Joint Optimization Configuration Method and System for On-vehicle Energy Storage System and Ground Charging System

Abstract

The present invention discloses a combined optimization configuration method and system for an on-vehicle energy storage system and a ground charging system, which are applied to the on-vehicle energy storage system and the ground charging system of a tram. The combined optimization configuration method includes: a traction calculation step: obtaining the parameter range of the on-vehicle energy storage system through traction calculation according to the target line information and the tram vehicle parameters; a combined scheme acquisition step: obtaining a plurality of combined schemes based on the parameter range according to the parameter configuration of the on-vehicle energy storage system and the parameter configuration of the charging pile; an optimized combined scheme acquisition step: taking the minimum full life cycle cost as the optimization goal, and traversing a plurality of combined schemes by using the grey wolf-event trigger state machine algorithm to obtain at least one optimized combined scheme; The present invention ensures the safe, reliable and stable operation of the on-vehicle capacitor energy storage type tram, and effectively reduces the line construction cost.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit, and more particularly to a method and system for jointly optimizing the configuration of an on-vehicle energy storage system and a ground charging system. Background Art

[0002] With the rapid development of society, people have higher and higher requirements for travel convenience. The carrying capacity of tramways is suitable for the passenger flow demand of some small and medium-sized cities, and the planning and construction in multiple cities are increasing. The current main power supply method for tramways still uses the overhead catenary power supply system, but due to the large number of complex line erections, it has a great impact on the urban beauty. The non-overhead catenary power supply methods mainly include electromagnetic induction power supply, third-rail power supply, and on-vehicle energy storage power supply. The electromagnetic induction system uses the principle of electromagnetic induction, with low efficiency, high investment and maintenance costs, and also generates electromagnetic pollution. The third-rail power supply has poor safety, high maintenance costs, complex system control, and high failure rates, so it is less used. The on-vehicle supercapacitor energy storage system for power supply, reasonably configuring charging piles at stations, and using the boarding and alighting time to supplement the power, has high safety, fast charging speed, high efficiency, and strong applicability, becoming the main trend of tramway development.

[0003] The parameter matching of the tramway supercapacitor energy storage system is mostly based on the parameter matching of independent subsystems based on power indicators, with high system redundancy, waste of on-vehicle equipment and high costs. The currently applied configuration methods for the charging piles matched with tramway lines are mainly the traversal method or the method of configuring charging piles at every other station. The traversal method configures charging piles at all stations along the line. This configuration method is not applicable to long and complex lines, and the number of charging piles is too large, greatly increasing the investment and construction costs; the method of configuring charging piles at every other station will increase the configuration of the energy storage system power. There is less research on other optimization configuration methods for the location of charging piles, especially the lack of a method for configuring charging piles in combination with the parameters of the supercapacitor energy storage system.

[0004] Therefore, there is an urgent need to develop a method and system for jointly optimizing the configuration of an on-vehicle energy storage system and a ground charging system to overcome the above-mentioned defects. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method for jointly optimizing the configuration of an on-vehicle energy storage system and a ground charging system, which is applied to the on-vehicle energy storage system and the ground charging system of a tramway, and the joint optimization configuration method includes:

[0006] Traction calculation step: obtaining the parameter range of the on-vehicle energy storage system through traction calculation according to the target line information and the tramway vehicle parameters;

[0007] Combination scheme acquisition step: Based on the parameter configurations of the on-vehicle energy storage system and the charging piles, multiple combination schemes are obtained through combination based on the parameter range;

[0008] Optimized combination scheme acquisition step: Taking the minimum full life cycle cost as the optimization objective, the Grey Wolf-Event Triggered State Machine algorithm is used to traverse multiple said combination schemes for optimization to obtain at least one optimized combination scheme;

[0009] Among them, each said optimized combination scheme and each said combination scheme include the parameter configuration of the on-vehicle energy storage system, the quantity configuration and location configuration of the charging piles.

