Truck suspension parameter optimization method for improving train braking safety performance

Abstract

The invention discloses a truck suspension parameter optimization method for improving the train braking safety performance. The truck suspension parameter optimization method comprises the following steps that S1, a heavy-duty truck-tread braking system dynamic coupling relation model is constructed; s2, constructing a heavy-load one-dimensional train longitudinal dynamical model and a three-dimensional truck dynamical model, and establishing a large-marshalling heavy-load train dynamical model in any marshalling form; and S3, a particle swarm optimization algorithm is adopted, optimization calculation is carried out with the vehicle operation safety and stability evaluation indexes as objects, and truck suspension parameter optimal matching oriented to long and large ramp braking performance improvement is achieved. According to the method, the suspension parameters are optimized in combination with the particle swarm optimization algorithm, the braking efficiency and the safety, stability and overall operation efficiency of the truck are improved, and then the transportation efficiency and economical efficiency of the truck are improved.

Description

Technical Field

[0001] The present invention relates to the field of transportation, and in particular to a method for optimizing suspension parameters of a truck for improving the braking safety performance of a train. Background Art

[0002] Railway freight is a popular mode of transportation that is environmentally friendly and resource-saving. Its advantages such as efficient transportation capacity, low cost and low carbon emissions are widely recognized. However, with the increase in train size, operating speed and train weight, especially during braking on long and steep slopes, the dynamic characteristics of the train have become extremely complex. In addition, inherent problems such as inconsistent braking performance of front and rear vehicles, coupler deflection, coupler gap and wheel-rail irritation during braking will further affect the dynamic behavior of the vehicle during braking and aggravate the vibration of the suspension device. These complexities have led to damage to key components of heavy-duty trains, such as cracks in the coupler and the coupler tail frame, and serious damage to the buffer. Under extreme conditions, the combined action of vertical and lateral loads may lead to safety accidents such as uncoupling, hook breakage and even derailment, which directly endangers the safety of people and property. Therefore, it is crucial to study the influence of truck suspension parameters on the dynamic behavior of heavy-duty trucks during braking on long and steep slopes and optimize the suspension parameters to ensure the safe operation of trains.

[0003] At present, there is still a relative lack of research on the optimization of suspension parameters for heavy-duty trucks. In actual train operation, with the increase in train formation size, the increase in axle weight, and the deterioration of service conditions, the dynamic behavior of the vehicle itself will become more complex and cannot be ignored. Existing research mainly focuses on the optimization design of locomotive structure, but rarely involves the impact of train longitudinal impact on vehicle vibration and wheel-rail dynamic interaction. This results in the inability of existing research to accurately reflect the relationship between suspension parameters and vehicle vibration under braking, and it is difficult to provide advice for vehicle structure design. In addition, the dynamic performance of heavy-duty trucks is significantly affected by the suspension system, especially the key suspension parameters. The reasonable selection of suspension parameters directly affects the safety and stability of train operation and is of great significance. Summary of the invention

[0004] In order to solve the problem that the prior art cannot accurately reflect the relationship between suspension parameters and vehicle vibration under braking conditions and the problem of optimizing suspension parameters, the present invention proposes a truck suspension parameter optimization method with improved train braking safety performance, which can reflect the interaction between the suspension system and vehicle vibration of heavy-loaded trucks under braking conditions on long slopes, optimize key suspension parameters, and solve the above-mentioned problems.

[0005] A method for optimizing suspension parameters of a freight car for improving train braking safety performance comprises the following steps:

[0006] S1. Construct a dynamic coupling relationship model of the vehicle-tread braking system of a heavy-duty truck;

[0007] S2. Construct a one-dimensional heavy-load train longitudinal dynamics model and a three-dimensional freight car dynamics model, and establish a long and large heavy-load train dynamics model under any formation form;

[0008] S3. The particle swarm optimization algorithm is used to optimize the calculation based on the vehicle evaluation index to achieve the optimal matching of the truck suspension parameters for improving the braking performance on long and steep slopes.

[0009] Preferably, the S1 comprises the following steps:

[0010] Establish brake shoe friction brake vibration model;

[0011] Establish the functional expression of brake shoe braking torque formed by air brake.

