DAB converter control method, system and device based on current stress optimization

Abstract

The application provides a DAB converter control method, system and device based on current stress optimization, and belongs to the technical field of power electronics. The method comprises the following steps: acquiring an input voltage and an output voltage of a DAB converter to determine a current stress model; according to the current stress model and a preset transmission power model, a phase shift ratio combination of the DAB converter is optimized by using a particle swarm algorithm to obtain a first value of the phase shift ratio combination; wherein the current stress model and the transmission power model both contain the phase shift ratio combination; according to the current stress model and the transmission power model, the first value of the phase shift ratio combination is iteratively updated by using a Lagrange multiplier method to obtain a second value of the phase shift ratio combination; and the DAB converter is subjected to extended phase shift control according to the second value of the phase shift ratio combination. The application can improve the control effect of the DAB converter.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to a control method, system and device for DAB converters based on current stress optimization. Background Technology

[0002] In DC microgrid architectures, the DAB (Dual Active Bridge) converter often serves as a key interface between the electrochemical energy storage system and the DC bus, primarily undertaking the core functions of bidirectional power conversion and power buffering. Its performance directly impacts the DC microgrid's renewable energy absorption capacity, power conversion efficiency, and dynamic response speed. Current technologies typically employ the traditional Lagrange multiplier method to iteratively optimize the shift ratio required to control the DAB converter. However, the shift ratio is usually randomly initialized before iteration, making the algorithm prone to divergence during iteration, hindering the acquisition of the optimal shift ratio solution, and consequently leading to poor control performance of the DAB converter. Summary of the Invention

[0003] The main purpose of this application is to propose a control method, system and device for DAB converter based on current stress optimization, which aims to improve the control effect of DAB converter.

[0004] To achieve the above objectives, one aspect of this application proposes a DAB converter control method based on current stress optimization, comprising: Obtain the input and output voltages of the DAB converter to determine the current stress model; Based on the current stress model and the preset transmission power model, the particle swarm optimization algorithm is used to optimize the shift ratio combination of the DAB converter to obtain the first value of the shift ratio combination; wherein, both the current stress model and the transmission power model include the shift ratio combination; Based on the current stress model and the transmission power model, the first value of the shift ratio combination is iteratively updated using the Lagrange multiplier method to obtain the second value of the shift ratio combination. Extended phase-shift control is performed on the DAB converter based on the second value of the phase-shift combination.

[0005] Furthermore, based on the current stress model and the preset transmission power model, the particle swarm optimization algorithm is used to optimize the shift ratio combination of the DAB converter, and the first value of the shift ratio combination is obtained, including: A fitness function is constructed based on the current stress model, the transmission power model, and the preset penalty coefficients for power constraints. Based on the fitness function, the particle swarm optimization algorithm is used to optimize the shift ratio combination to obtain the first value of the shift ratio combination.

[0006] Further, the step of iteratively updating the first value of the shift ratio combination using the Lagrange multiplier method based on the current stress model and the transmission power model to obtain the second value of the shift ratio combination includes: Based on the current stress model, the transmission power model, and the pre-introduced Lagrange multipliers, construct the Lagrange function; Differentiating the Lagrange function yields the first and second derivative results. Based on the first-order derivative result and the transmission power model, construct the residual matrix; The second first-order derivative result is obtained by differentiating the transmission power model. Based on the second-order derivative result and the second first-order derivative result, construct the Jacobian matrix; Based on the residual matrix and the Jacobian matrix, a numerical update model is constructed for the shift ratio combination and the Lagrange multiplier. Based on the numerical update model and the preset termination condition, the first value of the shift ratio combination is iteratively updated to obtain the second value of the shift ratio combination.

[0007] Furthermore, the DAB converter includes an input-side full-bridge circuit and an output-side full-bridge circuit, and the shift ratio combination includes an inner shift ratio of the input-side full-bridge circuit and an outer shift ratio between the input-side full-bridge circuit and the output-side full-bridge circuit.