[0010] For the above joint optimization configuration method, wherein, the combination scheme acquisition step includes:

[0011] Set the rated power of the on-vehicle energy storage system and the configured quantity of the charging piles as calculation variables, and use the required power and energy obtained in the traction calculation step, as well as the parameters and models of the on-vehicle energy storage system and the parameters and models of the charging piles as upper and lower limit vectors, and multiple said combination schemes are obtained through calculation.

[0012] For the above joint optimization configuration method, wherein, the optimized combination scheme acquisition step includes:

[0013] Multi-objective optimization function building step: Based on multiple said combination schemes, a multi-objective optimization function is built according to the full life cycle cost of the on-vehicle energy storage system and the charging piles;

[0014] Charging pile location configuration step: After the location of the charging piles is configured through the inner layer state machine algorithm according to the combination scheme, multiple charging pile location configuration schemes are obtained, the cost reference value of each said charging pile location configuration scheme is obtained through the multi-objective optimization function, and the smallest said cost reference value is selected as the cost value of the combination scheme;

[0015] On-vehicle energy storage system configuration and charging pile quantity configuration step: According to the cost value of each said combination scheme, at least one optimized combination scheme with global optimality is found through the outer layer Grey Wolf algorithm.

[0016] For the above joint optimization configuration method, wherein, the charging pile location configuration acquisition step includes:

[0017] The cost reference value of each charging pile location configuration scheme that satisfies the power range is calculated through the multi-objective optimization function, and the charging pile location configuration scheme corresponding to the smallest cost reference value among multiple said cost reference values is selected as the optimal charging pile location configuration scheme.

[0018] The above-mentioned joint optimization configuration method, wherein the steps of obtaining the configuration of the vehicle-mounted energy storage system and the number of charging piles include:

[0019] Compare the cost values of each of the combination schemes, and select at least one of the cost values as the optimized cost value; update the population position and iterate the combination scheme corresponding to the optimized cost value to obtain at least one optimized combination scheme.

[0020] The present invention also provides a joint optimization configuration system for a vehicle-mounted energy storage system and a ground charging system, which is applied to the vehicle-mounted energy storage system and the ground charging system of a tram. The joint optimization configuration system includes:

[0021] A traction calculation unit, which obtains the parameter range of the vehicle-mounted energy storage system through traction calculation according to the target line information and the tram vehicle parameters;

[0022] A combination scheme acquisition unit, which combines multiple combination schemes based on the parameter configuration of the vehicle-mounted energy storage system and the parameter configuration of the charging piles according to the parameter range;

[0023] An optimized combination scheme acquisition unit, with the minimum full life cycle cost as the optimization goal, traverses multiple combination schemes by using the gray wolf-event trigger state machine algorithm for optimization to obtain at least one optimized combination scheme;

[0024] Among them, each of the optimized combination schemes and each of the combination schemes includes the parameter configuration of the vehicle-mounted energy storage system, the number configuration and the position configuration of the charging piles.

[0025] The above-mentioned joint optimization configuration system, wherein the combination scheme acquisition unit includes:

[0026] Set the rated power of the vehicle-mounted energy storage system and the configured number of charging piles as calculation variables, and use the required power and energy obtained in the traction calculation step, as well as the parameters and models of the vehicle-mounted energy storage system and the parameters and models of the charging piles as the upper and lower limit vectors to calculate multiple combination schemes.

[0027] The above-mentioned joint optimization configuration system, wherein the optimized combination scheme acquisition unit includes:

[0028] Build a multi-objective optimization function module, and build a multi-objective optimization function based on multiple combination schemes according to the full life cycle cost of the vehicle-mounted energy storage system and the charging piles;

[0029] The charging pile location configuration module obtains multiple charging pile location configuration schemes through the inner state machine algorithm for the configuration of the charging pile locations according to the combination scheme, obtains the cost reference value of each charging pile location configuration scheme through the multi-objective optimization function, and selects the smallest cost reference value as the cost value of the combination scheme;

[0030] The on-vehicle energy storage system configuration and charging pile quantity configuration module searches for at least one globally optimal optimization combination scheme through the outer-layer gray wolf algorithm according to the cost value of each combination scheme.

