult for a user to determine the physical meaning of all variables, and it is more difficult to give reasonable iteration initial values, such as flow and specific enthalpy parameters in the fuel cell of the vehicle. Even given an initial value, there may be the following problems: (1) The iteration initial value is unreasonably set, for example, if the iteration initial value is not in the convergence domain of the accurate solution or is far away from the true solution, the iteration method is used for solving, and the iteration initial value may not converge or enter a limit cycle, so that the solution fails. (2) The iterative initial value is not within the domain of some equations, and abnormal mathematical operations may occur, such as dividing zero, or root-ing a negative number, resulting in a computational collapse. (3) iteratively solving for non-object understanding. Iterative algorithms with larger convergence domains, like the lunar algorithm, trust domain methods, etc., do not solve the problem for one time and all the time.
The traditional method takes an initial value or a default value given by a user as an iteration initial value, and can utilize a Newton-Lawson iteration method, a homotopy algorithm or convert a problem into an optimization problem for iteration solution. However, both of these methods require that the initial value and true solutions be sufficiently close, and that the initial value be within a domain defined by some system of nonlinear equations, if a given default value is not within the domain of the variables, then divide-by-zero or other abnormal mathematical operations may easily occur, causing the computation to crash.
Therefore, there is a need for a vehicle model simulation test simulation method of vehicle simulation software, which can automatically estimate an iteration initial value according to an initial value range when the iteration initial value is not given, avoid abnormal mathematical calculation, and improve the robustness of the vehicle simulation software.
Disclosure of Invention
In order to solve the technical problems, the invention provides a vehicle model simulation test simulation method, equipment and medium of vehicle simulation software, which can automatically search the value range of an approximate solution by adjusting the value range, automatically estimate an iteration initial value, avoid abnormal mathematical calculation and improve the robustness of the vehicle simulation software.
The invention provides a vehicle model simulation test simulation method of vehicle simulation software, which comprises the following steps:
s1, building a vehicle model in vehicle simulation software, wherein the vehicle model comprises a plurality of components;
s2, generating a corresponding nonlinear equation set by vehicle simulation software according to the current coupling relation of components of the vehicle model and actual problems of a simulation test;
s3, acquiring an initial variable of the nonlinear equation set by vehicle simulation software, wherein the initial variable comprises an iteration initial value or an initial value range, and the iteration initial value or the initial value range is determined according to the physical meaning of vehicle parameters of a vehicle model and actual problems of a simulation test;
and S4, the vehicle simulation software calculates according to the initial variable and the nonlinear equation set to obtain a simulation result corresponding to the current coupling relation of the components of the vehicle model.
Further, S4, the vehicle simulation software calculates according to the initial variable and the nonlinear equation set to obtain a simulation result corresponding to the current coupling relation of the components of the vehicle model, where the simulation result includes:
s41, executing a step S44 when the initial variable only comprises an iteration initial value; when the initial variable only includes the initial value range, step S42 is executed; when the initial variable does not include the iteration initial value and the initial value range, the default value range is taken as the initial value range, and step S42 is executed;
s42, adjusting the initial value range to obtain the value range of the approximate solution of the nonlinear equation set;
s43, estimating one or more iteration initial values through an intelligent optimization algorithm according to the value range of the approximate solution; the intelligent optimization algorithm comprises a simulated annealing algorithm, a particle swarm algorithm and a genetic algorithm;
s44, performing iterative computation according to the iteration initial value to obtain a high-precision solution of the nonlinear equation set; and (3) solving the simulation result corresponding to the current coupling relation of the components of the vehicle model with high precision.
