A method for balancing the wheel axle load of a five-module floating vehicle by adding pads and adjusting springs

Abstract

This invention discloses a method for balancing the axle load of a five-module floating car by adding shims and adjusting springs, relating to the field of five-module floating car manufacturing technology. The method includes: establishing a five-module floating car SIMPACK mechanical model in a SIMPACK environment, incorporating wheel-rail contact, suspension stiffness, and damping nonlinear characteristics; solving for the spring adjustment matrix of the five-module floating car based on the SIMPACK mechanical model; obtaining the axle weight data of the five-module floating car; establishing a vehicle spring adjustment model based on the spring adjustment matrix and the axle weight data; and calculating a shim-adding spring adjustment scheme, including the amount and position of shims, based on a genetic algorithm and the optimization index value and the vehicle spring adjustment model. The shim-adding spring adjustment scheme obtained using this invention can level the axle load deviation of a five-module floating car in one operation, meeting specifications, eliminating the need for repeated disassembly and reassembly of the car body and bogie, significantly improving operational efficiency and leveling accuracy.

Description

Technical Field

[0001] This invention relates to the field of five-module floating vehicle manufacturing technology, and more specifically, to a method for balancing the wheel axle load of a five-module floating vehicle by adding pads and adjusting springs. Background Technology

[0002] Axle load and wheel load control are critical aspects of the design process for urban rail vehicles. The weight load distribution of each wheel and the magnitude of axle load deviation directly affect the vehicle's adhesion and traction, as well as its overall dynamic performance. Therefore, it is essential to ensure that the axle load deviation rate is within the industry-specified range. Due to various reasons, sometimes after the assembly of newly manufactured vehicles, the measured axle load distribution does not meet the factory requirements. In such cases, shims need to be added to the primary or secondary springs to bring it up to production standards.

[0003] Due to its unique structure and complex stress distribution, the spring adjustment mechanism of the five-module floating car is more significantly affected by uneven wheel and axle weight distribution. Therefore, it is crucial and necessary to adjust the spring by adding pads to balance the wheel and axle weight distribution of the five-module floating car.

[0004] Currently, in actual production practice, the following methods are mainly used for adjusting the springs of the five-module floating car: one is that the workers adjust the springs manually multiple times based on their experience to achieve the desired effect; the other is to establish a spring adjustment model by listing the force equations in a separate body manner.

[0005] When adding adjusting shims on-site, workers often rely on experience, manually adjusting multiple times to achieve the desired effect. The entire adjustment and shim-addition process is extremely tedious, involving tasks such as lifting the vehicle and bogie. Even for the same model of rail vehicle, parameters can vary to some extent. Without a stable and reliable method for adjusting rail vehicle springs as a test basis, it would be very labor-intensive and resource-intensive, potentially affecting the overall production schedule. Therefore, it is necessary to study the impact of adding adjusting shims on the axle load of the vehicle, in order to achieve the ideal axle load with as few shim additions as possible.

[0006] The method of establishing a spring-adjusting model by decoupling the X, Y, and Z forces of the vehicle system into XZ and YZ directions, and then analyzing the wheel and axle loads, mainly involves decoupling the forces in these directions. However, it neglects the coupling effects from the X, Y, and Z directions, and the error increases significantly with the increase of the vehicle's center of gravity offset, which is unavoidable. Furthermore, this method establishes the spring-adjusting model by decoupling the force equations of a single vehicle body or several vehicle body groups. For different vehicle models, the entire equation set must be recalculated, resulting in low general applicability, a complex process with numerous equations, and making it unsuitable for engineers to quickly understand and apply. Summary of the Invention

[0007] The present invention provides a method for adjusting the axle load of a five-module floating wheel by adding pads and adjusting springs, which can alleviate the above-mentioned problems.

[0008] To alleviate the above problems, the technical solution adopted by the present invention is as follows:

[0009] This invention provides a method for balancing the axle load of a five-module floating wheel by adding shims and adjusting springs, comprising the following steps:

[0010] S1. In the SIMPACK environment, establish a five-module floating car SIMPACK mechanical model that includes wheel-rail contact, suspension stiffness, and damping nonlinear characteristics.

[0011] S2. Based on the five-module floating car SIMPACK mechanical model, solve the spring adjustment matrix of the five-module floating car and establish the vehicle spring adjustment model;

[0012] S3. Based on the wheel axle weight deviation requirements of the five-module floating car, the optimization index value is obtained;

[0013] S4. Based on the genetic algorithm, the wheel axle weight balance spring adjustment scheme, including the amount and position of the padding, is calculated according to the optimization index value and the vehicle spring adjustment model.