[0031] The above-mentioned joint optimization configuration system, wherein, the charging pile location configuration module includes:

[0032] Calculate the cost reference value of each charging pile location configuration scheme that meets the power range through the multi-objective optimization function, and select the charging pile location configuration scheme corresponding to the smallest cost reference value from multiple cost reference values as the optimal charging pile location configuration scheme.

[0033] The above-mentioned joint optimization configuration system, wherein, the on-vehicle energy storage system configuration and charging pile quantity configuration module includes:

[0034] Compare the cost values of each combination scheme and select at least one cost value as the optimized cost value; update the population position and iterate the combination scheme corresponding to the optimized cost value to obtain at least one optimized combination scheme.

[0035] The efficacy of the present invention relative to the prior art lies in:

[0036] The present invention calculates the traction calculation requirements for the tram to run on a fixed line, and at the same time considers working conditions such as charging pile failures and intersection stops, and adopts a gray wolf-event-triggered intelligent optimization algorithm to obtain the parameter configuration of the tram's supercapacitor energy storage system and the configuration of the number and location of charging piles, so that the total life cycle cost of the supercapacitor energy storage system and the charging pile system is the lowest, realizing the joint optimization of the parameter matching of the tram's supercapacitor energy storage system and the charging pile configuration. This method can ensure the safe, reliable and stable operation of the on-vehicle capacitive energy storage tram and effectively reduce the line construction cost.

[0037] Other features and advantages of the present invention will be described in the following description, and, in part, will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures pointed out in the description and the drawings. Brief Description of the Drawings

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0039] Figure 1 Topological diagram configured for vehicle-mounted energy storage system and charging pile;

[0040] Figure 2 Flowchart of the joint optimization configuration method of the present invention;

[0041] Figure 3 For Figure 2 Sub-step flowchart of step S3 in

[0042] Figure 4 For Figure 3 Application flowchart of step S32 in

[0043] Figure 5 For Figure 3 Application flowchart of step S33 in

[0044] Figure 6 Structural schematic diagram of the joint optimization configuration system of the present invention. Detailed implementation manners

[0045] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0046] The illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention. Additionally, elements / components using the same or similar reference numerals in the accompanying drawings and the embodiments are used to represent the same or similar parts.

[0047] Regarding the "first", "second", "S1", "S2",... used herein, they do not particularly refer to the meaning of order or sequence, nor are they used to limit the present invention. They are only used to distinguish elements or operations described with the same technical terms.

[0048] Regarding the directional terms used herein, such as: up, down, left, right, front or back, etc., they are only references to the directions in the accompanying drawings. Therefore, the directional terms used are for explanation and not for limiting this creation.

[0049] As used herein, terms such as "comprising", "including", "having", "containing", etc. are all open-ended terms, meaning including but not limited to.

[0050] As used herein, "and / or" includes any one or all combinations of the stated things.

[0051] "Plural" as used herein includes "two" and "more than two"; "multiple groups" as used herein includes "two groups" and "more than two groups".

[0052] As used herein, terms such as "substantially", "about", etc. are used to modify any quantity or error that can vary slightly, but these slight variations or errors do not change its essence. Generally, the range of such slight variations or errors modified by such terms can be 20% in some embodiments, 10% in some embodiments, 5% or other values in some embodiments. Those skilled in the art should understand that the aforementioned values can be adjusted according to actual needs and are not limited thereto.

[0053] Certain terms used to describe this application will be discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art regarding the description of this application.

