Co-design method, device and equipment of solid-state transformer and storage medium

Abstract

This application relates to a collaborative design method, apparatus, device, and storage medium for solid-state transformers. The method includes: determining first generation parameters for a power conversion component in the solid-state transformer, based on desired operating parameters of the power conversion component, the power conversion component including devices and / or magnetic components, and the first generation parameters including the component type and target operating parameters of the power conversion component; determining a control mode for the power conversion component based on the desired operating parameters; and determining second generation parameters for the solid-state transformer based on the first generation parameters and the control mode. This method enables the collaborative design of core components in a solid-state transformer.

Description

Technical Field

[0001] This application relates to the field of solid-state transformer technology, and in particular to a co-design method, apparatus, device and storage medium for solid-state transformers. Background Technology

[0002] With the development of power distribution networks, renewable energy, and electric transportation, traditional power frequency transformers have limitations in terms of bidirectional energy flow, power density improvement, dynamic regulation, and intelligent management. Solid-state transformers (SSTs), as a new type of power conversion equipment that integrates power electronic conversion, high-frequency magnetic integration, and digital control, have become key node equipment in the future power grid.

[0003] However, existing solid-state transformer designs lack a coordinated design for the core components within the solid-state transformer. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, apparatus, device, and storage medium for the collaborative design of solid-state transformers that can realize the collaborative design of core components in solid-state transformers, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a collaborative design method for solid-state transformers. The method includes:

[0006] Based on the desired operating parameters of the power conversion components in the solid-state transformer, a first generation parameter of the power conversion components is determined. The power conversion components include devices and / or magnetic components. The first generation parameter includes the component type of the power conversion components and the target operating parameters.

[0007] The control method of the power conversion component is determined based on the desired operating parameters;

[0008] The second generation parameters of the solid-state transformer are determined based on the first generation parameters and the control method.

[0009] In one embodiment, determining the second generation parameter of the solid-state transformer based on the first generation parameter and the control method includes:

[0010] Based on the first generation parameters and the control method, a multiphysics simulation model of the solid-state transformer is constructed;

[0011] Based on the first generated parameters and the multi-objective optimization algorithm, the multiphysics simulation model is iteratively solved to obtain the iterative solution results;

[0012] The second generation parameter is determined from the iterative solution result.

[0013] In one embodiment, the iterative solution of the multiphysics simulation model based on the first generated parameters and a multi-objective optimization algorithm to obtain the iterative solution result includes:

[0014] Obtain multiple sets of sample parameters from the first generated parameters;

[0015] The multiphysics simulation model is simulated using the multiple sets of sample parameters to obtain the performance data corresponding to the multiple sets of sample parameters. Based on the multiple sets of sample parameters and the performance data, a proxy model of the performance index is constructed.

[0016] The surrogate model is iteratively solved using a multi-objective genetic algorithm to obtain candidate results;

[0017] Candidate results that meet the preset convergence conditions are determined as the iterative solution results.

[0018] In one embodiment, the power conversion component includes a magnetic element, and determining the first generation parameters of the power conversion component based on the desired operating parameters of the power conversion component in the solid-state transformer includes:

[0019] The component type of the magnetic component is determined based on the desired operating parameters, the first constraint condition, and the preset circuit topology.

[0020] Based on the first constraint, the component type, the circuit topology, and the zero-voltage turn-on energy balance formula, determine the initial values ​​of the parasitic parameters and the initial leakage inductance of the magnetic component;

[0021] Based on the preset structural parameters of the magnetic component, the preset electromagnetic simulation model, and the electric field simulation model, the initial values ​​of the parasitic parameters and the initial leakage inductance are iteratively corrected to obtain the target values ​​of the parasitic parameters and the target leakage inductance.

[0022] In one embodiment, determining the control method of the power conversion component based on the desired operating parameters includes:

[0023] Based on the desired operating parameters, the first constraint condition, and the target leakage inductance, the modulation mode switching threshold under different load conditions is determined, wherein the modulation mode is related to phase shift modulation, pulse frequency modulation, and pulse width modulation.

[0024] Based on the modulation mode switching threshold, a multi-mode cooperative control model is constructed;

[0025] The optimal solution of the multi-mode cooperative control model is obtained by using a multi-objective optimization algorithm, and the combination of control parameters that satisfy the desired operating parameters and the first constraint condition is determined based on the optimal solution.

[0026] The control method is generated based on the combination of the control parameters.

[0027] In one embodiment, the power conversion component includes a device, and determining the first generation parameters of the power conversion component based on the desired operating parameters of the power conversion component in the solid-state transformer includes:

[0028] The initial component type of the device is determined based on the desired operating parameters, the second constraint, and the circuit topology of the dual active bridge.

[0029] Based on the initial component type, determine the circuit parameters and control parameters of the device;

[0030] Based on the circuit parameters and the control parameters, determine the conduction loss and switching loss corresponding to the device;

[0031] Based on the conduction loss, the switching loss, and the multiphysics coupling model, the initial component type is adjusted to determine the component type of the device.

[0032] In one embodiment, determining the control method of the power conversion component based on the desired operating parameters includes:

[0033] Based on the desired operating parameters, the second constraint, and the circuit topology of the dual active bridge, the energy balance control mode and the multi-mode cooperative modulation mode of the device are constructed.

[0034] The initial control parameters of the device are determined based on the energy balance control mode and the multi-mode cooperative modulation mode.

[0035] The initial control parameters are iteratively optimized using a multi-objective optimization algorithm to obtain target control parameters, and the control mode is determined based on the target control parameters.

[0036] Secondly, this application also provides a collaborative design apparatus for solid-state transformers. The apparatus includes:

[0037] The first determining module is used to determine the first generating parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer. The power conversion component includes devices and / or magnetic components. The first generating parameters include the component type of the power conversion component and the target operating parameters.

[0038] The second determining module is used to determine the control mode of the power conversion component based on the desired operating parameters;

[0039] The third determining module is used to determine the second generating parameters of the solid-state transformer based on the first generating parameters and the control method.

[0040] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in the first aspect.

[0041] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0042] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0043] The aforementioned collaborative design method, apparatus, equipment, and storage medium for solid-state transformers involve the terminal first determining first generated parameters, including the component type and target operating parameters of the power conversion components in the solid-state transformer, based on the desired operating parameters of these components. These power conversion components include devices and / or magnetic elements. Then, based on the desired operating parameters, the control method for the power conversion components is determined. Finally, based on the first generated parameters and the control method, second generated parameters for the solid-state transformer are determined. This process, by considering the first generated parameters and control method of the power conversion components during the determination of the second generated parameters, allows for precise matching of the physical characteristics and operating conditions of the devices and magnetic elements. This ensures that the solid-state transformer determined based on the second generated parameters meets the desired operating parameters, improving the reliability and overall performance of the solid-state transformer's energy conversion. Furthermore, it provides structured methodological support for the rapid design and performance improvement of solid-state transformers, enabling the collaborative design of core components within the solid-state transformer. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a collaborative design method for solid-state transformers in one embodiment;

[0046] Figure 2 This is a flowchart illustrating step 103 in one embodiment;

[0047] Figure 3 This is a flowchart illustrating step 202 in one embodiment;

[0048] Figure 4 This is a flowchart illustrating step 101 in one embodiment;

[0049] Figure 5 This is a flowchart illustrating step 102 in one embodiment;

[0050] Figure 6 This is a flowchart illustrating step 101 in another embodiment;

[0051] Figure 7 This is a flowchart illustrating step 102 in another embodiment;

[0052] Figure 8 This is a structural block diagram of a co-design device for a solid-state transformer in one embodiment;

[0053] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0054] 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 and not intended to limit the scope of this application.

[0055] In SST (Self-Transfer Streaming) devices that provide isolation and bidirectional power transmission via mid-frequency links, dual active bridges, inductor-inductor-capacitor resonant converters, or capacitor-inductor-inductor-capacitor resonant converters are mainly used. Among these, dual active bridges, with their bidirectional power flow and good controllability, have become the representative solution for mid-frequency links. However, existing dual active bridge designs have technical shortcomings in circuit topology, magnetic component and device design, device and drive technology, control strategies, system-level fault tolerance, and reliability, which restrict the large-scale application and industrialization of SST in distribution networks.

