Parameter matching design method and system for flexible topology architecture of inter-vehicle mobile microgrid

Abstract

The present invention provides a parameter matching design method and system for a flexible topology architecture of an inter-vehicle mobile microgrid, which belongs to the field of microgrid technology, and includes: determining and initializing the basic elements and performance parameters of the flexible topology architecture of an inter-vehicle mobile microgrid, and taking them as optimization objects; maximizing the transmission power, transmission efficiency or availability of the flexible topology architecture of an inter-vehicle mobile microgrid as an objective function; determining the upper and lower boundaries of the optimization object, and according to the objective function, using an improved transient search optimization algorithm to search for the basic elements and performance parameters of the optimal flexible topology architecture of an inter-vehicle mobile microgrid; wherein the improved transient search optimization algorithm introduces a position update method of a white-bone chicken optimization algorithm; and constructing a flexible topology architecture of an inter-vehicle mobile microgrid according to the basic elements and performance parameters of the optimal flexible topology architecture of an inter-vehicle mobile microgrid. The flexible topology architecture scheme of an inter-vehicle mobile microgrid generated by the method has better driving performance.

Description

Technical Field

[0001] The present invention relates to the technical field of inter-vehicle mobile microgrids, and in particular to a parameter matching design method for a flexible topology architecture of an inter-vehicle mobile microgrid. Background Art

[0002] The inter-vehicle mobile microgrid is a highly flexible and mobile power system that can form a dynamic microgrid for energy management and distribution by connecting multiple vehicles or mobile devices. In such a microgrid system, the flexible topology refers to its ability to flexibly adjust and reorganize the connection relationship according to real-time needs and environmental conditions to maximize system performance and efficiency. The parameter matching design of the flexible topology architecture of the inter-vehicle mobile microgrid is one of the key technologies for the success of land warfare platforms. Therefore, it is urgent and necessary to conduct in-depth research on the characteristics of the flexible topology architecture of the inter-vehicle mobile microgrid and carry out research on the parameter matching design of the flexible topology architecture of the inter-vehicle mobile microgrid.

[0003] Intelligent optimization algorithm is one of the hot topics in artificial intelligence research and has been widely used in optimization design in the power field. For example, Xiao Wei et al. studied a composite power supply parameter optimization method based on the intensity Pareto evolutionary algorithm (Xiao Wei et al. Composite power supply parameter optimization based on the intensity Pareto evolutionary algorithm [J]. Power Supply Technology, 2022, 46 (12): 1422-1427.); Chen Zonghai et al. invented a capacity matching method and system for a pure electric vehicle composite power supply system (Chen Zonghai et al. A capacity matching method and system for a pure electric vehicle composite power supply system [P]. Anhui: CN112434374A, 2021-03-02.). Transient search optimization algorithm (TSOA) is a new intelligent optimization algorithm that simulates the transient behavior of switching circuits containing storage elements such as inductors and capacitors. It can also be applied to the parameter matching design problem of the flexible topology architecture of vehicle-to-vehicle mobile microgrids.

[0004] However, in some studies, when transient search optimization algorithms are used to optimize the parameters of the flexible topology architecture of inter-vehicle mobile microgrids, the optimized parameters of the flexible topology architecture of inter-vehicle mobile microgrids are in local optimal situations, which often fail to achieve the ideal matching design effect, and thus fail to achieve reasonable energy configuration management of the flexible topology architecture of inter-vehicle mobile microgrids and fail to achieve the optimal utilization efficiency of the microgrid system. Summary of the invention

[0005] In order to obtain the optimal parameters of the flexible topology architecture of the inter-vehicle mobile microgrid, realize reasonable energy configuration management and achieve the best utilization efficiency of the topology architecture, the present invention provides a parameter matching design method for the flexible topology architecture of the inter-vehicle mobile microgrid:

[0006] The parameter matching design method of the vehicle-to-vehicle mobile microgrid flexible topology architecture includes the following steps:

[0007] Determine and initialize the basic elements and performance parameters of the flexible topology architecture of the inter-vehicle mobile microgrid;

[0008] The objective function is to maximize the transmission power, transmission efficiency or availability of the flexible topology architecture of the inter-vehicle mobile microgrid, and the basic elements and performance parameters of the flexible topology architecture of the inter-vehicle mobile microgrid are taken as the optimization objects;

[0009] Determine the upper and lower boundaries of the optimization object, and use an improved transient search optimization algorithm to search for the basic elements and performance parameters of the optimal vehicle-to-vehicle mobile microgrid flexible topology architecture according to the objective function; wherein the improved transient search optimization algorithm introduces the position update method of the white-bone chicken optimization algorithm, selects different position update modes according to the switching probability, the optimal position of the current iteration search agent, other random search agent positions within the population, and the chain motion mode factor to update the search agent;

[0010] According to the basic elements and performance parameters of the optimal inter-vehicle mobile microgrid flexible topology architecture, a flexible topology architecture of the inter-vehicle mobile microgrid is constructed.

