Rack damage equivalent model of electromechanical composite transmission system and optimization solution method, system, medium and equipment thereof

Abstract

The invention relates to the field of bench damage equivalent model solving, and discloses a bench damage equivalent model of an electromechanical composite transmission system, an optimization solving method and system of the bench damage equivalent model, a medium and equipment. Real vehicle data including load data under typical working conditions and damage conditions of key parts are obtained through a real vehicle test; wherein the key components comprise a gear, a motor winding and a controller; based on the real vehicle data, establishing a target damage model of the bench test as a damage equivalent model capable of effectively recovering the real vehicle damage on the test bench; and solving the damage equivalent model by using a particle swarm optimization algorithm, searching an optimal solution in a multi-dimensional parameter space through a PSO algorithm, and determining a proportionality coefficient of each working condition and a corresponding cycle time so as to realize optimization of a bench test loading scheme. According to the method, the optimal rack working condition loading strategy is obtained by optimizing and solving the damage equivalent model.

Description

Technical Field

[0001] The present invention relates to the technical field of bench damage equivalent model solution, and in particular to a bench damage equivalent model of an electromechanical composite transmission system and an optimization solution method, system, medium and equipment thereof. Background Art

[0002] With the popularity of electromechanical hybrid transmission systems in engineering applications, how to accurately evaluate the durability of the system under different working conditions has become a research focus. In order to simulate the damage accumulation in actual working conditions in a laboratory environment, the bench damage equivalent model came into being. Traditional bench tests apply specific loads to test components in a laboratory environment to simulate the stress and damage that they may experience under actual use conditions. However, due to the complexity and variability of actual working conditions, traditional bench tests often find it difficult to accurately reproduce the damage conditions in actual vehicle operation. Therefore, in recent years, researchers have begun to devote themselves to the development of more accurate bench damage equivalent technology, aiming to establish a bench test method that is highly correlated with actual vehicle damage through in-depth analysis of actual vehicle working conditions and damage. The development of these technologies enables bench tests to better simulate the cumulative damage of key components in actual vehicle operation, improving the effectiveness and reliability of the tests.

[0003] As the complexity of damage equivalent models continues to increase, traditional analytical and numerical solution methods gradually expose problems such as low efficiency and poor convergence when faced with multi-dimensional parameter space and complex nonlinear equations. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a test bench damage equivalent model of an electromechanical hybrid transmission system and its optimization solution method, system, medium and equipment, which, on the basis of ensuring damage equivalence, obtains the best test bench working condition loading strategy by optimizing the solution of the damage equivalent model.

[0005] To achieve the above-mentioned purpose, in the first aspect, the technical solution adopted by the present invention is: a bench damage equivalent model of an electromechanical composite transmission system and an optimization solution method thereof, which comprises: taking the stress load of the key components of the electromechanical composite transmission system as the research object, obtaining real vehicle data including load data under typical working conditions and damage conditions of the key components through real vehicle tests; wherein the key components include gears, motor windings and controllers; based on the real vehicle data, establishing a target damage model of the bench test as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench; solving the damage equivalent model using a particle swarm optimization algorithm, searching for the optimal solution in the multidimensional parameter space through the PSO algorithm, determining the proportional coefficient of each working condition and its corresponding cycle time, so as to optimize the bench test loading scheme.

[0006] Furthermore, the stress load of key components of the electromechanical hybrid transmission system is taken as the research object, and the real vehicle data including load data under typical working conditions and damage conditions of key components are obtained through real vehicle tests, including:

[0007] Loads are collected at the vehicle test site and the bench test site respectively to obtain load information for establishing durability correlation between the vehicle and the bench test site; the load information collected at the vehicle test site is the mileage, and the load information collected at the bench test site is the bench operation time;

[0008] Based on the load information of torque, speed and temperature of various road conditions in the actual vehicle test field, the pseudo-damage calculation method is adopted to calculate the pseudo-damage values ​​of key components under different working conditions.

[0009] Furthermore, based on the real vehicle data, a target damage model for bench test is established as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench, including:

[0010] According to the given bench load conditions, the damage value of the target channel under each condition is determined to form the bench coefficient matrix A;

[0011] According to the pseudo-damage values ​​of each channel in the real vehicle test field, determine the target damage matrix b applicable to the key components of the bench test field;

[0012] The target damage model is constructed based on the bench coefficient matrix A and the target damage matrix b.