[0008] Furthermore, the first first-order derivative result includes the third first-order derivative of the Lagrange function with respect to the outward shift ratio and the fourth first-order derivative of the Lagrange function with respect to the inward shift ratio; the residual matrix is ​​represented by the following expression: ; In the formula, Let be the residual matrix. The result of the third first-order derivative, The result of the fourth first-order derivative is... Referring to the aforementioned transmission power model, This is the preset reference transmission power per unit value.

[0009] Further, the second-order derivative results include the first and second-order mixed derivatives of the Lagrange function with respect to the outward shift ratio and the inward shift ratio, the second and second-order mixed derivatives of the Lagrange function with respect to the inward shift ratio and the outward shift ratio, the first and second-order derivatives of the Lagrange function with respect to the outward shift ratio, and the second and second-order derivatives of the Lagrange function with respect to the inward shift ratio; the second-order derivative results include the fifth and sixth-order derivatives of the transmission power model with respect to the outward shift ratio and the sixth and sixth-order derivatives of the transmission power model with respect to the inward shift ratio; the Jacobian matrix is ​​represented by the following expression: ; In the formula, Let be the Jacobian matrix. The result of the first and second derivatives, The result of the first and second order mixed derivatives, The result of the fifth first-order derivative is... This is the result of the second-order mixed derivative. This is the result of the second-order derivative. The result of the sixth-first order derivative is given.

[0010] Furthermore, the numerical update model is represented by the following expression: ; In the formula, The value of the outward shift is the value taken when entering the k-th iteration. The value of the outward shift after the k-th iteration is [value]. Let the value of the inward shift be the value taken when entering the k-th iteration. Let the value of the inward shift be the value after the k-th iteration. Let be the value of the Lagrange multiplier when entering the k-th iteration. Let be the value of the Lagrange multiplier after the k-th iteration.

[0011] To achieve the above objectives, another aspect of this application proposes a DAB converter control system based on current stress optimization, comprising: The first module is used to obtain the input voltage and output voltage of the DAB converter in order to determine the current stress model; The second module is used to optimize the shift ratio combination of the DAB converter using a particle swarm optimization algorithm based on the current stress model and the preset transmission power model, to obtain a first value of the shift ratio combination; wherein, both the current stress model and the transmission power model include the shift ratio combination; The third module is used to iteratively update the first value of the shift ratio combination based on the current stress model and the transmission power model using the Lagrange multiplier method, so as to obtain the second value of the shift ratio combination. The fourth module is used to perform extended phase shift control on the DAB converter based on the second value of the phase shift combination.

[0012] To achieve the above objectives, another aspect of this application proposes an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described DAB converter control method based on current stress optimization.

[0013] To achieve the above objectives, another aspect of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned DAB converter control method based on current stress optimization.

[0014] This application includes at least the following beneficial effects: by constructing the constraints required for the entire optimization process of the phase shift combination of the DAB converter based on the current stress model and the power transmission model, the first value of the phase shift combination is obtained by analysis using the particle swarm optimization algorithm, the first value of the phase shift combination is iteratively updated using the Lagrange multiplier method, and finally the extended phase shift control of the DAB converter is performed based on the updated second value of the phase shift combination. This can improve the power transmission efficiency of the DAB converter and its soft-switching performance over a wide input voltage range, and reduce the current stress of the DAB converter. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the composition of the DAB converter provided in the embodiments of this application; Figure 2 This is a flowchart illustrating a DAB converter control method based on current stress optimization provided in an embodiment of this application. Figure 3 This is a schematic diagram of the composition of a DAB converter control system based on current stress optimization provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of systems and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0017] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0018] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] In DC microgrid architectures, DAB (Dual Active Bridge) converters often serve as the key interface between electrochemical energy storage systems and the DC bus, primarily undertaking the core functions of bidirectional power conversion and power buffering. Their performance directly impacts the renewable energy absorption capacity, power conversion efficiency, and dynamic response speed of the DC microgrid. Therefore, it is necessary to develop reasonable control strategies for DAB converters.

[0021] In existing technologies, the traditional Lagrange multiplier method is generally used to iteratively optimize the shift ratio required to control the DAB converter. However, the shift ratio is usually randomly initialized before the iteration begins, which makes the algorithm prone to divergence during the iteration process. That is, the algorithm deviates from the expected convergence path during the iteration process and has difficulty converging to the optimal solution of the shift ratio. This leads to poor control of the DAB converter, such as reduced power transmission efficiency of the DAB converter (which can be understood as the DAB converter experiencing increased losses due to large return power when transmitting power), loss of soft-switching characteristics of the DAB converter over a wide input voltage range, and increased current stress of the DAB converter, which affects its dynamic response capability and stability.