[0012] Preferably, the brake shoe friction braking vibration model is:

[0013]

[0014] Among them, F bi is the braking force generated on the brake shoe, F st is the interaction force between the brake shoe and the structure;

[0015] Preferably, the vehicle-tread brake system dynamic coupling relationship model is:

[0016]

[0017] Among them, K i is the brake shoe pressure on each brake shoe, is the friction coefficient corresponding to the brake shoe, n k is the number of brake shoes installed on the vehicle, R0 is the wheel rolling radius, v i is the vehicle’s speed;

[0018] The vehicle-tread braking system adopts brake shoe friction braking, and the brake shoe pressure on each brake shoe is:

[0019]

[0020] Among them, d z is the brake cylinder diameter, is the brake cylinder pressure, η z is the transmission efficiency, γ z is the braking ratio, n z is the number of brake cylinders.

[0021] Preferably, the one-dimensional train longitudinal dynamics model is:

[0022]

[0023] Among them, m i is the vehicle mass, is the vehicle acceleration, F cxi-1 is the front hook force of the vehicle, F cxi is the rear hook force of the vehicle, F ri F is the vehicle running resistance, Ti is the locomotive traction force, F Di is the electric braking force of the locomotive, F Brake Air brake force.

[0024] Preferably, the three-dimensional truck dynamics model is:

[0025]

[0026] Among them, M c is the mass matrix of the vehicle body, M t is the mass matrix of the bogie, M w is the mass matrix of the wheelset, C c is the damping matrix of the vehicle body, C t is the bogie matrix of the vehicle body, C w is the damping matrix of the wheelset, K c is the stiffness matrix of the vehicle body, K t is the stiffness matrix of the bogie, K w is the stiffness matrix of the wheelset, q is the vehicle degree of freedom vector, is the first-order derivative of the vehicle degree of freedom vector, is the second-order derivative of the vehicle degree of freedom vector.

[0027] Preferably, the establishment of a dynamic model of a long and large heavy-load train in any marshaling form comprises the following steps:

[0028] In order to improve the computational efficiency, the train longitudinal dynamics model is used as the main body, the target vehicle position during braking is selected, and a three-dimensional truck vehicle is replaced at this position.

[0029] Preferably, S3 comprises the following steps:

[0030] S31. Under different types of braking processes, determining suspension parameters that need to be optimized, and determining optimization targets of these suspension parameters;

[0031] S32, initializing a particle swarm, where each particle represents a combination of suspension parameters;

[0032] S33, setting a fitness function to evaluate the influence of each set of braking parameters on the braking performance;

[0033] S34, by iteratively updating the speed and position of the particles, the suspension parameter combination is continuously adjusted to approach the global optimal solution;

[0034] S35, during the optimization process, the suspension parameters are adjusted according to the historical optimal position of the particle and the optimal position of all particles, so as to improve the stability and safety during the vertical lathe braking process, and finally the optimal suspension parameter combination that can significantly improve the braking performance is selected;

[0035] S36. Based on the vehicle evaluation index, determine the upper and lower limits of the fitness function influence index including the suspension parameters.

[0036] Preferably, the fitness function is:

[0037]

[0038] Among them, I K (K) is the influence index of suspension stiffness on braking performance. Its inverse value on performance is usually used in the fitness function, that is, the smaller the stiffness influence, the higher the fitness value; I C (C) is the influence index of suspension damping on braking performance. Its inverse value to performance is usually used in the fitness function, that is, the smaller the damping effect, the higher the fitness value; I L (L) is the influence index of the suspension installation position on the braking performance. The inverse value of the performance is usually used in the fitness function, that is, the smaller the position influence, the higher the fitness value; ω k is the weight of the suspension stiffness, ω C is the weight of the suspension damping, ω L is the weight of the suspension installation position, ω k ,ω C and ω L Used to balance the importance of different indicators during the optimization process.

[0039] The larger the value of the fitness function F, the more consistent the current suspension parameter combination is with the optimization target. By optimizing this fitness function using the particle swarm optimization algorithm, the optimal combination of suspension stiffness, damping, and installation position can be found, thereby significantly improving the safety and stability of the train during braking.

[0040] Preferably, the vehicle evaluation index includes a running stability index and a running safety index.

[0041] Beneficial effects of the present invention:

[0042] (1) The present invention establishes a one-dimensional train and a three-dimensional truck dynamics model through dynamics theory, and adopts a particle swarm optimization algorithm to obtain the optimal suspension parameters of a heavy-duty truck that is more suitable for braking on long and steep slopes, thereby improving the safety and stability of truck operation, enabling it to better adapt to various road conditions and braking conditions, and reducing the risk of accidents.