[0013] Furthermore, the establishment of the target damage model includes:

[0014] The damage equivalence problem is described as a multi-objective optimization problem, and constraints are set to determine the objective function, which is used as a damage equivalence model.

[0015] The constraints are:

[0016]

[0017] The objective function is:

[0018] f(x)=A×xb

[0019] In the formula, S is the total mileage of the actual vehicle; Aeq×x=beq represents the linear equality constraint of the variable x, Aeq represents the equivalent coefficient matrix, and beq represents the equivalent damage matrix; Ax≤b is the linear inequality constraint of the variable x; Σx is the total operating time of the test bench; A is the coefficient matrix, which is the pseudo-damage numerical matrix of each channel corresponding to the road conditions of the test bench test field; b is the target pseudo-damage matrix of the actual vehicle test field.

[0020] Furthermore, the particle swarm optimization algorithm is used to solve the damage equivalent model, including:

[0021] The number of the initial particle swarm is N, each particle corresponds to a possible solution, the particle position represents a solution vector, and the velocity represents the changing trend of the solution; the position and velocity of the initial particle are randomly generated;

[0022] The position of each particle represents a solution, which is substituted into the objective function f(x) to calculate its fitness value;

[0023] Set the particle's own historical optimal position p i best and the global optimal position g best The initial value of is the current initial position;

[0024] In each iteration, the particle position and velocity are updated;

[0025] For each particle, compare the fitness value of the current solution with the fitness value of its historical optimal position. If the current solution is better than the historical optimal position, update the historical optimal position of the particle. At the same time, compare the historical optimal positions of all particles with the current global optimal position g. best Compare and if there is a better solution, update the global optimal position;

[0026] Set the maximum number of iterations T or the convergence condition of the optimal solution. When the termination condition is met, stop the iteration, select multiple groups of solutions according to the needs, and calculate the error sum. Output the optimal solution g according to the principle of minimum error sum best , as the time ratio of each working condition, is used to design the loading strategy for the bench test.

[0027] Furthermore, the formula for updating the particle's position and velocity is:

[0028]

[0029] x i (t+1)=x i (t)+v i (t+1)

[0030] In the formula, v i (t+1) is the velocity of the ith particle at the t+1th iteration, v i (t) is the velocity of the i-th particle at the t-th iteration; x i (t+1) is the position of the i-th particle at the t+1th iteration, x i (t) is the position of the i-th particle at the t-th iteration; is the historical optimal position of the ith particle; g best(t) is the global optimal position; ω is the inertia weight, which controls the search ability of the particle; c1 and c2 are learning factors, representing the influence of particle self-cognition and group cognition respectively; r1 and r2 are random numbers between [0,1].

[0031] Furthermore, the error sum is:

[0032] error=error t +error s

[0033] In the formula, error is the error sum; error t is the total time error, error s is the total mileage error.

[0034] In the second aspect, the technical solution adopted by the present invention is: a bench damage equivalent model of an electromechanical composite transmission system and its optimization solution system, which includes: a real vehicle data acquisition module, which takes the stress load of the key components of the electromechanical composite transmission system as the research object, and obtains the real vehicle data including the load data under typical working conditions and the damage conditions of the key components through real vehicle tests; wherein the key components include gears, motor windings and controllers; a model establishment module, which establishes a target damage model of the bench test based on the real vehicle data, as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench; a model solution module, which solves the damage equivalent model using a particle swarm optimization algorithm, searches for the optimal solution in the multi-dimensional parameter space through the PSO algorithm, and determines the proportional coefficient of each working condition and its corresponding cycle time, so as to achieve the optimization of the bench test loading scheme.

