CN113326961B - Integrated optimization method for tramcar-mounted energy storage configuration and ground charging scheme - Google Patents

Abstract

The invention discloses an integrated optimization method for the on-board energy storage configuration of a tram and a ground charging scheme. The steps of the method are as follows: establish a basic data module, which includes a line data module, a train attribute data module, an ATO parameter module, a supercapacitor parameter module, and a charging station parameter module; establish a train operation simulation module, including a vehicle-mounted ATO model and a train model , on-board supercapacitor model, platform charging device model, train status update calculation model and train operating energy consumption calculation model; the optimization of on-board energy storage configuration and ground charging scheme is regarded as a multi-objective optimization problem, and the genetic algorithm NSGA-II is used for on-board storage. The integrated optimization of energy configuration and ground charging scheme, NSGA-II solves to obtain a uniformly distributed Pareto solution set, so as to determine the optimal on-board energy storage configuration and ground charging scheme. The method of the invention provides data and theoretical support for train type selection and line design in engineering projects, and has high use value and application prospect.

Description

Integrated optimization method of on-board energy storage configuration and ground charging scheme for trams

technical field

The invention relates to the technical field of urban rail transit, in particular to an integrated optimization method for on-board energy storage configuration and ground charging scheme of trams.

Background technique

The traditional catenary-powered tram lines have strict requirements on the clearance height, which not only poses certain safety hazards in the downtown section, but also adversely affects the expansion of the future line network. Therefore, the combination of on-board energy storage devices and catenary Power supply has become the current development trend. For this power supply mode, it is very important to reasonably select the on-board energy storage configuration and ground charging scheme in combination with the actual conditions of the line and the characteristic parameters of the train.

The existing research on on-board energy storage configuration and ground charging scheme is still immature, and has the following shortcomings: (1) The optimization of on-board energy storage configuration and ground charging scheme for modern trams is a multi-objective optimization problem. The method of weighting coefficients converts it into a single-objective optimization problem, although it simplifies the research of the problem, but the setting of the coefficients cannot guarantee the scientificity. Multi-objective optimization problems are analyzed. (2) The traversal method is used for the optimization of the ground charging scheme in the existing research, which is not suitable for long lines with multiple stations, and the search speed of the global optimal solution is low. (3) The train runs back and forth on the line, so the optimization simulation should not only consider the downward running process or the upward running process, but should consider the upward and downward running process as a whole.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a scientific, reliable and efficient integrated optimization method for on-board energy storage configuration and ground charging scheme of trams.

The technical solution for realizing the purpose of the present invention is: an integrated optimization method for on-board energy storage configuration and ground charging scheme of trams, comprising the following steps:

Step 1, establish a basic data module, the basic data module includes a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module, and a charging station parameter module;

Step 2, establishing a train operation simulation module, including an on-board ATO model, a train model, an on-board super capacitor model, a platform charging device model, a train state update calculation model and a train operation energy consumption calculation model;

Step 3: Taking the optimization of the on-board energy storage configuration and the ground charging scheme as a multi-objective optimization problem, the genetic algorithm NSGA-II is used to optimize the integration of the on-board energy storage configuration and the ground charging scheme, and NSGA-II is solved to obtain a uniformly distributed Pareto solution set. , so as to determine the optimal on-board energy storage configuration and ground charging scheme.

Compared with the prior art, the present invention has the following significant advantages: (1) from the perspective of integrated optimization, the on-board energy storage configuration and ground charging scheme optimization are analyzed as a multi-objective optimization problem; (2) the genetic algorithm is used to analyze NSGA-II is applied to the integrated optimization of on-board energy storage configuration and ground charging scheme. It adopts fast non-dominated sorting algorithm, crowded distance and crowded degree comparison operators, and elite and fitness sharing strategies, so that the final designed on-board energy storage configuration is similar to The ground charging scheme meets the requirements of the non-dominant standard, and at the same time, NSGA-II solves the uniformly distributed Pareto solution set, which is convenient to choose the most suitable on-board energy storage configuration and ground charging scheme based on the actual situation; Considering it as a whole, rather than optimizing only for the upward running process or the downward running process, the final on-board energy storage configuration and ground charging scheme are scientific and reliable.