[0022] In view of this, embodiments of this application provide a DAB converter control method, system, and device based on current stress optimization. This scheme constructs the constraints required for the entire optimization process of the phase shift combination of the DAB converter according to the current stress model and the power transmission model. First, the particle swarm optimization algorithm is used to analyze and obtain the first value of the phase shift combination. Then, the Lagrange multiplier method is used to iteratively update the first value of the phase shift combination. Finally, the DAB converter is extended phase shift control is performed based on the updated second value of the phase shift combination. This can improve the power transmission efficiency of the DAB converter and its soft-switching performance over a wide input voltage range, and reduce the current stress of the DAB converter, thereby improving the operational stability of the DAB converter.

[0023] Please see Figure 1 , Figure 1 This is a schematic diagram of the DAB converter provided in the embodiments of this application. The DAB converter includes an input-side full-bridge circuit, an output-side full-bridge circuit, and an input-side DC voltage source. Output side DC voltage source The auxiliary inductor L and the high-frequency isolation transformer HFT have the following turns ratio: The input-side full-bridge circuit and the output-side full-bridge circuit are symmetrical. The input-side DC voltage source... Used to provide power to the input-side full-bridge circuit so that it can generate a high-frequency AC voltage, which serves as the input voltage of the DAB converter; output-side DC voltage source. It is obtained by passing the high-frequency AC voltage through the high-frequency isolation transformer HFT and the full-bridge circuit on the output side in sequence. It is used to provide energy to the load R to meet its power requirements and serves as the output voltage of the DAB converter.

[0024] Specifically, the input-side full-bridge circuit includes a first switch S1, a second switch S2, a third switch S3, a fourth switch S4, a first diode T1 connected in anti-parallel to the first switch S1, a second diode T2 connected in anti-parallel to the second switch S2, a third diode T3 connected in anti-parallel to the third switch S3, a fourth diode T4 connected in anti-parallel to the fourth switch S4, and a first filter capacitor C1. The first switch S1 and the second switch S2 form the first bridge arm, the third switch S3 and the fourth switch S4 form the second bridge arm, the first filter capacitor C1 is connected in parallel with the first bridge arm, the first bridge arm is connected in parallel with the second bridge arm, the middle node of the first bridge arm is connected to the first primary terminal of the high-frequency isolation transformer HFT through an auxiliary inductor L, and the middle node of the second bridge arm is connected to the second primary terminal of the high-frequency isolation transformer HFT.

[0025] Specifically, the output-side full-bridge circuit includes a fifth switch S5, a sixth switch S6, a seventh switch S7, an eighth switch S8, a fifth diode T5 connected in antiparallel to the fifth switch S5, a sixth diode T6 connected in antiparallel to the sixth switch S6, a seventh diode T7 connected in antiparallel to the seventh switch S7, an eighth diode T8 connected in antiparallel to the eighth switch S8, and a second filter capacitor C2. The fifth switch S5 and the sixth switch S6 form the third bridge arm, and the seventh switch S7 and the eighth switch S8 form the fourth bridge arm. The third bridge arm and the fourth bridge arm are connected in parallel, and the fourth bridge arm is connected in parallel with the second filter capacitor C2. The middle node of the third bridge arm is connected to the first secondary terminal of the high-frequency isolation transformer HFT, and the middle node of the fourth bridge arm is connected to the second secondary terminal of the high-frequency isolation transformer HFT.

[0026] This application primarily proposes extended phase-shift control for DAB converters to achieve flexible power transfer and voltage regulation. To further improve the control effect of the DAB converter, it is necessary to optimize the combination of phase shift ratios used in extended phase-shift control, as described below. The phase shift ratio combination includes the internal phase shift ratio of the input-side full-bridge circuit. Compared to the external shift between the input-side full-bridge circuit and the output-side full-bridge circuit The inward shift of the input-side full-bridge circuit compared to This can be understood as the shift ratio between the first switch S1 and the fourth switch S4, and the external shift ratio between the input-side full-bridge circuit and the output-side full-bridge circuit. It can be understood as the shift ratio between the first switch S1 and the fifth switch S5. The shift ratio between two switches at a specific relative position refers to the ratio of the phase difference between the drive signals of the two switches to the switching period, which is used to control the direction and magnitude of power transmission.