[0043] (2) The present invention can improve the performance of the braking system by establishing a dynamic coupling relationship model and a truck dynamics model and optimizing the suspension parameters in combination with a particle swarm optimization algorithm. The optimized parameters can more effectively cooperate with the operation of the braking system and improve the braking efficiency.

[0044] (3) The optimized suspension parameters of the present invention can not only improve the safety and stability of the truck, but also help to improve the overall operating efficiency of the truck. The stable suspension system can reduce the energy consumption and vibration loss of the truck during transportation, thereby improving the transportation efficiency and economy of the truck. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic flow chart of a method for optimizing suspension parameters of a freight car for improving train braking safety performance according to an embodiment of the present invention;

[0046] Figure 2 A brake shoe braking torque diagram according to an embodiment of the present invention;

[0047] Figure 3 This is a diagram of a truck vehicle model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and examples.

[0049] The present application embodiment discloses a method for optimizing the suspension parameters of a freight car for improving the train braking safety performance. Figure 1 As shown, the following steps are included:

[0050] S1. Construct a dynamic coupling relationship model of the heavy-duty truck vehicle-tread braking system.

[0051] In the brake shoe friction braking model, heavy-duty trucks usually use the traditional tread braking method. This system mainly includes basic brake devices, brake shoes, and wheels. During the braking process, the air pressure in the brake cylinder is transmitted to the basic brake device through the piston, and then the basic brake device pushes the brake shoe to close to the wheel tread, thereby generating friction to hinder the vehicle from moving forward. In the modeling process, the brake shoe and the basic brake device are regarded as a whole and connected to the frame through spring-damper force elements. At the same time, the braking force generated by the air brake system is simulated by a function expression, thereby realizing the transmission of the braking force from the brake cylinder to the wheel.

[0052] The vibration model of brake shoe friction braking is established. According to the force characteristics and motion relationship of brake shoe friction braking, the vibration model of train brake shoe friction braking is:

[0053]

[0054] Among them, F bi is the braking force generated on the brake shoe, F st is the interaction force between the brake shoe and the structure.

[0055] Establish the functional expression of brake shoe braking torque formed by air brake.

[0056] The dynamic coupling relationship model of the vehicle-tread braking system is:

[0057]

[0058] Among them, K i is the brake shoe pressure on each brake shoe, is the friction coefficient corresponding to the brake shoe, n k is the number of brake shoes installed on the vehicle, R0 is the wheel rolling radius, v i is the vehicle's speed.

[0059] like Figure 2 As shown in the figure, ∑K is the brake shoe pressure, To calculate the friction coefficient, ∑B is the braking force, ∑B′ is the creep force, and O is the center of the wheelset. For the tread braking system of heavy-duty trucks, the brake shoe braking method of friction braking is used. During the braking process, the air pressure presses the brake shoe against the wheel tread through the brake cylinder piston and the basic braking device, thereby generating friction at the contact interface between the brake shoe and the wheel to form a braking force. When braking, the train travels at a constant speed v, and the wheelset rotates at an angular velocity. Assuming that the wheelset is an isolated body and ignoring the influence of other forces, then under the action of the total brake shoe pressure, the brake shoe pressure on each brake shoe on the wheelset is:

[0060]

[0061] Among them, d z is the brake cylinder diameter, is the brake cylinder pressure, η z is the transmission efficiency, γ z is the braking ratio, n z is the number of brake cylinders.

[0062] like Figure 3 As shown in the figure, J c is the moment of inertia of the vehicle body, M c is the vehicle mass, β cNod angle for the car body, X c is the longitudinal displacement of the vehicle body, K sz is the secondary suspension vertical stiffness, C sz is the secondary suspension vertical damping, K sx is the longitudinal stiffness of the secondary suspension, C sx is the longitudinal damping of the secondary suspension, J t is the structural moment of inertia, M t is the quality of the architecture, β t Nod to the architecture, X t is the longitudinal displacement of the structure, K pz is the vertical stiffness of the primary suspension, C pz is the primary suspension vertical damping, K cx is the primary suspension longitudinal stiffness, C px is the primary suspension longitudinal damping, M w is the wheelset mass, J w is the wheelset moment of inertia, β w is the wheelset rotation angle, F p is the brake shoe braking force, F creep is the creep force, P is the wheel-rail vertical force, T w is the braking torque, X w is the longitudinal displacement of the wheelset.