[0054] Please refer to Figure 1 , Figure 1 a topology diagram configured for an on-vehicle energy storage system and a charging pile. As Figure 1 shown, in order to meet the operation requirements of a supercapacitor energy storage tram, when designing the train, reasonable parameter configuration of the supercapacitor energy storage system is required, and the configuration of the line charging pile needs to cooperate with the energy storage system to achieve a better charging effect. According to the technical specification requirements of the supercapacitor energy storage system of the tram, the rated supply voltage of the energy storage system, the voltage change range, the space volume and weight limitations, etc. need to match the traction requirements of the whole vehicle; the line charging pile charges the train after it enters the station, and the charging time is not shorter than the parking time, and the stations equipped with charging piles can charge the up and down vehicles. Now, the parameter matching of the supercapacitor energy storage system of the tram and the configuration of the charging pile are jointly optimized. Due to different configurations of the charging pile positions, the charge and discharge depth and the SOC working range of the on-vehicle energy storage system are different, thus affecting the life of the supercapacitor and the life cycle cost. The present invention takes the minimum life cycle cost of the on-vehicle energy storage system and the charging pile configuration as the optimization goal, adopts the grey wolf-event triggered state machine algorithm, traverses all combination schemes to find the optimal combination of the supercapacitor energy storage system configuration and the number of charging piles, as well as the corresponding optimal charging pile position configuration result.

[0055] Please refer to Figure 2 , Figure 2This is the flowchart of the joint optimization configuration method of the present invention. As Figure 2 shown, a joint optimization configuration method for an on-vehicle energy storage system and a ground charging system of the present invention is applied to the on-vehicle energy storage system and the ground charging system of a tram. The joint optimization configuration method includes:

[0056] Traction calculation step S1: Obtain the parameter range of the on-vehicle energy storage system through traction calculation according to the target line information and tram vehicle parameters;

[0057] Combined scheme acquisition step S2: Based on the parameter configuration of the on-vehicle energy storage system and the parameter configuration of the charging pile, combine multiple combined schemes based on the parameter range;

[0058] Optimized combined scheme acquisition step S3: Taking the minimum full-life cycle cost as the optimization goal, use the grey wolf-event triggered state machine algorithm to traverse multiple combined schemes for optimization to obtain at least one optimized combined scheme;

[0059] Wherein, each optimized combined scheme and each combined scheme include the parameter configuration of the on-vehicle energy storage system, the quantity configuration and position configuration of the charging piles.

[0060] In step S1, in combination with the ramp, curve, speed limit, intersection stop information of the actual operation line, as well as vehicle load information, vehicle starting acceleration, braking deceleration dynamic performance, vehicle starting resistance and basic resistance, motor traction characteristics and electric braking characteristics, etc., perform traction calculation on the tram to obtain data such as the real-time operation power-speed curve, speed-operation time curve, traction energy consumption-speed curve and regenerative energy-speed curve of the train. According to the data such as the real-time operation power-speed curve, speed-operation time curve, traction energy consumption-speed curve and regenerative energy-speed curve of the train, determine the parameter range of the on-vehicle energy storage system of the tram, and ensure that the maximum demand power P of the tram re is greater than or equal to the output power P of the supercapacitor energy storage system sc , the output power E of the supercapacitor energy storage system sc is greater than or equal to the maximum interval demand power E re-max , the charging power Q of the charging pile ch is greater than or equal to the whole-line operation demand power Q re , the rated charge and discharge current I sc meets the parking charging current I re , the overall weight m of the supercapacitor energy storage system sc and the volume V sc meet the weight m max and volume V max limits of the vehicle design requirements.

[0061] Specifically, first, input the line ramp information, curve information, facility information, speed limit information, and intersection stop information; second, set the vehicle load, average starting acceleration, deceleration, maximum operating speed, resistance, traction characteristic parameters, and braking characteristic parameters; after inputting the gear transmission ratio, gear transmission efficiency, electromechanical efficiency, driving wheel diameter, and single vehicle auxiliary power parameter information, obtain data such as the real-time operating power-speed curve, speed-operating time curve, traction energy consumption-speed curve, and regenerative energy-speed curve of the train through traction calculation.

[0062] Further, the combination scheme obtaining step S2 includes:

[0063] Set the rated power of the on-vehicle energy storage system and the configured number of charging piles as calculation variables, and use the required power and energy obtained in the traction calculation step, as well as the parameters, models of the on-vehicle energy storage system, and the parameters, models of the charging piles as upper and lower limit vectors, and obtain multiple combination schemes through calculation.