Further, S42, adjusting the initial value range to obtain the value range of the approximate solution of the nonlinear equation set includes:
s421, defining a fitness function; the closer the value of the fitness function is to 1, the closer the corresponding point of the fitness function is to the high-precision solution of the nonlinear equation set;
s422, obtaining M discrete points in the initial value range;
s423, respectively calculating fitness function values at M discrete points;
s424, if all the values of the fitness function at the M discrete points are all calculated to be collapsed, expanding the value range to obtain a new value range, adding 1 to the number of times of adjustment of the value range, acquiring the M discrete points again in the new value range, and returning to the step S423;
s425, if the fitness function at M discrete points has a normal value, judging whether the absolute value of the difference between the maximum value of the fitness function calculated at this time and the maximum value of the fitness function calculated at last time is smaller than a preset difference value;
s426, if the absolute value of the difference between the maximum values of the fitness function is larger than or equal to a preset difference value, selecting a discrete point corresponding to the maximum value of the fitness function calculated at this time as a central point of a new value range, narrowing the value range, adding 1 to the number of times of adjustment of the value range, acquiring M discrete points again in the new value range, and returning to the step S423;
s427, if the absolute value of the difference between the maximum values of the fitness function is smaller than the preset difference value, stopping adjusting the value range, and taking the current value range as the value range of the approximate solution.
Further, S422, acquiring M discrete points in the initial value range includes:
calculating the number of discrete points M, m=p, from the dimension M of the variable to be solved and the number of points p chosen in each direction of the M-dimensional space m 。
Further, S422, acquiring M discrete points in the initial value range includes:
and randomly and uniformly scattering points in the initial value range to obtain M discrete points.
Further, when the number of times of adjustment of the value range exceeds the preset number of times of adjustment, the value range is stopped to be adjusted, and initialization failure information is output.
Further, in S44, after performing iterative computation according to the initial iteration value to obtain a high-precision solution of the nonlinear equation set, the method further includes:
judging whether all high-precision solutions are not converged or crashed;
if all the high-precision solutions do not converge or collapse, expanding the value range to obtain a new value range, adding 1 to the number of times of adjustment of the value range, re-acquiring M discrete points in the new value range, and returning to the execution step S423;
if the convergence or calculation normal high-precision solutions exist, outputting all the convergence or calculation normal high-precision solutions.
The invention also provides an electronic device, which comprises:
a processor and a memory;
the processor is configured to execute the steps of a vehicle model simulation test simulation method of a vehicle simulation software as described in any one of the above by calling a program or instructions stored in the memory.
The present invention also provides a computer-readable storage medium storing a program or instructions that cause a computer to execute the steps of a vehicle model simulation test simulation method of a vehicle simulation software as set forth in any one of the above.
The embodiment of the invention has the following technical effects:
when the vehicle simulation is carried out through the vehicle simulation software, when the iteration initial value is not given or is partially not given, the iteration initial value can be automatically estimated according to the initial value range, abnormal mathematical calculation is avoided, and the robustness of the vehicle simulation software can be improved; when the vehicle model has the multi-solution problem, all simulation results can be obtained at one time, so that a user can select according to simulation working conditions, and the applicability of software and the use experience of the user are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a vehicle model simulation test simulation method of vehicle simulation software provided by an embodiment of the invention;
FIG. 2 is a logic diagram of a vehicle model simulation test simulation method of vehicle simulation software provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the invention, are within the scope of the invention.
Fig. 1 is a flowchart of a vehicle model simulation test simulation method of vehicle simulation software provided by an embodiment of the present invention, and fig. 2 is a logic diagram of a vehicle model simulation test simulation method of vehicle simulation software provided by an embodiment of the present invention. Referring to fig. 1 and 2, the method specifically includes:
s1, building a vehicle model in vehicle simulation software, wherein the vehicle model comprises a plurality of components.
In particular, components of the vehicle model may include batteries, hubs, engines, plumbing, and the like. When the vehicle model is built, the required components are selected for combined connection according to the simulation problem to be solved, and the whole vehicle modeling is not needed.
And S2, generating a corresponding nonlinear equation set by vehicle simulation software according to the current coupling relation of components of the vehicle model and the actual problem of a simulation test.
Specifically, the current coupling relationship of the components of the vehicle model, that is, the connection relationship of the components of the vehicle model in the current state, and the nonlinear equation sets reflect the coupling relationship between the components of the vehicle model, and each component corresponds to an unknown variable in the nonlinear equation sets. The actual problem of the simulation test affects the design of the assembly, and the simulation test may include a vehicle crash test, a vehicle component durability test, and the like. Depending on the actual problems of the simulation test, the design conditions of the vehicle model assembly may be different, such as the number of battery cells in the battery pack, the connection mode of the pipelines, and the like.
