Heavy-load combined train braking optimization method, electronic equipment and medium

Abstract

The invention provides a heavy-load combination train braking optimization method, electronic equipment and a medium. According to the heavy-load combination train braking optimization method, a heavy-load combination train longitudinal dynamic model is established through a dynamic theory; optimizing braking parameters of the heavy-load combined train by adopting a particle swarm optimization algorithm, wherein the braking parameters comprise electric braking applying time, air braking applying time and the electric braking force of each train; the electric braking force and the electric braking and air braking applying time between different vehicles of the heavy-load combined train under different operation working conditions can be adjusted, the longitudinal impulse force is effectively reduced, optimization of a braking strategy is achieved, the operation safety and stability of the heavy-load combined train are improved, and the service life of the heavy-load combined train is prolonged. The system can better adapt to various road conditions and braking conditions, and accident risks are reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of vehicle braking optimization, and in particular relates to a braking optimization method, electronic equipment and medium for a heavy-load combination train. Background Art

[0002] Heavy-load combined train railway freight is highly favored as an environmentally friendly and resource-saving mode of transportation. Its advantages such as efficient transportation capacity, low cost and low carbon emissions are widely recognized and are regarded as the future development direction of railway transportation. However, with the increase in train formation scale, the increase in operating speed and the increase in vehicle weight, especially during braking on long and steep slopes, the longitudinal dynamic characteristics of heavy-load combined trains have become extremely complex, and the transmission time of the braking wave has increased significantly, resulting in a large longitudinal impact force on each locomotive and vehicle in the train. Since the performance of the basic equipment of the train cannot be greatly improved, it is very important to study the operating strategy of the train driver under braking conditions. Improper operating strategies will aggravate the deterioration of the longitudinal impulse force of the train, and in severe cases, accidents such as vehicle derailment and coupler breakage may occur.

[0003] With the increase in the number of coupled locomotives, the increase in axle weights and the deterioration of service conditions, the requirements for the driver's operating skills and the synchronization of the train control system have become higher, and the longitudinal dynamic problems generated by the train during operation have become more complex and cannot be ignored. However, existing research has mostly focused on controlling the hook force generated during the release after braking and using line slopes to reduce the longitudinal impulse force, which cannot meet the needs of various vehicle operating conditions and is difficult to provide more accurate suggestions for driver operation. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies in the prior art and to provide a heavy-load combined train braking optimization method, electronic equipment and medium, which reduces the longitudinal impulse force during braking by combining air braking and electric braking to adapt to various road conditions.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A heavy-load combined train braking optimization method includes the following processes:

[0007] A longitudinal dynamics model of a heavy-load combination train is established. The longitudinal dynamics model of a heavy-load combination train is as follows:

[0008]

[0009] With the goal of minimizing the longitudinal impulse of heavy-load combination trains, the particle swarm algorithm is used to optimize the combination of electric brake application time, air brake application time and electric brake force of each vehicle, and the optimal combination of electric brake application time, air brake application time and electric brake force of each vehicle is obtained.

[0010] Longitudinal impulse force F of heavy-load combined train C The expression is as follows:

[0011]

[0012] Among them, M is the mass matrix of the heavy-load combination train, C is the damping matrix of the heavy-load combination train, K is the stiffness matrix of the heavy-load combination train, F is the external force matrix of the heavy-load combination train, and the external forces of the heavy-load combination train include longitudinal impulse force, electric braking force, air braking force, and ramp force; q is the longitudinal displacement degree of freedom vector of the heavy-load combination train, is the first-order derivative of the longitudinal displacement degree of freedom vector of the heavy-load combination train, is the second-order derivative of the longitudinal displacement degree of freedom vector of the heavy-duty combined train; x is the longitudinal relative displacement of the front and rear vehicles, Δv is the relative speed of the front and rear vehicles, and f l (x) and f u (x) are the buffer loading and unloading characteristic curves respectively; sign is the judgment function, v f is the buffer conversion speed.

