DAB converter optimization method and device based on adaptive particle swarm optimization, equipment and medium

Abstract

The invention discloses a DAB converter optimization method and device, equipment and a medium, and belongs to the technical field of isolated bidirectional direct-current converters, and the method comprises the following steps: firstly, analyzing the transmission power and current stress characteristics of a dual active bridge DAB converter under dual phase shift control; adding a penalty coefficient to describe the relationship between the target function and the constraint function; the target function represents current stress, and the constraint function represents transmission power; introducing a self-adaptive inertia weight and a constraint factor to improve a particle swarm algorithm; and optimizing and solving an optimization function formed by the target function and the constraint function based on an improved particle swarm optimization algorithm, and obtaining a current stress minimum value of the DAB converter, thereby obtaining an optimal internal and external phase shift angle combination corresponding to the DAB converter. The development capability and the exploration capability of the particle swarm algorithm are greatly improved, the current stress is optimized through the adaptive particle swarm algorithm, and the efficiency of the DAB converter can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of isolated bidirectional direct current converters, and in particular to a DAB converter optimization method, device, equipment and medium based on an adaptive particle swarm algorithm. Background Art

[0002] Isolated DC converters can realize bidirectional energy flow, and can flexibly configure the voltage ratio between ports according to power demand and energy transmission, which is suitable for the access and consumption of distributed energy, so it has been widely studied. In the topological structure of the dual active bridge DC-DC converter (Dual Active Bridge, DAB), the front and rear H full bridge modules are symmetrical, and the forward and reverse flow of energy can be realized by phase shift control or frequency conversion control. In addition, a high-frequency transformer is added, which has the characteristics of electrical isolation and is more suitable for meeting the safety specifications required in actual use. In the prior art, the efficiency optimization research of DAB converters generally uses the Lagrange multiplier method to establish the mathematical relationship between the objective function and the constraint function. Therefore, the engineering design is more complex and the calculation is large. It is easy to fall into the local optimum and cannot accurately locate the optimal solution set. Particle swarm algorithm is widely used in the engineering field. It does not depend on the continuity and derivability of the objective function. It is simple, efficient and easy to implement. In view of the problem that the high current stress of DAB converter leads to high loss and low working efficiency, the optimal solution can be searched in a global range by combining particle swarm algorithm to avoid overfitting. However, the traditional particle swarm algorithm also has disadvantages such as slow convergence speed and inability to adjust parameters in a timely manner. Summary of the invention

[0003] The main purpose of the present invention is to suppress the current stress of the dual active bridge converter, improve the traditional particle swarm algorithm, and improve the working efficiency of the converter. By establishing a nonlinear constraint model of current stress and using an adaptive particle swarm algorithm to solve it, combined with improved inertia weight, the particle swarm optimization algorithm achieves a better balance between global exploration and local development. The purpose of the present invention is to provide a DAB converter optimization method and device based on an adaptive particle swarm algorithm to improve the performance and efficiency of the DAB converter under the existing technology.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A DAB converter optimization method, comprising:

[0006] Analyze the transmission power and current stress characteristics of DAB converter under dual phase shift control;

[0007] A penalty coefficient is added to describe the relationship between the objective function and the constraint function; by adjusting the penalty coefficient, the relationship between the objective function and the constraint condition is balanced in the process of finding the optimal solution, where the objective function represents the current stress and the constraint function represents the transmission power;

[0008] Adaptive inertia weight and constraint factor are introduced to improve particle swarm algorithm;

[0009] Based on the improved particle swarm algorithm, the optimization function composed of the objective function and the constraint function is solved, and the minimum current stress of the DAB converter is obtained, thereby obtaining the optimal inner and outer phase shift angle combination corresponding to the DAB converter.

[0010] Furthermore, the transmission power and current stress characteristics of the DAB converter under dual phase shift control are analyzed, including: under the premise of forward transmission, the transmission power P of the DAB converter in one switching cycle E And the per-unit value of the transmission power P pu , current stress I L and the per unit value of current stress I pu The expression is:

[0011] ;

[0012] ;

[0013] ;

[0014] ;

[0015] Where P E is the transmission power, P pu is the per-unit value of the transmission power, I L is the current stress, I pu is the per-unit value of current stress, k is the voltage conversion ratio, D1 is the inner phase shift, D2 is the outer phase shift angle, U1 is the input voltage, U2 is the output voltage, f is the frequency, and L is the inductance value.

