CN113591301A - 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-saving optimization. The multi-particle train operation energy consumption model fully considers the line 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 actual line position of each train of the train, meanwhile, the developed improved differential evolution algorithm dynamically distributes the range of the decision variable in the optimization process according to the mutual constraint relation, and is dedicated to searching the optimal value of the decision variable in each evolution, the global optimization capability and performance of the traditional evolution algorithm are enhanced, the energy consumption of train operation is reduced, and the theoretical basis and the engineering application guidance are provided for the 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 optimization algorithm for running parameters of an urban rail transit train.
Background
With the increasing demand of social trip, urban rail transit trains are favored by more and more cities due to high efficiency and convenience. Meanwhile, along with the annual increase of urban rail transit lines, the energy consumption of the urban rail transit lines is also increasing day by day. 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, most of the urban rail transit train energy consumption models established by the current research institute are single-substance point models, the models ignore the stress gradual change process of trains at the joint of curves and ramps, and certain errors exist between the models and the actual running conditions of the trains. On the other hand, the optimization problem of the urban rail transit train running track is a typical nonlinear optimization problem, strict constraint conditions are provided among decision variables, the initial range of the decision variables is determined mainly according to empirical values in the existing optimization algorithm, the existing optimization algorithm is easy to fall into a local optimal solution, and an ideal approximate optimal solution cannot be obtained.
Disclosure of Invention
The invention aims to provide an urban rail transit train operation parameter optimization algorithm, which solves the technical problems mentioned in the background technology and solves the energy-saving optimization problem of the urban rail transit train.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an optimization algorithm for running parameters of an urban rail transit train comprises the following steps:
step 1: dividing the running process of the train between stations into a traction stage, a cruise stage, an inertia stage and a braking stage according to a four-stage operation strategy, regarding the train as a quality band, analyzing and calculating the stress condition of each train according to the actual line position of each train, and establishing a multi-quality-point traction energy consumption model;
step 2: and optimizing key control parameters of train operation by adopting an improved differential evolution algorithm so as to minimize traction energy consumption.
Further, the specific process of establishing the multi-prime point traction energy consumption model in the step 1 is as follows:
setting the basic resistance of the train in operation to FbasicThe weight of the train is MmoWeight of trailer MtrAnd the train speed v is: fbasic(Mmo,Mtr,v)＝A+B·v+C·v2Wherein A, B and C are empirical coefficients determined from practical experience, which vary according to the type of train and the condition of the track, and the additional resistance F during the operation of the trainaddComprises the following steps: fadd＝(framp+fcurve+ftunnel)·MtotalG, in the formula, MtotalG is the gravitational acceleration, f is the total load of the trainranpAs a ramp resistance, fcurveAs resistance in bends, FtunnelFor tunnel resistance, three additional resistance calculation modes are respectively as follows:
in the formula, LtrainTotal length of train, κiIs the number of thousandths of the ramp on which the train is located lriThe length of the ramp occupied by the train,/ciThe length of the curve occupied by the train, RiIs the curve radius of the curve in which the train is located, ltiThe length of the tunnel occupied by the train is taken;
tractive effort F of train during whole operationtrComprises the following steps: ftr(Mmo,Mtr,v)＝Ftr,t∪Ftr,cTotal traction energy consumption of the train EtrComprises the following steps:
in the formula, T is the inter-station operating time, and the train traction energy consumption model is as follows:
in the formula, VlimFor interval speed limit, Δ T is the running time error, ξtFor allowable time error, Δ S is the running distance error, ξsIs an allowable distance error.
Further, the specific process of optimizing the key control parameters of train operation in step 2 is as follows:
step 2.1: selecting a traction force use coefficient alpha, a braking force use coefficient beta and a cruising speed v which are key control parameters of train operationcrInert spot ScoAnd a braking point SbrAs a decision variable, the train traction energy consumption is used as an adaptability value of an improved 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: initializing a population, setting 5 decision variables of an actual problem, and selecting an inert point S having a strict constraint relation with the rest decision variables according to the constraint relation among the decision variablescoThe gene information is used as the gene information to be optimized in the gene optimization process, and the other 4 decision variables are used as the gene information of the initial population individuals;
step 2.3: optimizing the gene of each individual population, and performing gene optimization and supplementation on each individual population;
step 2.4: selecting the optimal individuals from the new population after gene optimization as basis vectors in the variation process, and carrying out differential variation operation on the population individuals to generate a new population;
step 2.5: selecting the optimal individuals from the new population after gene optimization as target vectors in the crossing process, and carrying out 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 new population of individuals, and selecting the individuals with the optimal fitness value as the optimal individuals after evolution;
step 2.7: and judging whether the optimization termination condition is met, namely whether the evolution frequency reaches the set maximum frequency or whether the fitness value meets the requirement, if the optimization termination condition is met, stopping the evolution, outputting the objective function value of the optimal individual and the corresponding decision variable value, and if the optimization termination condition is not met, rejecting the supplemented gene information.
