Integrated Optimization Design Method, System and Medium for Inter-vehicle Mobile Microgrid Experimental System

Abstract

The present invention provides an integrated optimization design method, system and medium for an inter-vehicle mobile microgrid experimental system, belonging to the technical field of microgrids, including: determining and initializing the basic elements and performance parameters of the inter-vehicle mobile microgrid experimental system; taking the maximization of the transmission power, transmission efficiency or availability of the inter-vehicle mobile microgrid experimental system as the 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 optimal basic elements and performance parameters of the inter-vehicle mobile microgrid experimental system; the improved transient search optimization algorithm introduces the position update method of the sand cat swarm optimization algorithm; according to the optimal basic elements and performance parameters of the inter-vehicle mobile microgrid experimental system, constructing an inter-vehicle mobile microgrid experimental system. This method matches the optimal parameters of the inter-vehicle mobile microgrid experimental system and obtains accurate energy regulation simulation and test results.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle - to - vehicle mobile micro - grid, and particularly to an integrated optimization design method for a vehicle - to - vehicle mobile micro - grid experimental system. Background Technique

[0002] A vehicle - to - vehicle mobile micro - grid experimental system is an experimental platform for researching, verifying, and evaluating the concept of vehicle - to - vehicle mobile micro - grid. Such a system usually consists of hardware devices, software control systems, and experimental environments, aiming to simulate the energy interaction and management between vehicles under real - world scenarios. The vehicle - to - vehicle mobile micro - grid experimental system is one of the key technologies for verifying the advantages and disadvantages of vehicle - to - vehicle mobile micro - grid. Therefore, it is urgent and necessary to deeply study the technical characteristics of the vehicle - to - vehicle mobile micro - grid experimental system and carry out research on the integrated optimization design of the vehicle - to - vehicle mobile micro - grid experimental system.

[0003] However, in some studies, when conducting the integrated design of the vehicle - to - vehicle mobile micro - grid experimental system, the optimization of various basic parameters of the vehicle - to - vehicle mobile micro - grid experimental system is lacking. The optimized vehicle - to - vehicle mobile micro - grid experimental system obtained cannot match the micro - grid system architecture with the best energy utilization efficiency, and thus accurate energy regulation simulation and test results cannot be obtained, and effective reference and guidance for practical applications cannot be provided. Summary of the Invention

[0004] To match the optimal parameters of the vehicle - to - vehicle mobile micro - grid experimental system and obtain accurate energy regulation simulation and test results, the present invention provides an integrated optimization design method for a vehicle - to - vehicle mobile micro - grid experimental system:

[0005] The integrated optimization design method for a vehicle - to - vehicle mobile micro - grid experimental system includes the following steps:

[0006] Determine and initialize the basic elements and performance parameters of the vehicle - to - vehicle mobile micro - grid experimental system;

[0007] Maximize the transmission power, transmission efficiency, or availability of the vehicle - to - vehicle mobile micro - grid experimental system as the objective function, and use the basic elements and performance parameters of the vehicle - to - vehicle mobile micro - grid experimental system as the optimization objects;

[0008] Determine the upper and lower boundaries of the optimization objects, and according to the objective function, use an improved transient search optimization algorithm to search for the optimal basic elements and performance parameters of the vehicle - to - vehicle mobile micro - grid experimental system; wherein, the improved transient search optimization algorithm introduces the position update method of the sand cat swarm optimization algorithm, and updates the search agent position according to factors such as the optimal position of the search agent in the current iteration, the positions of other random search agents within the population, and the random angle values of each search agent;

[0009] Construct a vehicle - to - vehicle mobile micro - grid experimental system according to the optimal basic elements and performance parameters of the vehicle - to - vehicle mobile micro - grid experimental system.

[0010] Preferably, the basic elements and performance parameters of the inter-vehicle mobile microgrid experimental system include vehicle-mounted system platform parameters, power system parameters, transmitter inverter, transmitter coil, receiver coil, receiver converter parameters and load parameters.

[0011] Preferably, the method of searching for the optimal basic elements and performance parameters of the inter-vehicle mobile microgrid experimental system using the improved transient search optimization algorithm comprises the following steps:

[0012] Determine the upper and lower boundaries of the optimization object and initialize the search agent population position through Gaussian mapping;

[0013] Calculate the optimal fitness value according to the objective function, and search and record the optimal search agent position;

[0014] The position update method of the sand cat swarm optimization algorithm is introduced to improve the position update method of 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;

[0015] Bidirectional search for optimal agent positions sine Mutation: The search agent position with the best fitness value before and after mutation is used as the updated optimal search agent position;

[0016] The optimal search agent position is updated in sequence according to the preset maximum number of iterations to determine the optimal search agent position; the basic elements and performance parameters of the optimal inter-vehicle mobile microgrid experimental system are determined based on the optimal search agent position.