[0016] Furthermore, a penalty coefficient is added to describe the relationship between the objective function and the constraint function, including: the adaptive particle swarm algorithm cannot directly find the optimal solution under nonlinear constraints, and the penalty coefficient is introduced to establish the functional relationship:

[0017] ;

[0018] Where F(D1, D2) is the objective function, α and β are penalty coefficients, and p is the given transmission power. is the constraint function; is the objective function.

[0019] Furthermore, adaptive inertia weight and constraint factor are introduced to improve the particle swarm algorithm, including: using Circle mapping strategy to replace the random population generation method in the particle swarm algorithm, the particle velocity, position and Circle chaos mapping formula are:

[0020] ;

[0021] ;

[0022] ;

[0023] In the formula, v i t is the velocity of the ith particle after the tth iteration, x i t is the position of the ith particle after the tth iteration, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers uniformly distributed in [0,1], P besti is the optimal position of the i-th individual particle, P Gbest is the optimal position of the group particles, mod is the modulus operator, num y is the yth chaotic sequence, a is a constant value, and b is also a constant value.

[0024] Furthermore, the particle swarm algorithm is improved by introducing an adaptive inertia weight and a constraint factor, which also includes: introducing a nonlinear decreasing inertia weight w, and introducing a constraint factor μ in the exploration phase. The inertia weight w and the constraint factor μ are:

[0025] ;

[0026] ;

[0027] In the formula, w max is the maximum value of the inertia weight, w min is the minimum value of the inertia weight, t is the current iteration number, T max is the maximum number of iterations.

[0028] Furthermore, the particle swarm algorithm is improved by introducing adaptive inertia weight and constraint factor, and the rate and position formula of the particle swarm algorithm after using adaptive inertia weight and constraint factor is updated as follows:

[0029] ;

[0030] .

[0031] Furthermore, based on the improved particle swarm algorithm, the minimum current stress of the DAB converter is obtained by optimization and solution, so as to obtain the optimal inner and outer phase shift angle combination corresponding to the DAB converter, including: using the improved particle swarm algorithm to obtain the optimal (D1, D2) phase shift angle combination with the minimum current stress.

[0032] The present invention also provides a DAB converter optimization device, comprising:

[0033] The transmission power and current stress characteristics module is used to analyze the transmission power and current stress characteristics of the DAB converter under dual phase shift control;

[0034] A relationship description module; used to add a penalty coefficient to describe the relationship between the objective function and the constraint function; by adjusting the penalty coefficient, the relationship between the objective function and the constraint condition is balanced in the process of finding the optimal solution, where the objective function represents the current stress and the constraint function represents the transmission power;

[0035] Particle swarm algorithm improvement module; used to introduce adaptive inertia weight and constraint factor to improve particle swarm algorithm;

[0036] Phase shift angle acquisition module: Based on the improved particle swarm algorithm, the optimization function composed of the objective function and the constraint function is solved, and the minimum current stress of the DAB converter is obtained, so as to obtain the optimal inner and outer phase shift angle combination corresponding to the DAB converter.

[0037] Furthermore, the transmission power and current stress characteristics of the DAB converter under dual phase shift control are analyzed, including: under the premise of forward transmission, the transmission power P of the DAB converter in one switching cycle E And the per-unit value of the transmission power P pu , current stress I L And the per unit value of current stress I pu The expression is:

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] Where P E is the transmission power, P pu is the per-unit value of the transmission power, I L is the current stress, I puis the per-unit value of current stress, k is the voltage conversion ratio, D1 is the inner phase shift, D2 is the outer phase shift angle, U1 is the input voltage, U2 is the output voltage, f is the frequency, and L is the inductance value.