Description of drawings

FIG. 1 is a structural diagram of the integrated optimization method of the on-board energy storage configuration and ground charging scheme of a tram according to the present invention.

FIG. 2 is a schematic diagram of the overall structure of the train running simulation module in the present invention.

FIG. 3 is a flow chart of the Pareto solution of the on-board energy storage configuration and ground charging scheme solved by NSGA-II in the present invention.

Detailed ways

The integrated optimization method for the on-board energy storage configuration of the tram and the ground charging scheme of the present invention includes the following steps:

Step 1, establish a basic data module, the basic data module includes a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module, and a charging station parameter module;

Step 2, establishing a train operation simulation module, including an on-board ATO model, a train model, an on-board super capacitor model, a platform charging device model, a train state update calculation model and a train operation energy consumption calculation model;

Step 3: Taking the optimization of the on-board energy storage configuration and the ground charging scheme as a multi-objective optimization problem, the genetic algorithm NSGA-II is used to optimize the integration of the on-board energy storage configuration and the ground charging scheme, and NSGA-II is solved to obtain a uniformly distributed Pareto solution set. , so as to determine the optimal on-board energy storage configuration and ground charging scheme.

Further, the basic data module described in step 1 includes a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module, and a charging station parameter module, and the five modules are all data input modules, which are train operation simulation modules. Provide initial parameters, where:

Line data module, divided into station data, ramp data, curve data, speed limit data, catenary layout and charging station layout;

The train attribute data module provides basic operating parameters of train operation, including train formation, passenger capacity, basic resistance parameters, inverter efficiency, and traction braking characteristics;

The ATO configuration module configures the basic characteristic quantities of the ATO system, including the maximum traction acceleration, the maximum braking acceleration, the maximum impact limit, and the maximum running speed;

The supercapacitor parameter module provides the basic electrical parameters of the vehicle supercapacitor, including supercapacitor capacitance, maximum operating voltage, minimum operating voltage, and energy conversion efficiency;

The charging station parameter module provides basic electrical parameters of the station charging device, including charging current and energy conversion efficiency.

Further, the establishment of the train operation simulation module described in step 2 includes the on-board ATO model, the tram mechanics model, the on-board super capacitor model, the platform charging device model, the train state update calculation model and the train operation energy consumption calculation model, wherein:

On-board ATO model: Calculate the current train acceleration, realize the maintenance or transfer of the train condition, and transfer the acceleration value to the tram mechanics model;

Tram mechanics model: Calculate the traction or braking force of the train according to the acceleration data provided by the on-board ATO model, and transmit the traction or braking force value to the train running energy consumption calculation model and the train state update calculation model;

Vehicle supercapacitor model: Calculate the current voltage, current and power of the supercapacitor according to the supercapacitor parameters, train operating power requirements, the layout of the catenary segment and the layout of the platform charging device;

Platform charging device model: charging the on-board super capacitor according to the parameters of the charging device;

Train status update calculation model: According to the data provided by the tram mechanics model, perform dynamic operations to determine the current speed, running distance and running time of the train, and transmit the calculation results to the on-board ATO model;

Train operation energy consumption calculation model: According to the data provided by the tram mechanics model, the interval running time and traction energy consumption of the train are calculated.

Further, in step 3, the optimization of on-board energy storage configuration and ground charging scheme is regarded as a multi-objective optimization problem, and the genetic algorithm NSGA-II is used to optimize the integration of on-board energy storage configuration and ground charging scheme, and NSGA-II is solved to obtain a uniform distribution. The Pareto solution set of , so as to determine the optimal on-board energy storage configuration and ground charging scheme, as follows:

(1) Coding: Real number coding is used, and the objects of coding are on-board energy storage configuration and ground charging scheme;

(2) Determine the population size: determine the population size and iteration algebra according to the length of the interval;