[0017] Preferably, the initialization of the search agent population position by Gaussian mapping comprises the following steps:

[0018] Determine the size of the population N , search agent optimization lower bound LB and the search agent's upper bound UB ;

[0019] Generate the next random number through Gaussian mapping x t+1 :

[0020]

[0021] In the formula, mod(·) is the remainder function, x t is the current random number;

[0022] Initialize the search agent position using the generated Gaussian random number:

[0023]

[0024] In the formula, is the position of the initial search agent.

[0025] Preferably, the position is updated by the improved search agent position update method, and the optimal fitness value and the search agent position after the current iteration are recorded, including the following steps:

[0026] The sand cat swarm optimization algorithm is introduced, and the improved search agent position update formula is as follows:

[0027]

[0028] Where:

[0029]

[0030]

[0031]

[0032] Among them, is the position of the search agent at the t +1-th iteration; represents the best position of the search agent at the t -th iteration; is the position of the search agent at the current t -th iteration; is the position of the t -th randomly selected search agent at the current y -th iteration; and are random thermal resistance coefficients; is the attenuation coefficient, which gradually decreases from 2 to 0 as the number of iterations increases; is a constant (k = 0, 1, 2...); , and are random numbers within [0, 1]; is a random number within [0, 1]; y is a random integer between [1, N]; is a random angle value for each search agent, and a roulette wheel method is used to randomly select an angle value for each search agent;

[0033] Calculate the fitness value:

[0034]

[0035] In the formula, is the fitness value calculation function;

[0036] Record the optimal search agent in the current iteration.

[0037] Preferably, the two-way sine mutation of the optimal search agent position is performed, and the position of the search agent with the optimal fitness value before and after mutation is used as the updated optimal search agent position, including the following steps:

[0038] For dimension j , calculate the sine chaos value according to the current iteration number and switch the positive and negative directions with equal probability:

[0039]

[0040]

[0041] In the formula, rand is a random number from 0 to 1; x 0 is the iteration sequence value;

[0042] Perform mutation perturbation on the optimal position:

[0043]

[0044] In the formula: represents the optimal position of the t +(1)th iteration of the j dimension; is the optimal position after mutation perturbation of the j dimension;

[0045] Greedy update:

[0046]

[0047] After mutation in each dimension, stop the mutation.

[0048] An integrated optimization design system for an inter-vehicle mobile microgrid experimental system, the system includes:

[0049] A processor;

[0050] A memory, on which a computer program that can run on the processor is stored;

[0051] Wherein, when the computer program is executed by the processor, the steps of the integrated optimization design method for the inter-vehicle mobile microgrid experimental system are implemented.

[0052] A computer-readable storage medium, on which a data processing program is stored, and when the data processing program is executed by a processor, the steps of the integrated optimization design method for the inter-vehicle mobile microgrid experimental system are implemented.

[0053] Advantages of the present invention:

[0054] The present invention proposes an integrated optimization design method for an inter-vehicle mobile microgrid experimental system. An improved search agent optimization algorithm is used to generate the parameters of the inter-vehicle mobile microgrid experimental system, forming an integrated optimization design scheme for the inter-vehicle mobile microgrid experimental system. The improved search agent optimization algorithm adopted by this method improves the position update method of the search agent. The position update mechanism of the sand cat swarm optimization algorithm is introduced to improve the position update method of the search agent. Factors such as the optimal position of the search agent in this iteration, the positions of other random search agents within the population, and the random angle values of each search agent are comprehensively considered to update the position of the search agent, avoiding local optima in each iteration, thereby improving the global search ability of the transient search optimization algorithm, realizing an increase in the search range of the algorithm, enhancing the adaptability of the algorithm, and further improving the generation efficiency of the integrated optimization design scheme for the inter-vehicle mobile microgrid experimental system, the rationality of the entire integrated matching design scheme, and the optimized inter-vehicle mobile microgrid experimental system can be matched to the microgrid system architecture with the best energy utilization efficiency, and accurate energy regulation simulation and test results can be obtained, providing effective reference and guidance for practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of the integrated optimization design method for the inter-vehicle mobile microgrid experimental system according to an embodiment of the present invention;

[0056] Figure 2 is an iterative process curve of the integrated optimization design scheme for the inter-vehicle mobile microgrid experimental system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0058] Embodiment 1