[0063] The freight car model consists of a car body, bogie side frame, bolster, axle box and wheelset, and each component is connected by a suspension system. The car body is supported on the bogie side frame through a secondary suspension under the bolster, while the bogie side frame is supported on two wheelsets through a primary suspension. The axle box is mounted on the end of the wheelset through an axle box bearing. Each rigid body has a maximum of three degrees of freedom, namely longitudinal, vertical and nodal motion. In the vehicle dynamic simulation, the interaction between the components is considered and simulated by spring-damper units. In addition, the nonlinear characteristics of the secondary suspension friction damper are also considered, including the nonlinear effects of parameters such as stiffness and damping with speed and displacement.

[0064] The wheel-rail relationship is the core of connecting the vehicle system and the track system. The wheel-rail dynamic coupling acts on the vehicle system upward and on the track system downward through the wheel-rail load. Applying the Hertz nonlinear elastic contact theory, the vertical force between the wheel and the rail can be determined as:

[0065]

[0066] Among them, G is the wheel-rail contact constant, and δZ(t) is the elastic compression between the wheel and rail.

[0067] The elastic compression between the wheel and rail includes the static pressure of the wheel, which can be directly determined from the displacement of the wheel and rail at the wheel-rail contact point:

[0068] δZ=Zwi (t)-Z r (x wi , t)(i=1~4);

[0069] Among them, Z wi (t) is the displacement of the i-th wheel at time t, Z r (x wi , t) is the displacement of the rail under the i-th wheel at time t. In particular, when δZ < 0, the surface wheel and rail are separated from each other, and the wheel-rail force is 0.

[0070] When there is an uneven Z0(t) input in the wheel-rail section, the wheel-rail force expression is:

[0071]

[0072] The wheel-rail creep force is expressed by the following formula:

[0073] F creep =Q·μ;

[0074] Wherein, Q is the wheel-rail normal contact force, μ is the friction coefficient of the wheel-rail contact surface, which is determined by many complex factors, such as rail surface condition, rail surface roughness, train running speed and weather conditions, etc. In the model of this embodiment, the rail is set as a rigid rail, that is, the rail deformation is 0.

[0075] S2. Construct a one-dimensional heavy-load train longitudinal dynamics model and a three-dimensional freight car dynamics model, and establish a long and large heavy-load train dynamics model under any formation.

[0076] The one-dimensional train longitudinal dynamics model is:

[0077]

[0078] Among them, m i is the vehicle mass, is the vehicle acceleration, F cxi-1 is the front hook force of the vehicle, F cxi is the rear hook force of the vehicle, F ri F is the vehicle running resistance, Ti is the locomotive traction force, F Di is the electric braking force of the locomotive, F Brake Air brake force.

[0079] The three-dimensional truck dynamics model is:

[0080]

[0081] Among them, M c is the mass matrix of the vehicle body, M tis the mass matrix of the bogie, M w is the mass matrix of the wheelset, C c is the damping matrix of the vehicle body, C t is the bogie matrix of the vehicle body, C w is the damping matrix of the wheelset, K c is the stiffness matrix of the vehicle body, K t is the stiffness matrix of the bogie, K w is the stiffness matrix of the wheelset, q is the vehicle degree of freedom vector, is the first-order derivative of the vehicle degree of freedom vector, is the second-order derivative of the vehicle degree of freedom vector.

[0082] In order to improve the calculation efficiency, the longitudinal dynamics model of the train is taken as the main body. The target vehicle position during braking is selected and replaced with a three-dimensional freight vehicle at this position to establish a dynamic model of a long and large heavy-load train with any formation.

[0083] S3. Particle swarm optimization (PSO) algorithm is used to optimize the vehicle evaluation index and achieve the optimal matching of truck suspension parameters for improving braking performance on long and steep slopes.

[0084] S31. Under different types of braking processes (emergency braking, normal braking, etc.), determine the suspension parameters that need to be optimized, including parameters such as suspension stiffness, damping coefficient and suspension installation position, and determine the optimization targets of these suspension parameters, such as performance indicators such as braking stability, braking distance, wheel-rail interaction and vehicle vibration stability.

[0085] S32. Initialize a particle swarm, where each particle represents a combination of suspension parameters.