[0013] The present invention establishes a longitudinal dynamics model of a heavy-load combination train through dynamics theory, and adopts a particle swarm optimization algorithm to optimize the braking parameters of the heavy-load combination train (electric brake application time, air brake application time, and electric braking force of each vehicle). The present invention can adjust the electric braking force and the application time of electric brake and air brake between different vehicles of the heavy-load combination train under different operating conditions, effectively reduce the longitudinal impulse force, optimize the braking strategy, improve the operating safety and stability of the heavy-load combination train, enable it to better adapt to various road conditions and braking conditions, and reduce the risk of accidents.

[0014] Furthermore, the single-section vehicle dynamics model is as follows:

[0015]

[0016] Among them, m i is the mass of the vehicle in section i, c i is the damping of the i-th vehicle, k i is the vehicle stiffness of the i-th section, x i is the displacement of the i-th vehicle, is the speed of the i-th vehicle, is the acceleration of the i-th vehicle, F icis the longitudinal impulse force on the i-th vehicle, F ie is the electric braking force on the i-th vehicle, F ia is the air braking force on the i-th vehicle, F ip is the slope force exerted on the i-th vehicle, and n is the total number of vehicles.

[0017] Furthermore, the electric braking force F on the i-th vehicle ie The expression is as follows:

[0018]

[0019] Among them, v i is the speed of the i-th vehicle.

[0020] Furthermore, the air braking force F on the i-th vehicle ia The expression is as follows:

[0021]

[0022]

[0023] in, is the friction coefficient corresponding to the brake shoe, n k The number of brake shoes installed on the vehicle; z is the brake cylinder diameter, P i B (t) 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.

[0024] Based on the same inventive concept, the present invention also provides an electronic device, including:

[0025] one or more processors;

[0026] A memory having one or more programs stored thereon, which, when executed by the one or more processors, enables the one or more processors to implement the steps of the heavy-load combination train braking optimization method.

[0027] Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing a computer program, which implements the steps of the heavy-load combination train braking optimization method when executed by a processor.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention establishes a longitudinal dynamics model of a heavy-load combination train through dynamics theory, and adopts a particle swarm optimization algorithm to optimize the braking parameters of the heavy-load combination train (electric brake application time, air brake application time, and electric braking force of each vehicle). The present invention can adjust the electric braking force between different vehicles of the heavy-load combination train under different operating conditions and the application time of electric brake and air brake, effectively reduce the longitudinal impulse force, optimize the braking strategy, improve the operating safety and stability of the heavy-load combination train, enable it to better adapt to various road conditions and braking conditions, and reduce the risk of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a structural schematic diagram of a heavy-load combination train;

[0031] Figure 2 Loading and unloading characteristic curve diagrams for buffers;

[0032] Figure 3 It is a schematic diagram of the optimization result of the longitudinal impulse of the present invention;

[0033] Figure 4 It is a schematic flow chart of the braking optimization method of a heavy-load combination train of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be described in detail below in conjunction with the embodiments. It should be noted that the embodiments and features of the embodiments of the present invention can be combined with each other without conflict. For the convenience of description, if the words "upper", "lower", "left" and "right" appear in the following, they only indicate that the upper, lower, left and right directions are consistent with the drawings themselves, and do not limit the structure.

[0035] Example

[0036] like Figure 4 The heavy-load combination train braking optimization method of this embodiment includes the following steps:

[0037] 1. According to Figure 1 , construct a longitudinal dynamic model of a heavy-load combination train taking into account air braking force, electric braking force, ramp force, curve resistance, running resistance and coupler buffer, etc.; establish a function expression for the electric braking force of a heavy-load combination train and a function expression for the air braking force of a heavy-load combination train.

