High-degree-of-freedom adjustment strategy and optimization method applied to dual-active bridge converter

Abstract

The invention discloses a high-degree-of-freedom modulation strategy applied to a dual-active bridge converter and an optimization method. The high-degree-of-freedom modulation strategy is specifically implemented according to the following steps: step 1, determining a working mode of a DAB converter based on a conduction sequence and a time length of switches in the DAB converter; 2, solving steady-state characteristics of each working mode; step 3, taking the minimum peak value of the inductive current as an optimization target, and solving the minimum peak value current in each working mode by using a particle swarm algorithm; and 4, inputting the optimal phase shift value obtained in the step 3 into a digital signal processor. Through an innovative DAB converter modulation strategy, the effective value of inductive current is remarkably reduced, the soft switching working range of the DAB converter is greatly widened, double reduction of conduction loss and switching loss is achieved, and therefore remarkable effects are achieved in the aspects of improving circuit energy efficiency and stability and promoting technology industrialization.

Description

Technical Field

[0001] The invention belongs to the technical field of power electronic DC converters, and in particular relates to a high degree of freedom modulation strategy and an optimization method applied to a dual active bridge converter. Background Art

[0002] With the rapid development of distributed renewable energy (such as solar energy, wind energy, etc.) and the growing number of electric vehicle users, the power system has an increasingly urgent need for efficient and reliable power conversion equipment. In this context, the DC converter, as the core equipment for power conversion, undertakes the important task of converting DC power of different voltage levels. Its performance directly affects the efficiency and stability of the entire power system, so it has become a hot spot and difficulty in current research.

[0003] Among many DC converters, the Dual Active Bridge (DAB) converter stands out with its unique advantages and becomes the focus of DC power conversion equipment research. The DAB converter has significant characteristics such as structural symmetry, bidirectional energy flow, electrical isolation and high power density, which enable it to adapt to the complex and changing power environment and meet the diverse needs of renewable energy generation, electric vehicle charging and smart grid.

[0004] The basic structure of the DAB converter consists of two H-bridge circuits, which are connected through a high-frequency transformer and a power inductor, forming a structure for bidirectional energy flow. This structure enables the DAB converter to not only maintain high efficiency when transmitting electric energy in the forward direction, but also perform equally well when transmitting electric energy in the reverse direction, thereby greatly improving its application flexibility and adaptability.

[0005] However, despite the many advantages of DAB converters, they still face some technical challenges in practical applications, such as how to further improve conversion efficiency, how to optimize control strategies to achieve more stable power transmission, etc. Therefore, research on DAB converters is still deepening, in order to further improve their performance and reliability while maintaining their advantages, so as to meet the needs of future power systems for efficient and reliable power conversion equipment. Summary of the invention

[0006] The purpose of the present invention is to provide a high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter, which can improve the conversion efficiency and stability of the dual active bridge converter.

[0007] The technical solution adopted by the present invention is a high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter, which is specifically implemented according to the following steps:

[0008] Step 1, determining the operating mode of the DAB converter based on the turn-on sequence and duration of switches in the DAB converter;

[0009] Step 2, solving the steady-state characteristics of each working mode;

[0010] Step 3, taking the minimum inductor current peak as the optimization goal, the particle swarm algorithm is used to find the minimum peak current under each working mode;

[0011] Step 4: input the optimal phase shift value obtained in step 3 into the digital signal processor.

[0012] The technical solution of the present invention is also characterized in that:

[0013] The DAB converter in step 1 includes two full-bridge circuits, which are connected through a high-frequency transformer and a power inductor. The full-bridge with inductance is defined as the primary bridge, and the full-bridge without inductance is defined as the secondary bridge. The conduction sequence and duration of the switches are described by six degrees of freedom parameters.