[0086] S33. Setting a fitness function to evaluate the influence of each set of braking parameters on the braking performance, and also evaluating the vehicle operation safety and stability indicators to ensure that the dynamic performance of the vehicle in other working conditions also meets the requirements.

[0087] In the particle swarm optimization algorithm, the fitness function is used to measure the degree of compliance of each particle (i.e., each set of suspension parameter combinations) with the optimization target. For the suspension parameter optimization problem of train freight cars, the fitness function needs to comprehensively consider multiple indicators of braking safety performance. The fitness function is usually a value calculated based on the optimization target, reflecting the performance of a specific suspension parameter combination in actual operation. In this embodiment, the fitness function is defined as follows:

[0088] Suspension stiffness: Suspension stiffness affects the contact between the wheels and the rails and the stability of the train during braking. Higher suspension stiffness may reduce the vertical vibration of the vehicle body, but too high stiffness may worsen the wheel-rail interaction.

[0089] Suspension damping: Suspension damping determines the suspension system's ability to attenuate vibrations. Appropriate damping can effectively reduce the vibration of the vehicle body and wheelsets, thereby improving stability during braking.

[0090] Suspension installation position: The suspension installation position affects the force transfer path of the suspension system, which in turn affects the stability and comfort of the train.

[0091] Set the suspension stiffness, suspension damping and suspension installation position to K, C and L respectively, and define their influence index on the vibration characteristics of the truck as I K ,I K and I L , then the fitness function F can be expressed as:

[0092]

[0093] Among them, I K (K) is the influence index of suspension stiffness on braking performance. Its inverse value on performance is usually used in the fitness function, that is, the smaller the stiffness influence, the higher the fitness value; I C (C) is the influence index of suspension damping on braking performance. Its inverse value to performance is usually used in the fitness function, that is, the smaller the damping effect, the higher the fitness value; I L (L) is the influence index of the suspension installation position on the braking performance. The inverse value of the performance is usually used in the fitness function, that is, the smaller the position influence, the higher the fitness value; ω k is the weight of the suspension stiffness, ω C is the weight of the suspension damping, ω L is the weight of the suspension installation position, ω k ,ω C and ω L Used to balance the importance of different indicators during the optimization process.

[0094] The larger the value of the fitness function F, the more consistent the current suspension parameter combination is with the optimization target. By optimizing this fitness function using the particle swarm optimization algorithm, the optimal combination of suspension stiffness, damping, and installation position can be found, thereby significantly improving the safety and stability of the train during braking.

[0095] S34. By iteratively updating the particle speed and position, the suspension parameter combination is continuously adjusted to approach the global optimal solution.

[0096] S35. During the optimization process, the suspension parameters are adjusted according to the historical optimal position of the particle and the optimal position of all particles, so as to improve the stability and safety during the vertical car braking process, and finally the optimal suspension parameter combination that can significantly improve the braking performance is selected.

[0097] S36. Based on the vehicle evaluation index, determine the upper and lower limits of the fitness function influence index including the suspension parameters.

[0098] Furthermore, the suspension parameters (stiffness, damping, installation position, etc.) of the long slope braking vehicle are simulated and optimized, and the vehicle evaluation indicators include the running stability index W and the running safety index (derailment coefficient, wheel weight reduction rate, overturning coefficient); the upper and lower limits of the fitness function influencing index should not exceed the maximum limit specified by the vehicle evaluation index.

[0099] The present invention is mainly aimed at optimizing the suspension parameters of heavy-loaded trains when braking on long and steep slopes. First, the heavy-loaded trains on long and steep downhill lines are braked by tread braking. Secondly, by analyzing the train coupler force, the vehicle position where the maximum coupler force occurs is found, and the one-dimensional vehicle here is replaced by a three-dimensional truck. Then, taking the suspension parameters of the truck as the object, and taking the truck operation safety index and stability index as the limits of the optimization parameters, the optimization algorithm is used to optimize the suspension parameters and improve the braking performance and safety of the truck.

[0100] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing the suspension parameters of a freight car for improving the train braking safety performance, characterized in that: The following steps are involved: S1. Construct a dynamic coupling relationship model of the vehicle-tread braking system of a heavy-duty truck; S2. Construct a one-dimensional heavy-load train longitudinal dynamics model and a three-dimensional freight car dynamics model, and establish a long and large heavy-load train dynamics model under any formation form; S3. The particle swarm optimization algorithm is used to optimize the calculation based on the vehicle evaluation index to achieve the optimal matching of the truck suspension parameters for improving the braking performance on long and steep slopes.