[0056] Typically, Solid State Transformers (SSTs) employ a layered, decoupled design. For example, the device layer focuses on switching losses and thermal performance, while the magnetic component design concentrates on turns ratio and magnetic saturation losses. The control strategy independently adjusts phase shift or frequency based on the circuit model. Furthermore, the inadequate modeling and parametric design of mid-to-high frequency magnetic components leads to insufficient system-level fault tolerance and reconfiguration capabilities. When issues such as open circuits, short circuits, excessive temperature rise, or sensor drift occur, the system is prone to cascading failures, resulting in insufficient reliability and safety. Therefore, existing solid-state transformer designs lack a coordinated design approach for the core components within the solid-state transformer.

[0057] Based on this, this application proposes a collaborative design method for solid-state transformers to achieve collaborative design of core components in solid-state transformers, thereby improving the reliability and safety of solid-state transformers.

[0058] In one embodiment, such as Figure 1 As shown, a collaborative design method for solid-state transformers is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and can be implemented through the interaction between the terminal and the server.

[0059] The terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. Servers can be implemented using dedicated servers or server clusters composed of multiple servers.

[0060] In this embodiment, the method includes the following steps:

[0061] Step 101: Determine the first generation parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer.

[0062] The power conversion component includes devices and / or magnetic components.

[0063] It should be noted that the power conversion components of a solid-state transformer are the core power electronic devices that realize the conversion of electrical energy form, voltage level transformation, electrical isolation, and bidirectional power flow. They are also the core execution unit for the entire machine to complete energy transmission. The power conversion components can include input-stage power conversion components, isolation-stage power conversion components, and output-stage power conversion components. For example, the input-stage power conversion components include a high-power power electronic switching module, an input filter reactor, an input filter capacitor, and a high-voltage DC bus support capacitor; the isolation-stage power conversion components include a SiC / GaN wide-bandgap switch, a matching gate drive unit, a high-frequency isolation transformer, a resonant inductor, a magnetizing inductor, a resonant capacitor, and a DC-side bus support capacitor; and the output-stage power conversion components include a low-voltage power switching module, an output filter inductor, an output filter capacitor, and a low-voltage DC bus support capacitor.

[0064] In this embodiment, the power conversion components in a solid-state transformer are divided into devices and magnetic components. Devices refer to the core power electronic devices that directly participate in power conversion and realize switching / rectification / voltage regulation, as well as key components that are strongly related to the power circuit. Magnetic components refer to magnetic components that rely on electromagnetic induction to realize energy transmission, voltage conversion, energy storage, filtering, and resonance.

[0065] Among them, the expected operating parameters refer to the preset performance indicators and operational constraints that need to be ultimately met, and belong to the design input indicators. The first generated parameters refer to the core hardware parameters of the power conversion components (devices, magnetic components) that are initially determined based on the expected operating parameters.

[0066] Optionally, the desired operating parameters may include information such as electrical specifications, soft-switching parameters, efficiency parameters, topology requirements, operating condition requirements, electromagnetic interference (EMI) constraints, and temperature constraints. For example, the desired operating parameters may include the preset rated voltage, rated power, conversion efficiency, zero-voltage turn-on coverage, temperature rise limit, and electromagnetic interference constraints of the devices or magnets in the solid-state transformer.

[0067] Optionally, the first generated parameters include the component type of the power conversion component and the target operating parameters. For example, the component type may include the type of device and / or magnet. The target operating parameters may include the rated electrical parameters of the device or magnet, the resonant capacitor value, the target leakage inductance and turns ratio of the high-frequency isolation transformer, and the inductance of the resonant inductor and filter inductor.

[0068] In this embodiment, the terminal can receive the user's input of desired operating parameters and parse the desired operating parameters. From the parsing results, it extracts key indicator information such as rated power, input / output voltage, operating frequency, efficiency target, and power density requirements. Then, it selects an appropriate power conversion topology based on the key indicator information. Next, it selects the switching devices of the power conversion component, determines the capacitor parameters, and determines the auxiliary power device parameters based on the power conversion topology. Finally, it obtains the first generated parameters of the power conversion component based on the selection, capacitor parameters, and auxiliary power device parameters.

[0069] For example, if applied to high-voltage, high-power, bidirectional transmission scenarios, a dual active bridge topology can be selected; if applied to scenarios requiring high-efficiency bidirectional soft switching and adaptability to a wide voltage range, an inductor-inductor-capacitor resonant converter or a capacitor-inductor-inductor-capacitor resonant converter resonant topology can be selected. Furthermore, based on the hardware architecture of the power conversion components, the types and configurations of devices and magnetic components can be determined, such as whether to use independent resonant inductors or whether to configure dedicated electromagnetic interference filtering magnetic components and devices.

[0070] Step 102: Determine the control method for the power conversion component based on the desired operating parameters.

[0071] Among them, the control method refers to the switching control strategy, modulation mode, closed-loop regulation method and protection control logic adopted in order to match the power conversion components with the desired operating parameters, achieve stable operation, and meet the design goals such as soft switching, efficiency and electromagnetic interference.

[0072] Optionally, the control method may include voltage closed-loop control, power closed-loop control, and current closed-loop control. Voltage closed-loop control is used to maintain a constant output DC / AC voltage, adapting to constant voltage load scenarios; power closed-loop control is used to achieve rated power and limited power operation, meeting the needs of bidirectional power transmission and energy dispatch; current closed-loop control is used to limit peak current, achieve overcurrent protection, and simultaneously work with soft switching to achieve resonant cavity current regulation.

[0073] In this embodiment, the terminal can determine the electrical specifications and power flow direction based on the desired operating parameters, and determine the basic control architecture of the power conversion component based on the electrical specifications and power flow direction, namely the voltage and power dual closed-loop control strategy. Then, the real-time operating conditions are input into the offline simulation model or online prediction model, and the electromagnetic interference constraints and soft switching optimization objectives are solved based on the offline simulation model or online prediction model. Next, the optimization control logic is determined based on the electromagnetic interference constraints and soft switching optimization objectives. Then, the dual closed-loop control strategy is optimized using the optimization control logic to form a complete control mode for the power conversion component.

[0074] Step 103: Determine the second generation parameters of the solid-state transformer based on the first generation parameters and the control method.

[0075] The second generated parameter is a set of parameters adapted to the overall control and hardware coordination of the solid-state transformer. It includes both fine-tuning parameters for hardware adaptation and quantization tuning parameters for the control mode, and is the final execution parameter to achieve the desired operating parameters. Optionally, the second generated parameter may include modulation mode parameters, soft-switching coordination matching parameters, electromagnetic interference and loss constraint adaptation parameters, and overall closed-loop control tuning parameters.

[0076] In this embodiment, the terminal can take the first generated parameters as input, the control method as constraints and strategies, and substitute them into the complete mathematical and physical models corresponding to the solid-state transformer to perform steady-state and dynamic analysis, calculation and simulation, thereby deriving and optimizing a series of system-level indicators describing the final performance of the whole machine, and determining these indicators as the second generated parameters.

[0077] In the aforementioned collaborative design method for solid-state transformers, the terminal first determines first generated parameters, including the component type and target operating parameters of the power conversion components in the solid-state transformer, based on the expected operating parameters of these components. These power conversion components include devices and / or magnetic components. Then, based on the expected operating parameters, the control method for the power conversion components is determined. Finally, based on the first generated parameters and the control method, the second generated parameters for the solid-state transformer are determined. This approach, by considering the first generated parameters and control method of the power conversion components during the determination of the second generated parameters, accurately matches the physical characteristics and operating conditions of the devices and magnetic components. This ensures that the solid-state transformer determined based on the second generated parameters meets the expected operating parameters, improving the reliability and overall performance of the solid-state transformer's energy conversion. Furthermore, it provides structured methodological support for the rapid design and performance improvement of solid-state transformers, enabling the collaborative design of core components within the solid-state transformer.

[0078] In one exemplary embodiment, such as Figure 2 As shown, this embodiment relates to the process by which the terminal determines the second generation parameters of the solid-state transformer based on the first generation parameters and the control method. Step 103 includes:

[0079] Step 201: Based on the first generation parameters and control method, construct a multiphysics simulation model of the solid-state transformer.