[0014] Step 1 is implemented according to the following steps:

[0015] Step 1: There are two full bridges in the dual active bridge converter, and the two full bridges are isolated by an isolation transformer. The full bridge with inductance is defined as the primary bridge, and the full bridge without inductance is defined as the secondary bridge. The six degrees of freedom parameters satisfy the following constraints:

[0016]

[0017] Wherein, D1 is the on-time of S2 and S4, D2 is the phase shift time between S1 and S4, D3 is the on-time of S6 and S8, D4 is the phase shift time between S5 and S8, D5 is the phase shift time between S1 and S5, and D6 is the on-time of S2;

[0018] Step 1.2, the operation mode of the DAB converter is based on v AB and v CD The relative position of the model is divided and it is divided into 10 modes using the interpolation method.

[0019] In step 2, the steady-state characteristics of each working mode are solved, and the inductor current i in each mode is analyzed by the piecewise linear time domain method. L (t), and the transmission power and current stress in each mode are derived based on the change of inductor current.

[0020] Step 2 is implemented as follows:

[0021] Step 2.1, the piecewise linear time domain method is used to solve the inductor current i through the 6-DOF modulation scheme. L (t) can be expressed as:

[0022]

[0023] Among them, V1 is the primary DC side voltage, V2 is the secondary DC side voltage, V Cb is the voltage on the DC blocking capacitor, N is the transformer ratio, and t0-t8 is the inductor current node time;

[0024] From formula (2), we can see that there is the following relationship between frequency, degrees of freedom and time:

[0025]

[0026] In the formula, f s is the switching frequency;

[0027] Step 2.2, in order to improve the efficiency of the device, a more direct optimization target is needed. In this mode, the current rises from t0 to t4 and drops from t4 to t8, so the maximum current appears at t4 and the minimum current appears at t0. Therefore, the peak-to-peak current of the inductor can be obtained as follows:

[0028]

[0029] Step 2.3, from the above formula, we can see that the inductor peak current I p-p The expression of is relatively simple. It can be regarded as the sign of the RMS current under the same output power. The average output power is calculated as follows:

[0030]

[0031] Step 2.4: Repeat the above steps 2.1-2.3 to obtain the effective transmission power results and current stress results of each working mode.

[0032] In step 3, the minimum inductor current peak value is taken as the optimization goal, and the particle swarm algorithm is used to optimize the six degree-of-freedom parameters to solve the minimum peak current in each working mode.

[0033] Each particle in the particle swarm algorithm in step 3 represents a potential optimal solution, and its position is determined by the combination of six degrees of freedom parameters. The optimal solution is searched by iteratively updating the position and velocity of the particle; the individual learning factor c1 and the group learning factor c2 of the particle swarm algorithm are set according to the actual working conditions, the inertia weight ω is 0.5-1, and r1 and r2 are randomly generated numbers between 0 and 1; the output result of the particle swarm algorithm is the optimal phase shift value, which is used to control the DAB converter to optimize the transmission power and current stress.

[0034] Step 3 is implemented as follows:

[0035] Step 3.1, each individual particle represents a potential optimal solution, involving two feature indices: position X and velocity V:

[0036]

[0037] Step 3.2, initialize a set of particles, including random positions and velocities, and then perform the following iterations:

[0038]

[0039] Where m is the iteration index, ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are randomly generated numbers, N is the initial number of particles, and X is defined as i and V i are the position and velocity of the ith particle, Pxbest is the best position in the individual history, and Gxbest is the best position in the group history;

[0040] Step 3.3, if the maximum number of iterations is reached, exit the loop or continue;

[0041] Step 3.4, solve the peak-to-peak inductor current value of each particle;

[0042] Step 3.5, by comparing the current and historical fitness values ​​of a single particle, the better one is used as the current Pxbest. Similarly, by comparing the current fitness value and historical fitness value of the group, the better value is used as the current Gxbest.

[0043] Step 3.6, adjust the particle speed and position according to equation (7) until the optimal current stress is solved, and then the optimal phase shift value is obtained.

[0044] The beneficial effects of the present invention are:

[0045] 1) The effective value of the inductor current is significantly reduced: The present invention effectively reduces the effective value of the inductor current through innovative circuit design and modulation strategy, which not only reduces the heat loss of the system, but also improves the energy efficiency performance of the overall circuit, providing a more reliable foundation for the long-term stable operation of the equipment.