[0043] Furthermore, a penalty coefficient is added to describe the relationship between the objective function and the constraint function, including: The adaptive particle swarm algorithm cannot directly find the optimal solution under nonlinear constraints, and a penalty coefficient needs to be introduced to establish the functional relationship:

[0044] ;

[0045] Where F(D1, D2) is the objective function, α and β are penalty coefficients, and p is the given transmission power. is the constraint function; is the objective function.

[0046] Furthermore, adaptive inertia weight and constraint factor are introduced to improve the particle swarm algorithm, including: using Circle mapping strategy to replace the random population generation method in the particle swarm algorithm, the particle velocity, position and Circle chaos mapping formula are:

[0047] ;

[0048] ;

[0049] ;

[0050] In the formula, v i t is the velocity of the ith particle after the tth iteration, x i t is the position of the ith particle after the tth iteration, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers uniformly distributed in [0,1], P besti is the optimal position of the i-th individual particle, P Gbest is the optimal position of the group particles, mod is the modulus operator, num y is the yth chaotic sequence, a is a constant value, and b is also a constant value.

[0051] Furthermore, an adaptive inertia weight and constraint factor are introduced to improve the particle swarm algorithm, including: introducing a nonlinear decreasing inertia weight w, and introducing a constraint factor μ in the exploration phase. The inertia weight w and the constraint factor μ are:

[0052] ;

[0053] ;

[0054] In the formula, wmax is the maximum value of the inertia weight, w min is the minimum value of the inertia weight, t is the current iteration number, T max is the maximum number of iterations.

[0055] Furthermore, the particle swarm algorithm is improved by introducing adaptive inertia weights and constraint factors, including: the rate and position formula of the particle swarm algorithm after using adaptive weights and constraint factors is updated as follows:

[0056] ;

[0057] .

[0058] Furthermore, based on the improved particle swarm algorithm, the minimum current stress of the DAB converter is obtained by optimization and solution, so as to obtain the optimal inner and outer phase shift angle combination corresponding to the DAB converter, including: using the improved particle swarm algorithm to obtain the optimal (D1, D2) phase shift angle combination with the minimum current stress.

[0059] The present invention also proposes an electronic device, characterized in that it includes: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method.

[0060] The present invention also provides a computer-readable storage medium, characterized in that executable instructions are stored thereon, and when the instructions are executed by a processor, the processor implements the above method.

[0061] The present invention has the following beneficial effects:

[0062] 1. The present invention discloses a DAB converter optimization method and device based on an adaptive particle swarm algorithm, applies the particle swarm algorithm to a DC converter, and proves its feasibility in the engineering field.

[0063] 2. Compared with the traditional particle swarm algorithm, the present invention improves the particle swarm algorithm by adding Circle chaos mapping, adaptive inertia weight and constraint factor, thereby improving the quality and reliability of the solution, and better balancing the global exploration ability and local development ability of the particle swarm optimization algorithm in the exploration phase, making the particle swarm algorithm a powerful tool for solving engineering optimization problems.

[0064] 3. The DAB converter optimization method and device based on the adaptive particle swarm algorithm proposed in the present invention can reduce the current stress of the converter system, simplify the complexity of parameter calculation, make the DAB converter more reliable, and effectively improve the working efficiency of the DAB converter. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is the circuit topology diagram of DAB converter;

[0066] Figure 2 This is the working waveform diagram of the dual phase shift control of the DAB converter;

[0067] Figure 3 It is the optimization flow chart of adaptive particle swarm algorithm;

[0068] Figure 4 It is the convergence curve iteration diagram of 0≤D1≤D2≤1 adaptive particle swarm algorithm;

[0069] Figure 5 It is the convergence curve iteration diagram of 0≤D2≤D1≤1 adaptive particle swarm algorithm;

[0070] Figure 6 This is the current stress effect diagram of the DAB converter 0≤D1≤D2≤1 based on the adaptive particle swarm algorithm;

[0071] Figure 7 This is a current stress effect diagram of the DAB converter 0≤D2≤D1≤1 based on the adaptive particle swarm algorithm. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions 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. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0073] As shown in Figure 1, the topology of the dual-active-bridge (DAB) converter is shown in Figure 1, where N is the turns ratio of the medium-high frequency transformer, C1 and C2 are the capacitors on the input and output sides, respectively, L is the sum of the transformer leakage inductance and transition inductance, and the voltage conversion ratio k is defined as Among them, U1 is the input voltage and U2 is the output voltage.