(3) Set the population fitness equation: max{f _total1 ,f _total2 }, where f _total1 is the optimization evaluation function of the on-board energy storage configuration, and f _total2 is the optimization evaluation function of the ground charging scheme. The specific form is as follows:

Among them, pi is the traffic situation of the train in section _i , 1≤i≤n _section , if the train can pass through this section normally, the value of _pi is 1, otherwise the value of pi is 0; _{n section} is the one-way line of the line The number of intervals; x is the total value of the rated effective energy of the super capacitor, the unit is kWh, and the range is E _min ≤ x ≤ E _max ; E _min and E _max are the minimum and maximum value of the total rated effective energy of the super capacitor, respectively; y is the number of installed platform charging devices, 0≤y≤n _section +1; E _extra is the lowest point of supercapacitor SOE during train operation, which is a percentage;

(4) Calculate the individual fitness value of the parent population: calculate the individual fitness value of the parent population by the train operation simulation module described in step 2;

(5) Genetic operation: The genetic operation includes selection, crossover and mutation. The selection operation adopts the championship selection operator, the crossover operation adopts the simulated binary crossover, and the mutation operation adopts polynomial mutation to generate subpopulations;

(6) Calculate the individual fitness value of the sub-population: calculate the individual fitness value of the sub-population by the train operation simulation module described in step 2;

(7) Generating the next generation parent population: The parent population and the child population participate in the competition together, and adopt the elite and fitness value sharing strategy to obtain the next generation parent population;

(8) Judging whether the iteration satisfies the termination condition: Judging whether the iteration algebra reaches the maximum iteration algebra, if it is reached, it will end and enter (9), if not, return to (5);

(9) Output the optimal on-board energy storage configuration and ground charging scheme: select the on-board energy storage configuration and ground charging scheme using non-dominant criteria, energy consumption sensitivity criteria and time uniform distribution criteria.

Further, the object of the encoding described in step (1) is the on-board energy storage configuration and the ground charging scheme, specifically: for the on-board energy storage configuration, a real-number encoding method is adopted, and the total value of the rated effective energy is directly used as a real-number encoded value; for The ground charging scheme adopts the binary coding method. Since each station has two situations of installation or non-installation, directly use 0 to indicate no installation, and 1 to indicate installation.

Further, in step (2), the population size and iterative algebra are determined according to the interval length, specifically: when the interval length is less than 600m, the population size is set to 60; when the interval length is greater than 600m and less than 1000m, the population size is set to 80; When the interval length is greater than 1000m, the population size is set to 100; the iteration algebra is set to 100.

Further, the calculation steps of the individual fitness value in step (4) and step (6) include:

(a) Take the i-th individual in the population and decode the vehicle-mounted energy storage configuration and ground charging scheme corresponding to the individual. The initial value of i is 0;

(b) Transfer the on-board energy storage configuration and ground charging scheme decoded by individual chromosomes to the train operation simulation module;

(c) Carry out train operation simulation: the train operation simulation module performs operation simulation, and calculates the individual fitness value according to the train operation state information;

(d) Save the fitness value of the individual: the fitness 1 of the individual is the optimal evaluation function value of the on-board energy storage configuration, and the fitness of the individual 2 is the optimal evaluation function value of the ground charging scheme;

(e) Determine whether the current individual is the last individual in the population: if it is the last individual, the calculation ends; otherwise, i=i+1, and jump to (a).

Further, adopting the elite and fitness value sharing strategy described in step (7) to obtain the next generation parent population, specifically: combining the parent population and the child population generated by the parent population, and competing together to generate the next generation parent population, Make the excellent individuals in the parent generation into the next generation, and the optimal individual will not be lost.