[0014] In a preferred embodiment of the present invention, in step S1, the SIMPACK mechanical model of the five-module floating car is established in the SIMPACK software based on the lateral, longitudinal and vertical coupling of the wheel-rail contact, suspension stiffness and damping nonlinear characteristics of the five-module floating car, the parameters of the car body, bogie and wheelset, the car hinge position, and the stiffness parameters of the fixed hinge, elastic hinge and free hinge.

[0015] In a preferred embodiment of the present invention, in step S2, assuming there are n locations for adding springs to the five-module floating car and m wheel axle weights, then the spring adjustment matrix...

[0016]

[0017] The matrix elements represent the changes in wheel and axle weight at the positions where the five-module floating cars are fitted with shims and springs, and the sum of all elements is zero.

[0018] In a preferred embodiment of the present invention, the vehicle spring adjustment model is as follows:

[0019]

[0020] in, This is a spring adjustment scheme, where the elements are the shims added at the corresponding spring adjustment positions on the five-module floating cars. The deviation matrix consists of elements representing the difference between the measured wheel weight and the expected wheel weight of the corresponding axle of the five-module floating car, where the expected wheel weight is the average load on each wheel.

[0021] In a preferred embodiment of the present invention, in step S3, the optimization index value is...

[0022]

[0023] in,

[0024] This refers to the wheel load deviation rate. This refers to the dynamic axle load deviation rate. This refers to the axle load deviation rate.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] The padding and adjusting spring scheme obtained by the method of the present invention can level the axle load deviation of the five-module floating car in one go and meet the specification requirements, eliminating the need for repeated disassembly and hoisting of the car body and bogie, and significantly improving the work efficiency and leveling accuracy;

[0027] This invention addresses the uneven wheel axle load deviation during the manufacturing process of five-module floating cars. Taking into account wheel-rail contact, suspension stiffness, and damping nonlinearity, it proposes a shim-adding and spring-adjusting method for balancing the wheel axle load of five-module floating cars. This method can effectively simulate the impact of adding shims at the primary and secondary suspensions of the five-module floating car on the overall wheel axle load. Based on the spring-adjusting model, an algorithm optimization is performed to obtain a shim-adding and spring-adjusting scheme that balances the wheel axle load and meets the wheel axle load deviation requirements.

[0028] By analyzing the mechanical model of the five-module floating car and comprehensively considering the effects of the vehicle's lateral, longitudinal, and articulated coupling, a more realistic and accurate spring adjustment model can be obtained. This method uses a SIMPACK modeling approach to obtain the spring adjustment matrix, which characterizes the change in wheel and axle weight caused by a unit amount of spring adjustment. The study is conducted using both simulation and experimental methods. The results show that the results of this method are not affected by the vehicle's center of gravity offset and have high general adaptability, thus achieving wheel and axle weight balance in the five-module floating car.

[0029] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, embodiments of the present invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 Flowchart of the method for adding pads and adjusting springs for a five-module floating car;

[0032] Figure 2 A five-module floating wheel axle load mechanical model;

[0033] Figure 3 This is a schematic diagram of the genetic algorithm computation process;

[0034] Figure 4 This refers to the optimization process using a genetic algorithm.

[0035] Figure 5 Comparison of wheel weight deviation rates after adding shims and adjusting springs in SIMPACK simulation;

[0036] Figure 6 Comparison of axle load deviation rates after adding shims and adjusting springs in a SIMPACK simulation. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0038] This invention discloses a method for adjusting springs by adding pads to balance the axle load of a five-module floating wheel.

[0039] The structure of the five-module floating car is as follows: Figure 2 , 3 As shown, it has 26 rigid bodies in total, including five car body sections, three bogie frames, six axle bridges, and twelve independent wheels.

[0040] Please refer to Figure 1 The method for adding pads and adjusting springs to the five-module floating car includes the following steps:

[0041] S1. Establish a five-module floating vehicle SIMPACK mechanical model.

[0042] In this invention, based on the lateral, longitudinal, and vertical coupling of the wheel-rail contact, suspension stiffness, and damping nonlinear characteristics of the five-module floating car, the parameters of the car body, bogie, and wheelsets, the car hinge position, and the stiffness parameters of the fixed hinge, elastic hinge, and free hinge, a SIMPACK mechanical model of the five-module floating car is established in the SIMPACK software.

[0043] The car body, bogie, and axle each have 6 degrees of freedom: longitudinal, lateral, vertical, roll, pitch, and yaw. The left and right independent wheels each have one pitch degree of freedom, for a total of 96 degrees of freedom for the entire vehicle system.

[0044] Assuming the wheel and rail types are LM and China60kg respectively, the SIMPACK mechanical model of the five-module floating car is obtained as follows: Figure 2 As shown.