Further, the specific process of 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 the lazy point;
step 2.3.2: copying the initial population individuals according to the number of the 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 calculating the fitness value by adopting a binary iteration method in the interval;
step 2.3.4: and supplementing the optimal supplementary gene information found by the variation dichotomy to the initial population individuals 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-quality-point train operation energy consumption model established by the invention fully considers the line environments of ramps, curves, tunnels and the like, takes trains as quality zones, analyzes and calculates the stress condition of each train according to the actual line position of each train, is closer to the real operation environment of the trains compared with the common single-quality-point model in the conventional research results, and simultaneously, 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 is dedicated to searching the optimal value of the decision variables in each evolution, thereby enhancing the global optimization 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-phase steering strategy of the present invention;
FIG. 3 is a diagram of the stress conditions of the multi-quality-point train model at different junctions of curves and ramps;
fig. 4 is a comparison of the train running tracks before and after the 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 described in further detail below with reference to the accompanying drawings by way of examples of preferred embodiments. It should be noted, however, that the numerous details set forth in the description are merely for the purpose of providing the reader with a thorough understanding of one or more aspects of the present invention, which may be practiced without these specific details.
As shown in fig. 1-4, an optimization algorithm for operation parameters of an urban rail transit train is to establish a multi-particle train energy consumption model, and then obtain minimum operation parameters of traction energy consumption according to an improved differential evolution algorithm, which is specifically as follows: selecting key operation parameters of the train as decision variables, taking train traction energy consumption as a fitness value of an algorithm, and setting algorithm parameters; initializing a population, separating decision variables according to a mutual constraint relationship, and determining gene information to be optimized; performing gene optimization on the initial population by a variation dichotomy to generate a new population; carrying out variation and cross operation on the new population, and calculating the individual fitness of the new population; and judging whether the optimization termination condition is met. The method has stronger global optimization capability and stability, and can effectively reduce the traction energy consumption of the train.
The specific implementation process comprises the following steps:
according to a four-stage operation strategy, the operation process of the train between the stations is divided into a traction stage, a cruise stage, an inertia stage and a braking stage. Considering the complex running environment of the train between stations, regarding the train as a quality band, analyzing and calculating the stress condition of the train according to the actual line position of each train, and establishing a multi-particle train traction energy consumption equation and the constraint conditions thereof as follows:
in the formula, VlimFor interval speed limit, Δ T is the running time error, ξtFor allowable time error, Δ S is the running distance error, ξsIs an allowable distance error.
Simulation verification is carried out by adopting the Nanning subway line I running data, and the running condition of the train of the actual line between the national university station and the national wide-field station 10 and 9 in the uplink direction is simulated.
The following table is the relevant train operating parameters
TABLE 1 train operating parameters
In the initial stage of the optimization process, taking national university station to qingchuan station as an example, the determination process of the initial ranges of the other 3 key control parameters is as follows:
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 the average travelling speed between the stations in the actual operation of the train, and the value of the cruising speed is 64.67 km/h; the upper limit of the braking point is the inter-station running distance, the value of the upper limit is 2014.14m, the lower limit is determined according to 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, and the lower limit of the braking point is the upper limit minus the acceleration distance, and the value is also 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 when the train accelerates from the platform to the cruising speed is the lower limit of the inertia point, and the initial range of each control parameter between 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 traction force use coefficient alpha, a braking force use coefficient beta and a cruising speed v which are key control parameters of train operationcrInert spot ScoAnd a braking point SbrAs a decision variable. Energy consumption for train traction EtrAs fitness values of the improved differential evolution algorithm, the population size P is set to 50, the maximum number of evolutions G is set to 100, the variation factor Pm is set to 0.5, and the cross factor Pc is set to 0.8.
(2) Initializing a population, setting 5 decision variables of an actual problem, and selecting an inert point S having a strict constraint relation with the rest decision variables according to the constraint relation among the decision variablescoThe gene information to be optimized in the gene optimization process is used, and the other 4 decision variables are used as the gene information of the initial population individuals.
(3) And (4) carrying out gene optimization on individual population, and carrying out gene optimization and supplementation on each individual population. 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 inert point, the number of the nodes is determined according to the value range of the inert point, and the nodes are divided by the step length of 2 m.
And (3.2) copying the initial population individuals 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 adjacent individuals as the upper limit and the lower limit of the value range of the optimal supplementary gene. In the interval, a binary iteration method is adopted, and the value of the optimal supplementary gene is determined by calculating the fitness value.
And (3.4) supplementing the optimal supplementary gene information found by the variation dichotomy to the initial population individuals to obtain a new population after gene optimization.