[0046] 2) The soft switching range is greatly broadened: The present invention significantly broadens the soft switching operating range of the DAB converter by optimizing the switching timing and circuit parameters. The converter can achieve zero voltage switching (ZVS) or zero current switching (ZCS) under a wider range of working conditions, thereby greatly reducing energy loss and electromagnetic interference during the switching process and improving the stability and efficiency of the system.

[0047] 3) Double reduction of conduction loss and switching loss: The energy loss of the DAB converter of the present invention is effectively controlled both in the conduction state and during the switching process. This double loss reduction effect directly improves the conversion efficiency of the converter, making energy utilization more efficient, which is of great significance for energy conservation and emission reduction.

[0048] 4) The implementation method is simple and easy: The method of the present invention is not only technologically advanced, but also its implementation process is more concise and clear, and easy to apply in engineering. It not only reduces the complexity of technical implementation, but also shortens the product development cycle, which is conducive to the rapid promotion and industrialization of technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is the topology of dual active bridge DC-DC converter;

[0050] Figure 2 It is a schematic diagram of the 6-DOF modulation waveform of the present invention;

[0051] Figure 3 This is the result of the particle swarm algorithm optimization of the present invention with an input voltage of 100V and an output voltage of 50V;

[0052] Figure 4 This is the result of the particle swarm algorithm optimization of the present invention with an input voltage of 100V and an output voltage of 10V;

[0053] Figure 5 This is the experimental waveform after the particle swarm algorithm optimization of the present invention with an input voltage of 100V and an output voltage of 50V;

[0054] Figure 6 This is the experimental waveform after the particle swarm algorithm optimization of the present invention with an input voltage of 100V and an output voltage of 10V;

[0055] Figure 7 is a flow chart of the particle swarm algorithm of the present invention;

[0056] Figure 8 This is the first mode diagram of the modulation strategy of the present invention;

[0057] Fig. 9 It is a second mode diagram of the modulation strategy of the present invention;

[0058] Fig.10 It is the third mode diagram of the modulation strategy of the present invention;

[0059] Fig.11 It is the fourth mode diagram of the modulation strategy of the present invention;

[0060] Fig.12 It is the fifth mode diagram of the modulation strategy of the present invention;

[0061] Fig.13It is the sixth mode diagram of the modulation strategy of the present invention;

[0062] Fig.14 It is the seventh mode diagram of the modulation strategy of the present invention;

[0063] Fig.15 It is the eighth mode diagram of the modulation strategy of the present invention;

[0064] Fig.16 It is the ninth mode diagram of the modulation strategy of the present invention;

[0065] Fig.17 This is the tenth pattern diagram of the modulation strategy of the present invention. DETAILED DESCRIPTION

[0066] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] The high degree of freedom modulation strategy and optimization method of the present invention applied to the dual active bridge converter are specifically implemented according to the following steps:

[0068] Step 1, determining the operating mode of the DAB converter based on the turn-on sequence and duration of switches in the DAB converter;

[0069] Step 2, solving the steady-state characteristics of each working mode;

[0070] Step 3, taking the minimum inductor current peak as the optimization goal, the particle swarm algorithm is used to find the minimum peak current under each working mode;

[0071] Step 4: input the optimal phase shift value obtained in step 3 into the digital signal processor to control the DAB converter.

[0072] Example 1

[0073] High degree of freedom modulation strategy and optimization method applied to dual active bridge converter, such as Figure 1 As shown, the DAB converter in step 1 includes two full-bridge circuits, which are connected through a high-frequency transformer and a power inductor. The full-bridge containing the inductor is defined as the primary bridge, and the full-bridge without the inductor is defined as the secondary bridge. The conduction sequence and duration of the switches are described by six degrees of freedom parameters.