[0064] Specifically, after establishing system constraints according to the parameter range of the on-vehicle energy storage system, generate a charging pile configuration vector, that is, the rated power, rated power capacity of the supercapacitor energy storage system, and the configured number of charging piles (X; Y; Z) = (x1, x2,... x n ; y1, y2,... y n ; z1, z2,... z n ), and obtain multiple combination schemes through calculation.

[0065] Among them, the system constraints are:

[0066]

[0067] Among them, η1 is the motor efficiency, η2 is the power capacity margin of the supercapacitor energy storage system, and η3 is the charging efficiency of the charging pile. The above three variables are all values less than 1.

[0068] For example, according to the traction calculation results, take the rated power, rated power capacity of the supercapacitor energy storage system, and the configured number of charging piles as the population calculation quantities, and use the required power and energy obtained by the tram through traction calculation, as well as the common parameters and models of the supercapacitor energy storage system and the charging pile as the upper and lower limits (Formula 1), and initially obtain a feasible group of the rated power, rated power capacity of the supercapacitor energy storage system, and the number of charging piles. For example, there are a rated powers of the energy storage system, b rated power capacities of the energy storage system, and n numbers of charging piles, with a total of a * b * n combination schemes.

[0069] Please refer to Figure 3 , Figure 3 For Figure 2 the sub-step flowchart of step S3 in Figure 3As shown, the steps for obtaining the optimized combination scheme include:

[0070] Step S31 of building a multi-objective optimization function: Based on multiple said combination schemes, build a multi-objective optimization function according to the full life cycle cost of the on-vehicle energy storage system and the charging pile.

[0071] Step S32 of charging pile location configuration: According to the said combination scheme, obtain multiple charging pile location configuration schemes through the inner state machine algorithm, obtain the cost reference value of each charging pile location configuration scheme through the multi-objective optimization function, and select the smallest said cost reference value as the cost value of the combination scheme.

[0072] Step S33 of on-vehicle energy storage system configuration and charging pile number configuration: According to the cost value of each said combination scheme, find at least one optimized combination scheme with the global optimum through the outer gray wolf algorithm.

[0073] Among them, in step S31, the multi-objective optimization function is:

[0074] SC = m1 * SC re (P re , Q re , n) + m2 * SC y (P re , Q r ) + m3 * SC life (P re , Q re , n);

[0075] DC = m4 * DC re (P re , Q re , n) + m5 * DC y (P re , Q re , n) + m6 * DC life (P re , Q re , n);

[0076] C = k1 * SC(P re , Q re , n) + k2 * DC(P re , Q re , n);

[0077] Among them, k1 and k2 are weight coefficients, SC is the full life cycle cost function of the supercapacitor energy storage system, including the configuration power cost SC re , the maintenance cost SC of the supercapacitor energy storage system y and the remaining value SC at the end of the life of the supercapacitor energy storage system life, DC is the life - cycle cost function of the charging pile, including the cost of configuring the charging pile DC re , the maintenance cost of the charging pile DC y and the residual value of the charging pile at the end of its life DC life ; m1, m2, m3, m4, m5, and m6 are weight coefficients.

[0078] Among them, in step S32, for each combination scheme, the inner - layer state - machine algorithm is input for the configuration of the charging - pile location. For example, if the number of stations is m and the number of configured charging piles is n, the total number of charging - pile location configuration schemes is .

[0079] For each charging - pile location configuration scheme, set the vehicle to start running from the first station, and determine whether there is a charging pile. If there is a charging pile, determine whether the battery level meets the requirements. If the battery level meets the requirements, determine whether the route has been completed. If the route has been completed, enter the next station. If the route has not been completed, return to continue to determine whether there is a charging pile. Among them, if the judgment result is that there is no charging pile, directly determine whether the route has been completed. If the judgment result is that the battery level does not meet the requirements, charge the on - vehicle energy - storage system. After charging is completed, determine whether the route has been completed;

[0080] Finally, after entering the next station, update the battery level of the on - vehicle energy - storage system and determine whether the battery level meets the requirements. If the battery level meets the requirements, calculate the cost reference value of this charging - pile location configuration scheme according to the multi - objective optimization function. After traversing all the charging - pile location configuration schemes in this combination scheme, obtain multiple cost reference values, and select the smallest cost reference value as the cost value C1 of the combination scheme. That is, the charging - pile location configuration scheme corresponding to the cost value C1 is also the optimal charging - pile location configuration scheme of this combination scheme. Among them, if the battery level does not meet the requirements, set the cost to infinity.