[0027] Please see Figure 2 , Figure 2 This is an optional flowchart illustrating a current stress-based DAB converter control method provided in an embodiment of this application. The method may include, but is not limited to, the following steps S101 to S104: Step S101: Obtain the input voltage and output voltage of the DAB converter to determine the current stress model; Step S102: Based on the current stress model and the preset transmission power model, the particle swarm optimization algorithm is used to optimize the shift ratio combination of the DAB converter to obtain the first value of the shift ratio combination; wherein, both the current stress model and the transmission power model include the shift ratio combination; Step S103: Based on the current stress model and the transmission power model, the first value of the shift ratio combination is iteratively updated using the Lagrange multiplier method to obtain the second value of the shift ratio combination; Step S104: Perform extended phase shift control on the DAB converter according to the second value of the phase shift combination.

[0028] Steps S101 to S104 as shown in the embodiments of this application construct algorithm constraints based on the current stress model and the transmission power model, and sequentially use the particle swarm optimization algorithm and the Lagrange multiplier method to optimize the shift ratio combination of the DAB converter, which is beneficial to improving the control effect of the DAB converter.

[0029] In some embodiments, the current stress model can be represented by the following expression: ; In the formula, The current stress model refers to the per-unit value of the current stress. The current stress of a DAB converter refers to the peak current that its switching transistors withstand. Voltage conversion ratio and .

[0030] In some embodiments, the transmission power model can be represented by the following expression: ; In the formula, This refers to the transmission power model, whose output is the per-unit value of transmission power.

[0031] In some embodiments, step S102 may include, but is not limited to, the following steps S201 to S202: Step S201: Based on the current stress model, the transmission power model, and the preset penalty coefficients for power constraints, construct the fitness function, which can be expressed by the following expression: ; In the formula, Refers to the fitness function. This is a preset penalty coefficient related to power constraints. This is the preset reference transmission power per unit value.

[0032] Step S202: Based on the fitness function, the particle swarm optimization algorithm is used to optimize the shift ratio combination to obtain the first value of the shift ratio combination. The corresponding implementation process includes the following: Step A: Initialize the parameters of the particle swarm algorithm. This refers to initializing the size of the particle swarm, the position range and velocity range of each particle, the maximum number of iterations during algorithm execution, the number of optimization variables, and the parameters required to implement the velocity update strategy. The optimization variables include the shift ratio combination. Step B: Randomly initialize the position and velocity of the particle swarm, where the position of each particle represents the value of the shift ratio combination; Step C: Calculate the fitness value of each particle in the particle swarm based on the fitness function and the position of each particle in the particle swarm. Then select the particle with the best fitness value from the particle swarm and take its position as the global best position. For each particle in the particle swarm, obtain all the fitness values ​​calculated by the particle in all previous iterations and take the best fitness value from them. Take the position corresponding to the best fitness value as the historical best position of the particle. Step D: Based on the global optimal position and the historical optimal position of each particle in the particle swarm, update the velocity and position of each particle in the particle swarm using the following expression: , ; In the formula, Let be the velocity of the i-th particle when it enters the t-th iteration. Let be the velocity of the i-th particle after the t-th iteration, which can be understood as the velocity of the i-th particle when entering the (t+1)-th iteration. Inertial weights are used to control the inertia by which particles maintain their current state of motion. The historical best position of the i-th particle. Let be the position of the i-th particle when it enters the t-th iteration. Let be the position of the i-th particle after the t-th iteration, which can be understood as the position of the i-th particle when entering the (t+1)-th iteration. This is an individual learning factor used to control the speed at which a particle approaches its historical best position. The optimal position globally. This is a social learning factor used to control the speed at which particles approach the globally optimal position. and All are random numbers within the range [0,1]. Step E: Determine if the maximum number of iterations has been reached; if not, return to step C above; if yes, output the global optimal position as the first value of the shift ratio combination.