S3, the vehicle simulation software acquires an initial variable of the nonlinear equation set, wherein the initial variable comprises an iteration initial value or an initial value range, and the iteration initial value or the initial value range is determined according to the physical meaning of the vehicle parameters of the vehicle model and the actual problem of the simulation test.
Specifically, the iteration initial value or the initial value range is determined according to the physical meaning of the vehicle parameter of the vehicle model and the actual problem of the simulation test, and the vehicle parameter is assumed to be the engine water temperature, and the value range of the parameter should be within 0 ℃ to 100 ℃ according to the physical meaning of the water temperature, and according to the actual problem of the simulation test, such as the state of the engine under the current simulation, a user gives a more reasonable guess value as the iteration initial value or the initial value range of the vehicle parameter, such as the engine water temperature of 20 ℃. The user gives an iterative initial value or an initial value range of the vehicle parameters according to the vehicle model, or neither of them is given. When the physical meaning of a certain parameter of the vehicle model is not clear to the user or it is difficult to give a reasonable iteration initial value, only an initial value range of the parameter may be provided, for example, parameters such as flow rate, specific enthalpy and the like in a fuel cell of the vehicle.
And S4, the vehicle simulation software calculates according to the initial variable and the nonlinear equation set to obtain a simulation result corresponding to the current coupling relation of the components of the vehicle model.
Specifically, since the guess value given by the user may be very different from the actual value of the vehicle model simulation, iterative calculation is required to be performed according to the initial variable and the nonlinear equation set to obtain a simulation result corresponding to the current coupling relation of the components of the vehicle model, that is, the actual value of the vehicle parameter of the current simulation situation of the vehicle model.
S41, executing a step S44 when the initial variable only comprises an iteration initial value; when the initial variable only includes the initial value range, step S42 is executed; when the initial variable does not include the iteration initial value and the initial value range, the step S42 is executed with the default value range as the initial value range.
Specifically, when the initial variable given by the user includes only the iteration initial value, step S44 is executed, and the iteration calculation is directly performed according to the given iteration initial value. When the initial variable given by the user only includes the initial value range, step S42 is executed, where the initial value range is adjusted to estimate the iteration initial value, and then the iteration calculation is performed. When the initial variable does not include the iteration initial value and the initial value range, that is, when the user does not give the iteration initial value and the initial value range, acquiring a default value range as the initial value range, executing step S42, firstly adjusting the initial value range to estimate the iteration initial value, and then performing iterative calculation; the default value range is a larger range preset.
S42, adjusting the initial value range to obtain the value range of the approximate solution of the nonlinear equation set.
S421, defining a fitness function; the closer the value of the fitness function is to 1, the closer the corresponding point of the fitness function is to the high-precision solution of the nonlinear equation set.
Specifically, the fitness function is defined as:
（1）
wherein g (t) is the fitness function, t is the solution vector of the nonlinear equation set, including all unknowns of the nonlinear equation set, f i (t) is a nonlinear equation, n is the number of nonlinear equations in the nonlinear equation set, and i is the sequence number of the nonlinear equations in the nonlinear equation set.
If t is the solution of the nonlinear equation set, there is g (t) =1. As the fitness function is a continuous function, when the unknown quantity t is closer to the true solution t, the fitness function value is closer to 1; wherein true solution t is a high-precision solution of a nonlinear equation set. Since the calculation of the fitness function requires the calculation of the function value f i (t) if there is an abnormal mathematical operation in the calculation of the function value that causes a crash, the fitness function cannot be calculated, so that the fitness function can perceive whether the abnormal mathematical operation and the current point are close enough to a true solution.
S422, M discrete points are acquired in the initial value range.