[0038] For the air brake and electric brake models, the air brake of heavy-duty truck vehicles currently adopts the traditional tread braking method, and the electric brake of locomotive vehicles adopts the regenerative braking method. The braking force formed by the air brake system and the electric brake system is simulated through function expressions, thereby realizing the transmission of braking force during the braking process of heavy-duty combination trains.

[0039] F ie=F(v i )

[0040] In the formula, F ie is the electric braking force of the i-th vehicle, v i is the speed of the i-th vehicle.

[0041]

[0042] In the formula, F ia is the air braking force on the i-th vehicle, K i is the brake shoe pressure on each brake shoe, is the friction coefficient corresponding to the brake shoe, n k The number of brake shoes fitted to the vehicle.

[0043] The single-section vehicle dynamics model is:

[0044]

[0045] Among them, m i is the mass of the vehicle in section i, c i is the damping of the i-th vehicle, k i is the vehicle stiffness of the i-th section, x i is the displacement of the i-th vehicle, is the speed of the i-th vehicle, is the acceleration of the i-th vehicle, F ic is the longitudinal impulse force on the i-th vehicle, F ie is the electric braking force on the i-th vehicle, F ia is the air braking force on the i-th vehicle, F ip is the slope force exerted on the i-th vehicle, and n is the total number of vehicles.

[0046] The longitudinal dynamics model of heavy-duty combined train is established based on the single-section vehicle dynamics model:

[0047]

[0048]

[0049] F i =F ic +F ie +F ia +F ip ;

[0050] Among them, M is the mass matrix of the heavy-load combination train, C is the damping matrix of the heavy-load combination train, K is the stiffness matrix of the heavy-load combination train; F is the external force matrix of the heavy-load combination train, including longitudinal impulse force, electric braking force, air braking force, ramp force, etc.; q is the longitudinal displacement degree of freedom vector of the heavy-load combination train, is the first-order derivative of the longitudinal displacement degree of freedom vector of the heavy-load combination train, It is the second-order derivative of the longitudinal displacement degree of freedom vector of the heavy-load combination train.

[0051] The buffer model is the core of the longitudinal dynamics model, connecting the front and rear vehicles. The longitudinal impulse force between vehicles can be determined by applying the buffer velocity conversion nonlinear numerical model. The longitudinal impulse force of heavy-load combination trains is:

[0052]

[0053] In the formula, F C is the longitudinal impulse force of the heavy-load combined train, x is the longitudinal relative displacement of the front and rear vehicles, Δv is the relative speed of the front and rear vehicles, and f l (x) and f u (x) are the buffer loading and unloading characteristic curves, respectively. Figure 2 As shown; sign is the judgment function, v f is the buffer conversion speed.

[0054] Longitudinal impulse force is the force generated on the coupler buffers between vehicles and between locomotives and vehicles when a train or a group of locomotives is in motion.

[0055] For the braking system, the train uses the regenerative braking method in dynamic braking and the brake shoe braking method in friction braking. During the braking process, the regenerative braking converts the train's kinetic energy into electrical energy through the motor, and then feeds the electrical energy back to the power grid for use by other trains. In the early stage of regenerative braking, the traction motor is converted into a generator, and the electrical energy generated by the train braking is converted and transmitted back to the third rail (or contact network) and supplied to the auxiliary system of the train. The air brake presses the brake shoe against the wheel tread through the brake cylinder piston and the basic brake device, thereby generating friction at the contact interface between the brake shoe and the wheel to form a braking force.

[0056] The electric brake system adopts regenerative braking. The electric braking force F ie The expression is as follows:

[0057]

[0058] Among them, v i is the speed of the i-th vehicle.

[0059] The air brake system uses brake shoe friction braking, and the brake shoe pressure on each brake shoe is:

[0060]

[0061] Where, d z is the brake cylinder diameter, Pi B (t) 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] 2. The particle swarm optimization algorithm is used to construct a braking strategy optimization method for the distribution of electric braking force and the matching of braking application time for heavy-load combination trains; with the application time of electric braking and air braking of the train and the size of electric braking force as variables, and reducing the longitudinal impulse of the heavy-load combination train as the optimization goal, optimization calculations are performed to achieve the optimal matching of the application time of electric braking and air braking of the heavy-load combination train and the optimal distribution of electric braking force, effectively reducing the longitudinal impulse of the train.