[0033] In some embodiments, step S103 may include, but is not limited to, the following steps S301 to S307: Step S301: Construct the Lagrangian function based on the current stress model, the transmission power model, and the pre-introduced Lagrange multipliers.

[0034] In this step, based on the current stress model and the transmission power model, we first construct an optimization problem with power constraints as follows: ; Then, based on the pre-introduced Lagrange multipliers, the above optimization problem with power constraints is transformed to obtain the Lagrange function, which can be expressed by the following expression: ; In the formula, Refers to the Lagrange function, These are the pre-introduced Lagrange multipliers.

[0035] Step S302: Differentiate the Lagrange function to obtain the first-order derivative and the second-order derivative. The first-order derivative includes the third-order derivative of the Lagrange function with respect to the outward shift and the fourth-order derivative of the Lagrange function with respect to the inward shift. The second-order derivative includes the first and second-order mixed derivatives of the Lagrange function with respect to the outward shift and the inward shift, the second and second-order mixed derivatives of the Lagrange function with respect to the inward shift and the outward shift, the first and second-order derivatives of the Lagrange function with respect to the outward shift, and the second and second-order derivatives of the Lagrange function with respect to the inward shift.

[0036] Step S303: Based on the first-order derivative result and the transmission power model, construct the residual matrix, which can be represented by the following expression: ; In the formula, The residual matrix is... The result of the third first derivative of the Lagrange function with respect to the outward shift. This is the result of the fourth first derivative of the Lagrange function with respect to the inward shift ratio.

[0037] Step S304: Differentiate the transmission power model to obtain the second first-order derivative result, which includes the fifth first-order derivative result of the transmission power model compared with the outward shift and the sixth first-order derivative result of the transmission power model compared with the inward shift.

[0038] Step S305: Based on the second-order derivative and the second-first-order derivative, construct the Jacobian matrix, which can be represented by the following expression: ; In the formula, For Jacobian matrices, The result is the first and second derivatives of the Lagrange function compared to the outward shift. The result is the first and second mixed derivatives of the Lagrange function taken successively for the outward and inward shifts. This is the fifth first-order derivative of the transmission power model compared to the outward shift. This is the result of taking the second mixed derivative of the Lagrangian function with respect to both the inward and outward shifts. This is the second derivative of the Lagrange function with respect to the inward shift. This is the sixth-first derivative of the transmission power model with respect to the inward shift.

[0039] It should be noted that when solving the Jacobian matrix in the subsequent iteration process, the relevant second-order derivative results can be calculated by analytical derivation or numerical difference method (such as central difference method).

[0040] Step S306: Based on the residual matrix and the Jacobian matrix, construct a numerical update model for the shift ratio combination and Lagrange multipliers, which can be expressed by the following expression: ; In the formula, The value at the time of the k-th iteration is compared to the value at the time of the outward shift. The value of the outward shift comparison after the k-th iteration can be understood as the value of the outward shift comparison when entering the (k+1)-th iteration. The value of the inward shift is the value taken when entering the k-th iteration. The value of the inward shift comparison after the k-th iteration can be understood as the value of the inward shift comparison when entering the (k+1)-th iteration. Let the values ​​of the Lagrange multipliers be those obtained when entering the k-th iteration. The value of the Lagrange multiplier after the k-th iteration can be understood as the value of the Lagrange multiplier when entering the (k+1)-th iteration.

[0041] Step S307: Based on the numerical update model and the preset termination condition, iteratively update the first value of the shift ratio combination to obtain the second value of the shift ratio combination; wherein, the preset termination condition can be: satisfying... Or the current iteration count has reached the preset maximum iteration count. This is the preset convergence threshold.

