CN113591301B - Urban rail transit train operation parameter optimization algorithm - Google Patents

Abstract

The invention discloses an urban rail transit train operation parameter optimization algorithm, which belongs to the technical field of urban rail transit train energy conservation optimization. The multi-particle train operation energy consumption model fully considers the road environments such as ramps, curves, tunnels and the like. The train is regarded as a quality band, the stress condition of the train is analyzed and calculated according to the line position of each actual train of the train, meanwhile, a developed improved differential evolution algorithm dynamically distributes the range of a decision variable in the optimization process according to the mutual constraint relation and aims at searching the optimal value of the decision variable in each evolution, the global optimizing capability and performance of the traditional evolution algorithm are enhanced, the energy consumption of train operation is reduced, and theoretical basis and engineering application guidance are provided for train energy-saving driving.

Description

An optimization algorithm for urban rail transit train operating parameters

Technical field

The invention relates to the technical field of urban rail transit train energy-saving optimization, and in particular to an urban rail transit train operating parameter optimization algorithm.

Background technique

With the increasing demand for social travel, urban rail transit trains have been favored by more and more cities due to their efficiency and convenience. At the same time, as the number of urban rail transit lines increases year by year, its energy consumption is also increasing. Therefore, studying energy-saving optimization methods for urban rail transit trains has important theoretical significance and engineering application value for reducing energy consumption and achieving sustainable development of urban transportation.

The environment of urban rail transit lines is complex. Most of the energy consumption models of urban rail transit trains established in current research are single-particle models. Such models ignore the force gradient process of the train at the junction of curves and ramps, which is inconsistent with the actual operation of the train. There are certain errors in the situation. On the other hand, the urban rail transit train trajectory optimization problem is a typical nonlinear optimization problem with strict constraints on the decision variables. The current optimization algorithm mainly determines the initial range of the decision variables based on empirical values, and it is easy to fall into local Optimal solution, a more ideal approximate optimal solution cannot be obtained.

Contents of the invention

The purpose of the present invention is to provide an urban rail transit train operating parameter optimization algorithm to solve the technical problems mentioned in this background technology and solve the energy-saving optimization problem of urban rail transit trains.

In order to achieve the above objects, the technical solutions adopted by the present invention are as follows:

An urban rail transit train operating parameter optimization algorithm, the method includes the following steps:

Step 1: According to the four-stage control strategy, the train's operation process between stations is divided into traction stage, cruising stage, inertia stage and braking stage. The train is regarded as a quality belt. According to the actual line position of each vehicle of the train, To analyze and calculate its force conditions, and establish a multi-particle traction energy consumption model;

Step 2: Use an improved differential evolution algorithm to optimize key control parameters of train operation to minimize traction energy consumption.

Further, the specific process of establishing the multi-particle traction energy consumption model in step 1 is:

The basic resistance when the train is running is set to F _basic , the weight of the train is M _mo , the weight of the trailer is M _tr and the function of the train speed v is: F _basic (M _mo ,M _tr ,v)=A+B·v +C·v ² , where A, B and C are empirical coefficients, determined based on actual experience, which change according to changes in train type and line conditions. The additional resistance F _add when the train is running is: F _add = (f _ramp +f _curve +f _tunnel )·M _total ·g, where M _total is the total load of the train, g is the gravity acceleration, f _ranp is the slope resistance, f _curve is the curve resistance, F _tunnel is the tunnel resistance, three types The calculation methods of additional resistance are: In the formula, L _train is the total length of the train, κ _i is the thousandth of the ramp where the train is located, l _ri is the length of the ramp occupied by the train, l _ci is the length of the curve occupied by the train, and R _i is The curve radius of the curve where the train is located, l _ti is the length of the tunnel occupied by the train;

The traction force F _tr of the train during the entire operation is: F _tr (M _mo ,M _tr ,v)=F _tr,t ∪F _tr,c . The total traction energy consumption E _tr of the train is: In the formula, T is the inter-station running time, then the train traction energy consumption model is:

In the formula, V _lim is the interval speed limit, ΔT is the running time error, ξ _t is the allowable time error, ΔS is the running distance error, and ξ _s is the allowable distance error.