[0011] Preferably, the basic elements and performance parameters of the inter-vehicle mobile microgrid flexible topology architecture include vehicle-mounted system platform parameters, power system parameters and load parameters.

[0012] Preferably, the method of searching for the optimal basic elements and performance parameters of the inter-vehicle mobile microgrid flexible topology architecture using the improved transient search optimization algorithm comprises the following steps:

[0013] Determine the upper and lower boundaries of the optimization object and initialize the search agent population position through Gaussian mapping;

[0014] Calculate the optimal fitness value according to the objective function, and search and record the optimal search agent position;

[0015] The position update method of the white-bone optimization algorithm is introduced to improve the position update method in the original transient search optimization algorithm; the position is updated through the improved search agent position update method, and the optimal fitness value and search agent position after the current iteration are recorded;

[0016] Perform bidirectional sine mutation on the optimal search agent position, and use the search agent position with the best fitness value before and after mutation as the updated optimal search agent position;

[0017] The optimal search agent position is updated in sequence according to a preset maximum number of iterations to determine the optimal search agent position; and the basic elements and performance parameters of the optimal inter-vehicle mobile microgrid flexible topology architecture are determined according to the optimal search agent position.

[0018] Preferably, the initialization of searching the proxy population position by Gaussian mapping comprises the following steps:

[0019] Determine the size N of the population, search for the lower bound LB of the agent optimization and the upper bound UB of the agent optimization;

[0020] Generate the next random number x through Gaussian mapping t+1 :

[0021]

[0022] In the formula, mod(·) is the remainder function, x t is the current random number;

[0023] Initialize the search agent position using the generated Gaussian random number:

[0024] X(0) = (UB - LB) × x t +LB

[0025] Where X(0) is the position of the initial search agent.

[0026] Preferably, the method of performing position updating by using an improved search agent position updating method and recording the optimal fitness value and search agent position after the current iteration comprises the following steps:

[0027] The white-bone chicken optimization algorithm is introduced, and the improved search agent position update formula is as follows:

[0028]

[0029] in:

[0030] C(t)=k·z·λ3+1

[0031] T=2·z·λ2-z

[0032]

[0033] Where X(t+1) is the position of the search agent at the t+1th iteration; X(t-1) is the position of the search agent at the t-1th iteration; X bs (t) represents the optimal position of the search agent at the tth iteration; X(t) is the position of the search agent at the current tth iteration; X y(t) is the position of the randomly selected rth search agent at the current tth iteration; C(t) and T are random thermal resistance coefficients; z is the attenuation coefficient, which gradually decreases from 2 to 0 as the number of iterations increases; k is a constant (k = 0, 1, 2...); λ1, λ2 and λ3 are random numbers in [0, 1]; α and μ are random numbers in [0, 1]; y is a random integer between [1, N];

[0034] Calculate the fitness value:

[0035] fitness(t)=F(X(t+1))

[0036] Where F(·) is the fitness value calculation function;

[0037] Record the best search agent in the iteration.

[0038] Preferably, performing a bidirectional sine mutation on the optimal search proxy position and taking the search proxy position with the best fitness value before and after the mutation as the updated optimal search proxy position comprises the following steps:

[0039] For dimension j, the sine chaos value is calculated according to the current number of iterations, and the positive and negative directions are switched with equal probability:

[0040] sinValue=sin(πx0)

[0041]

[0042] In the formula, rand is a random number between 0 and 1; x0 is the iteration sequence value;

[0043] Perform mutation perturbation on the optimal position:

[0044] X bs(j) (t+1)'=X bs(j) (t+1)+SinValue×X bs(j) (t+1)

[0045] Where: X bs(j) (t+1) represents the optimal position X of the t+1th iteration bs The jth dimension of (t+1); X bs(j) (t+1)' is the optimal position X after the mutation disturbance bs The jth dimension of (t+1);

[0046] Greedy Update:

[0047]

[0048] After each dimension has been mutated, stop mutation.