[0074] Step 1 is implemented according to the following steps:

[0075] Step 1: There are two full bridges in the dual active bridge converter, and the two full bridges are isolated by an isolation transformer. The full bridge with inductance is defined as the primary bridge, and the full bridge without inductance is defined as the secondary bridge. The six degrees of freedom parameters satisfy the following constraints:

[0076]

[0077] Wherein, D1 is the on-time of S2 and S4, D2 is the phase shift time between S1 and S4, D3 is the on-time of S6 and S8, D4 is the phase shift time between S5 and S8, D5 is the phase shift time between S1 and S5, and D6 is the on-time of S2;

[0078] Step 1.2, such as Figure 2 As shown in the figure, the inductor voltage may be different during the switching cycle, which ultimately affects i L Therefore, the operation mode of the DAB converter depends on v AB and v CD The relative position of is divided, and the following 10 modes are obtained by using the interpolation method, such as Figure 8-Figure 17 shown.

[0079] Example 2

[0080] A high degree of freedom modulation strategy and optimization method for dual active bridge converters, wherein in step 2, the steady-state characteristics of each working mode are solved, and the inductor current i in each mode is analyzed by the piecewise linear time domain method. L (t), and the transmission power and current stress in each mode are derived based on the change of inductor current. The inductor current waveform is shown in Figure 2 As shown, it can be seen that the 6 degrees of freedom v AB、 v CD Waveform graph.

[0081] Example 3

[0082] High degree of freedom modulation strategy and optimization method applied to dual active bridge converter. In step 2, the steady-state characteristics of each working mode are solved. Since there are many modes, mode B is selected as an example to demonstrate the calculation and analysis in this section, such as Fig.10 As shown, the specific steps are described in detail. Other modes are similar, and the following assumptions are made:

[0083] 1) The converter operates in steady state;

[0084] 2) All switches are ideal;

[0085] 3) Transformers and inductors will not be magnetically saturated.

[0086] Step 2.1 will be solved using the piecewise linear time domain method. For analysis, all parameters are reflected on the primary side of the transformer. Therefore, through the 6-DOF modulation scheme, the inductor current i L (t) can be expressed as:

[0087]

[0088] Among them, V1 is the primary DC side voltage, V2 is the secondary DC side voltage, VCb is the voltage on the DC blocking capacitor, N is the transformer ratio, and t0-t8 is the inductor current node time;

[0089] From formula (2), we can see that there is the following relationship between frequency, degrees of freedom and time:

[0090]

[0091] In the formula, f s is the switching frequency;

[0092] In order to simplify the calculation, the following substitution is made:

[0093]

[0094] Where, M is the voltage conversion ratio;

[0095] Substituting equations (3) and (4) into equation (2), the inductor current value at the switching moment can be deduced as follows:

[0096]

[0097] Step 2.2: In order to improve the efficiency of the device, a more direct optimization target is needed. In this mode, the current rises from t0 to t4 and drops from t4 to t8. Therefore, the maximum current occurs at t4 and the minimum current occurs at t0. Therefore, the peak-to-peak current of the inductor can be obtained as follows:

[0098]

[0099] Step 2.3, from the above formula, we can see that the inductor peak current I p-p The expression of is relatively simple. It can be regarded as the sign of the RMS current under the same output power. The average output power is calculated as follows:

[0100]

[0101] Step 2.4: Repeat the above steps 2.1-2.3 to obtain the effective transmission power results of each working mode as follows:

[0102]

[0103]

[0104]

[0105] The current stress results of each working mode are as follows:

[0106]

[0107]

[0108] Example 4

[0109] A high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter. In step 3, the minimum inductor current peak value is taken as the optimization goal, and the particle swarm algorithm is used to optimize the six degree of freedom parameters to solve the minimum peak current in each working mode. Each particle in the particle swarm algorithm represents a potential optimal solution, and its position is determined by the combination of the six degree of freedom parameters. The optimal solution is searched by iteratively updating the position and speed of the particle; the individual learning factor c1 and the group learning factor c2 of the particle swarm algorithm are set according to the actual working conditions (actual input voltage, inductance, capacitance and other parameters), the inertia weight ω is 0.5-1, and r1 and r2 are randomly generated numbers between 0 and 1; the output result of the particle swarm algorithm is the optimal phase shift value, which is used to control the DAB converter to achieve the optimization of transmission power and current stress.