[0035] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0036] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0037] The present invention adopts the above technical solution, uses a multi-objective optimization model to construct a damage equivalent model, and uses a particle swarm optimization algorithm (PSO) to optimize and solve the proposed model, thereby obtaining the best bench load loading strategy. Therefore, the present invention has at least the following advantages: (1) diversified stress types, covering mechanical stress, electrical stress, thermal stress, etc.; (2) significant damage equivalent effect, which can accurately replicate the actual vehicle damage; (3) high model solving efficiency, fast and stable optimization process; (4) significantly improved the accuracy of bench test. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of a test bench damage equivalent model and an optimization solution method thereof of an electromechanical hybrid transmission system in an embodiment of the present invention;

[0039] Figure 2 : is a diagram of the optimization process of the PSO algorithm in an embodiment of the present invention;

[0040] Figure 3a is a comparison diagram of contact fatigue stress damage of a gear in a bench test and damage in a real vehicle in an embodiment of the present invention;

[0041] Figure 3b is a comparison diagram of the bending fatigue stress damage of the bench test gear and the damage of the actual vehicle in the embodiment of the present invention;

[0042] Figure 3c This is a comparison diagram of thermal stress damage of IGBT modules in bench tests and actual vehicle damage in an embodiment of the present invention;

[0043] Figure 3d is a comparison diagram of the electrical stress damage of the motor winding in the bench test and the damage of the actual vehicle in the embodiment of the present invention;

[0044] Figure 3e is a comparison diagram of the thermal stress damage of the motor winding in the bench test and the damage of the actual vehicle in the embodiment of the present invention; DETAILED DESCRIPTION

[0045] In the prior art, a variety of methods have been proposed for establishing a test bench damage equivalent model and its optimization solution. Currently, the damage to key components caused by mechanical stress is mainly considered. However, when the system is subjected to other types of stress (such as electrical stress, thermal stress, etc.) at the same time, the existing damage equivalent model is difficult to accurately reproduce the damage of the actual vehicle. In terms of optimization solution methods, the commonly used ones are the non-dominated sorting genetic algorithm II (NSGA-II) and the simulated annealing algorithm (SA). NSGA-II has a high implementation complexity and requires large computing resources; SA is suitable for single-objective optimization, but it is easy to converge to a suboptimal solution prematurely in multi-objective optimization tasks.

[0046] Although existing methods have made great progress in damage equivalent models and their optimization solutions, they usually only consider the damage of a single stress to the key components of the system. The limitation of this method is that the damage equivalent model cannot accurately reproduce the actual damage of the key components of the real vehicle. In addition, traditional analytical methods and numerical solution techniques are difficult to effectively shorten the calculation time while ensuring the accuracy of the solution when dealing with multi-dimensional parameter spaces and complex nonlinear equations. Therefore, the existing methods have the following disadvantages: 1) The damage of multiple stresses to key components is not comprehensively considered; 2) It is difficult to ensure both calculation accuracy and efficiency at the same time.

[0047] In order to solve the problems of low efficiency and poor convergence of traditional analytical and numerical solution methods, optimization algorithms, especially particle swarm optimization (PSO) algorithms, have gradually been applied and promoted in this field. Particle swarm optimization (PSO) algorithm is a global optimization method based on swarm intelligence. In recent years, it has shown great potential in solving complex damage equivalence problems. Traditional damage equivalence methods usually rely on empirical formulas or experimental adjustments, and it is difficult to simultaneously consider the complex relationship between multiple working conditions and multi-dimensional parameters. The PSO algorithm can efficiently search for the optimal solution in the high-dimensional parameter space by simulating the behavior of group collaboration in nature, and accurately calculate the equivalent relationship between real vehicle damage and bench damage. Specifically, the PSO algorithm continuously adjusts the working condition ratio and cycle time of the bench test so that the cumulative damage of the bench test matches the real vehicle damage, thereby optimizing the damage equivalence model. This damage equivalence method based on the PSO algorithm not only improves the accuracy of the bench test, but also greatly reduces the test cost, and promotes the further development of bench damage equivalence technology.

[0048] Therefore, for the electromechanical hybrid transmission system, the present invention provides a test bench damage equivalent model of the electromechanical hybrid transmission system and its optimization solution method, system, medium and equipment. First, the target damage values ​​of key components caused by mechanical stress, electrical stress and thermal stress are obtained using real vehicle data; then, the damage values ​​of the corresponding components of the test bench under stress are obtained by calculation; finally, the damage equivalent model is solved using the particle swarm optimization algorithm (PSO).