[0042] In this step, the first value of the shift ratio combination includes the outer shift ratio. Compared to the first value and the inward shift The first value, the second value of the shift-combination includes the outer shift-combination. Compared to the second value and the inward shift The second value; setting the Lagrange multipliers The initial value is preferably set to 0 in this application; the first iteration begins by shifting the value outward compared to... The first value is used as the outer shift comparison The value taken when entering the first iteration will be compared to the inward shift. The first value is used as the value at the beginning of the first iteration for the inward shift, and the Lagrange multipliers are also used. The initial value is used as the Lagrange multiplier. The values ​​taken at the beginning of the first iteration are then substituted into the numerical update model for calculation to obtain the outward shift ratio. Compared to inward movement and Lagrange multipliers After the first iteration, the value is taken; if the preset termination condition is not met, the second iteration begins, and the value is shifted outward compared to the previous value. Compared to inward movement and Lagrange multipliers After the first iteration, the values ​​are substituted into the numerical update model for calculation to obtain the outward shift ratio. Compared to inward movement and Lagrange multipliers The value is taken after the second iteration; this process continues until the preset termination condition is met, then the calculated outward shift is compared. The value after the last iteration is used as the outward shift comparison. The second value output, and the calculated inward shift compared to The value after the last iteration is used as the inward shift comparison. The second value is output.

[0043] In step S104 of some embodiments, the outward movement can be determined compared to... Compared to the second value and the inward shift After the second value is obtained, it is input to the EPS (Extended Phase Shift) modulator. Through the internally set pulse width modulation technology, all PWM (Pulse Width Modulation) signals are generated to control the DAB converter. That is, the corresponding switching transistors contained in the DAB converter are controlled according to each PWM signal, so that the current stress of the DAB converter is minimized.

[0044] The DAB converter control method based on current stress optimization provided in this application constructs the constraints required for the entire optimization process of the DAB converter's phase shift combination based on the current stress model and the power transmission model. First, a particle swarm optimization algorithm is used to analyze and obtain the first value of the phase shift combination. Then, the Lagrange multiplier method is used to iteratively update the first value of the phase shift combination. Finally, the DAB converter is extended phase shift control is performed based on the updated second value of the phase shift combination. This can improve the power transmission efficiency of the DAB converter and its soft-switching performance over a wide input voltage range, and reduce the current stress of the DAB converter, thereby improving the operational stability of the DAB converter.

[0045] Among them, the particle swarm optimization (PSO) algorithm has global search capability, which can avoid the solution of parameters getting trapped in local optima. However, relying solely on the penalty coefficient for constraint will lead to limited accuracy of the solution of parameters. The Lagrange multiplier method can improve the accuracy of the solution of parameters, but it requires good initial values ​​of parameters to avoid divergence. In this application, by using the PSO algorithm and the Lagrange multiplier method in combination, the PSO algorithm first provides robust initial values, and then the Lagrange multiplier method ensures convergence accuracy. This can obtain a better value of the shift ratio combination to further improve the control effect of the DAB converter.

[0046] Furthermore, this application mainly proposes to optimize the values ​​of the inner shift ratio of the input-side full-bridge circuit and the outer shift ratio between the input-side full-bridge circuit and the output-side full-bridge circuit when the DAB converter is used for extended phase-shift control. This facilitates the construction of relatively low-order nonlinear equations for fast iterative calculations during the application of the Lagrange multiplier method, thereby meeting the dynamic scheduling requirements of DC microgrids as much as possible.

[0047] Please see Figure 3 , Figure 3 This is an optional schematic diagram of a DAB converter control system based on current stress optimization provided in an embodiment of this application. It can implement the aforementioned DAB converter control method based on current stress optimization. The system may include, but is not limited to, the following: The first module 401 is used to obtain the input voltage and output voltage of the DAB converter in order to determine the current stress model; The second module 402 is used to optimize the shift ratio combination of the DAB converter using a particle swarm optimization algorithm based on the current stress model and the preset transmission power model, to obtain a first value of the shift ratio combination; wherein, both the current stress model and the transmission power model include the shift ratio combination; The third module 403 is used to iteratively update the first value of the shift ratio combination based on the current stress model and the transmission power model using the Lagrange multiplier method to obtain the second value of the shift ratio combination. The fourth module 404 is used to perform extended phase shift control on the DAB converter based on the second value of the phase shift combination.

[0048] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those implemented in the above method embodiments, and the beneficial effects achieved by this system embodiment are also the same as those achieved by the above method embodiments.