Further, the specific process of optimizing the key control parameters of train operation in step 2 is:

Step 2.1: Select the key control parameters of train operation: traction force usage coefficient α, braking force usage coefficient β, cruising speed v _cr , inert point S _co and braking point S _br as decision variables, and train traction energy consumption as an improved differential evolution algorithm Fitness value, set the population size P, the maximum number of evolutions G, the mutation factor Pm and the crossover Pc;

Step 2.2: Population initialization. There are 5 decision variables in the actual problem. According to the constraint relationship between the decision variables, the inert point S _co that has a strict constraint relationship with the other decision variables is selected as the gene information to be optimized in the genetic optimization process. The remaining 4 Decision variables serve as genetic information of individuals in the initial population;

Step 2.3: Gene optimization of individual populations, genetic optimization and supplementation for each individual population;

Step 2.4: Select the best individual from the genetically optimized new population as the basis vector in the mutation process, and perform differential mutation operations on the population individuals to generate a new population;

Step 2.5: Select the best individual from the genetically optimized new population as the target vector in the crossover process, and perform a crossover operation on the population individuals generated in step 2.4 to generate a new population;

Step 2.6: Calculate the fitness value of individuals in the new population, and select the individual with the best fitness value as the optimal individual after evolution;

Step 2.7: Determine whether the optimization termination conditions are met, that is, whether the number of evolutions reaches the set maximum number or whether the fitness value meets the requirements. If the termination conditions are met, the evolution stops, and the objective function value of the optimal individual and its corresponding decision variable are output. value, if the termination condition is not met, the supplementary genetic information will be eliminated.

Further, the specific process of step 2.3 is:

Step 2.3.1: Divide the value range of the decision variable into j nodes according to a certain step size, and the genetic information corresponding to each node is the value of the idle point;

Step 2.3.2: Copy the initial population individuals according to the number of nodes, and add the genetic information corresponding to each node to the individuals;

Step 2.3.3: Calculate the fitness value of each population after replication, select the individual with the best fitness value as the target individual, and the genetic information of its adjacent individuals as the upper and lower limits of the optimal supplementary gene value range, here Within the interval, the binary iteration method is used to determine the value of the optimal complementary gene by calculating the fitness value;

Step 2.3.4: Supplement the optimal complementary gene information found through the mutation dichotomy method to the initial population individuals to obtain a new genetically optimized population.

Since the present invention adopts the above technical solution, it has the following beneficial effects:

The multi-mass point train operation energy consumption model established by the present invention fully considers the line environment such as ramps, curves and tunnels, regards the train as a mass zone, and analyzes and calculates its impact according to the actual line position of each vehicle of the train. Compared with the single-particle model commonly used in previous research results, it is closer to the real operating environment of the train. At the same time, the developed improved differential evolution algorithm can dynamically allocate the range of decision variables in the optimization process based on mutual constraints. It is committed to finding the optimal value of the decision variable in each evolution, which enhances the global optimization capability and performance of the traditional evolutionary algorithm, effectively reduces the energy consumption of train operation, and provides a theoretical basis and engineering for energy-saving train driving. Application guidance.

Description of the drawings

Figure 1 is a flow chart of the method of the present invention;

Figure 2 is a schematic diagram of the four-stage manipulation strategy of the present invention;

Figure 3 is a diagram of the stress conditions of the multi-particle train model of the present invention at the junctions of different curves and ramps;

Figure 4 is a comparison chart of train running trajectories before and after optimization according to the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many details listed in the specification are merely to provide the reader with a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention may be implemented without these specific details.

As shown in Figure 1-4, an urban rail transit train operating parameter optimization algorithm first establishes a multi-particle train energy consumption model, and then obtains the minimum traction energy consumption operating parameters based on an improved differential evolution algorithm. The details are as follows: Select the key operation of the train The parameters are used as decision variables, and the train traction energy consumption is used as the fitness value of the algorithm to set the algorithm parameters; the population is initialized, the decision variables are separated according to mutual constraints, and the genetic information to be optimized is determined; the mutation bisection method is used to genetically optimize the initial population to generate New population; perform mutation and crossover operations on the new population, calculate the fitness of individuals in the new population; determine whether the optimization termination conditions are met. The method involved in the present invention has strong global optimization capability and stability, and can effectively reduce the traction energy consumption of the train.

