High-degree-of-freedom adjustment strategies and optimization methods applied to dual active bridge converters

Abstract

This invention discloses a high-degree-of-freedom modulation strategy and optimization method applied to dual active bridge converters (DABs), specifically implemented according to the following steps: Step 1, determine the operating mode of the DAB converter based on the conduction sequence and duration of the switches in the DAB converter; Step 2, solve for the steady-state characteristics of each operating mode; Step 3, using the minimum inductor current peak value as the optimization objective, calculate the minimum peak current in each operating mode using a particle swarm optimization algorithm; Step 4, input the optimal phase shift value obtained in Step 3 into a digital signal processor. This invention, through an innovative DAB converter modulation strategy, significantly reduces the effective value of the inductor current, greatly expands the soft-switching operating range of the DAB converter, and achieves a dual reduction in conduction loss and switching loss, thus achieving significant results in improving circuit energy efficiency and stability and promoting the industrialization of the technology.

Description

Technical Field

[0001] This invention belongs to the field of power electronic DC-DC converter technology, specifically relating to high-degree-of-freedom modulation strategies and optimization methods applied to dual active bridge converters. Background Technology

[0002] With the rapid development of distributed renewable energy sources (such as solar and wind power) and the increasing number of electric vehicle users, the demand for efficient and reliable power conversion equipment in power systems is becoming increasingly urgent. Against this backdrop, DC-DC converters, as the core equipment for power conversion, undertake the important task of converting DC power at different voltage levels. Their performance directly affects the efficiency and stability of the entire power system, thus becoming a current research hotspot and challenge.

[0003] Among numerous DC-DC converters, the Dual Active Bridge (DAB) converter stands out due to its unique advantages, becoming the focus of research on DC power conversion equipment. The DAB converter features symmetrical structure, bidirectional energy flow, electrical isolation, and high power density, enabling it to adapt to complex and ever-changing power environments and meet the diverse needs of renewable energy generation, electric vehicle charging, and smart grids.

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

[0005] However, despite the numerous advantages of DAB converters, they still face some technical challenges in practical applications, such as how to further improve conversion efficiency and how to optimize control strategies to achieve more stable power transmission. Therefore, research on DAB converters continues to deepen, aiming to further improve their performance and reliability while maintaining their advantages, in order to meet the future power system's demand for efficient and reliable power conversion equipment. Summary of the Invention

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

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

[0008] Step 1: Determine the operating mode of the DAB converter based on the conduction sequence and duration of the switches in the DAB converter.

[0009] Step 2: Solve for the steady-state characteristics of each operating mode;

[0010] Step 3: With the minimum inductor current peak value as the optimization objective, the minimum peak current under each operating mode is calculated using the particle swarm optimization algorithm.

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

[0012] The technical solution of this invention is also characterized by:

[0013] The DAB converter in step 1 contains two full-bridge circuits, which are connected by a high-frequency transformer and a power inductor. The full-bridge with inductor is defined as the primary bridge, and the full-bridge without inductor 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 in the following steps:

[0015] Step 1: In a dual active bridge converter, there are two full bridges separated by an isolation transformer. The full bridge containing an inductor is defined as the primary bridge, and the full bridge without an inductor is defined as the secondary bridge. The six degrees of freedom parameters satisfy the following constraints:

[0016]

[0017] Wherein, D1 is the conduction time of S2 and S4, D2 is the phase shift time between S1 and S4, D3 is the conduction 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 conduction time of S2.

[0018] Step 1.2, the operating mode of the DAB converter is based on v AB and v CD The relative positions of the components are used to divide the system into 10 modes using the insertion method.

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

[0020] Step 2 is implemented in the following steps:

[0021] Step 2.1 will use the piecewise linear time-domain method to solve the problem. Through a 6-degree-of-freedom modulation scheme, the inductor current i... L (t) can be expressed as:

[0022]

[0023] Where V1 is the primary DC-side voltage, V2 is the secondary DC-side voltage, and V Cb The voltage across the DC blocking capacitor is given by N, where N is the transformer turns ratio, and t0-t8 are the inductor current node times.