[0074] The working waveform of the dual phase shift control of the DAB converter is as follows Figure 2 As shown, D1 is the phase shift angle between S1 and S4, Q1 and Q4, called the inner phase shift angle; D2 is the phase shift angle between S1 and S4, called the outer phase shift angle.

[0075] The present invention provides a DAB converter optimization method, comprising:

[0076] Analyze the transmission power and current stress characteristics of dual active bridge DAB converter under dual phase shift control;

[0077] A penalty coefficient is added to describe the relationship between the objective function and the constraint function; including: by adjusting the penalty coefficient, the relationship between the objective function and the constraint condition is balanced in the process of finding the optimal solution, wherein the objective function represents the current stress and the constraint function represents the transmission power;

[0078] Adaptive inertia weight and constraint factor are introduced to improve particle swarm algorithm;

[0079] Based on the improved particle swarm algorithm, the optimization function composed of the objective function and the constraint function is solved, and the minimum current stress of the DAB converter is obtained, thereby obtaining the optimal inner and outer phase shift angle combination corresponding to the DAB converter.

[0080] like Figure 2 As shown, the transmission power and current stress characteristics of the DAB converter under dual phase shift control are analyzed, including: the transmission power P of the DAB converter in one switching cycle when the converter is under dual phase shift control E And per unit value P pu The expression is:

[0081] ;

[0082] ;

[0083] Current stress I L And per unit value I pu The expression is:

[0084] ;

[0085] ;

[0086] Where P E is the transmission power, P pu is the per-unit value of the transmission power, I L is the current stress, I pu is the per-unit value of current stress, k is the voltage conversion ratio, D1 is the inner phase shift, which is the phase shift angle between the switch tubes S1 and S4, D2 is the outer phase shift, which is the phase shift between the switch tubes S1 and Q1, and f is the frequency.

[0087] The objective function of the DAB converter is established by the mathematical function relationship. The present invention takes the current stress as the objective function and establishes a nonlinear model for the transmission power constraint condition and the phase shift constraint range as follows:

[0088] ;

[0089] Where F(D1, D2) is the objective function, α and β are penalty coefficients, and p is the given transmission power. is the constraint function; is the objective function.

[0090] However, the traditional particle swarm algorithm has some limitations when facing nonlinear constrained quadratic programming problems. In order to improve the convergence speed and accuracy and more effectively suppress current stress, the present invention proposes a DAB converter optimization method and device based on the particle swarm algorithm and the idea of ​​adaptively adjusting the inertia weight, which is an algorithm variant that better adapts to actual engineering needs.

[0091] like Figure 3 As shown, the input, output voltage and output current are obtained, and the individual and overall optimal fitness values ​​are updated through crossover, mutation and selection. The key steps are: introducing adaptive inertia weight and constraint factor to improve the particle swarm algorithm, and using Circle mapping to replace the random population generation method in the particle swarm algorithm. The particle speed, position and Circle chaos mapping formula are:

[0092] ;

[0093] ;

[0094] ;

[0095] In the formula, v i t is the velocity of the ith particle after the tth iteration, x i t is the position of the ith particle after the tth iteration, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers uniformly distributed in [0,1], P besti is the optimal position of the i-th individual particle, P Gbest is the optimal position of the group particles, mod is the modulus operator, num y is the yth chaotic sequence, a is a constant value of 0.5, and b is a constant value of 0.2.

[0096] The particle swarm algorithm is improved by introducing adaptive inertia weight and constraint factor, including: introducing a nonlinear decreasing inertia weight w, and introducing a constraint factor μ in the exploration phase. The inertia weight and constraint factor are:

[0097] ;

[0098] ;

[0099] In the formula, w max is the maximum value of the inertia weight, w max is the minimum value of the inertia weight, t is the current iteration number, Tmax is the maximum number of iterations.