[0080] It should be noted that during the operation of solid-state transformers, they may be subject to interference from multiple physical fields, including electrical, magnetic, thermal, and electromagnetic fields. Therefore, multiple physical fields need to be considered when determining the second generation parameter. The multiphysics simulation model refers to a simulation model that covers four core physical fields: circuit field, magnetic field, thermal field, and electromagnetic interference field. It achieves real-time bidirectional data transmission between each physical field and closed-loop linkage between the control module and the hardware model through cross-field coupling interfaces.

[0081] The core of the circuit field model is the SST power conversion main circuit, which maps the device switching characteristics and electromagnetic parameters of the magnetic components to the first generated parameters, simulating core electrical characteristics such as power transmission, soft switching implementation, and device / circuit loss calculation. The magnetic field model is constructed for core magnetic components such as high-frequency isolation transformers, resonant inductors, and filter magnetic components, mapping the core / winding structure and electromagnetic parameters to the first generated parameters, simulating magnetic characteristics such as energy conversion, core / winding losses, leakage flux distribution, and actual values ​​of parasitic parameters. The thermal field model uses the devices / magnetic components as the heat-generating core, mapping the thermal resistance / capacitance and heat conduction characteristics to the first generated parameters, and using the loss data output from the circuit / magnetic field as the heat source, simulating the temperature rise distribution, junction temperature change, and heat transfer laws of the core components. The electromagnetic interference field model uses the power loop, switching nodes, and leakage flux regions of the magnetic components as simulation objects, mapping the parasitic parameters to the first generated parameters and the device dv / dt / di / dt characteristics, simulating electromagnetic interference characteristics such as the amplitude and spectral distribution of conducted and radiated interference.

[0082] In this embodiment, the terminal can use the first generated parameters as geometric, material, and electrical parameterization definitions that can be used for simulation, and transform the control method into an executable control algorithm model. Then, in the co-simulation platform, circuit, electromagnetic field, and temperature field models are synchronously constructed based on these parameterization definitions, and a cross-physics coupling interface is built to realize bidirectional data transmission. Next, the control algorithm model is combined with the above-mentioned multi-physics coupling model through the co-simulation interface to construct a multi-physics simulation model.

[0083] Step 202: Based on the first generation parameters and the multi-objective optimization algorithm, the multiphysics simulation model is iteratively solved to obtain the iterative solution results.

[0084] Among them, multi-objective optimization algorithm refers to the solution algorithm that can handle the strong coupling relationship between devices, magnetic components and control parameters, and find the global optimal parameter combination through multiple rounds of iteration. Multi-objective optimization algorithm can simultaneously handle the optimization method of multiple conflicting objective functions, and find the optimal solution set that can balance each objective under the constraint conditions, rather than a single optimal solution.

[0085] The comprehensive objective function set corresponding to the multi-objective optimization algorithm is:

[0086]

[0087] ① Maximize efficiency:

[0088]

[0089] in, .

[0090] ② Maximize power density:

[0091]

[0092] constraint .

[0093] ③ Maximize the temperature rise margin:

[0094]

[0095] Its goal is to increase the safety margin (i.e., minimize hot spot temperature).

[0096] ④ Minimize EMI indicators:

[0097]

[0098] The goal is to reduce common-mode / differential-mode noise energy and improve the system's electromagnetic compatibility.

[0099] Multi-objective optimization can be achieved using weighted summation or Pareto boundary set methods:

[0100]

[0101] Among them, weight Customizable based on application scenarios (such as vehicle-mounted or grid-side).

[0102] In this embodiment, the terminal can extract the turn-on / turn-off energy, output capacitance, on-resistance, gate resistance, core material parameters, number of winding turns, target value of leakage inductance, interlayer insulation thickness, window utilization coefficient, etc. of the power conversion component from the first generated parameters as an initial parameter set. The initial parameter set is then mapped to decision variables using a real number encoding method. Then, an objective function is constructed based on the decision variables and the multiphysics simulation model. Next, a multi-objective optimization algorithm is used to iteratively solve the objective function to obtain the solution result.

[0103] Step 203: Determine the second generation parameter from the iterative solution results.

[0104] In this embodiment, the terminal can first eliminate solutions with overlapping performance and those that are not feasible in engineering from the iterative solution results to obtain feasible solutions. Then, it can adjust the weights of different types of solutions in the feasible solutions according to the application scenario. After that, it can select solutions that meet the expected working parameters from the feasible solutions according to the weights, and determine these solutions that meet the expected working parameters as the second generation parameters.

[0105] In this embodiment, the terminal first constructs a multiphysics simulation model of the solid-state transformer based on the first generation parameters and the control method. Then, it iteratively solves the multiphysics simulation model based on the first generation parameters and the multi-objective optimization algorithm to obtain the iterative solution results. The second generation parameters are then determined from the iterative solution results. In this way, the characteristics of the power conversion components and the control method can be accurately coupled and matched through the deep integration of the multiphysics simulation model and the multi-objective optimization algorithm, thereby improving the operational stability and reliability of the solid-state transformer designed according to the second generation parameters.

[0106] In one exemplary embodiment, such as Figure 3 As shown, this embodiment relates to the process by which the terminal iteratively solves a multiphysics simulation model based on the first generation parameters and a multi-objective optimization algorithm to obtain the iterative solution result. Step 202 includes:

[0107] Step 301: Obtain multiple sets of sample parameters from the first generation parameters.

[0108] Among them, the sample parameters refer to multiple sets of engineering parameter combinations selected from the first generated parameters. Each set of sample parameters includes parameters of magnetic components and devices, as well as control parameters, and covers different application scenarios.

[0109] In this embodiment, the terminal first parses the design variable space defined by the first generation parameter (including key parameters such as power device type, core geometry, winding structure and capacitor specifications), and then uses a design space sampling algorithm (such as Latin hypercube sampling, full factorial design or experience-based DoE method) to automatically generate multiple sets of sample parameter combinations that are uniformly distributed or representative within the allowable value range.

[0110] For example, if the core loss of the initial sample parameters exceeds the standard, the energy balance formula is mismatched, or the temperature rise exceeds the limit, then it is determined that the initial sample parameters do not meet the constraints.

[0111] Step 302: Use multiple sets of sample parameters to perform finite element simulation on the multiphysics simulation model to obtain the performance data corresponding to the multiple sets of sample parameters, and construct a proxy model of performance index based on the multiple sets of sample parameters and performance data.

[0112] Among them, the proxy model is a simplified mathematical model that uses low-cost computation to replace the complex multiphysics field finite element simulation, and quickly maps the relationship between the design parameters and performance indicators of solid-state transformers.

[0113] In this embodiment, the terminal first imports multiple sets of sample parameters into a pre-established multiphysics simulation model, drives the model to perform finite element simulation calculations involving electromagnetic-thermal-circuit coupling, and obtains key performance data (including indicators such as efficiency, loss distribution, peak temperature, stress, and electromagnetic interference level) corresponding to each set of samples. Subsequently, machine learning or statistical modeling methods (such as Kriging, radial basis function neural networks, or Gaussian process regression) are used, with sample parameters as input and performance data as output, to train and verify the model, and finally construct a performance index proxy model that can accurately approximate the behavior of multiphysics simulation.

[0114] Step 303: Use a multi-objective genetic algorithm to iteratively solve the surrogate model and obtain candidate results.

[0115] Among them, the multi-objective genetic algorithm is a heuristic optimization algorithm based on the theory of biological evolution. Its core is to find a non-dominated optimal solution among multiple conflicting objective functions, rather than a single optimal solution. In this embodiment, the non-dominated optimal solution needs to meet the requirements of collaborative optimization of four-dimensional objectives: efficiency, power density, temperature rise, and electromagnetic interference in multi-physics fields.

[0116] In this embodiment, the terminal can first set parameters such as population size, crossover probability, mutation probability, and maximum number of iterations for the multi-objective genetic algorithm. Then, it randomly generates an initial parameter combination that meets the parameter boundary constraints as the initial population of the algorithm. Next, in each generation of optimization, the performance indicators of each individual are quickly calculated through a surrogate model, and a non-dominated sorting and crowding calculation mechanism is used to screen the advantageous parameter combination. Then, a new generation of population is generated through selection, crossover, and mutation operations. This process is repeated to perform iterative optimization until the maximum number of iterations is reached. Finally, non-dominated solutions are selected from the last generation of population to form candidate results.

[0117] Step 304: The candidate results that meet the preset convergence conditions are determined as the iterative solution results.