(4) And selecting the optimal individuals from the new population after gene optimization as basis vectors in the variation process, and carrying out differential variation operation on the population individuals to generate a new population.
(5) And (4) selecting the optimal individuals from the new population after gene optimization as target vectors in the crossing process, and carrying out crossing operation on the population individuals generated in the step (4) to generate a new population.
(6) And calculating the fitness value of the new population of individuals, and selecting the individuals with the optimal fitness value as the optimal individuals after evolution.
(7) And judging whether the optimization 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 value-optimized data-raw data)
After the train running track is optimized by adopting the improved differential evolution algorithm, 30.13 percent of traction energy consumption is saved totally, and the train running tracks before and after optimization are shown in figure 4.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (4)
1. An optimization algorithm for operation parameters of an urban rail transit train is characterized by comprising the following steps: the method comprises the following steps:
step 1: dividing the running process of the train between stations into a traction stage, a cruise stage, an inertia stage and a braking stage according to a four-stage operation strategy, regarding the train as a quality band, analyzing and calculating the stress condition of each train according to the actual line position of each train, and establishing a multi-quality-point traction energy consumption model;
step 2: and optimizing key control parameters of train operation by adopting an improved differential evolution algorithm so as to minimize traction energy consumption.
2. The urban rail transit train operation parameter optimization algorithm according to claim 1, characterized in that: the specific process of establishing the multi-quality-point traction energy consumption model in the step 1 is as follows:
setting the basic resistance of the train in operation to FbasicThe weight of the train is MmoWeight of trailer MtrAnd the train speed v is: fbasic(Mmo,Mtr,v)＝A+B·v+C·v2Wherein A, B and C are empirical coefficients determined from practical experience, which vary according to the type of train and the condition of the track, and the additional resistance F during the operation of the trainaddComprises the following steps: fadd＝(framp+fcurve+ftunnel)·MtotalG, in the formula, MtotalG is the gravitational acceleration, f is the total load of the trainranpAs a ramp resistance, fcurveAs resistance in bends, FtunnelFor tunnel resistance, three additional resistance calculation modes are respectively as follows:
in the formula, LtrainTotal length of train, κiIs the number of thousandths of the ramp on which the train is located lriThe length of the ramp occupied by the train,/ciFor the length of the curve occupied by the train，RiIs the curve radius of the curve in which the train is located, ltiThe length of the tunnel occupied by the train is taken;
tractive effort F of train during whole operationtrComprises the following steps: ftr(Mmo,Mtr,v)＝Ftr,t∪Ftr,cTotal traction energy consumption of the train EtrComprises the following steps:
in the formula, T is the inter-station operating time, and the train traction energy consumption model is as follows:
minEtr
in the formula, VlimFor interval speed limit, Δ T is the running time error, ξtFor allowable time error, Δ S is the running distance error, ξsIs an allowable distance error.
3. The urban rail transit train operation parameter optimization algorithm according to claim 1, characterized in that: the specific process of optimizing the key control parameters of train operation in the step 2 is as follows:
step 2.1: selecting a traction force use coefficient alpha, a braking force use coefficient beta and a cruising speed v which are key control parameters of train operationcrInert spot ScoAnd a braking point SbrAs a decision variable, the train traction energy consumption is used as an adaptability value of an improved 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: initializing a population, setting 5 decision variables of an actual problem, and selecting an inert point S having a strict constraint relation with the rest decision variables according to the constraint relation among the decision variablescoThe gene information is used as the gene information to be optimized in the gene optimization process, and the other 4 decision variables are used as the gene information of the initial population individuals;
step 2.3: optimizing the gene of each individual population, and performing gene optimization and supplementation on each individual population;
step 2.4: selecting the optimal individuals from the new population after gene optimization as basis vectors in the variation process, and carrying out differential variation operation on the population individuals to generate a new population;
step 2.5: selecting the optimal individuals from the new population after gene optimization as target vectors in the crossing process, and carrying out 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 new population of individuals, and selecting the individuals with the optimal fitness value as the optimal individuals after evolution;
step 2.7: and judging whether the optimization termination condition is met, namely whether the evolution frequency reaches the set maximum frequency or whether the fitness value meets the requirement, if the optimization termination condition is met, stopping the evolution, outputting the objective function value of the optimal individual and the corresponding decision variable value, and if the optimization termination condition is not met, rejecting the supplemented gene information.
4. The urban rail transit train operation parameter optimization algorithm according to claim 3, wherein: 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 the lazy point;
step 2.3.2: copying the initial population individuals according to the number of the 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 calculating the fitness value by adopting a binary iteration method in the interval;
step 2.3.4: and supplementing the optimal supplementary gene information found by the variation dichotomy to the initial population individuals to obtain a new population after gene optimization.
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