[0100] like Figure 4 and Figure 5 As shown, the convergence curve iteration diagram of the particle swarm algorithm is improved by introducing adaptive inertia weights and constraint factors, including: the rate and position formula of the particle swarm algorithm after using adaptive weights and constraint factors is updated as follows:

[0101] ;

[0102] ;

[0103] like Figure 6 and Figure 7 As shown, based on the adaptive particle swarm algorithm, the minimum current stress of the DAB converter is solved to obtain the optimal inner and outer phase shift angle combination of the DAB converter, including: the optimal (D1, D2) phase shift angle combination with the minimum current stress can be obtained by using the improved particle swarm algorithm.

[0104] Finally, a simulation model of the DAB converter is built in MATLAB's Simulink. The parameter indicators are as follows: input voltage is 150V, output voltage is 100V, switching frequency f=50kHz, transformer ratio N=1:1, transition inductor L=22uH, input and output capacitors C1=620uF, and load R=20Ω. Figure 4 , Figure 5 It can be seen that after adding adaptive inertia weight and constraint factor, the convergence speed of particle swarm algorithm is faster and the accuracy is higher. Figure 6 , Figure 7 It can be seen that the current stress of the DAB converter based on the adaptive particle swarm algorithm is lower and the system working efficiency is improved.

[0105] The present invention further proposes a DAB converter optimization device, comprising:

[0106] The transmission power and current stress characteristics module is used to analyze the transmission power and current stress characteristics of the DAB converter under dual phase shift control;

[0107] A relationship description module; used to add a penalty coefficient to describe the relationship between the objective function and the constraint function; by adjusting the penalty coefficient, the relationship between the objective function and the constraint condition is balanced in the process of finding the optimal solution, where the objective function represents the current stress and the constraint function represents the transmission power;

[0108] Particle swarm algorithm improvement module; used to introduce adaptive inertia weight and constraint factor to improve particle swarm algorithm;

[0109] Phase shift angle acquisition module: Based on the improved particle swarm algorithm, the optimization function composed of the objective function and the constraint function is solved, and the minimum current stress of the DAB converter is obtained, so as to obtain the optimal inner and outer phase shift angle combination corresponding to the DAB converter.

[0110] Furthermore, the transmission power and current stress characteristics of the DAB converter under dual phase shift control are analyzed, including: under the premise of forward transmission, the transmission power P of the DAB converter in one switching cycle E And per unit value P pu , current stress I L And per unit value I pu The expression is:

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] Where P E is the transmission power, P pu is the per-unit value of the transmission power, I L is the current stress, I pu is the per-unit value of current stress, k is the voltage conversion ratio, D1 is the inner phase shift, which is the phase shift angle between the switch tubes S1 and S4, D2 is the outer phase shift, which is the phase shift between the switch tubes S1 and Q1, and f is the frequency.

[0116] Furthermore, a penalty coefficient is added to describe the relationship between the objective function and the constraint function, including: The adaptive particle swarm algorithm cannot directly find the optimal solution under nonlinear constraints, and a penalty coefficient needs to be introduced to establish the functional relationship:

[0117] ;

[0118] Where F(D1, D2) is the objective function, α and β are penalty coefficients, and p is the given transmission power. is the constraint function; is the objective function.

[0119] Furthermore, adaptive inertia weight and constraint factor are introduced to improve the particle swarm algorithm, including: using Circle mapping strategy to replace the random population generation method in the particle swarm algorithm, the particle velocity, position and Circle chaos mapping formula are:

[0120] ;

[0121] ;

[0122] ;

[0123] In the formula, v i t is the velocity of the ith particle after the tth iteration, x i t is the position of the ith particle after the tth iteration, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers uniformly distributed in [0,1], P besti is the optimal position of the i-th individual particle, P Gbest is the optimal position of the group particles, mod is the modulus operator, num y is the yth chaotic sequence, a is a constant value of 0.5, and b is a constant value of 0.2.

[0124] Furthermore, adaptive inertia weight and constraint factor are introduced to improve the particle swarm algorithm, including: introducing a nonlinear decreasing inertia weight w, and introducing a constraint factor μ in the exploration phase. The inertia weight and constraint factor are:

[0125] ;

[0126] ;

[0127] In the formula, w max is the maximum value of the inertia weight, w max is the minimum value of the inertia weight, t is the current iteration number, T max is the maximum number of iterations.