Specific implementation process:

According to the four-stage control strategy, the train's operation process between stations is divided into traction stage, cruising stage, inertia stage and braking stage. Taking into account the complex operating environment of trains between stations, the train is regarded as a mass belt, and the stress situation of each train is analyzed and calculated according to the actual line position of each train, and a multi-mass point train traction energy consumption equation and its constraints are established. for:

In the formula, V _lim is the interval speed limit, ΔT is the running time error, ξ _t is the allowable time error, ΔS is the running distance error, and ξ _s is the allowable distance error.

The operation data of Nanning Metro Line 1 were used for simulation verification to simulate the train operation conditions of the actual line in the 10 stations and 9 sections from the University of China for Nationalities Station to the Minzu Square Station in the upward direction.

The following table shows the relevant train operating parameters

Table 1 Train operating parameters

In the initial stage of the optimization process, taking Minzu University Station to Qingchuan Station as an example, the determination process of the initial range of the remaining three key control parameters is as follows:

The upper limit of the cruising speed is the interval speed limit, its value is 80km/h, the lower limit is the average travel speed between stations in actual operation of the train, its value is 64.67km/h; the upper limit of the braking point is the running distance between stations, its value is 2014.14m. The lower limit is determined based on the cruising speed. The braking process of the train from the braking point to the platform can be regarded as the acceleration process of the train from the platform to the braking point. Then the lower limit of the braking point is the upper limit minus the upper limit. To accelerate distance, this value is also the upper limit of the inert point; the lower limit of the inert point is also determined based on the cruising speed. The distance traveled by the train when accelerating from the platform to the cruising speed is the lower limit of the inert point. The initial range of the control parameters between the remaining stations It can be determined based on the above principles.

The proposed improved differential evolution algorithm is used for optimization. The process is as follows:

(1) Select the key control parameters of train operation: traction force usage coefficient α, braking force usage coefficient β, cruising speed v _cr , inert point S _co and braking point S _br as decision variables. The train traction energy consumption E _tr is used as the fitness value of the improved differential evolution algorithm, and the population size P=50, the maximum number of evolutions G=100, the mutation factor Pm=0.5, and the crossover factor Pc=0.8.

(2) Population initialization. There are 5 decision variables in the actual problem. According to the constraint relationship between the decision variables, the inert point S _co that has a strict constraint relationship with the other decision variables is selected as the gene information to be optimized in the genetic optimization process. The remaining 4 Decision variables serve as genetic information of individuals in the initial population.

(3) Gene optimization of individual populations, genetic optimization and supplementation for each individual population. Specific steps are as follows:

(3.1) Divide the value range of the decision variable into j nodes according to a certain step size. The genetic information corresponding to each node is the value of the lazy point. The number of nodes is determined according to the value range of the lazy point. Divide 2m long.

(3.2) Copy the initial population individuals according to the number of nodes, and add the genetic information corresponding to each node to the individuals.

(3.3) Calculate the fitness value of each population after replication, select the individual with the optimal fitness value as the target individual, and use the genetic information of its adjacent individuals as the upper and lower limits of the optimal supplementary gene value range. Within this interval, the binary iteration method is used to determine the value of the optimal complementary gene by calculating the fitness value.

(3.4) Supplement the optimal complementary gene information found through the mutation dichotomy method to the initial population individuals to obtain a new genetically optimized population.

(4) Select the best individual from the genetically optimized new population as the basis vector in the mutation process, and perform differential mutation operations on the individuals in the population to generate a new population.

(5) Select the best individual from the genetically optimized new population as the target vector in the crossover process, and perform a crossover operation on the population individuals generated in (4) to generate a new population.

(6) Calculate the fitness value of the new population individuals, and select the individual with the best fitness value as the optimal individual after evolution.

(7) Determine whether the optimization termination conditions are met, that is, whether the number of evolutions reaches the set maximum number or whether the fitness value meets the requirements. If the termination condition is met, the evolution stops, and the objective function value of the optimal individual and its corresponding decision variable value are output. If the termination conditions are not met, the supplementary genetic information is eliminated and jumps to step (3).

The optimization results are shown in the table below

Table 2 Optimization results

Data format: original data/optimized data (difference = optimized data – original data)

After using the improved differential evolution algorithm to optimize the train trajectory, a total of 30.13% of traction energy consumption was saved. The train trajectory before and after optimization is shown in Figure 4.

The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be made. regarded as the protection scope of the present invention.