[0045] S2. Based on the five-module floating car SIMPACK mechanical model, solve the spring adjustment matrix of the five-module floating car and establish the vehicle spring adjustment model.

[0046] Suppose there are n locations for adding shims and adjusting springs on the five-module floating car, and m wheel weights. Let the unit shim amount ΔX at the i-th adjusting spring position be... i =1 (i=1,2,...n), causing m wheel weight changes a ji (j=1,2,...m), then the displacement ΔX of the n adjusting springs i (i = 1, 2, ..., n) and m wheel weight changes ΔP j The relational expression between (j = 1, 2, ..., m) is the spring adjustment matrix of the five-module floating car, i.e.

[0047]

[0048] Abbreviated as

[0049] [A][ΔX] = [ΔP].

[0050] Where [A] is the spring adjustment matrix, and [△X] is the spring adjustment scheme. △P j (j=1,2,...m) represents the difference between the wheel weight before spring adjustment and the desired wheel weight. The wheel weight before spring adjustment is the actual measured value of the five-module floating wheel axle on the weighing platform. The desired wheel weight is the weight of each wheel evenly loaded, i.e., the sum of all measured wheel weights divided by the number of wheels. Therefore, the weight △P after the train is weighed is... j That is, the known items.

[0051] Using SIMPACK multibody dynamics software, a lateral, longitudinal, and vertical coupled vehicle dynamics model considering wheel-rail contact, suspension stiffness, and damping nonlinear characteristics is established. Then, the spring adjustment matrix [A] is solved, and the spring adjustment scheme [△X] is obtained based on this.

[0052] For the five-module floating car, there are a total of 12 wheel weights, 24 primary spring padding positions, and 12 secondary spring padding positions.

[0053] The steps to solve for the spring tuning matrix [A] are as follows:

[0054] For the whole vehicle model, only add a 1mm shim to position 1 of the primary spring, and calculate the change in wheel weight before and after adding the shim to obtain the first column of the spring adjustment matrix [A]; only add a 1mm shim to position 2 of the primary spring, and calculate the change in wheel weight before and after adding the shim to obtain the second column of the spring adjustment matrix.

[0055] Following the same steps as above, solve for the remaining 34 padding positions sequentially to obtain the final spring adjustment matrix [A]. Figure 2 As shown, the sum of the elements of the spring-tuning matrix ∑a ji =0 (i = 1, 2, ... 36; j = 1, 2, ... 12). Because adding pads to the first and second series springs will not cause additional forces to the system, but will only cause a redistribution of the internal forces of the system, whether the sum of the elements of the spring adjustment matrix is ​​zero is also a criterion for verifying the correctness of the spring adjustment matrix.

[0056] S3. Based on the wheel axle weight deviation requirements of the five-module floating car, the optimization index value is obtained.

[0057] The initial and spring-adjusted wheel weight test data of the vehicle were obtained through a weighing test bench.

[0058] After filtering the actual wheel load data, wheel and axle load distributions with more severe working conditions were selected. These severe working conditions were categorized as follows: wheel load deviation exceeding limits; axle load deviation exceeding limits; and both wheel load deviation and axle load deviation exceeding limits.

[0059] By analyzing the actual measured wheel load, we can obtain △Pj (j=1,2,...m), which is the difference between the measured wheel load and the expected wheel load, where the expected wheel load is the load evenly distributed across all wheels.

[0060] The ultimate goal of spring adjustment is to ensure that all parameters meet the standards when the vehicle is weighed. Therefore, the index value is based on the standard vehicle wheel and axle load deviation rate. Since a single index value is used, the wheel load deviation rate, the driving axle load deviation rate, and the trailer axle load deviation rate are considered together. Because the deviation rate percentages are different, in order to ensure that several parameters meet the standards simultaneously, the percentage of deviation rate exceeding the limit is increased to 4%, that is, when the optimal index value ζ is sought... 算法 A shim allowance of less than 4% is sufficient to ensure that all three vehicle parameters are within the standard range. Secondly, the spring shim allowance must be rounded down; a shim allowance of 4mm or less is recommended for optimal results.

[0061] Therefore, the optimal index value

[0062]

[0063] in,

[0064] This refers to the wheel load deviation rate. This refers to the dynamic axle load deviation rate. This refers to the axle load deviation rate.