2. The method for optimizing suspension parameters of freight cars for improving train braking safety performance according to claim 1, characterized in that: The S1 comprises the following steps: Establish brake shoe friction brake vibration model; Establish the functional expression of brake shoe braking torque formed by air brake.

3. The method for optimizing suspension parameters of a freight car for improving train braking safety performance according to claim 2, characterized in that: The brake shoe friction braking vibration model is: Among them, F bi is the braking force generated on the brake shoe, F st is the interaction force between the brake shoe and the structure.

4. The method for optimizing suspension parameters of a freight car for improving train braking safety performance according to claim 3, characterized in that: The dynamic coupling relationship model of the vehicle-tread brake system is: Among them, K i is the brake shoe pressure on each brake shoe, is the friction coefficient corresponding to the brake shoe, n k is the number of brake shoes installed on the vehicle, R0 is the wheel rolling radius, v i is the vehicle’s speed; The vehicle-tread braking system adopts brake shoe friction braking, and the brake shoe pressure on each brake shoe is: Among them, d z is the brake cylinder diameter, is the brake cylinder pressure, η z is the transmission efficiency, γ z is the braking ratio, n z is the number of brake cylinders.

5. The method for optimizing suspension parameters of freight cars for improving train braking safety performance according to claim 4, characterized in that: The one-dimensional train longitudinal dynamics model is: Among them, m i is the vehicle mass, is the vehicle acceleration, F cxi-1 is the front hook force of the vehicle, F cxi is the rear hook force of the vehicle, F ri F is the vehicle running resistance, Ti is the locomotive traction force, F Di is the electric braking force of the locomotive, F Brake Air brake force.

6. The method for optimizing suspension parameters of freight cars for improving train braking safety performance according to claim 5, characterized in that: The three-dimensional truck dynamics model is: Among them, M c is the mass matrix of the vehicle body, M t is the mass matrix of the bogie, M w is the mass matrix of the wheelset, C c is the damping matrix of the vehicle body, C t is the bogie matrix of the vehicle body, C w is the damping matrix of the wheelset, K c is the stiffness matrix of the vehicle body, K t is the stiffness matrix of the bogie, K w is the stiffness matrix of the wheelset, q is the vehicle degree of freedom vector, is the first-order derivative of the vehicle degree of freedom vector, is the second-order derivative of the vehicle degree of freedom vector.

7. The method for optimizing suspension parameters of freight cars for improving train braking safety performance according to claim 6, characterized in that: The establishment of a dynamic model of a long and large-sized heavy-load train under any marshaling form comprises the following steps: Taking the train longitudinal dynamics model as the main body, the target vehicle position during braking is selected and replaced by a three-dimensional truck vehicle at that position.

8. The method for optimizing suspension parameters of freight cars for improving train braking safety performance according to claim 7, characterized in that: The S3 comprises the following steps: S31. Under different types of braking processes, determining suspension parameters that need to be optimized, and determining optimization targets of these suspension parameters; S32, initializing a particle swarm, where each particle represents a combination of suspension parameters; S33, setting a fitness function to evaluate the influence of each set of braking parameters on the braking performance; S34, by iteratively updating the speed and position of the particles, the combination of suspension parameters is continuously adjusted to approach the global optimal solution; S35, during the optimization process, the suspension parameters are adjusted according to the historical optimal position of the particle and the optimal position of all particles, so as to improve the stability and safety during the vertical lathe braking process, and finally the optimal suspension parameter combination that can significantly improve the braking performance is selected; S36. Based on the vehicle evaluation index, determine the upper and lower limits of the fitness function influence index including the suspension parameters.

9. The method for optimizing suspension parameters of freight cars for improving train braking safety performance according to claim 8, characterized in that: The fitness function is: Among them, I K (K) is the index of the influence of suspension stiffness on braking performance, I C (C) is the index of the influence of suspension damping on braking performance, I L (L) is the influence index of suspension installation position on braking performance, ω k is the weight of the suspension stiffness, ω C is the weight of the suspension damping, ω L The weight for the suspension mounting position.

10. The method for optimizing suspension parameters of freight cars for improving train braking safety performance according to claim 9, characterized in that: The vehicle evaluation index includes a running stability index and a running safety index.

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