[0118] The convergence condition refers to the conditions that satisfy the multi-physics collaborative screening, which need to meet the efficiency threshold, soft-switching coverage threshold, temperature rise threshold, and electromagnetic compatibility constraints. For example, the convergence condition could be that the comprehensive objective function value of the best individual in the population tends to stabilize, the non-dominated solution does not change significantly after n consecutive generations of optimization, and the candidate results include at least m sets of solutions that satisfy the constraints.

[0119] In this embodiment, the terminal first determines whether the candidate result set meets the preset convergence conditions based on the target space solution set distribution, generational improvement rate and population diversity index recorded during the algorithm iteration process. If it meets the conditions and the average crowding degree reaches stability, the optimization is determined to be converged. The candidate result set that meets the non-dominant relationship and covers a wide target space is determined as the iterative solution result of multi-objective collaborative optimization.

[0120] In this embodiment, the terminal first obtains multiple sets of sample parameters from the first generation parameters, performs finite element simulation on the multiphysics simulation model using the multiple sets of sample parameters, obtains the performance data corresponding to the multiple sets of sample parameters, and constructs a surrogate model of performance indicators based on the multiple sets of sample parameters and performance data. The surrogate model is then iteratively solved using a multi-objective genetic algorithm to obtain candidate results. The candidate results that meet the preset convergence conditions are determined as the iterative solution results. This process realizes deep collaborative optimization of devices, magnetic components and control, effectively broadens the coverage of soft switching, significantly improves system efficiency and power density, and precisely controls temperature rise and electromagnetic interference levels. It solves the problems of fragmented design, low efficiency under light load and insufficient reliability of traditional solid-state transformers, and provides efficient and robust technical support for the large-scale application and industrialization of medium and high frequency solid-state transformers.

[0121] In one exemplary embodiment, the power conversion component includes a magnetic element, such as... Figure 4 As shown, this embodiment relates to the process by which the terminal determines the first generation parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer. Step 101 includes:

[0122] Step 401: Determine the component type of the magnetic component based on the desired operating parameters, the first constraint condition, and the preset circuit topology.

[0123] The component type refers to the material selection for the magnetic core of the magnetic component. For example, the component type may be ferrite, nanocrystalline / amorphous material.

[0124] The first constraint is set around the performance, physical characteristics, and manufacturing requirements of the high-frequency link (20–100kHz) of the medium-frequency solid-state transformer, including the following four categories: thermal and magnetic constraints, switching and control constraints, geometric and manufacturing constraints, and device and drive constraints. For example, loss density can be... Effective cross-sectional area Magnetic circuit length Window area Curie temperature and magnetic permeability temperature drift are used as selection criteria for component types.

[0125] The preset circuit topology can be a dual active bridge topology, in which the power stages include a primary full-bridge circuit, a high-frequency isolation transformer, and a secondary full-bridge circuit. The core component may include a high-frequency transformer. It supports bidirectional energy transfer, adapting to the bidirectional energy flow needs of power distribution networks, renewable energy sources, and electric transportation.

[0126] In this embodiment, the terminal analyzes the desired operating parameters (such as rated power, input / output voltage, and operating frequency) to clarify the core functions that the magnetic component needs to achieve, namely high-frequency electrical isolation and a specific voltage transformation ratio. Then, based on the specific requirements of the magnetic component according to the preset circuit topology (such as dual active bridge DAB), the key performance target parameters of the magnetic component are determined, including the target transformation ratio n and the target leakage inductance range L. lk Then, using the first constraint conditions (such as cost limit, maximum allowable volume and weight, temperature rise limit) as the screening boundary, all candidate types that do not meet the constraints are evaluated and eliminated from the magnetic component type library (such as independent transformer + discrete inductor, split core transformer with integrated leakage inductance, planar magnetic integrated structure, etc.). Finally, the specific magnetic component functional architecture that meets the electrical performance target and best conforms to all constraints is selected and determined as the magnetic component type of the power conversion component in the solid-state transformer.

[0127] For example, the component type may be an EE-type magnetic core high-frequency transformer with separate primary and secondary windings and an adjustable air gap.

[0128] In some embodiments, the first constraint may include:

[0129] ① Thermal and magnetic confinement:

[0130]

[0131] in, Typically, 40–60℃ is used. .

[0132] ② Switching and control constraints:

[0133]

[0134]

[0135] ③ Geometric and manufacturing constraints, and must meet insulation distance, creepage distance, and machining tolerances:

[0136]

[0137] Step 402: Determine the initial values ​​of the parasitic parameters and the initial leakage inductance of the magnetic component based on the first constraint condition, component type, circuit topology, and zero-voltage turn-on energy balance formula.

[0138] The zero-voltage turn-on energy balance formula means that before the switching device is turned on, the energy stored in the inductor in the circuit must be sufficient to cover the commutation energy of the device's equivalent capacitance.

[0139] Among them, the initial value of parasitic parameters refers to the initial setting value of parameters that naturally exist in magnetic components and circuits, which are not the design target but affect performance.

[0140] The initial leakage inductance is the target value of leakage inductance initially determined in the early stages of magnetic component design, based on circuit topology requirements, zero-voltage turn-on energy balance formula, and rated operating conditions.

[0141] In this embodiment, the terminal first sets the physical design boundary of the magnetic component according to the first constraint condition. Then, based on the power transmission requirements of the circuit topology and the component type of the magnetic component, the target range of leakage inductance required to meet the rated power transmission is derived using the power formula of the topology. Subsequently, the zero-voltage turn-on energy balance formula is applied to iteratively solve and verify within the target range, and a value that can both ensure that the soft switching condition is met within the full load range and meet the first constraint condition is selected, which is determined as the initial leakage inductance. Next, based on the expected physical structure of the magnetic component (such as the number of winding layers, spacing, and insulation thickness) to achieve the initial leakage inductance, the initial values ​​of parasitic capacitances such as inter-winding layer capacitance and winding-to-ground capacitance are estimated using approximation methods such as the parallel plate capacitor model, which together constitute the initial value set of parasitic parameters of the magnetic component.

[0142] In this embodiment, the process of determining the initial leakage inductance can be expressed as follows:

[0143]

[0144] in The average length of the leakage flux. It depends on the floor spacing / column arrangement.

[0145] In this embodiment, the process of determining the initial value of the parasitic parameter can be expressed as follows:

[0146]

[0147] Typically, the following conditions must be met first. Then, through shielding / layering... Fine-tuning, in conjunction with DV / DT management on the control side.

[0148] Step 403: Based on the preset structural parameters of the magnetic component, the preset electromagnetic simulation model, and the electric field simulation model, the initial values ​​of parasitic parameters and the initial leakage inductance are iteratively corrected to obtain the target values ​​of parasitic parameters and the target leakage inductance.

[0149] Among them, the preset structural parameters of the magnetic components are the core parameters that determine the physical form and electromagnetic properties of the magnetic components, which are pre-set based on design requirements.

[0150] Among them, the electromagnetic simulation model is a numerical model used to simulate the magnetic coupling, energy transmission, and loss characteristics of magnetic components at medium and high frequencies. The electric field simulation model is a numerical model used to analyze the internal electric field distribution, parasitic capacitance, and insulation performance of magnetic components. Its function is to accurately extract parasitic capacitance parameters and verify insulation reliability.

[0151] In this embodiment, the terminal first takes the initial leakage inductance and initial parasitic parameter values ​​as input, and drives a preset electromagnetic simulation model to perform finite element calculations based on preset magnetic component structure parameters (such as core geometry, winding wire diameter, interlayer spacing, shielding layer position, etc.) to obtain a more accurate simulated leakage inductance value. Simultaneously, it drives an electric field simulation model to calculate the corresponding simulated parasitic capacitance value. Then, it compares the deviation between the simulated value and the initial value. If the difference between the initial leakage inductance and the simulated leakage inductance value, or the difference between the initial parasitic parameter value and the simulated parasitic capacitance value, exceeds the allowable error, the preset structural parameters are automatically adjusted (e.g., fine-tuning the winding spacing to change the leakage inductance, adjusting the interlayer insulation thickness to change the capacitance), and the electromagnetic and electric field simulations are re-executed. This iterative "simulation-comparison-adjustment" process is repeated until the simulated leakage inductance and parasitic capacitance values ​​simultaneously meet the preset accuracy requirements, and the corresponding magnetic component structure still conforms to the first constraint condition. Finally, the terminal determines the leakage inductance value verified by simulation after iterative convergence as the target leakage inductance, and determines the corresponding parasitic capacitance value as the target value of the parasitic parameter.