Claims (1)

1. An urban rail transit train operation parameter optimization algorithm is characterized in that: the method comprises the following steps:

step 1: dividing the running process of the train between stations into a traction stage, a cruising stage, an inertia stage and a braking stage according to a four-stage operating strategy, regarding the train as a mass band, analyzing and calculating the stress condition of the train according to the line position of each actual train of the train, and establishing a multi-particle traction energy consumption model;

step 2: optimizing key control parameters of train operation by adopting an improved differential evolution algorithm so as to minimize traction energy consumption;

the specific process for establishing the multi-particle traction energy consumption model in the step 1 is as follows:

setting the basic resistance of the train to F _basic The weight of the train motor car is M _mo Trailer weight M _tr And train speed v is a function of: f (F) _basic (M _mo ,M _tr ,v)＝A+B·v+C·v ² Wherein A, B and C are empirical coefficients, which are determined empirically based on changes in the type of train and the line conditions, and the additional resistance F during the operation of the train _add The method comprises the following steps: f (F) _add ＝(f _ramp +f _curve +f _tunnel )·M _total G, where M _total G is gravity acceleration, f is the total load of the train _ramp For ramp resistance, f _curve For curve resistance, F _tunnel For tunnel resistance, three additional resistance calculation modes are respectively: wherein L is _train Kappa is the total length of the train _i Is the number of thousandths of the ramp where the train is located, l _ri The length of the ramp occupied by the train is l _ci R is the length of the curve occupied by the train _i Is the curve radius of the curve where the train is located, l _ti The length of the tunnel occupied by the train;

traction force F of train in whole running process _tr The method comprises the following steps: f (F) _tr (M _mo ,M _tr ,v)＝F _tr,t ∪F _tr,c Total traction energy consumption E of train _tr The method comprises the following steps:in the formula, T is the running time between stations, and then the train traction energy consumption model is:

min E _tr

wherein V is _lim For interval speed limit, ΔT is the run time error, ζ _t For allowable time error, ΔS is the travel distance error, ζ _s Is an allowable distance error;

the specific process for optimizing the key control parameters of train operation in the step 2 is as follows:

step 2.1: selecting a key control parameter of train operation, namely traction force use coefficient alpha, braking force use coefficient beta and cruising speed v _cr Inertia point S _co And a braking point S _br As decision variables, train traction energy consumption is used as an adaptability value for improving a differential evolution algorithm, and a population scale P, a maximum evolution frequency G, a variation factor Pm and a cross Pc are set;

step 2.2: the method comprises the steps of initializing a population, wherein decision variables of actual problems are 5, and selecting inert points S with strict constraint relation with the rest decision variables according to constraint relation among the decision variables _co The other 4 decision variables are used as the gene information of the individuals in the initial population as the gene information to be optimized in the gene optimization process;

step 2.3: optimizing the population individual genes, and optimizing and supplementing the genes aiming at each population individual;

step 2.4: selecting optimal individuals from the new population after gene optimization as basis vectors in the mutation process, and performing differential mutation operation on the individuals of the population to generate a new population;

step 2.5: selecting optimal individuals from the new population after gene optimization as target vectors in the crossing process, and performing crossing operation on the population individuals generated in the step 2.4 to generate a new population;

step 2.6: calculating the fitness value of the individuals of the new population, and selecting the individuals with the optimal fitness value as the evolved optimal individuals;

step 2.7: judging whether an optimized termination condition is met, namely whether the evolution times reach the set maximum times or whether the fitness value meets the requirements, if the termination condition is met, stopping evolution, outputting the objective function value of the optimal individual and the corresponding decision variable value, and if the termination condition is not met, removing the supplementary gene information;

the specific process of the step 2.3 is as follows:

step 2.3.1: dividing the value range of the decision variable into j nodes according to a certain step length, wherein the gene information corresponding to each node is the value of an idle point;

step 2.3.2: copying individuals of the initial population according to the number of nodes, and supplementing gene information corresponding to each node into the individuals;

step 2.3.3: calculating the fitness value of each copied population, selecting an individual with the optimal fitness value as a target individual, taking the gene information of adjacent individuals as the upper limit and the lower limit of the value range of the optimal supplementary gene, and determining the value of the optimal supplementary gene by adopting a binary iteration method through calculating the fitness value in the interval;

step 2.3.4: and supplementing the optimal supplementary gene information found by the mutation dichotomy to individuals in the initial population to obtain a new population after gene optimization.