[0049] A vehicle-to-vehicle mobile microgrid flexible topology architecture parameter matching design system, the system comprising:

[0050] processor;

[0051] a memory having stored thereon a computer program executable on the processor;

[0052] Wherein, when the computer program is executed by the processor, the steps of the vehicle-to-vehicle mobile microgrid flexible topology architecture parameter matching design method are implemented.

[0053] Beneficial effects of the present invention:

[0054] The present invention proposes a vehicle-to-vehicle mobile microgrid flexible topology architecture parameter matching design method, adopts an improved search agent optimization algorithm to generate vehicle-to-vehicle mobile microgrid flexible topology architecture parameters, and forms a vehicle-to-vehicle mobile microgrid flexible topology architecture parameter matching design scheme. The method adopts an improved search agent optimization algorithm to improve the position update method of the search agent, introduces the position update mechanism of the white bone crested chicken optimization algorithm to improve the search agent position update method, comprehensively considers the selection of different position update modes according to the switching probability, the optimal position of the search agent in this iteration, other random search agent positions within the population, chain movement mode and other factors to update the search agent position, avoids the occurrence of local optimality in each iteration, and thus improves the global search capability of the transient search optimization algorithm, realizes the increase of the algorithm search range, and enhances the adaptability of the algorithm, thereby improving the generation efficiency of the vehicle-to-vehicle mobile microgrid flexible topology architecture parameter matching design scheme, the rationality of the entire integrated matching design scheme, and the optimized vehicle-to-vehicle mobile microgrid flexible topology architecture can realize reasonable energy configuration management and achieve the best utilization efficiency of the topology architecture. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a parameter matching design method for a flexible topology architecture of an inter-vehicle mobile microgrid according to an embodiment of the present invention;

[0056] Figure 2 It is an iterative process curve of the parameter matching design scheme of the vehicle-to-vehicle mobile microgrid flexible topology architecture according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] Example 1

[0059] The transient search optimization algorithm (TSOA) is a new intelligent optimization algorithm that simulates the transient behavior of switching circuits containing storage elements such as inductors and capacitors. It can also be applied to the parameter matching design problem of the flexible topology architecture of inter-vehicle mobile microgrids. In some studies, when the transient search optimization algorithm is used to optimize the parameters of the flexible topology architecture of inter-vehicle mobile microgrids, the optimized parameters of the flexible topology architecture of inter-vehicle mobile microgrids are in a local optimum, which often fails to achieve the ideal matching design effect, and thus fails to achieve reasonable energy configuration management of the flexible topology architecture of inter-vehicle mobile microgrids and fails to achieve the best utilization efficiency of the microgrid system.

[0060] This embodiment proposes a method for designing parameter matching of a flexible topology architecture of a vehicle-to-vehicle mobile microgrid. The flow chart is as follows: Figure 1 As shown, the specific steps include:

[0061] S1: Initialize the basic structure and parameter information of the inter-vehicle mobile microgrid flexible topology architecture according to the basic elements and performance parameters of the inter-vehicle mobile microgrid flexible topology architecture to be designed (for example, the vehicle platform, various power sources and loads, etc.).

[0062] S2: Establish the objective function of the parameter matching design scheme of the flexible topology architecture of the inter-vehicle mobile microgrid based on the improved transient search optimization algorithm, such as transmission power, transmission efficiency, availability, etc.

[0063] S3: Parameter setting, mainly including: the size of the search agent population (ie, the number of search agent individuals) N; the maximum number of iterations (ie, the condition for stopping iteration) Miter; the search agent optimization lower boundary LB; the search agent optimization upper boundary UB.

[0064] S4: Initialize the search agent population position using Gaussian mapping.

[0065] Gaussian map random number generation:

[0066]

[0067] Initialize the search agent position using the generated Gaussian random number:

[0068] X(0) = (UB - LB) × x t +LB

[0069] Where: X(0) is the position of the initial search agent.

[0070] S5: Search agent location update.

[0071] In the original transient search optimization algorithm, only the optimal search agent position is used for guidance to update the search agent position. In order to more effectively improve the global search capability of the search agent, the position update mechanism of the white-bone chicken optimization algorithm is introduced to improve the search agent position update method. The search agent position is updated by comprehensively considering factors such as selecting different position update modes according to the switching probability, the optimal position of the search agent in this iteration, other random search agent positions within the population, and the chain movement mode, so as to avoid local optimality in each iteration, thereby improving the global search capability of the transient search optimization algorithm.