[0110] Example 5

[0111] A high degree of freedom modulation strategy and optimization method for dual active bridge converters. In step 3, the optimization goal is to minimize the peak-to-peak inductor current value in mode B. Formula (1) is a constraint condition. As shown in formula (8), the particle swarm algorithm is used to find the minimum peak current in each working mode. The flow chart of the particle swarm algorithm is as follows: Figure 7 shown.

[0112]

[0113] Example 6

[0114] The high degree of freedom modulation strategy and optimization method applied to the dual active bridge converter, step 3 is specifically implemented according to the following steps:

[0115] Step 3.1, since the position of each particle is determined by the combination of (D1, D2, D3, D4, D5, D6), the spatial dimension d is 6, and each individual particle represents a potential optimal solution involving two feature indices: position X and velocity V:

[0116]

[0117] Step 3.2, initialize a set of particles, including random positions and velocities, and then perform the following iterations:

[0118]

[0119] Where m is the iteration index, ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are randomly generated numbers, N is the initial number of particles, and X is defined as i and Vi are the position and velocity of the ith particle, Pxbest is the best position in the individual history, and Gxbest is the best position in the group history;

[0120] Step 3.3, if the maximum number of iterations is reached, exit the loop or continue;

[0121] Step 3.4, solve the peak-to-peak inductor current value of each particle;

[0122] Step 3.5, by comparing the current and historical fitness values ​​of a single particle, the better one is used as the current Pxbest. Similarly, by comparing the current fitness value and historical fitness value of the group, the better value is used as the current Gxbest.

[0123] Step 3.6, adjust the particle speed and position according to equation (10) until the optimal current stress is solved, and then the optimal phase shift value is obtained.

[0124] The optimal current stresses with different boost ratios after particle swarm optimization are: Figure 3 The results of the particle swarm algorithm optimization of the present invention with an input voltage of 100V and an output voltage of 50V are as follows: Figure 4 This is the result of particle swarm algorithm optimization with input voltage of 100V and output voltage of 10V.

[0125] Example 7

[0126] Step 4 inputs the optimal phase shift value into the digital signal processor to control the DAB converter. The optimal current stresses of different boost ratios after particle swarm optimization are: Figure 5 The experimental waveform of the invention after the particle swarm algorithm optimization with input voltage of 100V and output voltage of 50V, Figure 6 This is the experimental waveform after the particle swarm algorithm optimization of the present invention with an input voltage of 100V and an output voltage of 10V.

[0127] The present invention is applied to a high degree of freedom modulation strategy and optimization method of a dual active bridge converter. Through an innovative DAB converter modulation strategy, the effective value of the inductor current is significantly reduced, the soft switching operating range of the DAB converter is greatly broadened, and the dual reduction of conduction loss and switching loss is achieved, thereby achieving remarkable results in improving circuit energy efficiency and stability and promoting technology industrialization.

Claims

1. A high degree of freedom modulation strategy and optimization method for dual active bridge converters, characterized in that: Follow the steps below to implement it: Step 1, determining the operating mode of the DAB converter based on the turn-on sequence and duration of switches in the DAB converter; Step 2, solving the steady-state characteristics of each working mode; Step 3, taking the minimum inductor current peak as the optimization goal, the particle swarm algorithm is used to find the minimum peak current under each working mode; Step 4: input the optimal phase shift value obtained in step 3 into the digital signal processor.

2. The high degree of freedom modulation strategy and optimization method for dual active bridge converter according to claim 1, characterized in that: The DAB converter in step 1 includes two full-bridge circuits, which are connected via a high-frequency transformer and a power inductor. The full-bridge with inductance is defined as the primary bridge, and the full-bridge without inductance is defined as the secondary bridge. The conduction sequence and duration of the switches are described by six degrees of freedom parameters.