[0049] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0050] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0051] In one embodiment of the present invention, Figure 1 As shown, a bench damage equivalent model of an electromechanical composite transmission system and its optimization solution method, system, medium and equipment are provided. The present invention is divided into two main stages: the first stage is the establishment of a bench damage equivalent model. Specifically, the present invention takes the stress load of key components (such as gears, motor windings and controllers) of the electromechanical composite transmission system as the research object, and obtains the load data and damage conditions of key components under typical working conditions through actual vehicle tests. Based on these actual vehicle data, a target damage model of the bench test is established, thereby forming a damage equivalent model that can effectively replicate the actual vehicle damage on the test bench. The second stage is to solve the bench damage equivalent model using an optimization algorithm. The present invention constructs a mathematical model based on linear equality constraints and uses a particle swarm optimization algorithm (PSO) for optimization and solution. The PSO algorithm can efficiently search for the optimal solution in a multidimensional parameter space, determine the proportional coefficient of each working condition and its corresponding cycle time, thereby realizing the optimization of the bench test loading scheme. Through the above method, the present invention realizes the accurate reproduction of the actual vehicle damage on the bench, and effectively improves the efficiency and accuracy of the bench loading scheme through the optimization algorithm. Specifically, in this embodiment, the method includes the following steps:

[0052] 1) Taking the stress load of key components of the electromechanical hybrid transmission system as the research object, the actual vehicle data including load data under typical working conditions and damage conditions of key components are obtained through actual vehicle tests; among them, the key components include but are not limited to gears, motor windings and controllers (IGBT modules);

[0053] 2) Based on the real vehicle data, a target damage model for bench test is established as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench;

[0054] 3) The particle swarm optimization algorithm is used to solve the damage equivalent model, and the optimal solution is searched in the multidimensional parameter space through the PSO algorithm to determine the proportional coefficient of each working condition and its corresponding cycle time to achieve the optimization of the bench test loading scheme.

[0055] In the above step 1), the stress load of the key components of the electromechanical hybrid transmission system is taken as the research object, and the real vehicle data including the load data under typical working conditions and the damage conditions of the key components are obtained through real vehicle tests, which includes the following steps:

[0056] 1.1) Loads are collected at the vehicle test site and the bench test site respectively to obtain load information for establishing durability correlation between the vehicle and the bench test site; the load information collected at the vehicle test site is the mileage, and the load information collected at the bench test site is the bench operation time;

[0057] 1.2) Based on the load information of torque, speed, temperature and other road conditions in the actual vehicle test field, the pseudo-damage calculation method is used to calculate the pseudo-damage values ​​of key components under different working conditions.

[0058] The pseudo damage value is:

[0059] S=S f ×(N) -ε

[0060] Where S is the stress amplitude of cyclic loading, N is the fatigue life (number of cycles) of the material under the corresponding stress amplitude, and S f is the fatigue strength coefficient of the material, and ε is the fatigue strength exponent.

[0061] In the above step 2), based on the real vehicle data, a target damage model of the bench test is established as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench, including the following steps:

[0062] 2.1) According to the given bench load conditions, determine the damage value of the target channel under each condition and form the bench coefficient matrix A;

[0063] In this embodiment, the target channel is determined as follows: the stress type of the key components is determined according to the working environment of the electromechanical hybrid transmission system and the stress is used as the target channel of the model. The target channel includes but is not limited to the contact fatigue stress and bending fatigue stress of the gear, the electrical stress and thermal stress of the motor winding, and the thermal stress of the IGBT module.

[0064] In this embodiment, the number of the bench loading conditions is set to n conditions, and each condition corresponds to m target channels. Therefore, the coefficient matrix A has a dimension of m×n.

[0065] 2.2) According to the pseudo-damage values ​​of each channel in the real vehicle test field, determine the target damage matrix b applicable to the key components of the bench test field;

[0066] In this embodiment, a target matrix b is obtained by determining m target channels, and the dimension of the target matrix b is m×1.

[0067] 2.3) Construct the target damage model based on the bench coefficient matrix A and the target damage matrix b to achieve the bench test field damage equivalence.

[0068] In this embodiment, the establishment of the target damage model is specifically as follows: describing the damage equivalence problem as a multi-objective optimization problem, setting constraints, determining the objective function, and using the objective function as the damage equivalence model.

[0069] Specifically, the multi-objective optimization problem is:

[0070] min[f1(x),f2(x),...,f m (x)]

[0071] In the formula, f i (x) is the objective function to be optimized, and x is the variable to be optimized.