[0072] The white-bone chicken optimization algorithm is introduced, and the improved search agent position update formula is as follows:

[0073]

[0074] in:

[0075] C(t)=k·z·λ3+1

[0076] T=2·z·λ2-z

[0077]

[0078] Where X(t+1) is the position of the search agent at the t+1th iteration; X(t-1) is the position of the search agent at the t-1th iteration; X bs (t) represents the optimal position of the search agent at the tth iteration; X(t) is the position of the search agent at the current tth iteration; X y (t) is the position of the randomly selected r-th search agent in the current t-th iteration; C(t) and T are random thermal resistance coefficients; z is the attenuation coefficient, which gradually decreases from 2 to 0 as the number of iterations increases; k is a constant (k = 0, 1, 2…); λ1, λ2 and λ3 are random numbers in [0, 1]; α and μ are random numbers in [0, 1]; y is a random integer between [1, N].

[0079] S6: Calculate the fitness value.

[0080] fitness(t)=F(X(t+1))

[0081] Where F(·) is the fitness function used to calculate the fitness value.

[0082] S7: Record information. Record the best search agent in the current iteration.

[0083] S8: Perform bidirectional sine mutation on the optimal search agent in each dimension. For dimension j, first calculate the sine chaos value according to the current number of iterations, and switch the positive and negative directions with equal probability.

[0084] SinValue=sin(πx0)

[0085]

[0086] Then perform mutation perturbation on the optimal position

[0087] X bs(j) (t+1)'=X bs(j) (t+1)+SinValue×X bs(j) (t+1)

[0088] Where: X bs(j) (t+1) represents the optimal position X of the t+1th iteration bs The j-th dimension of (t+1).

[0089] Greedy Update:

[0090]

[0091] After each dimension has been mutated, stop mutation.

[0092] S9: Record information. Record the best search agent in the current iteration.

[0093] S10: Repeat steps S5 to S9. When the maximum number of iterations Miter is reached, the algorithm stops and the optimal search agent is output to obtain the optimal parameter matching design scheme of the inter-vehicle mobile microgrid flexible topology architecture, that is, the inter-vehicle mobile microgrid flexible topology architecture scheme is obtained.

[0094] In this embodiment:

[0095] Using MATLAB as the simulation platform, the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO) and Transient search optimization algorithm (TSOA) were selected for comparison with the ITSOA method proposed in this embodiment.

[0096] To ensure the fairness of the experiment, the population size of all algorithms is set to 30, the maximum number of iterations is set to 300, LB = 0, UB = 100, and other parameter settings are shown in Table 1.

[0097] Table 1 Algorithm parameter settings:

[0098]

[0099] Figure 2 is the iterative process curve. Figure 2It can be intuitively found that the convergence speed of the ITSOA method is faster than that of the other three algorithms, and the convergence accuracy of the ITSOA method is better than that of the other three algorithms. The simulation results show that the ITSOA algorithm has stronger search ability and obtains a better parameter matching design scheme for the flexible topology architecture of the inter-vehicle mobile microgrid, verifying the effectiveness of the algorithm.

[0100] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A parameter matching design method for a flexible topology architecture of an inter-vehicle mobile microgrid, characterized in that: The following steps are involved: Determine and initialize the basic elements and performance parameters of the flexible topology architecture of the inter-vehicle mobile microgrid; The objective function is to maximize the transmission power, transmission efficiency or availability of the flexible topology architecture of the inter-vehicle mobile microgrid, and the basic elements and performance parameters of the flexible topology architecture of the inter-vehicle mobile microgrid are taken as the optimization objects; Determine the upper and lower boundaries of the optimization object, and use an improved transient search optimization algorithm to search for the basic elements and performance parameters of the optimal vehicle-to-vehicle mobile microgrid flexible topology architecture according to the objective function; wherein the improved transient search optimization algorithm introduces the position update method of the white-bone chicken optimization algorithm, selects different position update modes according to the switching probability, the optimal position of the current iteration search agent, other random search agent positions within the population, and the chain motion mode factor to update the search agent; According to the basic elements and performance parameters of the optimal inter-vehicle mobile microgrid flexible topology architecture, a flexible topology architecture of the inter-vehicle mobile microgrid is constructed.