Specifically, the format of the initial value range is the initial value range of the first unknown quantity x the initial value range of the second unknown quantity x the initial value range of the third unknown quantity … …, and so on. For example, when there are two unknown amounts, the initial range of values given by the user may be (-10, 10) × (-10, 10). The method for evenly dividing the initial value range can be adopted to obtain M discrete points:
the number of variables to be solved in a nonlinear equation set is marked as m, namely the dimension of the variables to be solved is marked as m, an initial value range is regarded as an m-dimensional hypercube taking a certain point as a center, the number p of points selected in each direction of an m-dimensional space is determined, each direction represents one variable to be solved, and when the coordinates of discrete points are determined, the value range in each direction is required to be evenly split, so that the selected points are evenly distributed in each direction. Calculating the number of discrete points M, m=p, from the dimension M of the variable to be solved and the number of points p chosen in each direction of the M-dimensional space m 。
Illustratively, assume that the variables to be solvedThe number m=3, the origin of coordinates in m-dimensional space is point o, the axes a, b and c extend in three directions, and the value range of the given variable a to be calculated is [0,1 ]]The value range of the variable b to be calculated is [1,5]The value range of the variable c to be calculated is [0,10]Taking 3 points in each direction. Uniformly dividing the value ranges of the variables a, b and c to be solved into p-1 parts, namely uniformly dividing the value ranges of the variables a, b and c to be solved into 2 parts, and obtaining the points selected in the direction a as 0,1/2,1 and the points selected in the direction b as 1,3,5 and 10. Discrete points (a, b, c) are formed from combinations of points selected in each direction, respectively (0, 1, 0), (0, 1, 5), (0, 3, 1), (0,3,5), (0, 5, 0), (0, 5), (1/2, 1, 0), (1, 3, 10) … … according to m=p m The total number of discrete points selected in the initial value range can be found to be 27.
In some embodiments, the M discrete points may also be obtained by randomly and evenly scattering the points within the initial range of values using an even distribution or gaussian distribution.
S423, calculating fitness function values at M discrete points respectively.
S424, if all the values of the fitness function at the M discrete points are all calculated to be collapsed, expanding the value range to obtain a new value range, adding 1 to the number of times of adjustment of the value range, acquiring the M discrete points again in the new value range, and returning to the step S423.
Specifically, the length of the value range in the k direction in the m-dimensional space is defined as L k Expanding the range of values, i.e. extending the range of values L in the k-direction k Expanding, by way of example, the value range L in the k direction can be set k Expanded to 2L k . Taking the expanded value range as a new value range, recording the adjustment times of the value range and adding 1, re-acquiring M discrete points in the new value range, and re-calculating the fitness function.
S425, if the fitness function at M discrete points has a normal value, judging whether the absolute value of the difference between the maximum value of the fitness function calculated at this time and the maximum value of the fitness function calculated at last time is smaller than a preset difference value.
Specifically, the maximum value of the fitness function is the fitness function value closest to 1. And comparing the absolute value of the difference between the maximum value of the fitness function obtained by calculating the current value range and the maximum value of the fitness function obtained by calculating the last value range with a preset difference value.
S426, if the absolute value of the difference between the maximum values of the fitness function is greater than or equal to the preset difference, selecting the discrete point corresponding to the maximum value of the fitness function calculated at this time as the center point of the new value range, narrowing the value range, adding 1 to the number of times of adjustment of the value range, re-acquiring M discrete points in the new value range, and returning to the step S423.
Specifically, if the absolute value of the difference between the maximum values of the fitness function is greater than or equal to the preset difference, the adaptability function is indicated to be greatly changed, and the difference between the value range and the true value range of the nonlinear equation set is great at the moment, so that continuous adjustment is needed. The closer the value of the fitness function is to 1, the closer the discrete point corresponding to the fitness function is to the true solution of the nonlinear equation set, so that the discrete point corresponding to the maximum value of the fitness function calculated in the current value range is selected as the center point of the new value range. Defining length L of value range in k direction in m-dimensional space k Narrowing the range of values means narrowing the range of values L in the k direction k Narrowing, for example, the value range L in the k direction can be k Is reduced to L k /2. And obtaining a new value range according to the central point of the new value range and the reduced value range in the k direction, recording the adjustment times of the value range and adding 1, re-acquiring M discrete points in the new value range, and re-calculating the fitness function.