[0081] Among them, in step S33, after comparing the cost values of each combination scheme, select at least one of the cost values as the optimized cost value; update the population position and iterate for the combination scheme corresponding to the optimized cost value to obtain at least one optimized combination scheme

[0082] Specifically, please refer to Figure 5 , Figure 5 is Figure 3 the application flowchart of step S33 in. The following combines Figure 5 to specifically describe step S33. In this embodiment, step S33 obtains at least one optimized combination scheme through an outer - layer algorithm, that is, the gray - wolf algorithm.

[0083] First, initialize the algorithm;

[0084] Secondly, compare all the cost values of the first three combination schemes, and sort the cost values in ascending order to obtain the optimal result α, the sub-optimal result β, and the second sub-optimal result γ;

[0085] Thirdly, compare and update the cost values of all the remaining combination schemes with the optimal result α, the sub-optimal result β, and the second sub-optimal result γ respectively to obtain the final optimal result α, the sub-optimal result β, and the second sub-optimal result γ. The final optimal result α, the sub-optimal result β, and the second sub-optimal result γ are finally selected as the optimized cost values. For example, when the cost value of the fourth combination scheme is less than the optimal result α, the cost value of the fourth combination scheme is set as the optimal result α, the original optimal result α becomes the sub-optimal result β, and the original sub-optimal result β becomes the second sub-optimal result γ;

[0086] Finally, take the optimal result α, the sub-optimal result β, and the second sub-optimal result γ as the optimized cost values, and update the population positions and iterate the corresponding combination schemes of the three optimized cost values respectively to obtain three optimized combination schemes:

[0087] As the number of iterations increases, its range gradually converges. The constant q determines the search range of the algorithm, and its calculation method is:

[0088] q = 2 - 2c iter / c maxiter

[0089] In the formula, q is a value that changes linearly with the number of iterations, C iter is the current number of iterations, and C maxiter is the maximum number of iterations;

[0090] For the j-th specific position in the i-th population, its update method is:

[0091]

[0092]

[0093] In the formula, P'(i, j) is the new position of the j-th variable in the i-th population after update, P(i, j) is the position of the j-th variable in the i-th population, ε1, ε2, and ε3 are the position information updated according to the corresponding coordinates of the optimal solutions α, β, and γ respectively, P α (j) is the size of the j-th variable corresponding to the optimal solution α, P β (j) is the size of the j-th variable corresponding to the sub-optimal solution β, P γ (j) is the size of the j-th variable corresponding to the second sub-optimal solution γ, and ξ1 - ξ6 are randomly generated values in the range of 0 - 1.

[0094] Please refer to Figure 6 , Figure 6This is a schematic structural diagram of the joint optimization configuration system of the present invention. As Figure 6 shown, a joint optimization configuration system of an on-vehicle energy storage system and a ground charging system according to the present invention is applied to the on-vehicle energy storage system and the ground charging system of a tram. The joint optimization configuration system includes:

[0095] A traction calculation unit 11, which obtains the parameter range of the on-vehicle energy storage system through traction calculation according to the target line information and the tram vehicle parameters;

[0096] A combination scheme acquisition unit 12, which obtains multiple combination schemes by combining the parameter configuration of the on-vehicle energy storage system and the parameter configuration of the charging pile based on the parameter range;

[0097] An optimized combination scheme acquisition unit 13, with the minimum life cycle cost as the optimization goal, traverses multiple said combination schemes using the grey wolf-event triggered state machine algorithm for optimization to obtain at least one optimized combination scheme;

[0098] Among them, each said optimized combination scheme and each said combination scheme include the parameter configuration of the on-vehicle energy storage system, the quantity configuration and the position configuration of the charging piles.