[0072] To ensure that the optimization problem meets the constraints of actual working conditions, the constraints are:

[0073]

[0074] The objective function to be optimized is the solution equation that needs to be established in the present invention, and the objective function is:

[0075] f(x)=A×xb

[0076] In the formula, S is the total mileage of the actual vehicle; Aeq×x=beq represents the linear equality constraint of variable x, Aeq represents the equivalent coefficient matrix, beq represents the equivalent damage matrix; Ax≤b is the linear inequality constraint of variable x; ∑x is the total operating time of the test bench; A is the coefficient matrix, which is the corresponding pseudo-damage numerical matrix of each channel under the road conditions of the test bench test field; b is the target pseudo-damage matrix of the actual vehicle test field. This model is the damage equivalent model to be solved.

[0077] In the above step 3), based on the solution of the multi-objective optimization algorithm, the optimal solution is obtained through continuous iteration until the preset termination condition is met. Specifically, the damage equivalent model is solved using the particle swarm optimization algorithm, including the following steps:

[0078] 3.1) The number of the initial particle swarm is N, each particle corresponds to a possible solution, the position of the particle represents a solution vector, and the speed represents the changing trend of the solution; the position and speed of the initial particle are randomly generated;

[0079] The initialization formula for each particle position is:

[0080] x i (0) = x min +rand()×(x max -x min )

[0081] In the formula, x i (0) is the initial position of the ith particle, x min and xmax are the lower and upper bounds of the solution space respectively, and rand() is a random number between [0,1].

[0082] 3.2) The position of each particle represents a solution, which is substituted into the objective function f(x) to calculate its fitness value; the fitness value reflects the quality of the current solution.

[0083] 3.3) Set the particle's own historical optimal position and the global optimal position g best The initial value of is the current initial position; in each iteration, the position and velocity of the particle are updated;

[0084] 3.4) For each particle, compare the fitness value of the current solution with the fitness value of its historical optimal position. If the current solution is better than the historical optimal position, update the particle's historical optimal position p ibest (t+1)=x i (t+1), at the same time, the historical optimal position of all particles is compared with the current global optimal position g best Compare and if there is a better solution, update the global optimal position;

[0085] 3.5) Set the maximum number of iterations T or the convergence condition of the optimal solution (such as the change in the objective function value is less than a certain threshold). When the termination condition is met, stop the iteration, select multiple groups of solutions (for example, 30 groups of solutions) according to the needs, and calculate the error sum. Output the optimal solution g according to the principle of minimum error sum best , as the time ratio of each working condition, is used to design the loading strategy for the bench test.

[0086] 3.6) Carry out loading test on the test bench according to the optimal loading strategy obtained by optimization solution, so as to obtain the damage value of the test bench.

[0087] In the above step 3.3), the formula for updating the position and velocity of the particle is:

[0088]

[0089] x i (t+1)=x i (t)+v i (t+1)

[0090] In the formula, v i (t+1) is the velocity of the ith particle at the t+1th iteration, v i (t) is the velocity of the i-th particle at the t-th iteration; x i (t+1) is the position of the i-th particle at the t+1th iteration, x i (t) is the position of the i-th particle at the t-th iteration; is the historical optimal position of the ith particle; g best (t) is the global optimal position; ω is the inertia weight, which controls the search ability of the particle; c1 and c2 are learning factors, representing the influence of particle self-cognition and group cognition respectively; r1 and r2 are random numbers between [0,1].

[0091] In the above step 3.5), the error expression is:

[0092]

[0093] Where x is the total time (total mileage) obtained by the optimization algorithm, and y is the total time (total mileage) required by the target.

[0094] Then the error sum is:

[0095] error=error t +error s

[0096] In the formula, error is the error sum; error t is the total time error, error s is the total mileage error.

[0097] Implementation case, in order to further verify the effectiveness of the method of the present invention, the motor speed, motor torque, vehicle speed, and voltage data of the actual vehicle equipped with the electromechanical hybrid transmission system were collected. 96 loading conditions were determined as bench test conditions. The coefficient matrix and target matrix were obtained through the calculation formula of pseudo damage as shown in Table 1 and Table 2.

[0098] Table 1 Coefficient matrix

[0099]

[0100]

[0101] Table 2 Target matrix

[0102]

[0103] The damage equivalent model is constructed through the coefficient matrix and the target matrix, and the five constraints are determined as follows: (1) the linear equality constraint of the variable x is Aeqx=beq, (2) the linear inequality constraint of the variable x is Ax≤b, (3) the variable x is a non-negative value, (4) the sum of the variable x is 400 hours, and (5) the total mileage is 10,000 kilometers.