S427, if the absolute value of the difference between the maximum values of the fitness function is smaller than the preset difference value, stopping adjusting the value range, and taking the current value range as the value range of the approximate solution.
Specifically, if the absolute value of the difference between the maximum values of the fitness function is smaller than the preset difference, the fitness function is indicated to be stable in change, the value range is close to the true value range of the nonlinear equation set, the adjustment of the value range is stopped, and the current value range is used as the value range of the approximate solution.
Further, when the number of times of adjustment of the value range exceeds the preset number of times of adjustment, the value range is stopped to be adjusted, and initialization failure information is output.
S43, estimating one or more iteration initial values through an intelligent optimization algorithm according to the value range of the approximate solution.
Specifically, the intelligent optimization algorithm is a calculation method for approximately solving an optimization problem, and the intelligent optimization algorithm can comprise a simulated annealing algorithm, a particle swarm algorithm and a genetic algorithm, wherein the simulated annealing algorithm can calculate a plurality of iteration initial values at a time, and the particle swarm algorithm and the genetic algorithm can only calculate one iteration initial value at a time.
S44, performing iterative computation according to the iteration initial value to obtain a high-precision solution of the nonlinear equation set.
Specifically, performing iterative computation according to the iteration initial value to obtain a high-precision solution of the nonlinear equation set, wherein the high-precision solution is a simulation result corresponding to the current coupling relation of the components of the vehicle model. Iterative methods of performing iterative calculations include, but are not limited to: newton-Lawson iterative method, improved by Levenberg-Marquardt (Levenberg-Marquardt) algorithm based on solving optimization problem. The occurrence of non-convergence or limit cycles can be effectively avoided by adopting a Levenberg-Marquardt (Levenberg-Marquardt) algorithm based on improvement of solving the optimization problem.
In the embodiment of the invention, when the vehicle simulation is carried out through the vehicle simulation software, when the iteration initial value is not given or is partially not given, the iteration initial value can be automatically estimated according to the initial value range, abnormal mathematical calculation is avoided, and the robustness of the vehicle simulation software can be improved; when the vehicle model has the multi-solution problem, all simulation results can be obtained at one time, so that a user can select according to simulation working conditions, and the applicability of software and the use experience of the user are improved.
Further, after step S44, the method further includes:
and judging whether all the high-precision solutions are not converged or crashed. If all the high-precision solutions do not converge or collapse, the value range is enlarged to obtain a new value range, the number of times of adjustment of the value range is increased by 1, and M discrete points are reacquired in the new value range, and the step S423 is executed. The method for expanding the value range is the same as that in step S424, and will not be described again. For example, when the user only gives the iteration initial value and does not give the initial value range, if all the high-precision solutions do not converge or collapse, the default value range can be used as the initial value range to perform range adjustment. If the convergence or calculation normal high-precision solutions exist, outputting all the convergence or calculation normal high-precision solutions.
Illustratively, when the vehicle simulation software generates the following set of nonlinear equations:
（2）
wherein x and y are unknown variables, namely vehicle parameters corresponding to components of the vehicle model, and the nonlinear equation set reflects the coupling relation among the components of the vehicle model.
The jacobian matrix of the nonlinear system of equations is:
（3）
the jacobian matrix is a matrix formed by arranging first-order partial derivatives in a certain way, and can represent an optimal linear approximation of a micro equation and a given point. Obviously, the matrix is singular at the origin (0, 0), if (0, 0) is taken as an iteration initial value, the computational breakdown is solved by using an iteration method. Therefore, the method of the invention is adopted for solving, and the effectiveness of the method for solving the equation is shown in two cases.