The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

1, the integrated optimization method of the on-board energy storage configuration and ground charging scheme of the modern tram of the present invention includes the following steps:

Step 1, establish a basic data module;

The basic data module includes a line data module, a train attribute data module, an ATO parameter module, a supercapacitor parameter module, and a charging station parameter module. These five modules are all data input modules, which integrate the on-board energy storage configuration with the ground charging scheme. Optimization provides initial parameters, where:

The train attribute data module provides basic operating parameters of train operation, including train formation, passenger capacity, basic resistance parameters, inverter efficiency, traction braking characteristics and other parameters;

The ATO configuration module configures the basic characteristic quantities of the ATO system, including parameters such as maximum traction acceleration, maximum braking acceleration, maximum impact limit, and maximum operating speed;

The supercapacitor parameter module provides the basic electrical parameters of the vehicle supercapacitor, including the supercapacitor capacitance, maximum working voltage, minimum working voltage, energy conversion efficiency and other parameters;

The charging station parameter module provides the basic electrical parameters of the charging device on the station, including parameters such as charging current and energy conversion efficiency.

Step 2, establish an evaluation module for on-board energy storage configuration and ground charging scheme, and evaluate the pros and cons of the current on-board energy storage configuration and ground charging scheme;

With reference to Figure 2, the establishment of the on-board energy storage configuration and ground charging scheme evaluation module is to establish a train operation simulation module, including:

On-board ATO model: Calculate the current train acceleration, realize the maintenance or transfer of the train condition, and transfer the acceleration value to the train model and the operation calculation model;

Train model: Calculate the traction or braking force of the train according to the acceleration data provided by the on-board ATO model, and transmit the traction or braking force value to the operation calculation model;

On-board supercapacitor model: Calculate the voltage, current and power of the current supercapacitor according to the current train's operating power requirements, the layout of the catenary segment and the layout of the platform charging device;

Platform charging device model: charge the vehicle super capacitor according to the current charging device parameter settings;

Train status update calculation model: According to the data provided by the on-board ATO model and the train model, perform dynamic operations to determine the current speed, running distance and running time of the train, and transmit the calculation results to the energy consumption and time calculation models;

Train operation energy consumption calculation model: According to the data provided by the operation calculation model, the interval running time and traction energy consumption of the train are calculated.

Step 3: Establish an integrated optimization method for on-board energy storage configuration and ground charging scheme based on multi-objective genetic algorithm NSGA-II, and determine the optimal on-board energy storage configuration and ground charging scheme, as shown in Figure 3. The specific steps are as follows:

(1) Coding: Real number coding is used, and the objects of coding are on-board energy storage configuration and ground charging scheme;

(2) Determine the population size: determine the population size and iteration algebra according to the length of the interval;

(3) Set the population fitness equation: max{f _total1 ,f _total2 }, where f _total1 is the optimization evaluation function of the on-board energy storage configuration, and f _total2 is the optimization evaluation function of the ground charging scheme. The specific form is as follows:

Among them, pi is the traffic situation of the train in the section _i (1≤i≤n _section ), if the train can pass through this section normally, the value is 1, otherwise the value is 0;

n _section is the number of one-way sections of the line;

x is the total value of the rated effective energy of the supercapacitor, the unit is kWh, and the range is _{Emin≤x≤Emax} , _Emin and _Emax are the minimum and maximum value of the total rated effective energy of the _{supercapacitor} , respectively;

y is the number of installed station charging devices, 0≤y≤n _section +1;

E _extra is the lowest point of supercapacitor SOE during train operation, and its value is a percentage;

(4) Calculate the individual fitness value of the parent population: calculate the individual fitness value of the parent population by the on-board energy storage configuration and ground charging scheme evaluation module described in step 2;

(5) Genetic operation: The genetic operation includes selection, crossover and mutation. The selection operation adopts the championship selection operator, the crossover operation adopts the simulated binary crossover, and the mutation operation adopts polynomial mutation to generate subpopulations;

(6) Calculate the individual fitness value of the sub-population: The individual fitness value of the sub-population is calculated by the on-board energy storage configuration and ground charging scheme evaluation module described in step 2. The individual fitness value in steps (4) and (6) The value calculation steps include:

(a) Take the i-th individual in the population and calculate the vehicle-mounted energy storage configuration and ground charging scheme corresponding to the individual, where the initial value of i is 0;

(b) Transfer the on-board energy storage configuration and ground charging scheme transformed by individual chromosomes to the train operation simulation module;

(c) Carry out train operation simulation: call the on-board energy storage configuration and ground charging scheme evaluation module to carry out operation simulation, and calculate the individual fitness value according to the train operation state information;

(d) Save the fitness value of the individual: the fitness 1 of the individual is the optimal evaluation function value of the on-board energy storage configuration, and the fitness of the individual 2 is the optimal evaluation function value of the ground charging scheme;

(e) Determine whether the current individual is the last individual in the population: if it is the last individual, the calculation ends; otherwise, i=i+1, and jump to (a).