[0152] In this embodiment, the process of determining the target leakage inductance, combined with the dual active bridge, can be expressed as follows:

[0153]

[0154] Given rating With the plan (generally Inverse solution:

[0155]

[0156] Considering the zero-voltage switching (ZVS) requirement: at the four switching points Must meet:

[0157]

[0158] If the ZVS is insufficient under light load, increase it appropriately. Alternatively, switch to PFM frequency reduction under light load.

[0159] In this embodiment, the terminal first determines the component type of the magnetic component based on the desired operating parameters, the first constraint condition, and the preset circuit topology. Then, based on the first constraint condition, component type, circuit topology, and zero-voltage turn-on energy balance formula, it determines the initial values ​​of the parasitic parameters and the initial leakage inductance of the magnetic component. Subsequently, based on the preset structural parameters of the magnetic component, the preset electromagnetic simulation model, and the electric field simulation model, the initial values ​​of the parasitic parameters and the initial leakage inductance are iteratively corrected to obtain the target values ​​of the parasitic parameters and the target leakage inductance. In this way, the magnetic component parameters are deeply adapted to the high-frequency link in the solid-state transformer. This not only allows the magnetic component to have the core functions of electrical isolation and controlled leakage inductance, but also ensures the basic conditions for soft switching through the zero-voltage turn-on energy balance formula. Furthermore, with the iterative correction of electromagnetic and electric field simulations, the parasitic parameters and leakage inductance are precisely controlled, effectively overcoming the problems of parameter matching imbalance, narrow soft-switching window, and excessive temperature rise and electromagnetic interference in traditional designs. Ultimately, this improves system efficiency, power density, and operational reliability, providing key technical support for the stable mass production and large-scale application of medium- and high-frequency solid-state transformers.

[0160] In one exemplary embodiment, such as Figure 5 As shown, this embodiment relates to the process by which the terminal determines the control method of the power conversion component based on the desired operating parameters. Step 102 above includes:

[0161] Step 501: Determine the modulation mode switching threshold under different load conditions based on the desired operating parameters, the first constraint condition, and the target leakage inductance.

[0162] Among them, the modulation mode is related to phase shift modulation, pulse frequency modulation, and pulse width modulation.

[0163] The modulation mode switching threshold refers to a series of preset operating boundary conditions. When the real-time operating state of the system crosses these boundaries, the terminal will automatically trigger the switching of the modulation mode in order to maintain the optimal system performance.

[0164] Phase-shift control (PS) controls power transmission by adjusting the phase difference between the two full-bridge output voltage square waves. It is the most basic control method for dual active bridges and is highly efficient in medium-to-high load regions. Pulse frequency modulation (PFM) changes the equivalent impedance by adjusting the switching frequency (f_s), thereby controlling power and extending the soft-switching range. It is often used in light-load or voltage mismatch conditions to reduce circulating current losses. Pulse width modulation (PWM) effectively changes the output voltage amplitude by adjusting the duty cycle (D) of the bridge arm output voltage square wave. It is used to suppress circulating current in cases of severe voltage mismatch and is often used in combination with PS or PFM.

[0165] Optionally, the load conditions can be divided into light load conditions, medium load conditions, and heavy load conditions.

[0166] In this embodiment, the terminal performs a full load condition scan and performance evaluation based on the expected operating parameters of the solid-state transformer (including rated power, input and output voltage range, and operating frequency), the first constraint conditions (such as maximum allowable temperature rise, efficiency lower limit, and EMI limit), and the optimized target leakage inductance. It uses a built-in loss model and soft-switching boundary analysis algorithm to calculate and compare the system efficiency, temperature rise, and soft-switching holding capability of different modulation modes at each load point. Then, based on the criterion of optimal overall performance, a series of clear load current or power boundary values ​​are determined as modulation mode switching thresholds.

[0167] For example, parameter boundary values ​​may include zero-voltage turn-on coverage ≥95%, temperature rise ≤60℃, dv / dt∈20–50kV / μs, and inductor root mean square current ≤ device rated limit.

[0168] For example, the threshold I for switching from light load to medium load th1 It can be calculated based on the segmented current model of a dual active bridge. Where, I sw With load current I L The correlation is linear, and I is derived by combining the target leakage inductance. th1 ≈0.2Irated, for example, according to The fine-tuning process can be: When it is too large, I th1 It can be reduced to 15%; it can rise to 25% in smaller cases.

[0169] Threshold I for switching from medium load to heavy load th2 It can be when I L / I rated When the current is greater than 80%, the circulating current loss in PS mode increases significantly. Superimposing PWM duty cycle control can suppress current spikes, therefore I is set... th2 ≈0.8Irated When it is small, I th2 It can be reduced to 75% to avoid excessive temperature rise.

[0170] In some embodiments, the structural hierarchy in the control method may include power / bus voltage control in the outer loop and phase shift angle modulation in the inner loop. The outer loop is responsible for system energy balance and power command tracking, and can employ one of the following two methods depending on the SST level:

[0171] ① Constant Voltage Mode (VDCMode): Maintains the output bus voltage Stablize;

[0172] ② Constant Power Mode (PMode): Based on the power command from the upstream bus. Control the flow of energy.

[0173] The outer loop typically uses PI or low-bandwidth MPC (Model Predictive Control):

[0174]

[0175] When a bus voltage loop exists:

[0176]

[0177] The outer ring bandwidth is usually set to The switching frequency is adjusted to prevent modulation interference.

[0178] The inner ring is based on the phase shift angle. As the main control variable, its basic power relationship is:

[0179]

[0180] Based on the unimodal characteristic of this function (in (At maximum power), the controller must: ensure To avoid ZVS loss; automatically shrinks under light load. Reduce circulating current; appropriately expand during voltage mismatch. To maintain power output.

[0181] The inner loop is implemented based on digital proportional feedforward plus integral correction;

[0182] The feedforward term is obtained by linearizing the power model:

[0183]

[0184] That is, the initial value is obtained by looking up a table;

[0185] The real-time compensation term is adjusted by the inner-loop PI:

[0186]

[0187] The following provides an example of different modulation modes.

[0188] In some embodiments, when light load or voltage ratio deviates ( When the switching frequency is low, the circulating current is significant and ZVS fails under simple PS control. At this point, PFM mode is entered, which reduces the switching frequency. In the same It increases the commutation current; expands the soft-switching range; and reduces idle loss under light load.

[0189] Frequency modulation mode can be represented as:

[0190]

[0191] That is, the smaller the current, the lower the frequency. PFM mode and PS mode together form a continuous range, and there is no need for mode switching delay.

[0192] In some embodiments, under partial load or voltage imbalance scenarios, to suppress excessively high dv / dt and parasitic circulating current, a duty cycle control (PWM) can be superimposed, that is, adjusting the square wave amplitude of the bridge arm output from ±V to ±D·V; by adjusting the duty cycle... Correct the effective output voltage; control objective: minimize the RMS current while ensuring ZVS.

[0193] The joint relationship of the three variables is approximated as follows:

[0194]

[0195] Among them, the controller adjusts in real time. The combination minimizes the target power error while maintaining ZVS.

[0196] Step 502: Construct a multi-mode cooperative control model based on the modulation mode switching threshold.

[0197] Among them, the multi-mode cooperative control model is an adaptive and intelligent advanced control architecture for power electronic converters. The multi-mode cooperative control model does not rely on a single fixed control strategy, but automatically selects or integrates multiple basic modulation modes according to the real-time operating conditions of the system to achieve optimal comprehensive performance across the entire operating range.

[0198] In this embodiment, the terminal designs a logic decision-maker based on a determined modulation mode switching threshold. This decision-maker takes the real-time collected system operating conditions as input and outputs the currently optimal modulation mode selection command according to the threshold logic. Next, around this decision-maker, an independent controller library matching each modulation mode is integrated (including a phase shift angle calculator for the PS, a frequency regulator for the PFM, a duty cycle regulator for the PWM, and their corresponding closed-loop compensation algorithms), and a smooth switching mechanism (such as state transfer and disturbance-free transition logic) is designed. Finally, the decision-maker, the multi-mode controller library, and the switching mechanism are integrated into a multi-mode cooperative control model.