[0104] After obtaining the above damage equivalent model, the PSO algorithm is used to solve the model. The specific parameters are: the lower bound of the variable is defined as 0, and there is no requirement for the upper bound. The particle swarm size is set to 200, and the maximum number of iterations T is 300. The calculated optimization algorithm solution is shown in Table 3.

[0105] Table 3 Optimization algorithm solution

[0106] Serial number <![CDATA[x1]]> <![CDATA[x2]]> <![CDATA[x3]]> ··· <![CDATA[x 96 ]]> 1 3.2591 11.0607 10.7594 ··· 8.2744 2 0.6636 0 0.0069 ··· 0.5897 3 7.7905 5.0601 5.1867 ··· 0.0097 4 5.1603 13.8692 0.2799 ··· 1.2245 … ··· ··· ··· ··· ··· 30 0.3202 1.0718 1.2884 ··· 0.0153

[0107] The error sum formula is used to calculate the error sum corresponding to each set of solutions. The results are shown in Table 4.

[0108] Table 4 Errors and corresponding to 30 solutions

[0109]

[0110]

[0111] The error and minimum value are selected as 0.0002, corresponding to a total time of 400.0526 hours and a total mileage of 9999.1201 kilometers. The strategy for bench loading is the solution corresponding to number 25. The damage obtained by adding this strategy to the bench and the comparison of the damage of the actual vehicle are shown in the attached figure. Figures 3a to 3e shown.

[0112] In one embodiment of the present invention, a test bench damage equivalent model of an electromechanical hybrid transmission system and an optimization solution system thereof are provided, which include:

[0113] The real vehicle data acquisition module takes the stress load of the key components of the electromechanical hybrid transmission system as the research object, and obtains the real vehicle data including the load data under typical working conditions and the damage conditions of the key components through real vehicle tests; among which, the key components include gears, motor windings and controllers;

[0114] Model building module, based on real vehicle data, builds the target damage model of bench test as a damage equivalent model that can effectively replicate the damage of real vehicles on the test bench;

[0115] The model solving module uses the particle swarm optimization algorithm to solve the damage equivalent model, searches for the optimal solution in the multi-dimensional parameter space through the PSO algorithm, determines the proportional coefficient of each working condition and its corresponding cycle time, so as to optimize the bench test loading scheme.

[0116] In the above embodiment, the stress load of the key components of the electromechanical hybrid transmission system is taken as the research object, and the real vehicle data including the load data under typical working conditions and the damage conditions of the key components are obtained through real vehicle tests, including:

[0117] Loads are collected at the vehicle test site and the bench test site respectively to obtain load information for establishing durability correlation between the vehicle and the bench test site; the load information collected at the vehicle test site is the mileage, and the load information collected at the bench test site is the bench operation time;

[0118] Based on the load information of torque, speed and temperature of various road conditions in the actual vehicle test field, the pseudo-damage calculation method is adopted to calculate the pseudo-damage values ​​of key components under different working conditions.

[0119] In the above embodiment, based on the real vehicle data, a target damage model of the bench test is established as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench, including:

[0120] According to the given bench load conditions, the damage value of the target channel under each condition is determined to form the bench coefficient matrix A;

[0121] According to the pseudo-damage values ​​of each channel in the real vehicle test field, the target damage matrix b suitable for the durability evaluation of key components in the bench test field is determined;

[0122] The target damage model is constructed based on the bench coefficient matrix A and the target damage matrix b.

[0123] The establishment of the target damage model includes:

[0124] The damage equivalence problem is described as a multi-objective optimization problem, and constraints are set to determine the objective function, which is used as a damage equivalence model.

[0125] The constraints are:

[0126]

[0127] The objective function is:

[0128] f(x)=A×xb

[0129] In the formula, S is the total mileage of the actual vehicle; Aeq×x=beq represents the linear equality constraint of the variable x, Aeq represents the equivalent coefficient matrix, and beq represents the equivalent damage matrix; Ax≤b is the linear inequality constraint of the variable x; Σx is the total operating time of the test bench; A is the coefficient matrix, which is the pseudo-damage numerical matrix of each channel corresponding to the road conditions of the test bench test field; b is the target pseudo-damage matrix of the actual vehicle test field.