[0049] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned DAB converter control method based on current stress optimization. This electronic device can include any smart terminal such as a tablet computer, laptop computer, or desktop computer.

[0050] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those implemented by the above method embodiments, and the beneficial effects achieved by the present device embodiments are also the same as those achieved by the above method embodiments.

[0051] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating the hardware structure of an electronic device according to another embodiment. The electronic device includes: The processor 501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 502 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store the operating system and other applications. When the technical solutions provided in the embodiments of this application are implemented through software or firmware, the relevant program code is stored in the memory 502 and is called and executed by the processor 501. The input / output interface 503 is used to implement information input and output; The communication interface 504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 505 transmits information between various components of the device (e.g., processor 501, memory 502, input / output interface 503, and communication interface 504); The processor 501, memory 502, input / output interface 503 and communication interface 504 are connected to each other within the device via bus 505.

[0052] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described DAB converter control method based on current stress optimization.

[0053] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented by this storage medium embodiment are the same as those implemented by the above method embodiments, and the beneficial effects achieved by this storage medium embodiment are also the same as those achieved by the above method embodiments.

[0054] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0055] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0056] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0057] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0059] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0060] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0061] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0062] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0064] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A control method for a DAB converter based on current stress optimization, characterized in that, include: Obtain the input and output voltages of the DAB converter to determine the current stress model; Based on the current stress model and the preset transmission power model, the particle swarm optimization algorithm is used to optimize the shift ratio combination of the DAB converter to obtain the first value of the shift ratio combination; wherein, both the current stress model and the transmission power model include the shift ratio combination, the DAB converter includes an input-side full-bridge circuit and an output-side full-bridge circuit, and the shift ratio combination includes the inner shift ratio of the input-side full-bridge circuit and the outer shift ratio between the input-side full-bridge circuit and the output-side full-bridge circuit; Based on the current stress model and the transmission power model, the first value of the shift ratio combination is iteratively updated using the Lagrange multiplier method to obtain the second value of the shift ratio combination. Based on the second value of the phase shift combination, the DAB converter is subjected to extended phase shift control; The step of iteratively updating the first value of the shift ratio combination using the Lagrange multiplier method based on the current stress model and the transmission power model to obtain the second value of the shift ratio combination includes: Based on the current stress model, the transmission power model, and the pre-introduced Lagrange multipliers, construct the Lagrange function; Differentiating the Lagrange function yields a first-order derivative and a second-order derivative. The first-order derivative includes the third-order derivative of the Lagrange function with respect to the outward shift ratio and the fourth-order derivative of the Lagrange function with respect to the inward shift ratio. The second-order derivative includes the first and second-order mixed derivatives of the Lagrange function with respect to the outward shift ratio and the inward shift ratio, the second and second-order mixed derivatives of the Lagrange function with respect to the inward shift ratio and the outward shift ratio, the first and second-order derivatives of the Lagrange function with respect to the outward shift ratio, and the second and second-order derivatives of the Lagrange function with respect to the inward shift ratio. Based on the first-order derivative result and the transmission power model, a residual matrix is ​​constructed, which is expressed by the following expression: In the formula, Let be the residual matrix. The result of the third first-order derivative, The result of the fourth first-order derivative is... Referring to the aforementioned transmission power model, This is the preset reference transmission power per-unit value; The transmission power model is differentiated to obtain a second first-order derivative result, which includes the fifth first-order derivative result of the transmission power model with respect to the outward shift ratio and the sixth first-order derivative result of the transmission power model with respect to the inward shift ratio. Based on the second-order derivative and the second-first-order derivative, a Jacobian matrix is ​​constructed, which is expressed by the following expression: In the formula, Let be the Jacobian matrix. The result of the first and second derivatives, The result of the first and second order mixed derivatives, The result of the fifth first-order derivative is... This is the result of the second-order mixed derivative. This is the result of the second-order derivative. The result of the sixth-first derivative; Based on the residual matrix and the Jacobian matrix, a numerical update model is constructed for the shift ratio combination and the Lagrange multiplier. Based on the numerical update model and the preset termination condition, the first value of the shift ratio combination is iteratively updated to obtain the second value of the shift ratio combination.