[0199] For example, the pattern judgment logic can be shown in the following table:

[0200] condition Main mode Auxiliary modulation <![CDATA[I L >I mid ZVS is normal. PS / <![CDATA[I L min ZVS is invalid. ​ PFM Downclocking <![CDATA[V1 / V2>1.2 or <0.8]]> PS+PWM Adjust duty cycle D Fault or overheating derating Frequency reduction + amplitude limiting /

[0201] Step 503: Use a multi-objective optimization algorithm to solve for the optimal solution of the multi-mode cooperative control model, and determine the combination of control parameters that satisfy the desired operating parameters and the first constraint condition based on the optimal solution.

[0202] In this embodiment, the terminal first encodes the adjustable key variables in the multi-mode cooperative control model into real-valued decision vectors. Secondly, it establishes a multi-objective function centered on system overall efficiency, peak temperature rise, and electromagnetic interference level, and transforms the first constraint into an optimization boundary or penalty function. Subsequently, in each iteration, for each set of decision vectors generated by the algorithm, a multi-physics simulation model or a high-precision surrogate model is invoked to simulate its full-condition operation under multi-mode cooperative control, thereby calculating the corresponding multi-objective function value. Finally, through iterative optimization using multiple generations of non-dominated sorting and elite retention, the terminal selects the optimal solution from the converged Pareto front that best balances the objectives and strictly satisfies the first constraint, and decodes this solution into a set of deterministic control parameter combinations, thus obtaining a deployable and executable optimal control strategy that simultaneously meets the desired operating parameter performance requirements and the first constraint.

[0203] Step 504: Generate a control mode based on the combination of control parameters.

[0204] In this embodiment, the terminal uses the combination of control parameters as a basis and embeds the combination of control parameters into a three-layer architecture of mode decision-core control-parameter adaptation to obtain the parameter combination that satisfies the adjustment logic in each mode. Then, a control strategy is generated based on the parameter combination that satisfies the adjustment logic in each mode.

[0205] In this embodiment, the terminal determines the modulation mode switching threshold under different load conditions based on the desired operating parameters, the first constraint condition, and the target leakage inductance. The modulation mode is related to phase shift modulation, pulse frequency modulation, and pulse width modulation. Based on the modulation mode switching threshold, a multi-mode cooperative control model is constructed. A multi-objective optimization algorithm is used to solve the optimal solution of the multi-mode cooperative control model. Based on the optimal solution, a combination of control parameters that satisfies the desired operating parameters and the first constraint condition is determined. A control mode is generated based on the combination of control parameters. In this way, not only is stable coverage of soft switching under a wide load range achieved, effectively suppressing light load circulating current loss and heavy load current spikes, and improving system efficiency and power density, but temperature rise control and electromagnetic interference suppression are also taken into account, thereby ensuring the efficient, stable, and safe operation of the high-frequency link in the solid-state transformer under all operating conditions.

[0206] In one exemplary embodiment, such as Figure 6 As shown, this embodiment relates to the process by which the terminal determines the first generation parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer. Step 101 includes:

[0207] Step 601: Determine the initial component type of the device based on the desired operating parameters, the second constraint condition, and the circuit topology of the dual active bridge.

[0208] The second constraint refers to the comprehensive set of restrictions that must be met in system design and operation, in addition to basic electrical performance. These mainly include cost limits, permissible volume and weight, temperature rise limits for thermal reliability, electromagnetic compatibility standards, and long-term operational reliability requirements. These conditions together constitute the boundary framework for device selection, control strategy design, and performance optimization.

[0209] In this embodiment, the terminal can first parse the device categories required by the circuit topology, including power switching devices, high-frequency transformers, resonant inductors and DC support capacitors; then, based on the desired operating parameters and the second constraint conditions, it can determine the voltage and current stress calculation and soft switching condition analysis, and automatically match and output the initial component type from the preset device database.

[0210] For example, silicon carbide MOSFETs can be selected as high-frequency switching devices.

[0211] In some embodiments, device selection objectives include: meeting rated power and voltage levels (typically 800–1500VDC links); ensuring soft-switching capability under all operating conditions; controlling dv / dt within the range of <50kV / μs to prevent common-mode interference to magnetic components and control systems; and maintaining low on-resistance and predictable switching energy over a wide temperature range (–40°C to 150°C).

[0212] Selection criteria can be:

[0213] ① At 40kHz~60kHz, select A module with mJ (20A, 800V);

[0214] ② Temperature rise rate value;

[0215] ③ The current should not exceed 200pF at 800V to ensure that the soft-switching range is controllable;

[0216] ④ It needs to support the Kelvin Source pin to reduce gate circuit interference.

[0217] Step 602: Determine the circuit parameters and control parameters of the device based on the initial component type.

[0218] The circuit parameters may include the gate resistance, dead time, and drive voltage of the switching devices; the number of turns, target leakage inductance, winding structure, and interlayer capacitance of the high-frequency transformer; the specific inductance and saturation current of the resonant inductor; and the capacitance and voltage rating of the DC support capacitor. Control parameters may include the initial phase shift angle, switching frequency operating point, frequency modulation range, and duty cycle adjustment boundary of the dual active bridge.

[0219] In this embodiment, the terminal can automatically determine the specific circuit parameters and control parameters of the device based on the determined initial component type, combined with the expected operating parameters of the solid-state transformer and the second constraint conditions, through a multiphysics coupling model and parametric design rules.

[0220] Step 603: Determine the conduction loss and switching loss of the device based on the circuit parameters and control parameters.

[0221] Among them, conduction loss refers to the energy dissipation caused by the on-state resistance of a power device during conduction. Its magnitude mainly depends on the effective value of the on-state current and the on-state resistance of the device.

[0222] Switching loss refers to the transient energy loss caused by voltage and current overlap and parasitic capacitance charging and discharging during the turn-on and turn-off processes of power devices. Its magnitude is affected by the switching frequency, device switching characteristics, driving parameters and bus voltage.

[0223] In this embodiment, the terminal can automatically determine the conduction loss and switching loss of the device under the target operating conditions based on the determined circuit parameters and control parameters and through the loss calculation model.

[0224] Step 604: Adjust the initial component type based on conduction loss, switching loss, and multiphysics coupling model to determine the component type of the device.

[0225] In this embodiment, the terminal can perform a comprehensive analysis based on the calculated conduction loss and switching loss data, combined with a multi-physics coupling model (covering the interactive effects of multiple physical domains such as electricity, heat, and magnetism), and automatically adjust the initial component type to optimize system performance, ultimately determining the component type of the device.

[0226] For example, if the loss analysis indicates that the thermal stress or efficiency of a particular device does not meet the second constraint, the terminal will initiate an iterative adjustment mechanism. For instance, if the switching loss of a silicon carbide MOSFET exceeds the allowable range at high frequencies, its component type may be adjusted to a new type of device with lower switching characteristics.

[0227] For example, the key electrical parameters of the device can be shown in the table below:

[0228] parameter symbol Design significance On resistance RdsonT Determines conduction losses and thermal balance; temperature coefficient needs to be evaluated. Switching energy Eon / offI,V,T Switching losses are determined by current and junction temperature, and vary significantly. Output capacitor CossV Affecting ZVS conditions, dv / dt, and EMI Gate charge Qg, Qgd Affects driving energy and switching speed Junction-shell thermal resistance RθJC Used for thermal modeling and cyclic load life analysis Transient thermal resistance Zθt Heat accumulation when evaluating pulsating power Reverse recovery charge Qrr Determines the cross loss of bridge arms and the risk of soft switching failure.

[0229] In this embodiment, the terminal determines the initial component type of the device based on the desired operating parameters, the second constraint condition, and the circuit topology of the dual active bridge. Based on the initial component type, it determines the device's circuit parameters and control parameters. Based on the circuit parameters and control parameters, it determines the corresponding conduction loss and switching loss of the device. Based on the conduction loss, switching loss, and multi-physics coupling model, the initial component type is adjusted to determine the final component type of the device. This closed-loop method ensures a high degree of coordination between device selection, circuit operating point, control strategy, and multi-physics constraints. This significantly improves the overall efficiency of the solid-state transformer, expands the soft-switching range, improves thermal uniformity, and reduces electromagnetic interference while meeting requirements for size, cost, and reliability. Ultimately, it achieves comprehensive optimization of power density, energy efficiency, and reliability.