[0130] In the above embodiment, the damage equivalent model is solved by using a particle swarm optimization algorithm, including:

[0131] The number of the initial particle swarm is N, each particle corresponds to a possible solution, the particle position represents a solution vector, and the velocity represents the changing trend of the solution; the position and velocity of the initial particle are randomly generated;

[0132] The position of each particle represents a solution, which is substituted into the objective function f(x) to calculate its fitness value;

[0133] Set the particle's own historical optimal position p i_best and the global optimal position g best The initial value of is the current initial position;

[0134] In each iteration, the particle position and velocity are updated;

[0135] For each particle, compare the fitness value of the current solution with the fitness value of its historical optimal position. If the current solution is better than the historical optimal position, update the historical optimal position of the particle. At the same time, compare the historical optimal positions of all particles with the current global optimal position g. best Compare and if there is a better solution, update the global optimal position;

[0136] Set the maximum number of iterations T or the convergence condition of the optimal solution. When the termination condition is met, stop the iteration, select multiple groups of solutions according to the needs, and calculate the error sum. Output the optimal solution g according to the principle of minimum error sum best , as the time ratio of each working condition, is used to design the loading strategy for the bench test.

[0137] In the above embodiment, the formula for updating the position and velocity of the particle is:

[0138]

[0139] x i (t+1)=x i (t)+v i (t+1)

[0140] In the formula, v i (t+1) is the velocity of the ith particle at the t+1th iteration, v i (t) is the velocity of the i-th particle at the t-th iteration; x i (t+1) is the position of the i-th particle at the t+1th iteration, x i (t) is the position of the i-th particle at the t-th iteration; is the historical optimal position of the ith particle; g best (t) is the global optimal position; ω is the inertia weight, which controls the search ability of the particle; c1 and c2 are learning factors, representing the influence of particle self-cognition and group cognition respectively; r1 and r2 are random numbers between [0,1].

[0141] In the above embodiment, the error sum is:

[0142] error=errort +error s

[0143] In the formula, error is the error sum; error t is the total time error, error s is the total mileage error.

[0144] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0145] In one embodiment of the present invention, a computing device is provided, which may be a terminal, and may include: a processor, a communication interface, a memory, a display screen, and an input device. Among them, the processor, the communication interface, and the memory communicate with each other through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory, and the non-volatile storage medium stores an operating system and a computer program. When the computer program is executed by the processor, the method in each of the above embodiments is implemented; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a management network, NFC (near field communication) or other technologies. The display screen may be a liquid crystal display screen or an electronic ink display screen, and the input device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computing device, or an external keyboard, touchpad or mouse, etc. The processor may call the logic instructions in the memory.

[0146] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0147] In one embodiment of the present invention, a computer program product is provided, wherein the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0148] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores server instructions, and the computer instructions enable a computer to execute the methods provided in the above embodiments.

[0149] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A test bench damage equivalent model of a mechatronic transmission system and its optimization solution method, characterized in that: include: The stress load of key components of the electromechanical hybrid transmission system is taken as the research object, and the real vehicle data including load data under typical working conditions and damage conditions of key components are obtained through real vehicle tests; among them, the key components include gears, motor windings and controllers; Based on the real vehicle data, a target damage model for bench test is established as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench; The particle swarm optimization algorithm is used to solve the damage equivalent model. The PSO algorithm is used to search for the optimal solution in the multidimensional parameter space, and the proportional coefficient of each working condition and its corresponding cycle time are determined to optimize the bench test loading scheme.

2. The test bench damage equivalent model and optimization solution method of the electromechanical hybrid transmission system as claimed in claim 1, characterized in that: Taking the stress load of key components of the electromechanical hybrid transmission system as the research object, the real vehicle data including load data under typical working conditions and damage conditions of key components are obtained through real vehicle tests, including: Loads are collected at the vehicle test site and the bench test site respectively to obtain load information for establishing durability correlation between the vehicle and the bench test site; the load information collected at the vehicle test site is the mileage, and the load information collected at the bench test site is the bench operation time; Based on the load information of torque, speed and temperature of various road conditions in the actual vehicle test field, the pseudo-damage calculation method is adopted to calculate the pseudo-damage values ​​of key components under different working conditions.