2. The method for designing the parameter matching of the flexible topology architecture of the inter-vehicle mobile microgrid according to claim 1 is characterized in that: The basic elements and performance parameters of the inter-vehicle mobile microgrid flexible topology architecture include vehicle system platform parameters, power system parameters and load parameters.

3. The method for designing the parameter matching of the flexible topology architecture of the inter-vehicle mobile microgrid according to claim 1 is characterized in that: The method of using the improved transient search optimization algorithm to search for the basic elements and performance parameters of the optimal vehicle-to-vehicle mobile microgrid flexible topology architecture includes the following steps: Determine the upper and lower boundaries of the optimization object and initialize the search agent population position through Gaussian mapping; Calculate the optimal fitness value according to the objective function, and search and record the optimal search agent position; The position update method of the white-bone optimization algorithm is introduced to improve the position update method in the original transient search optimization algorithm; the position is updated through the improved search agent position update method, and the optimal fitness value and search agent position after the current iteration are recorded; Perform bidirectional sine mutation on the optimal search agent position, and use the search agent position with the best fitness value before and after mutation as the updated optimal search agent position; The optimal search agent position is updated in sequence according to a preset maximum number of iterations to determine the optimal search agent position; and the basic elements and performance parameters of the optimal inter-vehicle mobile microgrid flexible topology architecture are determined according to the optimal search agent position.

4. The method for designing the parameter matching of the flexible topology architecture of the inter-vehicle mobile microgrid according to claim 3 is characterized in that: The process of initializing the search agent population position by Gaussian mapping includes the following steps: Determine the size N of the population, search for the lower bound LB of the agent optimization and the upper bound UB of the agent optimization; Generate the next random number x through Gaussian mapping t+1 : In the formula, mod(·) is the remainder function, x t is the current random number; Initialize the search agent position using the generated Gaussian random number: X(0)=(UB-LB)×x t +LB Where X(0) is the position of the initial search agent.

5. The method for designing the parameter matching of the flexible topology architecture of the inter-vehicle mobile microgrid according to claim 3 is characterized in that: The improved search agent location update method is used to update the location, and the optimal fitness value and search agent location after the current iteration are recorded. The following steps are involved: The white-bone chicken optimization algorithm is introduced, and the improved search agent position update formula is as follows: in: C(t)=k·z·λ3+1 T=2·z·λ2-z Where X(t+1) is the position of the search agent at the t+1th iteration; X(t-1) is the position of the search agent at the t-1th iteration; X bs (t) represents the optimal position of the search agent at the tth iteration; X(t) is the position of the search agent at the current tth iteration; X y (t) is the position of the randomly selected rth search agent at the current tth iteration; C(t) and T are random thermal resistance coefficients; z is the attenuation coefficient, which gradually decreases from 2 to 0 as the number of iterations increases; k is a constant k = 0, 1, 2…; λ1, λ2 and λ3 are random numbers in [0, 1]; α and μ are random numbers in [0, 1]; y is a random integer between [1, N]; Calculate the fitness value: fitness(t)=F(X(t+1)) In the formula, F(·) is the fitness value calculation function; Record the best search agent in the iteration.

6. The method for designing the parameter matching of the flexible topology architecture of the inter-vehicle mobile microgrid according to claim 5 is characterized in that: The method of performing bidirectional sine mutation on the optimal search proxy position and taking the search proxy position with the best fitness value before and after the mutation as the updated optimal search proxy position includes the following steps: For dimension j, the sine chaos value is calculated according to the current number of iterations, and the positive and negative directions are switched with equal probability: sin Value = sin(πx0) In the formula, rand is a random number between 0 and 1; x0 is the iteration sequence value; Perform mutation perturbation on the optimal position: X bs(j) (t+1)'=X bs(j) (t+1)+SinValue×X bs(j) (t+1) Where: X bs(j) (t+1) represents the optimal position X of the t+1th iteration bs The jth dimension of (t+1); X bs(j) (t+1)' is the optimal position X after the mutation disturbance bs The jth dimension of (t+1); Greedy Update: After each dimension has been mutated, stop mutation.

7. A vehicle-to-vehicle mobile microgrid flexible topology architecture parameter matching design system, characterized in that: The system comprises: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the vehicle-to-vehicle mobile microgrid flexible topology architecture parameter matching design method as described in any one of claims 1 to 6 are implemented.

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