(7) Generating the next-generation parent population: The parent population and the child population participate in the competition together, and adopt the elite and fitness value sharing strategy to obtain the next-generation parent population, specifically: combining the parent population with the child population generated by the parent population, Common competition produces the next generation of parent population, ensuring that the excellent individuals in the parent generation enter the next generation, and the optimal individual will not be lost.

(8) Judging whether the iteration satisfies the termination condition: Judging whether the iteration algebra reaches the maximum iteration algebra, if it is reached, end and enter (9), if not, return to (5).

(9) Output the optimal on-board energy storage configuration and ground charging scheme: select the on-board energy storage configuration and ground charging scheme using non-dominant criteria, energy consumption sensitivity criteria and time uniform distribution criteria.

Example 1

Now take a modern tram line in urban rail transit as an example, the design steps of its on-board energy storage configuration and ground charging scheme are as follows:

First, input line data, train attribute data, ATO parameters, super capacitor parameters, and charging station parameters to determine the simulation interval. If the data is correct, the computer enters the vehicle energy storage configuration and ground charging scheme design module;

Next, enter the vehicle energy storage configuration and ground charging scheme design module, the specific steps include:

Step 1: Encoding, that is, encoding each population. For the on-board energy storage configuration, the real number coding method is adopted, and the total value of its rated effective energy is directly used as the real number coding value; for the ground charging scheme, the binary coding method is adopted. Since each station has two situations, whether it is installed or not, it is directly represented by 0 Do not install, 1 means install.

Step 2: Determine the population size and generation, and initialize the first-generation parent population. Determine the population size according to the interval length. When the interval length is less than 600m, the population size is set to 60; when the interval length is greater than 600m and less than 1000m, the population size is set to 80; when the interval length is greater than 1000m, the population size is set to 100; is 100.

Step 3: Set the fitness equation of the population. The goal is to minimize the configuration of on-board energy storage and the minimum number of charging devices on the platform. The integrated optimization of on-board energy storage configuration and ground charging scheme is a two-objective optimization problem. The mathematical model is: max{f _total1 ,f _total2 }, where f _total1 is the optimization evaluation function of on-board energy storage configuration, and f _total2 is ground charging. The scheme optimization evaluation function, the specific form is as follows:

Among them, pi is the traffic situation of the train in the section _i (1≤i≤n _section ), if the train can pass through this section normally, the value is 1, otherwise the value is 0;

n _section is the number of one-way sections of the line;

x is the total value of the rated effective energy of the supercapacitor, the unit is kWh, and the range is _{Emin≤x≤Emax} , _Emin and _Emax are the minimum and maximum value of the total rated effective energy of the _{supercapacitor} , respectively;

y is the number of installed station charging devices, 0≤y≤n _section +1;

E _extra is the lowest point of supercapacitor SOE during train operation, and its value is a percentage.

Step 4: Pass the parent population to the fitness calculation model, which calculates the fitness value of each individual in the population.

Step 5: Genetic manipulation: The parent population generates a subpopulation through genetic manipulation, which mainly includes selection, crossover and mutation. The selection operation adopts the tournament selection operator, the crossover operation adopts the simulated binary crossover, and the mutation operation adopts the polynomial mutation to generate subpopulations.

Step 6: Subpopulation fitness function calculation: The subpopulation is passed to the fitness calculation model, which calculates the fitness value of each individual in the population.