3. The high degree of freedom modulation strategy and optimization method for dual active bridge converter according to claim 2, characterized in that: The step 1 is specifically implemented according to the following steps: Step 1: There are two full bridges in the dual active bridge converter, and the two full bridges are isolated by an isolation transformer. The full bridge with inductance is defined as the primary bridge, and the full bridge without inductance is defined as the secondary bridge. The six degrees of freedom parameters satisfy the following constraints: Wherein, D1 is the on-time of S2 and S4, D2 is the phase shift time between S1 and S4, D3 is the on-time of S6 and S8, D4 is the phase shift time between S5 and S8, D5 is the phase shift time between S1 and S5, and D6 is the on-time of S2; Step 1.2, the operation mode of the DAB converter is based on v AB and v CD The relative position of the model is divided and it is divided into 10 modes using the interpolation method.

4. The high degree of freedom modulation strategy and optimization method for dual active bridge converter according to claim 1, characterized in that: In step 2, the steady-state characteristics of each working mode are solved, and the inductor current i in each mode is analyzed by the piecewise linear time domain method. L (t), and the transmission power and current stress in each mode are derived based on the change of inductor current.

5. The high degree of freedom modulation strategy and optimization method for dual active bridge converter according to claim 4, characterized in that: The step 2 is specifically implemented according to the following steps: Step 2.1, the piecewise linear time domain method is used to solve the inductor current i through the 6-DOF modulation scheme. L (t) can be expressed as: Among them, V1 is the primary DC side voltage, V2 is the secondary DC side voltage, V Cb is the voltage on the DC blocking capacitor, N is the transformer ratio, and t0-t8 is the inductor current node time; From formula (2), we can see that there is the following relationship between frequency, degrees of freedom and time: In the formula, f s is the switching frequency; Step 2.2: In order to improve the efficiency of the device, a more direct optimization target is needed. In this mode, the current rises from t0 to t4 and drops from t4 to t8. Therefore, the maximum current occurs at t4 and the minimum current occurs at t0. Therefore, the peak-to-peak current of the inductor can be obtained as follows: Step 2.3, from the above formula, we can see that the inductor peak current I p-p The expression of is relatively simple. It can be regarded as the sign of the RMS current under the same output power. The average output power is calculated as follows: Step 2.4: Repeat the above steps 2.1-2.3 to obtain the effective transmission power results and current stress results of each working mode.

6. The high degree of freedom modulation strategy and optimization method for dual active bridge converter according to claim 1, characterized in that: In step 3, the minimum inductor current peak value is taken as the optimization goal, and the particle swarm algorithm is used to optimize the six degree-of-freedom parameters to solve the minimum peak current in each working mode.

7. The high degree of freedom modulation strategy and optimization method for dual active bridge converter according to claim 6, characterized in that: Each particle in the particle swarm algorithm in step 3 represents a potential optimal solution, and its position is determined by a combination of six degrees of freedom parameters. The optimal solution is searched by iteratively updating the position and velocity of the particle; the individual learning factor c1 and the group learning factor c2 of the particle swarm algorithm are set according to the actual working conditions, the inertia weight ω is 0.5-1, and r1 and r2 are randomly generated numbers between 0 and 1; The output result of the particle swarm algorithm is the optimal phase shift value, which is used to control the DAB converter to optimize the transmission power and current stress.

8. The high degree of freedom modulation strategy and optimization method for dual active bridge converter according to claim 6, characterized in that: The step 3 is specifically implemented according to the following steps: Step 3.1, each individual particle represents a potential optimal solution, involving two feature indices: position X and velocity V: Step 3.2, initialize a set of particles, including random positions and velocities, and then perform the following iterations: Where m is the iteration index, ω is the inertia weight, c1 is the individual learning factor, c2 is the group learning factor, r1 and r2 are randomly generated numbers, N is the initial number of particles, and X is defined as i and V i are the position and velocity of the ith particle, Pxbest is the best position in the individual history, and Gxbest is the best position in the group history; Step 3.3, if the maximum number of iterations is reached, exit the loop or continue; Step 3.4, solve the peak-to-peak inductor current value of each particle; Step 3.5, by comparing the current and historical fitness values ​​of a single particle, the better one is used as the current Pxbest. Similarly, by comparing the current fitness value and historical fitness value of the group, the better value is used as the current Gxbest. Step 3.6, adjust the particle speed and position according to equation (7) until the optimal current stress is solved, and then the optimal phase shift value is obtained.

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