Illustratively, when the user gives an initial defined value range ofWhen the method is used, the initial value range given by a user is adjusted to obtain the value range of the approximate solution as follows。
If genetic algorithm is adopted for intelligent optimization, the iteration initial value estimated in the value range of the approximate solution isThe fitness function value is->. Further adopting Newton-Lawson iteration method to obtain high-accuracy solution (0.705965,1.5139823) with error of 3.32X10 -9 。
If the simulated annealing algorithm is adopted for intelligent optimization, the initial iteration value estimated in the value range of the approximate solution is shown in table 1, and the corresponding relation between the initial iteration value obtained by the simulated annealing algorithm and the high-precision solution under the condition that the solution range is given by a user is shown:
TABLE 1 initial iteration value and high-precision solution obtained by simulated annealing algorithm under condition of user given solution range
Obviously, the method provided by the invention can avoid solving breakdown, can solve a high-precision approximate solution under the condition of a given range of a user, and can solve a plurality of solutions by using a simulated annealing algorithm under the condition that a plurality of solutions exist in an equation.
Illustratively, when the user does not give the initial fixed value range, the default value rangeAs the initial value range, the value range of the obtained approximate solution is。
If genetic algorithm is adopted for intelligent optimization, the iteration initial value estimated in the value range of the approximate solution isThe fitness function value is->. Further adopting Newton-Lawson iteration method to obtain high-accuracy solution (0.187732, -12.001575) with error of 2.83×10 -7 。
If the simulated annealing algorithm is adopted for intelligent optimization, the iteration initial value estimated in the value range of the approximate solution is shown in table 2, and the corresponding relation between the iteration initial value obtained by the simulated annealing algorithm and the high-precision solution under the condition that the solution range is not given by a user is shown:
TABLE 2 initial iteration values and high-precision solutions obtained by simulated annealing algorithm without user given solution scope
Obviously, the method provided by the invention can avoid solving breakdown, can solve a high-precision approximate solution under the condition of a given range of a user, and can solve a plurality of solutions by using a simulated annealing algorithm under the condition that a plurality of solutions exist in an equation.
In the embodiment of the invention, when the vehicle simulation is carried out through the vehicle simulation software, when the iteration initial value is not given or is partially not given, the iteration initial value can be automatically estimated according to the initial value range, abnormal mathematical calculation is avoided, and the robustness of the vehicle simulation software can be improved; when the vehicle model has the multi-solution problem, all simulation results can be obtained at one time, so that a user can select according to simulation working conditions, and the applicability of software and the use experience of the user are improved.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, electronic device 500 includes one or more processors 501 and memory 502.
The processor 501 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities and may control other components in the electronic device 500 to perform desired functions.
Memory 502 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that may be executed by the processor 501 to implement a vehicle model simulation test emulation method and/or other desired functions of the vehicle simulation software of any of the embodiments of the present application described above. Various content such as initial arguments, thresholds, etc. may also be stored in the computer readable storage medium.
In one example, the electronic device 500 may further include: an input device 503 and an output device 504, which are interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 503 may include, for example, a keyboard, a mouse, and the like. The output device 504 may output various information to the outside, including early warning prompt information, braking force, etc. The output device 504 may include, for example, a display, speakers, a printer, and a communication network and remote output apparatus connected thereto, etc.
Of course, only some of the components of the electronic device 500 that are relevant to the present application are shown in fig. 3 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 500 may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of a vehicle model simulation test simulation method of vehicle simulation software provided by any of the embodiments of the present application.
The computer program product may write program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium, on which computer program instructions are stored, which, when executed by a processor, cause the processor to perform the steps of a vehicle model simulation test simulation method of vehicle simulation software provided by any embodiment of the present application.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application. As used in this specification, the terms "a," "an," "the," and/or "the" are not intended to be limiting, but rather are to be construed as covering the singular and the plural, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus 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, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements.
It should also be noted that the positional or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the positional or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Unless specifically stated or limited otherwise, the terms "mounted," "connected," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.