[0099] Further, the combination scheme acquisition unit 12 sets the rated power of the on-vehicle energy storage system and the configured quantity of the charging piles as calculation variables, and uses the required power and energy obtained in the traction calculation step, as well as the parameters and models of the on-vehicle energy storage system and the parameters and models of the charging piles as upper and lower limit vectors to obtain multiple said combination schemes through calculation.

[0100] Still further, the optimized combination scheme acquisition unit 13 includes:

[0101] Build a multi-objective optimization function module 131, and build a multi-objective optimization function based on multiple said combination schemes according to the life cycle costs of the on-vehicle energy storage system and the charging piles;

[0102] A charging pile position configuration module 132, which obtains multiple charging pile position configuration schemes through inner layer state machine algorithm for the position configuration of the charging piles according to the combination scheme, obtains the cost reference value of each said charging pile position configuration scheme through the multi-objective optimization function, and selects the smallest said cost reference value as the cost value of the combination scheme;

[0103] An on-vehicle energy storage system configuration and charging pile quantity configuration module 133, which finds at least one globally optimal optimized combination scheme through the outer layer grey wolf algorithm according to the cost value of each said combination scheme.

[0104] Among them, the charging pile location configuration module 132 calculates the cost reference value of each charging pile location configuration scheme that meets the power range through the multi-objective optimization function, and selects the charging pile location configuration scheme corresponding to the minimum cost reference value from multiple cost reference values as the optimal charging pile location configuration scheme.

[0105] Among them, the vehicle-mounted energy storage system configuration and charging pile quantity configuration module 133 compares the cost values of each combination scheme and selects at least one of the cost values as the optimized cost value; after updating the population position and iterating the combination scheme corresponding to the optimized cost value, at least one optimized combination scheme is obtained.

[0106] In summary, the present invention calculates the traction calculation requirements for a tram running on a fixed line, and at the same time considers working conditions such as charging pile failures and intersection stops. By using the gray wolf-event-triggered intelligent optimization algorithm, the parameter configuration of the tram's supercapacitor energy storage system and the quantity and location configuration of the charging piles are obtained, so that the total life cycle cost of the supercapacitor energy storage system and the charging pile system is the lowest, realizing the joint optimization of the parameter matching of the tram's supercapacitor energy storage system and the charging pile configuration; at the same time, the optimal parameter matching results of the tram's energy storage system and the quantity and configuration location of the charging piles are obtained through the present invention, reducing unnecessary redundancy in the vehicle-mounted energy storage system and the charging pile configuration. Based on this method, the charging and discharging of the supercapacitor energy storage system can be optimized to work in the high-efficiency range to the greatest extent, effectively extending the service life of the supercapacitor energy storage system and achieving the optimal total life cycle.

[0107] Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A combined optimization configuration method for a vehicle-mounted energy storage system and a ground charging system, characterized in that On - vehicle energy storage system and ground charging system applied to tramcars, the combined optimization configuration method includes: Traction calculation step: Obtain the parameter range of the on - vehicle energy storage system through traction calculation according to the target line information and tramcar vehicle parameters; Combined - scheme acquisition step: Based on the parameter configuration of the on - vehicle energy storage system and the parameter configuration of the charging piles, obtain multiple combined schemes by combination based on the parameter range; Optimized - combined - scheme acquisition step: Taking the minimum life - cycle cost as the optimization goal, use the grey - wolf event - triggered state - machine algorithm to traverse multiple combined schemes for optimization to obtain at least one optimized combined scheme; Among them, each optimized combined scheme and each combined scheme include the parameter configuration of the on - vehicle energy storage system, the quantity configuration and location configuration of the charging piles; Among them, the optimized - combined - scheme acquisition step includes: Multi - objective optimization function construction step: Based on multiple combined schemes, construct a multi - objective optimization function according to the life - cycle costs of the on - vehicle energy storage system and the charging piles; Charging - pile location configuration step: After configuring the locations of the charging piles through the inner - layer state - machine algorithm according to the combined scheme, obtain multiple charging - pile location configuration schemes, obtain the cost reference value of each charging - pile location configuration scheme through the multi - objective optimization function, and select the minimum cost reference value as the cost value of the combined scheme; On - vehicle energy storage system configuration and charging - pile quantity configuration step: According to the cost value of each combined scheme, use the outer - layer grey - wolf algorithm to find at least one globally optimal optimized combined scheme.