[0128] Furthermore, adaptive inertia weights and constraint factors are introduced to improve the particle swarm algorithm, including: the rate and position formula of the particle swarm algorithm after using adaptive weights and constraint factors is updated as follows:

[0129] ;

[0130] ;

[0131] Furthermore, the minimum current stress of the DAB converter is solved based on the adaptive particle swarm algorithm, so as to obtain the optimal inner and outer phase shift angle combination of the DAB converter, including: the optimal (D1, D2) phase shift angle combination with the minimum current stress can be obtained by using the improved particle swarm algorithm.

[0132] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is 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 DAB converter optimization method, characterized in that: include: Analyze the transmission power and current stress characteristics of dual active bridge DAB converter under dual phase shift control; A penalty coefficient is added to describe the relationship between the objective function and the constraint function; including: by adjusting the penalty coefficient, the relationship between the objective function and the constraint condition is balanced in the process of finding the optimal solution, wherein the objective function represents the current stress and the constraint function represents the transmission power; Adaptive inertia weight and constraint factor are introduced to improve particle swarm algorithm; Based on the improved particle swarm algorithm, the optimization function composed of the objective function and the constraint function is solved, and the minimum current stress of the DAB converter is obtained, thereby obtaining the optimal inner and outer phase shift angle combination corresponding to the DAB converter.

2. A DAB converter optimization method as claimed in claim 1, characterized in that: The transmission power and current stress characteristics of the DAB converter under dual phase shift control are analyzed, including: Under the premise of forward transmission, the transmission power P of the DAB converter in one switching cycle E And the per-unit value of the transmission power P pu , current stress I L and the per unit value of current stress I pu The expression is: ； ； ； ； Where P E is the transmission power, P pu is the per-unit value of the transmission power, I L is the current stress, I pu is the per-unit value of current stress, k is the voltage conversion ratio, D1 is the inner phase shift, D2 is the outer phase shift angle, U1 is the input voltage, U2 is the output voltage, f is the frequency, and L is the inductance value.

3. A DAB converter optimization method as claimed in claim 1, characterized in that: The penalty coefficient is added to describe the relationship between the objective function and the constraint function, including: The adaptive particle swarm algorithm cannot directly find the optimal solution under nonlinear constraints, and the penalty coefficient is introduced to establish the functional relationship: ； Where F(D1, D2) is the optimization function, α and β are penalty coefficients, and p is the given transmission power. is the constraint function; is the objective function.

4. A DAB converter optimization method as claimed in claim 1, characterized in that: Adaptive inertia weight and constraint factor are introduced to improve the particle swarm algorithm, including: using Circle mapping strategy to replace the random population generation method in the particle swarm algorithm, the particle velocity, position and Circle chaos mapping formula are: ； ； ； In the formula, v i t is the velocity of the ith particle after the tth iteration, x i t is the position of the ith particle after the tth iteration, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers uniformly distributed in [0,1], P besti is the optimal position of the i-th individual particle, P Gbest is the optimal position of the group particles, mod is the modulus operator, num y is the yth chaotic sequence, a is a constant value, and b is also a constant value.

5. A DAB converter optimization method as claimed in claim 4, characterized in that: The particle swarm algorithm is improved by introducing adaptive inertia weight and constraint factor, which also includes: introducing a nonlinear decreasing inertia weight w, and introducing a constraint factor μ in the exploration stage. The inertia weight w and the constraint factor μ are: ； ； In the formula, w max is the maximum value of the inertia weight, w min is the minimum value of the inertia weight, t is the current iteration number, T max is the maximum number of iterations.

6. A DAB converter optimization method as claimed in claim 4, characterized in that: The particle swarm algorithm is improved by introducing adaptive inertia weight and constraint factor, and also includes: the rate and position formula of the particle swarm algorithm after using adaptive inertia weight and constraint factor is updated as follows: ； 。 7. A DAB converter optimization method as claimed in claim 1, characterized in that: Based on the improved particle swarm algorithm, the minimum current stress of the DAB converter is obtained by optimization and solution, so as to obtain the optimal inner and outer phase shift angle combination corresponding to the DAB converter, including: using the improved particle swarm algorithm to obtain the optimal (D1, D2) phase shift angle combination with the minimum current stress.