Claims (7)
1. A vehicle model simulation test simulation method of vehicle simulation software is characterized by comprising the following steps:
s1, building a vehicle model in vehicle simulation software, wherein the vehicle model comprises a plurality of components;
s2, the vehicle simulation software generates a corresponding nonlinear equation set according to the current coupling relation of the components of the vehicle model and the actual problem of the simulation test;
s3, the vehicle simulation software acquires an initial variable of the nonlinear equation set, wherein the initial variable comprises an iteration initial value or an initial value range, and the iteration initial value or the initial value range is determined according to the physical meaning of the vehicle parameters of the vehicle model and the actual problem of the simulation test;
s4, the vehicle simulation software calculates according to the initial variable and the nonlinear equation set to obtain a simulation result corresponding to the current coupling relation of the components of the vehicle model;
the method specifically comprises the following steps:
s41, executing a step S44 when the initial variable only comprises the iteration initial value; when the initial variable only includes the initial value range, executing step S42; when the initial variable does not include the iteration initial value and the initial value range, taking a default value range as the initial value range, and executing step S42;
s42, adjusting the initial value range to obtain the value range of the approximate solution of the nonlinear equation set;
s421, defining a fitness function; the closer the value of the fitness function is to 1, the closer the corresponding point of the fitness function is to the high-precision solution of the nonlinear equation set;
s422, obtaining M discrete points in the initial value range;
s423, respectively calculating fitness function values at M discrete points;
s424, if all the values of the fitness functions at the M discrete points are calculated to be crashed, expanding the value range to obtain a new value range, adding 1 to the number of times of adjustment of the value range, acquiring the M discrete points again in the new value range, and returning to the step S423;
s425, if the fitness function at the M discrete points has a normal value, judging whether the absolute value of the difference between the maximum value of the fitness function calculated at this time and the maximum value of the fitness function calculated at last time is smaller than a preset difference value;
s426, if the absolute value of the difference between the maximum values of the fitness function is larger than or equal to a preset difference value, selecting a discrete point corresponding to the maximum value of the fitness function calculated at this time as a central point of a new value range, narrowing the value range, adding 1 to the number of times of adjustment of the value range, acquiring M discrete points again in the new value range, and returning to the step S423;
s427, if the absolute value of the difference between the maximum values of the fitness function is smaller than the preset difference value, stopping adjusting the value range, and taking the current value range as the value range of the approximate solution;
s43, estimating one or more iteration initial values through an intelligent optimization algorithm according to the value range of the approximate solution; the intelligent optimization algorithm comprises a simulated annealing algorithm, a particle swarm algorithm and a genetic algorithm;
s44, performing iterative computation according to the iterative initial value to obtain a high-precision solution of the nonlinear equation set; and the high-precision solution is a simulation result corresponding to the current coupling relation of the components of the vehicle model.
2. The vehicle model simulation test simulation method of the vehicle simulation software according to claim 1, wherein the step S422 of obtaining M discrete points in the initial value range includes:
calculating the number of discrete points M, m=p, from the dimension M of the variable to be solved and the number of points p chosen in each direction of the M-dimensional space m 。
3. The vehicle model simulation test simulation method of the vehicle simulation software according to claim 1, wherein the step S422 of obtaining M discrete points in the initial value range includes:
and randomly and uniformly scattering points in the initial value range to obtain M discrete points.
4. The vehicle model simulation test simulation method of the vehicle simulation software according to claim 1, wherein: when the adjustment times of the value range exceeds the preset adjustment times, stopping adjusting the value range, and outputting initialization failure information.
5. The vehicle model simulation test simulation method of the vehicle simulation software according to claim 1, wherein after the step S44 of performing the iterative calculation according to the iterative initial value to obtain the high-precision solution of the nonlinear equation set, the method further comprises:
judging whether all high-precision solutions are not converged or crashed;
if all the high-precision solutions do not converge or collapse, expanding the value range to obtain a new value range, adding 1 to the number of times of adjustment of the value range, re-acquiring M discrete points in the new value range, and returning to the execution step S423;
if the convergence or calculation normal high-precision solutions exist, outputting all the convergence or calculation normal high-precision solutions.
6. An electronic device, the electronic device comprising:
a processor and a memory;
the processor is configured to execute the steps of a vehicle model simulation test simulation method of a vehicle simulation software according to any one of claims 1 to 5 by calling a program or instructions stored in the memory.
7. A computer-readable storage medium storing a program or instructions that cause a computer to execute the steps of a vehicle model simulation test simulation method of a vehicle simulation software according to any one of claims 1 to 5.
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