[0059] In-depth research on the technical characteristics of the inter-vehicle mobile microgrid experimental system, and it is urgent and necessary to carry out research on the integrated optimization design of the inter-vehicle mobile microgrid experimental system. In some studies, when conducting the integrated design of the inter-vehicle mobile microgrid experimental system, the optimization of each basic parameter of the inter-vehicle mobile microgrid experimental system is lacking. The optimized inter-vehicle mobile microgrid experimental system obtained cannot be matched to the microgrid system architecture with the best energy utilization efficiency, and thus accurate energy regulation simulation and test results cannot be obtained, and effective reference and guidance cannot be provided for practical applications. Therefore, this embodiment proposes an integrated optimization design method for the inter-vehicle mobile microgrid experimental system, and the flowchart is as Figure 1As shown in the figure, it specifically includes the following steps:

[0060] S1: Initialize the basic structure and parameter information of the vehicle-to-vehicle mobile microgrid experimental system according to the basic elements and performance parameters of the vehicle-to-vehicle mobile microgrid experimental system to be designed (for example, vehicle-mounted platform, power source, transmitter inverter, transmitting coil, receiving coil, receiver converter, load, etc.).

[0061] S2: Establish the objective function of the integrated optimization design scheme of the vehicle-to-vehicle mobile microgrid experimental system based on the improved transient search optimization algorithm funtion , such as transmission power, transmission efficiency, availability, etc.

[0062] S3: Conduct parameter settings, mainly including: the size of the search agent population (i.e., the number of search agent individuals) N ; the maximum number of iterations (i.e., the condition for stopping iteration) Miter ; the lower boundary of search agent optimization LB ; the upper boundary of search agent optimization UB .

[0063] S4: Initialize the positions of the search agent population using the Gaussian mapping.

[0064] Generation of Gaussian mapping random numbers:

[0065]

[0066] Initialize the positions of the search agents using the generated Gaussian random numbers:

[0067]

[0068] In the formula: is the position of the initial search agent.

[0069] S5: Update the positions of the search agents.

[0070] 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 ability of the search agent, the position update mechanism of the sand cat swarm optimization algorithm is introduced to improve the search agent position update method. The positions of the search agents are updated by comprehensively considering factors such as the optimal position of the search agent in this iteration, the positions of other random search agents within the population, and the random angle values of each search agent, avoiding local optima in each iteration, and thus improving the global search ability of the transient search optimization algorithm.

[0071] Introduce the sand cat swarm optimization algorithm, and the updated formula for the positions of the improved search agents is as follows:

[0072]

[0073] Among them:

[0074]

[0075]

[0076]

[0077] Among them, is the position of the search agent in the t +(1)th iteration; represents the best position of the search agent in the t th iteration; is the position of the search agent in the current t th iteration; is the position of the t th randomly selected search agent in the current y th iteration; and are random thermal resistance coefficients; is the attenuation coefficient, which gradually decreases from 2 to 0 as the number of iterations increases; is a constant (k = 0, 1, 2...); , and are random numbers within [0, 1]; is a random number within [0, 1]; y is a random integer between [1, N]; is the random angle value of each search agent, and a roulette wheel method is used to randomly select an angle value for each search agent.

[0078] S6: Calculate the fitness value.

[0079]

[0080] In the formula, is the fitness function when calculating the fitness value.

[0081] S7: Record information. Record the optimal search agent in the current iteration.

[0082] S8: Perform two-way sine mutation on each dimension of the optimal search agent. For dimension j . First, calculate the sine chaotic value according to the current iteration number. And switch the positive and negative directions with equal probability.

[0083]

[0084]

[0085] Then, mutate and perturb the optimal position

[0086]

[0087] In the formula: represents the optimal position at the (t + 1)-th iteration of the j-th dimension.

[0088] Greedy update:

[0089]

[0090] After mutating each dimension, stop the mutation.

[0091] S9: Record information. Record the optimal search agent in the current iteration.

[0092] S10: Repeat steps S5 - S9. After reaching the maximum number of iterations Miter the algorithm stops, outputs the optimal search agent, and obtains the optimal integrated optimization design scheme for the vehicle - to - vehicle mobile micro - grid experimental system, that is, obtains the vehicle - to - vehicle mobile micro - grid experimental system scheme.

[0093] In this embodiment:

[0094] Taking MATLAB as the simulation platform, select the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Transient search optimization algorithm (TSOA), and compare them with the ITSOA method proposed in this embodiment.

[0095] 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 parameters are set as shown in Table 1.

[0096] Table 1 Algorithm parameter settings:

[0097]

[0098] Figure 2 is the iteration process curve. It can be intuitively found from Figure 2 that the convergence speed of the ITSOA method is faster than the other three algorithms, and the convergence accuracy of the ITSOA method is better than the other three algorithms. The simulation results show that the ITSOA algorithm has stronger search ability, obtains a better integrated optimization design scheme for the vehicle - to - vehicle mobile micro - grid experimental system, and verifies the effectiveness of the algorithm.