[0024] From equation (2), it can be seen that there is the following relationship between frequency, degrees of freedom, and time:

[0025]

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

[0027] Step 2.2: To improve device efficiency, a more direct optimization target is needed. In this mode, the current rises from t0 to t4 and falls from t4 to t8. Therefore, the maximum current occurs at time t4, and the minimum current occurs at time t0. Thus, the peak-to-peak inductor current can be obtained as follows:

[0028]

[0029] Step 2.3, from the above formula, it can be seen that the peak inductor current I p-p The expression is relatively simple, and can be regarded as the symbol of the root mean square current under the same output power. The average output power is calculated as follows:

[0030]

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

[0032] In step 3, with the minimum peak inductor current as the optimization objective, the particle swarm optimization algorithm is used to optimize the six degrees of freedom parameters in order to solve for the minimum peak current in each operating mode.

[0033] In step 3, each particle in the particle swarm optimization algorithm represents a potential optimal solution. 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 particles. The individual learning factor c1 and the swarm learning factor c2 of the particle swarm optimization algorithm are set according to the actual working conditions. The inertia weight ω is set to 0.5-1, and r1 and r2 are randomly generated numbers between 0 and 1. The output of the particle swarm optimization 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 in the following steps:

[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] In the formula, m is the iteration exponent, 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... i and V i Let Pxbest be the position and velocity of the i-th particle, Pxbest be the individual's best historical position, and Gxbest be the group's best historical position.

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

[0041] Step 3.4: Solve for the peak-to-peak inductance 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 taken as the current Pxbest. Similarly, by comparing the current fitness value and historical fitness value of the group, the better value is taken as the current Gxbest.

[0043] Step 3.6: Adjust the particle velocity and position according to equation (7) until the optimal current stress is obtained, and then obtain the optimal phase shift value.

[0044] The beneficial effects of this invention are:

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

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

[0047] 3) Dual reduction of conduction and switching losses: The DAB converter of the present invention effectively controls energy loss in both the conduction state and the switching process. This dual reduction of losses directly improves the conversion efficiency of the converter, making energy utilization more efficient and of great significance for energy conservation and emission reduction.

[0048] 4) The implementation method is simple and easy to implement: The method of the present invention is not only technically advanced, but its implementation process is also simpler and clearer, which is 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 the technology. Attached Figure Description

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

[0050] Figure 2 This 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 optimized for an input voltage of 100V and an output voltage of 50V in this invention.

[0052] Figure 4 This is the result of the particle swarm algorithm optimized for an input voltage of 100V and an output voltage of 10V in this invention;

[0053] Figure 5 These are experimental waveforms after optimization of the particle swarm algorithm of this invention with an input voltage of 100V and an output voltage of 50V.

[0054] Figure 6 These are experimental waveforms after the particle swarm algorithm of this invention has been optimized with an input voltage of 100V and an output voltage of 10V.

[0055] Figure 7 This is a flowchart of the particle swarm optimization algorithm of this invention;

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

[0057] Figure 9 This is a second mode diagram of the modulation strategy of the present invention;

[0058] Figure 10 This is the third mode diagram of the modulation strategy of the present invention;

[0059] Figure 11 This is the fourth mode diagram of the modulation strategy of the present invention;

[0060] Figure 12 This is the fifth mode diagram of the modulation strategy of the present invention;

[0061] Figure 13This is the sixth mode diagram of the modulation strategy of the present invention;

[0062] Figure 14 This is the seventh mode diagram of the modulation strategy of the present invention;

[0063] Figure 15 This is the eighth mode diagram of the modulation strategy of the present invention;

[0064] Figure 16 This is the ninth mode diagram of the modulation strategy of the present invention;

[0065] Figure 17 This is the tenth mode diagram of the modulation strategy of this invention. Detailed Implementation

[0066] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0067] This invention relates to a high-degree-of-freedom modulation strategy and optimization method for dual active bridge converters, and is implemented according to the following steps:

[0068] Step 1: Determine the operating mode of the DAB converter based on the conduction sequence and duration of the switches in the DAB converter.

[0069] Step 2: Solve for the steady-state characteristics of each operating mode;

[0070] Step 3: With the minimum inductor current peak value as the optimization objective, the minimum peak current under each operating mode is calculated using the particle swarm optimization algorithm.

[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 strategies and optimization methods applied to dual active bridge converters, such as Figure 1 As shown, the DAB converter in step 1 contains two full-bridge circuits. These two full-bridge circuits are connected by a high-frequency transformer and a power inductor. The full-bridge with inductor is defined as the primary bridge, and the full-bridge without 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 in the following steps:

[0075] Step 1: In a dual active bridge converter, there are two full bridges separated by an isolation transformer. The full bridge containing an inductor is defined as the primary bridge, and the full bridge without an inductor is defined as the secondary bridge. The six degrees of freedom parameters satisfy the following constraints:

[0076]

[0077] Wherein, D1 is the conduction time of S2 and S4, D2 is the phase shift time between S1 and S4, D3 is the conduction 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 conduction time of S2.