Step 7: The parent population and the child population compete together, adopt the elite and fitness value sharing strategy to obtain the next generation parent population, which is conducive to ensuring that the excellent individuals in the parent generation enter the next generation, and through the classification of all individuals in the population Stored, so that the optimal individual will not be lost. At the same time, the fitness sharing strategy of NSGA-II is based on the crowding distance operator to maintain the diversity and uniform distribution of the population;

Step 8: Determine whether the iteration satisfies the termination condition.

Step 9: Using non-dominant criteria, obtain the optimal solution set of on-board energy storage configuration and ground charging scheme.

To sum up, the method of the present invention can obtain the optimal on-board energy storage configuration of the line and the solution set of the ground charging scheme, provide data and theoretical support for the selection of trains and the design of lines in engineering projects, and has high use value and reliability. application prospects.

Claims (6)

1. an integrated optimization method for the on-board energy storage configuration of a tram and a ground charging scheme, is characterized in that, comprises the following steps: Step 1, establish a basic data module, the basic data module includes a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module, and a charging station parameter module; Step 2, establishing a train operation simulation module, including an on-board ATO model, a train model, an on-board super capacitor model, a platform charging device model, a train state update calculation model and a train operation energy consumption calculation model; Step 3: Taking the optimization of the on-board energy storage configuration and the ground charging scheme as a multi-objective optimization problem, the genetic algorithm NSGA-II is used to optimize the integration of the on-board energy storage configuration and the ground charging scheme, and NSGA-II is solved to obtain a uniformly distributed Pareto solution set. , so as to determine the optimal on-board energy storage configuration and ground charging scheme; The basic data module described in step 1 includes a line data module, a train attribute data module, an ATO parameter module, a super capacitor parameter module, and a charging station parameter module. The five modules are all data input modules that provide initial parameters for the train operation simulation module. ,in: Line data module, divided into station data, ramp data, curve data, speed limit data, catenary layout and charging station layout; The train attribute data module provides basic operating parameters of train operation, including train formation, passenger capacity, basic resistance parameters, inverter efficiency, and traction braking characteristics; The ATO configuration module configures the basic characteristic quantities of the ATO system, including the maximum traction acceleration, the maximum braking acceleration, the maximum impact limit, and the maximum running speed; The supercapacitor parameter module provides the basic electrical parameters of the vehicle supercapacitor, including supercapacitor capacitance, maximum operating voltage, minimum operating voltage, and energy conversion efficiency; The charging station parameter module provides basic electrical parameters of the station charging device, including charging current and energy conversion efficiency; The establishment of a train operation simulation module described in step 2 includes an on-board ATO model, a tram mechanics model, an on-board super capacitor model, a platform charging device model, a train state update calculation model and a train operation energy consumption calculation model, wherein: On-board ATO model: Calculate the current train acceleration, realize the maintenance or transfer of the train condition, and transfer the acceleration value to the tram mechanics model; Tram mechanics model: Calculate the traction or braking force of the train according to the acceleration data provided by the on-board ATO model, and transmit the traction or braking force value to the train running energy consumption calculation model and the train state update calculation model; Vehicle supercapacitor model: Calculate the current voltage, current and power of the supercapacitor according to the supercapacitor parameters, train operating power requirements, the layout of the catenary segment and the layout of the platform charging device; Platform charging device model: charging the on-board super capacitor according to the parameters of the charging device; Train status update calculation model: According to the data provided by the tram mechanics model, perform dynamic operations to determine the current speed, running distance and running time of the train, and transmit the calculation results to the on-board ATO model; Train operation energy consumption calculation model: According to the data provided by the tram mechanics model, the interval running time and traction energy consumption of the train are calculated. 2 . The integrated optimization method for on-board energy storage configuration and ground charging scheme of trams according to claim 1 , wherein in step 3, the optimization of on-board energy storage configuration and ground charging scheme is regarded as a multi-objective optimization problem , the genetic algorithm NSGA-II is used to optimize the integration of on-board energy storage configuration and ground charging scheme, and NSGA-II solves to obtain a uniformly distributed Pareto solution set, thereby determining the optimal on-board energy storage configuration and ground charging scheme, as follows: (1) Coding: Real number coding is used, and the objects of coding are on-board energy storage configuration and ground charging scheme; (2) Determine the population size: determine the population size and iteration algebra according to the length of the interval; (3) Set the population fitness equation: max{f _total1 ,f _total2 }, where f _total1 is the optimization evaluation function of the on-board energy storage configuration, and f _total2 is the optimization evaluation function of the ground charging scheme. The specific form is as follows:

Among them, pi is the traffic situation of the train in section _i , 1≤i≤n _section , if the train can pass through this section normally, the value of _pi is 1, otherwise the value of pi is 0; _{n section} is the one-way line of the line The number of intervals; x is the total value of the rated effective energy of the super capacitor, the unit is kWh, and the range is E _min ≤ x ≤ E _max ; E _min and E _max are the minimum and maximum value of the total rated effective energy of the super capacitor, respectively; y is the number of installed platform charging devices, 0≤y≤n _section +1; E _extra is the lowest point of supercapacitor SOE during train operation, which is a percentage; (4) Calculate the individual fitness value of the parent population: calculate the individual fitness value of the parent population by the train operation simulation module described in step 2; (5) Genetic operation: The genetic operation includes selection, crossover and mutation. The selection operation adopts the championship selection operator, the crossover operation adopts the simulated binary crossover, and the mutation operation adopts polynomial mutation to generate subpopulations; (6) Calculate the individual fitness value of the sub-population: calculate the individual fitness value of the sub-population by the train operation simulation module described in step 2; (7) Generating the next generation parent population: The parent population and the child population participate in the competition together, and adopt the elite and fitness value sharing strategy to obtain the next generation parent population; (8) Judging whether the iteration satisfies the termination condition: Judging whether the iteration algebra reaches the maximum iteration algebra, if it is reached, it will end and enter (9), if not, return to (5); (9) Output the optimal on-board energy storage configuration and ground charging scheme: select the on-board energy storage configuration and ground charging scheme using non-dominant criteria, energy consumption sensitivity criteria and time uniform distribution criteria. 3. The integrated optimization method of the on-board energy storage configuration of the tram according to claim 2 and the ground charging scheme, it is characterized in that, the object of the coding described in step (1) is the on-board energy storage configuration and the ground charging scheme, Specifically, the real number coding method is used for the on-board energy storage configuration, and the total value of the rated effective energy is directly used as the real number coding value; for the ground charging scheme, the binary coding method is used. 0 means not installed, 1 means installed. 4. The integrated optimization method of the on-board energy storage configuration of the tram and the ground charging scheme according to claim 2, characterized in that, in step (2), the population size and iterative algebra are determined according to the interval length, specifically: When the interval length is less than 600m, the population size is set to 60; when the interval length is greater than 600m and less than 1000m, the population size is set to 80; when the interval length is greater than 1000m, the population size is set to 100; 5. The integrated optimization method of the on-board energy storage configuration of the tram and the ground charging scheme according to claim 2, wherein the calculation step of the individual fitness value in step (4) and step (6) comprises: (a) Take the i-th individual in the population and decode the vehicle-mounted energy storage configuration and ground charging scheme corresponding to the individual. The initial value of i is 0; (b) Transfer the on-board energy storage configuration and ground charging scheme decoded by individual chromosomes to the train operation simulation module; (c) Carry out train operation simulation: the train operation simulation module performs operation simulation, and calculates the individual fitness value according to the train operation state information; (d) Save the fitness value of the individual: the fitness 1 of the individual is the optimal evaluation function value of the on-board energy storage configuration, and the fitness of the individual 2 is the optimal evaluation function value of the ground charging scheme; (e) Determine whether the current individual is the last individual in the population: if it is the last individual, the calculation ends; otherwise, i=i+1, and jump to (a). 6. The integrated optimization method for the on-board energy storage configuration of the tram and the ground charging scheme according to claim 2, wherein the step (7) adopts the elite and fitness value sharing strategy to obtain the next generation parent. Population, specifically: combine the parent population with the child population generated by the parent population, and compete together to generate the next generation parent population, so that the excellent individuals in the parent generation enter the next generation, and the optimal individual will not be lost.

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