2. The DAB converter control method based on current stress optimization according to claim 1, characterized in that, The step of optimizing the shift ratio combination of the DAB converter using a particle swarm optimization algorithm based on the current stress model and a preset transmission power model, and obtaining the first value of the shift ratio combination, includes: A fitness function is constructed based on the current stress model, the transmission power model, and the preset penalty coefficients for power constraints. Based on the fitness function, the particle swarm optimization algorithm is used to optimize the shift ratio combination to obtain the first value of the shift ratio combination.

3. The DAB converter control method based on current stress optimization according to claim 1, characterized in that, The numerical update model is represented by the following expression: In the formula, The value of the outward shift is the value taken when entering the k-th iteration. The value of the outward shift after the k-th iteration is [value]. Let the value of the inward shift be the value taken when entering the k-th iteration. Let the value of the inward shift be the value after the k-th iteration. Let be the value of the Lagrange multiplier when entering the k-th iteration. Let be the value of the Lagrange multiplier after the k-th iteration. Let be the Jacobian matrix. Let be the residual matrix.

4. A DAB converter control system based on current stress optimization, characterized in that, include: The first module is used to obtain the input voltage and output voltage of the DAB converter in order to determine the current stress model; The second module is used to optimize the shift ratio combination of the DAB converter using a particle swarm optimization algorithm based on the current stress model and the preset transmission power model, to obtain a first value of the shift ratio combination; wherein, both the current stress model and the transmission power model include the shift ratio combination, the DAB converter includes an input-side full-bridge circuit and an output-side full-bridge circuit, and the shift ratio combination includes the inner shift ratio of the input-side full-bridge circuit and the outer shift ratio between the input-side full-bridge circuit and the output-side full-bridge circuit; The third module is used to iteratively update the first value of the shift ratio combination based on the current stress model and the transmission power model using the Lagrange multiplier method, so as to obtain the second value of the shift ratio combination. The fourth module is used to perform extended phase shift control on the DAB converter based on the second value of the phase shift combination. The step of iteratively updating the first value of the shift ratio combination using the Lagrange multiplier method based on the current stress model and the transmission power model to obtain the second value of the shift ratio combination includes: Based on the current stress model, the transmission power model, and the pre-introduced Lagrange multipliers, construct the Lagrange function; Differentiating the Lagrange function yields a first-order derivative and a second-order derivative. The first-order derivative includes the third-order derivative of the Lagrange function with respect to the outward shift ratio and the fourth-order derivative of the Lagrange function with respect to the inward shift ratio. The second-order derivative includes the first and second-order mixed derivatives of the Lagrange function with respect to the outward shift ratio and the inward shift ratio, the second and second-order mixed derivatives of the Lagrange function with respect to the inward shift ratio and the outward shift ratio, the first and second-order derivatives of the Lagrange function with respect to the outward shift ratio, and the second and second-order derivatives of the Lagrange function with respect to the inward shift ratio. Based on the first-order derivative result and the transmission power model, a residual matrix is ​​constructed, which is expressed by the following expression: In the formula, Let be the residual matrix. The result of the third first-order derivative, The result of the fourth first-order derivative is... Referring to the aforementioned transmission power model, This is the preset reference transmission power per-unit value; The transmission power model is differentiated to obtain a second first-order derivative result, which includes the fifth first-order derivative result of the transmission power model with respect to the outward shift ratio and the sixth first-order derivative result of the transmission power model with respect to the inward shift ratio. Based on the second-order derivative and the second-first-order derivative, a Jacobian matrix is ​​constructed, which is expressed by the following expression: In the formula, Let be the Jacobian matrix. The result of the first and second derivatives, The result of the first and second order mixed derivatives, The result of the fifth first-order derivative is... This is the result of the second-order mixed derivative. This is the result of the second-order derivative. The result of the sixth-first derivative; Based on the residual matrix and the Jacobian matrix, a numerical update model is constructed for the shift ratio combination and the Lagrange multiplier. Based on the numerical update model and the preset termination condition, the first value of the shift ratio combination is iteratively updated to obtain the second value of the shift ratio combination.

5. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the DAB converter control method based on current stress optimization as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the DAB converter control method based on current stress optimization as described in any one of claims 1 to 3.

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