[0078] Step 1.2, as follows Figure 2 As shown, the inductor voltage may vary during the switching cycle, ultimately affecting i. L The trend leads to different patterns. Therefore, the operating mode of the DAB converter depends on v. AB and v CD Based on their relative positions, the following 10 modes were obtained using the insertion method, such as... Figures 8-17 As shown.

[0079] Example 2

[0080] A high-degree-of-freedom modulation strategy and optimization method are applied to a dual active bridge converter. In step 2, the steady-state characteristics of each operating mode are solved, and the inductor current i in each mode is analyzed by piecewise linear time-domain method. L (t), and based on the change in inductor current, the transmission power and current stress in each mode are derived. The inductor current waveform is as follows: Figure 2 As shown, the 6-DOF v of this invention can also be seen. AB、 v CD Waveform diagram.

[0081] Example 3

[0082] The high-degree-of-freedom modulation strategy and optimization method applied to dual active bridge converters, in step 2, solves the steady-state characteristics of each operating mode. Since there are many modes, mode B is selected as an example to demonstrate the calculation and analysis in this section. Figure 10 As shown, the specific steps are detailed; other modalities 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 become magnetically saturated.

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

[0087]

[0088] Where V1 is the primary DC-side voltage, V2 is the secondary DC-side voltage, and VCb The voltage across the DC blocking capacitor is given by N, where N is the transformer turns ratio, and t0-t8 are the inductor current node times.

[0089] From equation (2), it can be seen that there is the following relationship between frequency, degrees of freedom, and time:

[0090]

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

[0092] To simplify the calculations, the following substitution is made:

[0093]

[0094] In the formula, M is the voltage conversion ratio;

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

[0096]

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

[0098]

[0099] Step 2.3, from the above formula, it can be seen that the peak inductor current I p-p The expression is relatively simple, and can be regarded as the symbol of the root mean square current under the same output power. The average output power is calculated as follows:

[0100]

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

[0102]

[0103]

[0104]

[0105] The current stress results for each operating 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 is presented. In step 3, the optimization objective is to minimize the peak inductor current. The particle swarm optimization (PSO) algorithm is used to optimize the six degrees of freedom parameters to find the minimum peak current in each operating mode. Each particle in the PSO algorithm represents a potential optimal solution, and its position is determined by the combination of the six degrees of freedom parameters. The optimal solution is searched by iteratively updating the position and velocity of the particles. The individual learning factor c1 and the swarm learning factor c2 of the PSO algorithm are set according to the actual operating conditions (actual input voltage, inductance, capacitance, etc.). The inertia weight ω is set to 0.5-1, and r1 and r2 are randomly generated numbers between 0 and 1. The output of the PSO algorithm is the optimal phase shift value, which is used to control the DAB converter to optimize the transmission power and current stress.

[0110] Example 5

[0111] The high-degree-of-freedom modulation strategy and optimization method applied to dual active bridge converters, in step 3, takes the minimum peak-to-peak inductor current value of mode B as the optimization objective, and uses formula (1) as the constraint condition, as shown in formula (8). The minimum peak current of each working mode is obtained by using the particle swarm optimization algorithm; the flowchart of the particle swarm optimization algorithm is shown below. Figure 7 As shown.

[0112]

[0113] Example 6

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

[0115] Step 3.1: Since the position of each particle is determined by a combination of (D1, D2, D3, D4, D5, D6), the spatial dimension d is 6. 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] In the formula, m is the iteration exponent, 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... i and Vi Let Pxbest be the position and velocity of the i-th particle, Pxbest be the individual's best historical position, and Gxbest be the group's best historical position.

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

[0121] Step 3.4: Solve for the peak-to-peak inductance 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 taken as the current Pxbest. Similarly, by comparing the current fitness value and historical fitness value of the group, the better value is taken as the current Gxbest.

[0123] Step 3.6: Adjust the particle velocity and position according to equation (10) until the optimal current stress is solved, and then obtain the optimal phase shift value.