8. A DAB converter optimization device, characterized in that: include: The transmission power and current stress characteristics module is used to analyze the transmission power and current stress characteristics of the DAB converter under dual phase shift control; Relationship description module; Used to add penalty coefficients to describe the relationship between the objective function and the constraint function; By adjusting the penalty coefficient, the relationship between the objective function and the constraint condition is balanced in the process of finding the optimal solution, where the objective function represents the current stress and the constraint function represents the transmission power; Particle swarm algorithm improvement module; used to introduce adaptive inertia weight and constraint factor to improve particle swarm algorithm; Phase shift angle acquisition module: Based on the improved particle swarm algorithm, the optimization function composed of the objective function and the constraint function is solved, and the minimum current stress of the DAB converter is obtained, so as to obtain the optimal inner and outer phase shift angle combination corresponding to the DAB converter.

9. The DAB converter optimization device according to claim 8, characterized in that: The transmission power and current stress characteristics of the DAB converter under dual phase shift control are analyzed, including: Under the premise of forward transmission, the transmission power P of the DAB converter in one switching cycle E And the per-unit value of the transmission power P pu , current stress I L and the per unit value of current stress I pu The expression is: ； ； ； ； Where P E is the transmission power, P pu is the per-unit value of the transmission power, I L is the current stress, I pu is the per-unit value of current stress, k is the voltage conversion ratio, D1 is the inner phase shift, D2 is the outer phase shift angle, U1 is the input voltage, U2 is the output voltage, f is the frequency, and L is the inductance value.

10. A DAB converter optimization device as claimed in claim 8, characterized in that: The penalty coefficient is added to describe the relationship between the objective function and the constraint function, including: The adaptive particle swarm algorithm cannot directly find the optimal solution under nonlinear constraints, and the penalty coefficient needs to be introduced to establish the functional relationship: ； Where F(D1, D2) is the optimization function, α and β are penalty coefficients, and p is the given transmission power. is the constraint function; is the objective function.

11. A DAB converter optimization device as claimed in claim 8, characterized in that: Adaptive inertia weight and constraint factor are introduced to improve the particle swarm algorithm, including: using Circle mapping strategy to replace the random population generation method in the particle swarm algorithm, the particle velocity, position and Circle chaos mapping formula are: ； ； ； In the formula, v i t is the velocity of the ith particle after the tth iteration, x i t is the position of the ith particle after the tth iteration, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are random numbers uniformly distributed in [0,1], P besti is the optimal position of the i-th individual particle, P Gbest is the optimal position of the group particles, mod is the modulus operator, num y is the yth chaotic sequence, a is a constant value, and b is also a constant value.

12. A DAB converter optimization device as claimed in claim 11, characterized in that: The particle swarm algorithm is improved by introducing adaptive inertia weight and constraint factor, including: introducing a nonlinear decreasing inertia weight w, and introducing a constraint factor μ in the exploration phase. The inertia weight w and constraint factor μ are: ； ； In the formula, w max is the maximum value of the inertia weight, w min is the minimum value of the inertia weight, t is the current iteration number, T max is the maximum number of iterations.

13. A DAB converter optimization device as claimed in claim 11, characterized in that: Adaptive inertia weights and constraint factors are introduced to improve the particle swarm algorithm, including: the particle swarm algorithm rate and position formula after using adaptive weights and constraint factors is updated as follows: ； 。 14. A DAB converter optimization device as claimed in claim 8, characterized in that: Based on the improved particle swarm algorithm, the minimum current stress of the DAB converter is obtained by optimization and solution, so as to obtain the optimal inner and outer phase shift angle combination corresponding to the DAB converter, including: using the improved particle swarm algorithm to obtain the optimal (D1, D2) phase shift angle combination with the minimum current stress.

15. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

16. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by a processor, the processor implements the method according to any one of claims 1 to 8.

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