[0063] The particle swarm algorithm considers the speed and displacement of each particle (variable); each particle searches for the optimal solution in the search space and records it as the current individual extreme value; the individual extreme value is shared with other particles in the entire particle swarm, and the optimal individual extreme value is found as the current global optimal solution of the entire particle swarm; all particles in the particle swarm adjust their speed and position according to the current individual extreme value they have found and the current global optimal solution shared by the entire particle swarm. Among them, the particle displacement and speed iteration format is as follows:

[0064]

[0065] Where ω is the inertia factor, v′ i is the particle speed, rand() is a random number between (0,1), x′ i is the current position of the particle, c1 and c2 are learning factors, pbest i and gbest i is an extreme value.

[0066] During the calculation process, the number of calculation iterations is set to start searching for the optimal parameters; in the process of continuous iterations, the particles continuously update their own speed and position until the set number of iterations is reached or the solution accuracy meets the requirements, the results are optimized and the position of the particles is obtained.

[0067] Taking the application time of the train's electric brake and air brake and the size of the electric brake force as variables, and minimizing the longitudinal impulse of the heavy-load combination train as the optimization goal, the optimal matching of the application time of the electric brake and air brake of the heavy-load combination train and the optimal distribution of the electric brake force are achieved, effectively reducing the longitudinal impulse of the train.

[0068] The specific steps are as follows: First, the train's electric brake and air brake application time (t e and t a) and the braking force of each vehicle are used as optimization variables. Next, by establishing the dynamic model of the train to establish the objective function, the braking process of the train under different electric braking and air braking conditions can be simulated, and the longitudinal impact force in each case can be calculated. The particle swarm optimization algorithm randomly initializes a group of particles at the beginning, and each particle represents a solution (that is, a specific set of braking time and braking force parameters). The position of each particle is the combination of braking time and braking force. They calculate the objective function based on their current position (decision variable), that is, under these time and force conditions, each particle will calculate the corresponding objective function value, simulate the braking process of the train, and calculate the longitudinal impact force or acceleration.

[0069] As the algorithm progresses, each particle will continuously adjust its speed and position according to its historical best position and the group's best position, moving closer to the target of a smaller longitudinal impact force. After multiple iterations, the particle swarm will gradually converge and find the best parameter combination to minimize the longitudinal impact force. Ultimately, the optimal solution output by the algorithm is the electric brake and air brake application time and the electric brake force between different locomotives that can produce the minimum longitudinal impact force under given constraints. The optimization results for the longitudinal impulse force are shown in Figure 2. Figure 3 .

[0070] The braking strategy optimization method for heavy-load combination trains also includes: based on the longitudinal impulse force evaluation index, using the longitudinal dynamics model of the heavy-load combination train and the braking optimization method to simulate and optimize the parameters in the braking strategy (distribution of the electric braking force of the master and slave locomotives, the application time of the electric brake and the air brake, etc.).

[0071] Simulate and optimize the braking strategy parameters of heavy-load combination trains (distribution of electric braking force between master and slave locomotives, application time of electric braking and air braking, etc.), and the longitudinal acceleration of the vehicle a≤10m / s 2 When the vehicle is in operation, including normal braking, the maximum coupler force is less than or equal to 1000kN.

[0072] The heavy-load combination train braking optimization method of this embodiment establishes a longitudinal dynamic model of the heavy-load combination train through dynamics theory, and adopts a particle swarm optimization algorithm to obtain the optimal braking parameters of the heavy-load combination train, thereby improving the operating safety and stability of the heavy-load combination train, enabling it to better adapt to various road conditions and braking conditions, and reducing the risk of accidents.