[0230] In one exemplary embodiment, such as Figure 7 As shown, this embodiment relates to the process by which the terminal determines the first generation parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer. Step 102 includes:

[0231] Step 701: Based on the desired operating parameters, the second constraint condition, and the circuit topology of the dual active bridge, construct the energy balance control mode and the multi-mode cooperative modulation mode of the device.

[0232] Among them, the energy balance control mode is a control method with the core objective of maintaining the instantaneous or periodic energy supply and demand balance of the system. In this mode, the terminal ensures that the energy flow on the input and output sides matches the command by adjusting the transmission power of the dual active bridges in real time (such as through phase shift angle control), while maintaining the DC bus voltage stability, and actively managing the circulating energy to minimize lossless circulation in the process.

[0233] Among them, the multi-mode cooperative modulation mode is an intelligent control architecture that dynamically switches or integrates multiple modulation strategies according to the operating conditions. For example, PS is used under heavy load to ensure power transmission, PFM is switched in under light load to maintain soft switching and reduce losses, and PWM is introduced to suppress circulating current when there is a severe voltage mismatch, thereby achieving the optimal trade-off between efficiency, dynamic performance and electromagnetic compatibility across the entire operating range.

[0234] In this embodiment, the terminal first analyzes the power transmission model of the dual active bridge, calculates the reference phase shift angle and switching frequency required for steady-state power transmission based on the desired operating parameters, and introduces a voltage and current closed-loop regulation mechanism to construct an energy balance control mode. Then, based on the second constraint condition and the performance requirements under different operating conditions, a multi-mode cooperative modulation mode is constructed.

[0235] Step 702: Determine the initial control parameters of the device based on the energy balance control mode and the multi-mode cooperative modulation mode.

[0236] In this embodiment, firstly, based on the core objective of the energy balance control mode, the terminal maintains the system's power supply and demand balance and DC bus stability. Utilizing a dual active bridge power transfer model, it solves the power equations under the desired operating parameters to calculate the reference phase shift angle, switching frequency, and corresponding power feedforward, forming the basis for steady-state control. Simultaneously, combining the operating logic of the multi-mode cooperative modulation mode, it adaptively switches or merges between PS, PFM, and PWM modes based on real-time operating conditions such as load current and voltage ratio. It retrieves a preset mode switching threshold table and modulation parameter mapping library to assign initial parameters for each possible operating mode.

[0237] Step 703: The initial control parameters are iteratively optimized using a multi-objective optimization algorithm to obtain the target control parameters, and the control mode is determined based on the target control parameters.

[0238] In this embodiment, firstly, the terminal takes the initial control parameters as a starting point and uses efficiency, power density, temperature rise margin, and electromagnetic interference level as multi-objective optimization functions, and evaluates the system performance in combination with a multi-physics coupling model. Then, a multi-objective optimization algorithm is used to iteratively solve the problem within the set parameter search space. New combinations of control parameters are continuously generated through selection, crossover, and mutation operations, and their performance is evaluated using a surrogate model or fast simulation. Finally, a set of optimized solutions, i.e., the target control parameters, is obtained.

[0239] In this embodiment, the terminal first constructs the energy balance control mode and multi-mode cooperative modulation mode of the device based on the desired operating parameters, the second constraint condition, and the circuit topology of the dual active bridge. Based on the energy balance control mode and the multi-mode cooperative modulation mode, the initial control parameters of the device are determined, providing the system with the underlying control logic to maintain power stability, minimize circulating current, and achieve efficient soft switching under all operating conditions. Then, a multi-objective optimization algorithm is used to iteratively optimize the initial control parameters to obtain the target control parameters. Based on the target control parameters, the control mode is determined, so that the determined control strategy can dynamically match the real-time operating conditions. This ensures that the solid-state transformer maintains excellent comprehensive performance of high efficiency, high reliability, and low electromagnetic interference under the full load range and voltage fluctuation conditions, thereby significantly improving the overall energy efficiency of the system, expanding the stable operating range, enhancing dynamic response capability, and effectively supporting the design goals of high power density and long life.

[0240] To facilitate understanding by those skilled in the art, the co-design method for solid-state transformers provided in this application will be described in detail below. This method may include:

[0241] S1. Determine the component type of the magnetic component based on the desired operating parameters, the first constraint condition, and the preset circuit topology.

[0242] S2. Based on the first constraint, component type, circuit topology, and zero-voltage turn-on energy balance formula, determine the initial values ​​of the parasitic parameters and the initial leakage inductance of the magnetic component.

[0243] S3, based on the preset structural parameters of the magnetic components, the preset electromagnetic simulation model and electric field simulation model, iteratively corrects the initial values ​​of parasitic parameters and the initial leakage inductance to obtain the target operating parameters, including the target values ​​of parasitic parameters and the target leakage inductance.

[0244] S4. Based on the desired operating parameters, the first constraint condition, and the target leakage inductance, determine the modulation mode switching threshold under different load conditions.

[0245] Among them, the modulation mode is related to phase shift modulation, pulse frequency modulation, and pulse width modulation.

[0246] S5. Construct a multi-mode cooperative control model based on the modulation mode switching threshold.

[0247] S6. A multi-objective optimization algorithm is used to solve the optimal solution of the multi-mode cooperative control model, and the combination of control parameters that satisfy the desired operating parameters and the first constraint condition is determined based on the optimal solution.

[0248] S7 generates the control mode corresponding to the magnetic components in the solid-state transformer based on the combination of control parameters.

[0249] S8. Determine the initial component type of the device based on the desired operating parameters, the second constraint, and the circuit topology of the dual active bridge.

[0250] S9. Determine the circuit parameters and control parameters of the device based on the initial component type.

[0251] S10, based on circuit parameters and control parameters, determine the target operating parameters of the device, including conduction loss and switching loss.

[0252] S11. Based on the conduction loss, switching loss, and multi-physics coupling model, the initial component type is adjusted to determine the component type of the device.

[0253] S12, based on the desired operating parameters, the second constraint condition and the circuit topology of the dual active bridge, construct the energy balance control mode and the multi-mode cooperative modulation mode of the device.

[0254] S13, determine the initial control parameters of the device based on the energy balance control mode and the multi-mode cooperative modulation mode.

[0255] S14. The initial control parameters are iteratively optimized using a multi-objective optimization algorithm to obtain the target control parameters, and the control mode corresponding to the device in the solid-state transformer is determined based on the target control parameters.

[0256] S15. Based on the component type, target operating parameters, and corresponding control method of the magnetic component, the component type, target operating parameters, and corresponding control method of the device, construct a multiphysics simulation model of the solid-state transformer and obtain multiple sets of sample parameters.

[0257] S16. Finite element simulation is performed on the multiphysics simulation model using multiple sets of sample parameters to obtain performance data corresponding to the multiple sets of sample parameters. Based on the multiple sets of sample parameters and performance data, a proxy model of performance index is constructed.

[0258] S17. The surrogate model is solved iteratively using a multi-objective genetic algorithm to obtain candidate results.

[0259] S18, the candidate results that meet the preset convergence conditions are determined as the iterative solution results of the multiphysics simulation model.

[0260] S19. The second generation parameters of the solid-state transformer are determined from the iterative solution results, and the solid-state transformer is designed based on the second generation parameters.

[0261] It should be noted that the descriptions in S1-S19 above can be found in the relevant descriptions in the above embodiments, and their effects are similar, so they will not be repeated here.

[0262] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0263] Based on the same inventive concept, this application also provides a solid-state transformer collaborative design apparatus for implementing the above-described collaborative design method for solid-state transformers. The solution provided by this apparatus is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the solid-state transformer collaborative design apparatus provided below can be found in the limitations of the solid-state transformer collaborative design method described above, and will not be repeated here.

[0264] In one embodiment, such as Figure 8As shown, a collaborative design device for solid-state transformers is provided, comprising: a first determining module 801, a first determining module 802, and a first determining module 803, wherein:

[0265] The first determining module 801 is used to determine the first generating parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer. The power conversion component includes devices and / or magnetic components. The first generating parameters include the component type of the power conversion component and the target operating parameters.