[0099] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An integrated optimization design method for an inter-vehicle mobile microgrid experimental system, characterized in that, Including the following steps: Determine and initialize the basic elements and performance parameters of the in-vehicle mobile microgrid experimental system; Take the maximization of the transmission power, transmission efficiency, or availability of the in-vehicle mobile microgrid experimental system as the objective function, and take the basic elements and performance parameters of the in-vehicle mobile microgrid experimental system as the optimization objects; Determine the upper and lower boundaries of the optimization objects. According to the objective function, use the improved transient search optimization algorithm to search for the optimal basic elements and performance parameters of the in-vehicle mobile microgrid experimental system; wherein, the improved transient search optimization algorithm introduces the position update method of the sand cat swarm optimization algorithm, and updates the search agent position according to the factors of the optimal position of the search agent in the current iteration, the positions of other random search agents within the population, and the random angle values of each search agent; Construct the in-vehicle mobile microgrid experimental system according to the optimal basic elements and performance parameters of the in-vehicle mobile microgrid experimental system; The step of using the improved transient search optimization algorithm to search for the optimal basic elements and performance parameters of the in-vehicle mobile microgrid experimental system includes the following steps: Determine the upper and lower boundaries of the optimization objects, and initialize the positions of the search agent population through Gaussian mapping; Calculate the optimal fitness value according to the objective function, and search for and record the optimal search agent position; Introduce the position update method of the sand cat swarm optimization algorithm to improve the position update method in the original transient search optimization algorithm; perform position update through the improved search agent position update method, and record the optimal fitness value and search agent position after the current iteration; Bidirectionally sine mutate the position of the optimal search agent, and use the position of the search agent with the best fitness value before and after mutation as the updated position of the optimal search agent; Update the optimal search agent position in sequence according to the preset maximum number of iterations, and determine the optimal search agent position; determine the optimal basic elements and performance parameters of the in-vehicle mobile microgrid experimental system according to the optimal search agent position; The step of initializing the positions of the search agent population through Gaussian mapping includes the following steps: Determine the size of the population N , search agent optimization lower bound LB and search agent optimization upper bound UB ; Generate the next random number through the Gaussian mapping x t+1 : where mod(·) is the remainder function, x t is the current random number; Initialize the search agent position by using the generated Gaussian random numbers: In the formula, is the position of the initial search agent; The step of performing position update through the improved search agent position update method and recording the optimal fitness value and search agent position after the current iteration includes the following steps: Introduce the sand cat swarm optimization algorithm, and the improved search agent position update formula is as follows: Wherein: Wherein, is the position of the search agent at the t +(1)th iteration; represents the best position of the search agent at the t th iteration; is the position of the search agent at the current t th iteration; is the position of the t th randomly selected search agent at the current y th iteration; and are random thermal resistance coefficients; is the attenuation coefficient, which gradually decreases from 2 to 0 as the number of iterations increases; is a constant k = 0, 1, 2...; , and are random numbers within [0, 1]; is a random number within [0, 1]; y is a random integer between [1, N]; is a random angular value for each search agent, and a roulette wheel method is used to randomly select an angular value for each search agent; Calculate the fitness value: In the formula, is the fitness value calculation function; Record the optimal search agent in the current iteration.

2. The integrated optimization design method of the vehicle-to-vehicle mobile microgrid experimental system according to claim 1, wherein The basic elements and performance parameters of the in-vehicle mobile microgrid experimental system include vehicle-mounted system platform parameters, power supply system parameters, transmitter inverters, transmitter coils, receiver coils, receiver-side converter parameters, and load parameters.

3. The integrated optimization design method of the vehicle-to-vehicle mobile microgrid experimental system according to claim 1, characterized in that, The two-way sine mutation is performed on the position of the optimal search agent, and the position of the search agent with the best fitness value before and after the mutation is used as the updated position of the optimal search agent, including the following steps: For the dimension j , calculate according to the current iteration number sine the chaos value, and switch the positive and negative directions with equal probability: In the formula, rand is a random number from 0 to 1; x 0 is the iterative sequence value; Perform mutation perturbation on the optimal position: Wherein: represents the t optimal position at the th j iteration + 1; is the th j dimension of the optimal position after mutation perturbation; Greedy update: After mutating each dimension, stop mutating.

4. An integrated optimization design system for an in-vehicle mobile microgrid experimental system, characterized in that, The system includes: A processor; A memory having a computer program stored thereon that can be run on the processor; Wherein, when the computer program is executed by the processor, it implements the steps of the integrated optimization design method of the in-vehicle mobile microgrid experimental system as described in any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that, A data processing program is stored on the computer-readable storage medium. When the data processing program is executed by a processor, the steps of the integrated optimization design method of the vehicle-to-vehicle mobile microgrid experimental system according to any one of claims 1 to 3 are implemented.

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