[0073] The optimized braking strategy can not only improve the safety and stability of freight cars, but also help improve the overall operating efficiency of heavy-load combination trains and reduce the energy consumption of heavy-load combination trains during transportation.

[0074] Under the premise of air-electric combined braking mode and ensuring the safety of train operation, this braking strategy fully utilizes the electric braking force (regenerative braking) of the locomotive to improve the braking capacity. It can convert kinetic energy into electrical energy and input it into the power grid, thereby recovering energy and improving the transportation efficiency and economy of the train.

[0075] Another embodiment of the present invention provides an electronic device, including:

[0076] one or more processors;

[0077] A memory having one or more programs stored thereon, which, when executed by one or more processors, enables the one or more processors to implement the steps of the heavy-load combination train braking optimization method.

[0078] In some implementations, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0079] In some other implementations, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors of various types, which are not limited herein.

[0080] Another embodiment of the present invention provides a computer-readable storage medium storing a computer program, which implements the steps of the heavy-load combination train braking optimization method when the computer program is executed by a processor.

[0081] The contents explained in the above embodiments should be understood as these embodiments are only used to more clearly illustrate the present invention, and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

Claims

1. A heavy-load combination train braking optimization method, characterized in that: The process includes: A longitudinal dynamics model of a heavy-load combination train is established. The longitudinal dynamics model of a heavy-load combination train is as follows: With the goal of minimizing the longitudinal impulse of heavy-load combination trains, the particle swarm algorithm is used to optimize the combination of electric brake application time, air brake application time and electric brake force of each vehicle, and the optimal combination of electric brake application time, air brake application time and electric brake force of each vehicle is obtained. Longitudinal impulse force F of heavy-load combined train C The expression is as follows: Among them, M is the mass matrix of the heavy-load combination train, C is the damping matrix of the heavy-load combination train, K is the stiffness matrix of the heavy-load combination train, F is the external force matrix of the heavy-load combination train, and the external forces of the heavy-load combination train include longitudinal impulse force, electric braking force, air braking force, and ramp force; q is the longitudinal displacement degree of freedom vector of the heavy-load combination train, is the first-order derivative of the longitudinal displacement degree of freedom vector of the heavy-load combination train, is the second-order derivative of the longitudinal displacement degree of freedom vector of the heavy-duty combined train; x is the longitudinal relative displacement of the front and rear vehicles, Δv is the relative speed of the front and rear vehicles, and f l (x) and f u (x) are the buffer loading and unloading characteristic curves respectively; sign is the judgment function, v f is the buffer conversion speed.

2. The heavy-load combination train braking optimization method according to claim 1, characterized in that: The single-section vehicle dynamics model is as follows: Among them, m i is the mass of the vehicle in section i, c i is the damping of the i-th vehicle, k i is the vehicle stiffness of the i-th section, x i is the displacement of the i-th vehicle, is the speed of the i-th vehicle, is the acceleration of the i-th vehicle, F ic is the longitudinal impulse force on the i-th vehicle, F ie is the electric braking force on the i-th vehicle, F ia is the air braking force on the i-th vehicle, F ip is the slope force exerted on the i-th vehicle, and n is the total number of vehicles.

3. The heavy-load combination train braking optimization method according to claim 2 is characterized in that: The electric braking force F on the i-th vehicle ie The expression is as follows: Among them, v i is the speed of the i-th vehicle.

4. The heavy-load combination train braking optimization method according to claim 2, characterized in that: The air braking force F on the i-th vehicle ia The expression is as follows: in, is the friction coefficient corresponding to the brake shoe, n k The number of brake shoes installed on the vehicle; z is the brake cylinder diameter, P i B (t) 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. An electronic device, characterized in that: include: one or more processors; A memory having one or more programs stored thereon, which, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the steps of the method according to any one of claims 1 to 4.

6. A computer-readable storage medium, characterized in that: The computer program is stored therein, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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