[0266] The second determining module 802 is used to determine the control mode of the power conversion component based on the desired operating parameters;

[0267] The third determining module 803 is used to determine the second generating parameters of the solid-state transformer based on the first generating parameters and the control method.

[0268] In one embodiment, the third determining module 803 includes:

[0269] The first building unit is used to build a multiphysics simulation model of a solid-state transformer based on the first generation parameters and control method.

[0270] The first solving unit is used to iteratively solve the multiphysics simulation model based on the first generation parameters and the multi-objective optimization algorithm to obtain the iterative solution results.

[0271] The first determining unit is used to determine the second generation parameters from the iterative solution results.

[0272] In one embodiment, the above-mentioned solving unit is specifically used for:

[0273] Obtain multiple sets of sample parameters from the first generation parameters;

[0274] Finite element simulation of a multiphysics simulation model is performed using multiple sets of sample parameters to obtain performance data corresponding to the multiple sets of sample parameters. Based on the multiple sets of sample parameters and performance data, a proxy model of performance index is constructed.

[0275] A multi-objective genetic algorithm is used to iteratively solve the surrogate model to obtain candidate results;

[0276] Candidate results that meet the preset convergence conditions are determined as the iterative solution results.

[0277] In one embodiment, the power conversion component includes a magnetic component, and the first determining module 801 includes:

[0278] The second determining unit is used to determine the component type of the magnetic component based on the desired operating parameters, the first constraint condition, and the preset circuit topology.

[0279] The third determining unit is used to determine the initial values ​​of the parasitic parameters and the initial leakage inductance of the magnetic component based on the first constraint condition, component type, circuit topology and zero-voltage turn-on energy balance formula.

[0280] The correction unit is used to iteratively correct the initial values ​​of parasitic parameters and the initial leakage inductance based on the preset structural parameters of the magnetic component, the preset electromagnetic simulation model, and the electric field simulation model, so as to obtain the target values ​​of parasitic parameters and the target leakage inductance.

[0281] In one embodiment, the second determining module 802 includes:

[0282] The third determining unit is used to determine the modulation mode switching threshold under different load conditions based on the expected operating parameters, the first constraint condition and the target leakage inductance. The modulation mode is related to phase shift modulation, pulse frequency modulation and pulse width modulation.

[0283] The second building unit is used to build a multi-mode cooperative control model based on the modulation mode switching threshold.

[0284] The second solution unit is used to solve the optimal solution of the multi-mode cooperative control model using a multi-objective optimization algorithm, and to determine the combination of control parameters that satisfy the desired operating parameters and the first constraint condition based on the optimal solution.

[0285] The generation unit is used to generate a control mode based on the combination of control parameters.

[0286] In one embodiment, the power conversion component includes a device, and the first determining module 801 includes:

[0287] The fourth determining unit is used to determine the initial component type of the device based on the desired operating parameters, the second constraint condition, and the circuit topology of the dual active bridge.

[0288] The fifth determining unit is used to determine the circuit parameters and control parameters of the device based on the initial component type;

[0289] The sixth determining unit is used to determine the conduction loss and switching loss of the device based on the circuit parameters and control parameters.

[0290] The seventh determining unit is used to adjust the initial component type based on conduction loss, switching loss, and multiphysics coupling model, and to determine the component type of the device.

[0291] In one embodiment, the second determining module 802 includes:

[0292] The third building unit is used to build the energy balance control mode and multi-mode cooperative modulation mode of the device based on the desired operating parameters, the second constraint condition and the circuit topology of the dual active bridge.

[0293] The eighth determining unit is used to determine the initial control parameters of the device based on the energy balance control mode and the multi-mode cooperative modulation mode.

[0294] The ninth determining unit is used to iteratively optimize the initial control parameters using a multi-objective optimization algorithm to obtain the target control parameters, and to determine the control mode based on the target control parameters.

[0295] The modules in the aforementioned collaborative design device for solid-state transformers can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0296] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a co-design method for solid-state transformers. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0297] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0298] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0299] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0300] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0301] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0302] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0303] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0304] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A collaborative design method for solid-state transformers, characterized in that, The method includes: Based on the desired operating parameters of the power conversion components in the solid-state transformer, a first generation parameter of the power conversion components is determined. The power conversion components include devices and / or magnetic components. The first generation parameter includes the component type of the power conversion components and the target operating parameters. The control method of the power conversion component is determined based on the desired operating parameters; The second generation parameters of the solid-state transformer are determined based on the first generation parameters and the control method.

2. The method according to claim 1, characterized in that, The step of determining the second generation parameter of the solid-state transformer based on the first generation parameter and the control method includes: Based on the first generation parameters and the control method, a multiphysics simulation model of the solid-state transformer is constructed; Based on the first generated parameters and the multi-objective optimization algorithm, the multiphysics simulation model is iteratively solved to obtain the iterative solution results; The second generation parameter is determined from the iterative solution result.

3. The method according to claim 2, characterized in that, The iterative solution of the multiphysics simulation model based on the first generated parameters and the multi-objective optimization algorithm to obtain the iterative solution result includes: Obtain multiple sets of sample parameters from the first generated parameters; The multiphysics simulation model is simulated using the multiple sets of sample parameters to obtain the performance data corresponding to the multiple sets of sample parameters. Based on the multiple sets of sample parameters and the performance data, a proxy model of the performance index is constructed. The surrogate model is iteratively solved using a multi-objective genetic algorithm to obtain candidate results; Candidate results that meet the preset convergence conditions are determined as the iterative solution results.

4. The method according to claim 1, characterized in that, The power conversion component includes magnetic components. Determining the first generation parameters of the power conversion component based on the desired operating parameters of the power conversion component in the solid-state transformer includes: The component type of the magnetic component is determined based on the desired operating parameters, the first constraint condition, and the preset circuit topology. Based on the first constraint, the component type, the circuit topology, and the zero-voltage turn-on energy balance formula, determine the initial values ​​of the parasitic parameters and the initial leakage inductance of the magnetic component; Based on the preset structural parameters of the magnetic component, the preset electromagnetic simulation model, and the electric field simulation model, the initial values ​​of the parasitic parameters and the initial leakage inductance are iteratively corrected to obtain the target values ​​of the parasitic parameters and the target leakage inductance.

5. The method according to claim 4, characterized in that, Determining the control mode of the power conversion component based on the desired operating parameters includes: Based on the desired operating parameters, the first constraint condition, and the target leakage inductance, the modulation mode switching threshold under different load conditions is determined, wherein the modulation mode is related to phase shift modulation, pulse frequency modulation, and pulse width modulation. Based on the modulation mode switching threshold, a multi-mode cooperative control model is constructed; The optimal solution of the multi-mode cooperative control model is obtained by using a multi-objective optimization algorithm, and the combination of control parameters that satisfy the desired operating parameters and the first constraint condition is determined based on the optimal solution. The control method is generated based on the combination of the control parameters.

6. The method according to claim 1, characterized in that, The power conversion component includes devices, and determining the first generation parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer includes: The initial component type of the device is determined based on the desired operating parameters, the second constraint, and the circuit topology of the dual active bridge. Based on the initial component type, determine the circuit parameters and control parameters of the device; Based on the circuit parameters and the control parameters, determine the conduction loss and switching loss corresponding to the device; Based on the conduction loss, the switching loss, and the multiphysics coupling model, the initial component type is adjusted to determine the component type of the device.

7. The method according to claim 6, characterized in that, Determining the control mode of the power conversion component based on the desired operating parameters includes: Based on the desired operating parameters, the second constraint, and the circuit topology of the dual active bridge, the energy balance control mode and the multi-mode cooperative modulation mode of the device are constructed. The initial control parameters of the device are determined based on the energy balance control mode and the multi-mode cooperative modulation mode. The initial control parameters are iteratively optimized using a multi-objective optimization algorithm to obtain target control parameters, and the control mode is determined based on the target control parameters.

8. A collaborative design device for solid-state transformers, characterized in that, The device includes: The first determining module is used to determine the first generating parameters of the power conversion component based on the expected operating parameters of the power conversion component in the solid-state transformer. The power conversion component includes devices and / or magnetic components. The first generating parameters include the component type of the power conversion component and the target operating parameters. The second determining module is used to determine the control mode of the power conversion component based on the desired operating parameters; The third determining module is used to determine the second generating parameters of the solid-state transformer based on the first generating parameters and the control method.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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