[0065] S4, such as Figure 3 As shown, based on the genetic algorithm, and according to the optimization index value and the vehicle spring adjustment model, a wheel-axle weight-balanced spring adjustment scheme, including the amount and position of the shims, is calculated. The genetic algorithm calculation process is as follows: Figure 3 As shown, the algorithm first checks the axle weight deviation of each wheelset, converting the variables in the optimization problem into binary format to obtain the individual's 'DNA'. Then, it edits the population size and randomly initializes each individual, encoding and initializing the axle weight data for each wheelset. Next, it obtains the fitness of each individual using a fitness function to find the current optimal solution. It then checks if the generation number is less than or equal to the maximum allowed generation number. If the condition is not met, it exits the genetic iteration loop, outputs the optimal solution for this iteration, and terminates the algorithm. If the condition is met, it calculates the axle weight deviation of each wheelset. If the termination condition is met, i.e., ζ... 算法 If the fitness level is ≤4%, the genetic iteration terminates, the optimal solution is output, and the algorithm ends. If the termination condition is not met, the simulation of natural selection begins (individuals with higher fitness are more likely to survive), performing selection, mutation, and other genetic operations. Individuals then randomly mate and reproduce, generating random mutations during reproduction to obtain new population variations. Individual fitness is evaluated again, and the calculation loop begins. Once the termination condition is met, the calculation terminates, and the optimal solution is output. This optimal solution is the spring adjustment scheme calculated by the genetic algorithm. Because the numbers in the padding vector δ are strictly arranged according to positional order when establishing the padding vector, the calculated series of optimal solutions already know the padding positions and amounts.

[0066] In this embodiment of the program design: the selected population has 200 individuals, a chromosome length of 15, a maximum number of generations of evolution of 250, and crossover and mutation probabilities of 0.8 and 0.2, respectively. Elite selection is used in the selection process. After 14 iterations, the optimal solution of 1.1739 was found in the 15th generation. The corresponding individual chromosome, after decoding, is the value of the independent variable, i.e., the padding position at each spring. The algorithm found the global optimum in the 15th generation. The average fitness function value of the population in each generation decreased rapidly in the first 20 evolutions, and then leveled off around the 30th generation. After the 45th generation, the maximum and average fitness values ​​of the population became very flat, indicating that the algorithm had converged and the parameter settings were reasonable.

[0067] The change of the algorithm's index value with the number of algorithm iterations can be obtained using a genetic algorithm, as shown in the following results. Figure 4 As shown, the maximum wheel weight deviation decreased from 5.52% to 1.17%, and the corresponding spring shim amount and shim position were obtained, thus realizing the solution for adjusting the spring for wheel axle weight with severe deviation.

[0068] The spring adjustment scheme [△X] obtained by the genetic algorithm was verified based on the SIMPACK numerical simulation results. The wheel axle weight deviation results of the SIMPACK simulation of the padding scheme are as follows: Figure 5 and Figure 6As shown, the spring adjustment matrix obtained by establishing the SIMPACK mechanical model can significantly reduce the axle weight deviation of each wheel and meet the specification requirements, verifying the effectiveness of the spring adjustment algorithm and realizing the wheel and axle weight balance of the five-module floating car.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for balancing the axle load of a five-module floating wheel with shims and adjusting springs, characterized in that, Includes the following steps: S1. In the SIMPACK environment, establish a five-module floating car SIMPACK mechanical model that includes wheel-rail contact, suspension stiffness, and damping nonlinear characteristics. S2. Based on the five-module floating car SIMPACK mechanical model, solve the spring adjustment matrix of the five-module floating car and establish the vehicle spring adjustment model; S3. Based on the wheel axle weight deviation requirements of the five-module floating car, the optimization index value is obtained; S4. Based on the genetic algorithm, the wheel axle weight balancing spring adjustment scheme, including the amount and position of the padding, is calculated according to the optimization index value and the vehicle spring adjustment model. In step S1, the SIMPACK mechanical model of the five-module floating car is established in the SIMPACK software based on the lateral, longitudinal and vertical coupling of the wheel-rail contact, suspension stiffness and damping nonlinear characteristics of the five-module floating car, the parameters of the car body, bogie and wheelset, the car hinge position, and the stiffness parameters of the fixed hinge, elastic hinge and free hinge. In step S2, assuming there are n locations for adding springs to the five-module floating car and m wheel and axle weights, then the spring adjustment matrix is... [A]= ， The matrix elements represent the changes in wheel and axle weight at the positions where the shims are adjusted for springs on the five-module floating cars, and the sum of all elements is zero. The vehicle spring adjustment model is ， in, This is a spring adjustment scheme, where the elements are the shims added at the corresponding spring adjustment positions on the five-module floating cars. The deviation matrix consists of elements representing the difference between the measured wheel weight and the expected wheel weight of the corresponding axle of the five-module floating car, where the expected wheel weight is the average load on each wheel.

2. The method for balancing the axle load of a five-module floating wheel as described in claim 1, characterized in that, In step S3, the optimization index value is... ， in, , This refers to the wheel load deviation rate. This refers to the dynamic axle load deviation rate. This refers to the axle load deviation rate.

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