3. The bench damage equivalent model and optimization solution method of the electromechanical hybrid transmission system as claimed in claim 1, characterized in that: Based on the real vehicle data, the target damage model of the bench test is established as a damage equivalent model that can effectively replicate the damage of the real vehicle on the test bench, including: According to the given bench load conditions, the damage value of the target channel under each condition is determined to form the bench coefficient matrix A; According to the pseudo-damage values ​​of each channel in the real vehicle test field, determine the target damage matrix b applicable to the key components of the bench test field; The target damage model is constructed based on the bench coefficient matrix A and the target damage matrix b.

4. The test bench damage equivalent model and optimization solution method of the electromechanical hybrid transmission system as claimed in claim 3, characterized in that: The establishment of the target damage model includes: The damage equivalence problem is described as a multi-objective optimization problem, and constraints are set to determine the objective function, which is used as a damage equivalence model. The constraints are: The objective function is: f(x)=A×xb In the formula, S is the total mileage of the actual vehicle; Aeq×x=beq represents the linear equality constraint of the variable x, Aeq represents the equivalent coefficient matrix, and beq represents the equivalent damage matrix; Ax≤b is the linear inequality constraint of the variable x; ∑x is the total operating time of the test bench; A is the coefficient matrix, which is the pseudo-damage numerical matrix of each channel corresponding to the road conditions of the test bench test field; b is the target pseudo-damage matrix of the actual vehicle test field.

5. The test bench damage equivalent model and optimization solution method of the electromechanical hybrid transmission system as claimed in claim 1, characterized in that: The particle swarm optimization algorithm is used to solve the damage equivalent model, including: The number of the initial particle swarm is N, each particle corresponds to a possible solution, the particle position represents a solution vector, and the velocity represents the changing trend of the solution; the position and velocity of the initial particle are randomly generated; The position of each particle represents a solution, which is substituted into the objective function f(x) to calculate its fitness value; Set the particle's own historical optimal position p i_best and the global optimal position g best The initial value of is the current initial position; In each iteration, the particle position and velocity are updated; For each particle, compare the fitness value of the current solution with the fitness value of its historical optimal position. If the current solution is better than the historical optimal position, update the historical optimal position of the particle. At the same time, compare the historical optimal positions of all particles with the current global optimal position g. best Compare and if there is a better solution, update the global optimal position; Set the maximum number of iterations T or the convergence condition of the optimal solution. When the termination condition is met, stop the iteration, select multiple groups of solutions according to the needs, and calculate the error sum. Output the optimal solution g according to the principle of minimum error sum best , as the time ratio of each working condition, is used to design the loading strategy for the bench test.

6. The bench damage equivalent model and optimization solution method of the electromechanical hybrid transmission system as claimed in claim 5, characterized in that: The formula for updating the particle's position and velocity is: x i (t+1)=x i (t)+v i (t+1) In the formula, v i (t+1) is the velocity of the ith particle at the t+1th iteration, v i (t) is the velocity of the i-th particle at the t-th iteration; x i (t+1) is the position of the i-th particle at the t+1th iteration, x i (t) is the position of the i-th particle at the t-th iteration; is the historical optimal position of the ith particle; g best (t) is the global optimal position; ω is the inertia weight, which controls the search ability of the particle; c1 and c2 are learning factors, representing the influence of particle self-cognition and group cognition respectively; r1 and r2 are random numbers between [0,1].

7. The bench damage equivalent model and optimization solution method of the electromechanical hybrid transmission system as claimed in claim 5, characterized in that: The error sum is: error=error t +error s Where error is the error sum; error t is the total time error, error s is the total mileage error.

8. A test bench damage equivalent model of an electromechanical hybrid transmission system and its optimization solution system, characterized in that: include: The real vehicle data acquisition module takes the stress load of the key components of the electromechanical hybrid transmission system as the research object, and obtains the real vehicle data including the load data under typical working conditions and the damage conditions of the key components through real vehicle tests; among which, the key components include gears, motor windings and controllers; Model building module, based on real vehicle data, builds the target damage model of bench test as a damage equivalent model that can effectively replicate the damage of real vehicles on the test bench; The model solving module uses the particle swarm optimization algorithm to solve the damage equivalent model, searches for the optimal solution in the multi-dimensional parameter space through the PSO algorithm, determines the proportional coefficient of each working condition and its corresponding cycle time, so as to optimize the bench test loading scheme.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7.

10. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods described in claims 1 to 7.

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