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

[0125] Example 7

[0126] Step 4 inputs the optimal phase shift value to the digital signal processor to control the DAB converter. The optimal current stresses for different boost ratios, optimized using the particle swarm optimization algorithm, can be obtained as follows: Figure 5 The experimental waveforms of the particle swarm optimization algorithm after this invention, with an input voltage of 100V and an output voltage of 50V, are shown below. Figure 6 The experimental waveforms are those of the particle swarm algorithm optimized for an input voltage of 100V and an output voltage of 10V, as presented in this invention.

[0127] This invention relates to a high-degree-of-freedom modulation strategy and optimization method for dual active bridge converters. Through an innovative DAB converter modulation strategy, it significantly reduces the effective value of the inductor current, greatly expands the soft-switching operating range of the DAB converter, and achieves a dual reduction in conduction loss and switching loss. As a result, it has achieved remarkable results in improving circuit energy efficiency and stability and promoting the industrialization of the technology.

Claims

1. A high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter, characterized in that, The method is implemented according to the following steps: Step 1, determine the working mode of the DAB converter based on the on-off sequence and time length of the switches in the DAB converter; Step 2, solve the steady-state characteristics of each working mode; Step 3, take the minimum peak current of the inductor current as the optimization goal, and use the particle swarm algorithm to obtain the minimum peak current in each working mode; Step 4, input the optimal phase shift value obtained in step 3 into the digital signal processor; 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 circuit with inductor is defined as the primary bridge, and the full-bridge circuit without inductor is defined as the secondary bridge. The on-off sequence and time length of the switches are described by six degrees of freedom parameters; In step 3, the particle swarm algorithm is used to optimize the six degrees of freedom parameters to obtain the minimum peak current in each working mode, with the minimum peak current of the inductor current as the optimization goal; Each particle in the particle swarm algorithm in step 3 represents a potential optimal solution, and its position is determined by the combination of the six degrees of freedom parameters. The position and velocity of the particle are updated through iteration to search for the optimal solution. The individual learning factor c1 and the group learning factor c2 of the particle swarm algorithm are set according to the actual working condition, the inertia weight ω is 0.5-1, and r1 and r2 are random 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 and optimize the transmission power and current stress.

2. The high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter according to claim 1, characterized in that, Step 1 is implemented according to the following steps: Step 1, in the dual active bridge converter, the two full-bridge circuits are separated by an isolation transformer. The full-bridge circuit with inductor is defined as the primary bridge, and the full-bridge circuit without inductor is defined as the secondary bridge. The six degrees of freedom parameters satisfy the following constraints: （1） Where 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 operating mode of the DAB converter is divided according to v AB and the relative position of v CD is divided into 10 modes by the interpolation method. 3.The high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter of claim 1, wherein, In step 2, the steady-state characteristics of each operating mode are solved, and the inductor current i L (t) is analyzed by piecewise linear time-domain method, and the transmission power and current stress in each mode are derived based on the change of inductor current.

4. The high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter according to claim 3, characterized in that, Step 2 is implemented according to the following steps: Step 2.1, will be solved using piecewise linear time domain method, through 6 degrees of freedom modulation scheme, inductor current i L (t) can be expressed as: （2） Wherein, 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, t0-t8 is the inductor current node time; From equation (2), the following relationship exists between frequency, degree of freedom and time: （3） 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 goal is needed. In this mode, the current rises from t0 to t4 and falls from t4 to t8. Therefore, the maximum value of the current appears at t4, and the minimum value of the current appears at t0. Therefore, the inductor peak-to-peak current can be obtained as follows: （4）； Step 2.

3. From the above equation, the expression for the inductor peak current I p-p is relatively simple to see as the sign of the RMS current for the same output power, the average output power is calculated as follows: （5）； Step 2.4, repeat steps 2.1-2.3 to obtain the effective transmission power results and current stress results of each working mode.

5. The high degree of freedom modulation strategy and optimization method applied to a dual active bridge converter according to claim 1, characterized in that, Step 3 is implemented according to the following steps: Step 3.1, each individual particle represents a potential optimal solution, involving two characteristic indices: position X and velocity V: （6） Step 3.2, initialize a set of particles, including random position and velocity, then execute the following iteration: （7） 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 defined as i and V i are the position and velocity of the i-th particle, respectively, Pxbest is the individual historical optimal position, and Gxbest is the group historical optimal position. 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 the individual particles, the better one is taken as the current Pxbest, similarly, by comparing the current fitness values and historical fitness values of the population, the better value is taken as the current Gxbest; Step 3.6, the particle velocity and position are adjusted according to formula (7) until the optimal current stress is solved, and then the optimal phase shift value is obtained.

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