2. The combined optimization configuration method according to claim 1, wherein The combined - scheme acquisition step includes: Set the rated power of the on - vehicle energy storage system and the configured quantity of the charging piles as calculation variables, and use the required power and energy obtained in the traction calculation step, as well as the parameters and models of the on - vehicle energy storage system and the parameters and models of the charging piles as upper and lower limit vectors, and calculate to obtain multiple combined schemes.

3. The combined optimization configuration method according to claim 2, wherein, The charging - pile location configuration acquisition step includes: Calculate the cost reference value of each charging - pile location configuration scheme that meets the power range through the multi - objective optimization function, and select the charging - pile location configuration scheme corresponding to the minimum cost reference value as the optimal charging - pile location configuration scheme.

4. The combined optimization configuration method according to claim 3, wherein The on - vehicle energy storage system configuration and charging - pile quantity configuration acquisition step includes: Compare the cost values of each combined scheme, and select at least one cost value as the optimized cost value; update the population position and iterate the combined scheme corresponding to the optimized cost value to obtain at least one optimized combined scheme.

5. A combined optimization configuration system for an in-vehicle energy storage system and a ground charging system, characterized in that, On - vehicle energy storage system and ground charging system applied to tramcars, the combined optimization configuration system includes: Traction calculation unit, which obtains the parameter range of the on - vehicle energy storage system through traction calculation according to the target line information and tramcar vehicle parameters; Combined - scheme acquisition unit, which obtains multiple combined schemes by combination based on the parameter configuration of the on - vehicle energy storage system and the parameter configuration of the charging piles based on the parameter range; An optimized combination scheme acquisition unit takes the minimum life cycle cost as the optimization goal and uses the Grey Wolf-Event Triggered State Machine algorithm to traverse multiple said combination schemes for optimization to obtain at least one optimized combination scheme; Among them, each said optimized combination scheme and each said combination scheme include the parameter configuration of the on-vehicle energy storage system, the quantity configuration and location configuration of the charging piles; Among them, the optimized combination scheme acquisition unit includes: Build a multi-objective optimization function module, and build a multi-objective optimization function based on multiple said combination schemes according to the life cycle costs of the on-vehicle energy storage system and the charging piles; A charging pile location configuration module configures the locations of the charging piles through the inner layer state machine algorithm according to the combination scheme to obtain multiple charging pile location configuration schemes, obtains the cost reference value of each said charging pile location configuration scheme through the multi-objective optimization function, and selects the smallest said cost reference value as the cost value of the combination scheme; An on-vehicle energy storage system configuration and charging pile quantity configuration module searches for at least one globally optimal optimized combination scheme through the outer layer Grey Wolf algorithm according to the cost value of each said combination scheme.

6. The joint optimization configuration system according to claim 5, wherein The combination scheme acquisition unit includes: Set the rated power of the on-vehicle energy storage system and the configured quantity of the charging piles as calculation variables, and use the required power and energy obtained by the traction calculation unit, as well as the parameters and models of the on-vehicle energy storage system and the parameters and models of the charging piles as upper and lower limit vectors, and calculate to obtain multiple said combination schemes.

7. The joint optimization configuration system according to claim 6, wherein The charging pile location configuration module includes: Calculate the cost reference value of each said charging pile location configuration scheme that meets the power range through the multi-objective optimization function, and select the charging pile location configuration scheme corresponding to the smallest cost reference value from multiple said cost reference values as the optimal charging pile location configuration scheme.

8. The combined optimization configuration system according to claim 7, wherein The on-vehicle energy storage system configuration and charging pile quantity configuration module includes: Compare the cost values of each said combination scheme and select at least one said cost value as the optimized cost value; update the population positions and iterate the combination scheme corresponding to the optimized cost value to obtain at least one said optimized combination scheme.

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