CN113591301B - Urban rail transit train operation parameter optimization algorithm - Google Patents
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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
Technical Field
The invention relates to the technical field of energy-saving optimization of urban rail transit trains, in particular to an urban rail transit train operation parameter optimization algorithm.
Background
With the increasing demands of society for traveling, urban rail transit trains are favored by more and more cities due to the high efficiency and convenience of the urban rail transit trains. Meanwhile, with the annual increase of urban rail transit lines, the energy consumption of the urban rail transit lines is also increasing. Therefore, the research on the energy-saving optimization method of the urban rail transit train has important theoretical significance and engineering application value for reducing energy consumption and realizing sustainable development of urban traffic.
The urban rail transit line environment is complex, the energy consumption model of the urban rail transit train established by the current research is mostly a simple substance point model, the model ignores the stress gradual change process of the train at the joint of a curve and a ramp, and a certain error exists between the model and the actual running condition of the train. On the other hand, the urban rail transit train running track optimization problem is a typical nonlinear optimization problem, a strict constraint condition exists among decision variables, the initial range of the decision variables is mainly determined according to an empirical value by the existing optimization algorithm, the local optimal solution is easy to fall into, and a more ideal approximate optimal solution cannot be obtained.
Disclosure of Invention
The invention aims to provide an urban rail transit train operation parameter optimization algorithm, solve the technical problems mentioned in the background art and solve the energy-saving optimization problem of the urban rail transit train.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
an urban rail transit train operation parameter optimization algorithm, the method comprising the steps of:
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: the key control parameters of train operation are optimized by adopting an improved differential evolution algorithm so as to minimize traction energy consumption.
Further, the specific process of 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 ranp 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:
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.
Further, the specific process of optimizing the key control parameters of the 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.
Further, 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.
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
the multi-particle train operation energy consumption model established by the invention fully considers the line environments such as ramps, curves and tunnels, takes the train as a quality zone, analyzes and calculates the stress condition according to the line position of each actual train of the train, is closer to the actual operation environment of the train compared with the simple substance point model commonly used in the past research results, and meanwhile, the developed improved differential evolution algorithm can dynamically allocate the range of decision variables in the optimization process according to the mutual constraint relation and aims at searching the optimal value of the decision variables in each evolution, thereby enhancing the global optimizing capability and performance of the traditional evolution algorithm, effectively reducing the energy consumption of train operation and providing theoretical basis and engineering application guidance for train energy-saving driving.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a four-stage maneuver strategy of the present invention;
FIG. 3 is a diagram of the stress situation of a multi-mass point train model of the present invention at different curve and ramp junctions;
fig. 4 is a graph comparing train movement tracks before and after optimization of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below by referring to the accompanying drawings and by illustrating preferred embodiments. It should be noted, however, that many of the details set forth in the description are merely provided to provide a thorough understanding of one or more aspects of the invention, and that these aspects of the invention may be practiced without these specific details.
As shown in fig. 1-4, an urban rail transit train operation parameter optimization algorithm firstly establishes a multi-particle train energy consumption model, and then obtains traction energy consumption minimum operation parameters according to an improved differential evolution algorithm, and the algorithm is specifically as follows: selecting key operation parameters of a train as decision variables, taking the traction energy consumption of the train as an adaptability value of an algorithm, and setting algorithm parameters; initializing a population, separating decision variables according to a mutual constraint relation, and determining gene information to be optimized; performing gene optimization on the initial population by adopting a mutation dichotomy to generate a new population; performing mutation and crossover operation on the new population, and calculating the fitness of individuals of the new population; and judging whether the optimization termination condition is met. The method has stronger global optimizing capability and stability, and can effectively reduce the traction energy consumption of the train.
The specific implementation process comprises the following steps:
the running process of the train between stations is divided into a traction phase, a cruising phase, an inertia phase and a braking phase according to a four-phase operating strategy. Considering the complex running environment of the trains among stations, regarding the trains as a mass band, analyzing and calculating the stress condition of each train according to the line position of each actual train, and establishing a multi-particle train traction energy consumption equation and constraint conditions thereof as follows:
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.
And (3) carrying out simulation verification by adopting first line operation data of the nan Ning subway, and simulating train operation conditions of actual lines from national university stations to national wide-field stations 10 and 9 in the uplink direction.
The following table is the relevant train operation parameters
TABLE 1 train operation parameters
In the initial stage of the optimization process, taking the university station to Qingchuan station as an example, the initial range of the other 3 key control parameters is determined by the following steps:
the upper limit of the cruising speed is interval speed limit, the value of the cruising speed is 80km/h, the lower limit of the cruising speed is average travel speed between stations in actual running of a train, and the value of the cruising speed is 64.67km/h; the upper limit of the braking point is the running distance between stations, the value of the upper limit is 2014.14m, the lower limit is determined according to the cruising speed, the braking process from the braking point to the station can be regarded as the acceleration process of the train from the station to the braking point, the lower limit of the braking point is the upper limit minus the acceleration distance, and the value is the upper limit of the inertia point; the lower limit of the inertia point is also determined according to the cruising speed, the distance traveled by the train from the platform to the cruising speed is the lower limit of the inertia point, and the initial range of each control parameter among the rest of the stations can be determined according to the principle.
The proposed improved differential evolution algorithm is adopted for optimization, and the process is as follows:
(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. Traction energy consumption E of train tr As fitness values for improving the differential evolution algorithm, population sizes p=50, maximum evolutionary numbers g=100, variation factors pm=0.5, and crossover factors pc=0.8 were set.
(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 As the gene information to be optimized in the gene optimization process, the other 4 decision variables are used as the gene information of the individuals in the initial population.
(3) Population individual gene optimization, and gene optimization and supplementation are carried out on each population individual. The method comprises the following specific steps:
and (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 the idle point, the number of the nodes is determined according to the value range of the idle point, and the nodes are divided by the step length of 2 m.
And (3.2) copying the individuals in the initial population according to the number of the nodes, and supplementing the gene information corresponding to each node into the individuals.
And (3.3) calculating the fitness value of each copied population, selecting an individual with the optimal fitness value as a target individual, and taking the gene information of the adjacent individuals as the upper limit and the lower limit of the optimal complementary gene value range. In the interval, a binary iteration method is adopted, and the value of the optimal complementary gene is determined by calculating the fitness value.
And (3.4) 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.
(4) And 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 in the population to generate the new population.
(5) And (3) 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 (4) to generate a new population.
(6) And calculating the fitness value of the individuals of the new population, and selecting the individuals with the optimal fitness value as the optimal individuals after evolution.
(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 requirement. If the termination condition is met, the evolution is stopped, and the objective function value of the optimal individual and the corresponding decision variable value are output. If the termination condition is not met, the supplementary gene information is removed, and the step (3) is skipped.
The optimization results are shown in the following table
Table 2 optimization results
Data format: raw data/optimized data (difference = optimized data-raw data)
After the train running track is optimized by adopting the improved differential evolution algorithm, the traction energy consumption is saved by 30.13%, and the train running track before and after optimization is shown in fig. 4.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the 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.
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