Modeling method, device and equipment of magnetic coupling resonance type multi-device wireless power transmission system

Abstract

The application discloses a modeling method, device and equipment of a magnetic coupling resonance type multi-device wireless power transmission system, and relates to the technical field of wireless power transmission. The method reserves the full coupling topological relationship of the multi-device cluster system, avoids the precision deviation caused by the fixed simplification of the coupling relationship, by constructing a circuit model containing multiple transmission modules and multiple receiving modules and mutual inductive magnetic coupling between any modules and deriving the corresponding coupling mode equation. The operation overhead of complex number solving is eliminated, and the calculation complexity of modeling and solving is reduced, by decomposing the mode amplitude in the coupling mode equation into real and imaginary parts and separating to obtain the real value coupling mode equation. The discrete time state average model is obtained by processing the real value coupling mode equation through the discrete time state average method, which can accurately depict the transient response characteristics of the system and effectively support the dynamic load adjustment, stability analysis and fault diagnosis of the multi-device wireless power transmission cluster system.

Description

Technical Field

[0001] This application relates to the field of wireless power transmission technology, and in particular to a modeling method, apparatus and equipment for a magnetically coupled resonant multi-device wireless power transmission system. Background Technology

[0002] Magnetic coupling resonant wireless power transfer technology has been widely used for wireless power supply of various monitoring devices, sensors and inspection equipment in power distribution systems, and the power transfer process of the system is usually analyzed through circuit models or coupled-mode equations.

[0003] However, when dealing with multi-device cluster systems, existing modeling schemes either rely on solving a large number of nonlinear equations, resulting in high computational complexity and limited real-time analysis capabilities, or they simplify the coupling relationships by fixing them, making it difficult to accurately characterize mutual inductance changes and transient responses, thus making it difficult to support dynamic load adjustment, stability analysis, and fault diagnosis.

[0004] Therefore, how to reduce the computational complexity of modeling and improve the accuracy of describing dynamic characteristics and disturbance response in multi-device wireless power transmission systems has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a modeling method, apparatus, device, and equipment for a magnetically coupled resonant multi-device wireless power transfer system to solve the aforementioned technical problems. The method is designed for magnetically coupled resonant multi-device wireless power transfer systems comprising multiple transmitting modules and multiple receiving modules. It constructs a model that uniformly characterizes the transmitting end, receiving end, and their mutual coupling characteristics, based on the magnetic coupling energy transfer relationship between the multiple modules in the system. On this basis, it generates modeling results suitable for dynamic system analysis, thus balancing modeling efficiency in multi-device scenarios with the ability to describe dynamic behavior and disturbance responses.

[0006] In a first aspect, this application provides a modeling method for a magnetically coupled resonant multi-device wireless power transfer system, including:

[0007] Obtain a circuit model of a multi-device wireless power transmission system; the multi-device wireless power transmission system includes multiple transmitting modules and multiple receiving modules, each of the transmitting and receiving modules contains a resonant circuit, and magnetic coupling is achieved between any two transmitting modules, between any two receiving modules, and between the transmitting module and the receiving module through mutual inductance;

[0008] Based on the circuit model, the coupling mode equations of the multi-device wireless power transmission system are determined.

[0009] The magnitude value in the coupled mode equation is decomposed into real and imaginary parts, and the decomposed real and imaginary parts are substituted into the coupled mode equation to separate the real and imaginary parts of the coupled mode equation, thus obtaining the real-valued coupled mode equation;

[0010] The discrete-time state-average model of the multi-device wireless power transmission system is obtained by processing the real-valued coupled-mode equations using the discrete-time state-average method.

[0011] In one possible design, the real-valued coupled-mode equations are expressed as:

[0012] ;

[0013] in, Indicates the module number. N is the total number of all modules; Let be the real component of the modulus value of the i-th module; is the imaginary part of the modulus value of the i-th module; k is the module number of the summation traversal, representing other modules that are magnetically coupled to the i-th module; This represents the resonant angular frequency of a multi-device wireless power transfer system. This represents the mutual inductance between the coil of the i-th module and the coil of the k-th module, and characterizes the magnetic coupling strength between the two coils. This represents the coil inductance of the i-th module; This represents the coil inductance of the k-th module; This represents the loss resistance of the i-th module; This represents the load resistance of the i-th receiving module; This represents the module type identifier coefficient, used to distinguish between the transmitting module and the receiving module; This represents the effective value of the AC power supply voltage for the i-th transmitting module; This indicates the operating angular frequency of the AC power supply.

[0014] In one possible design, the process of processing the real-valued coupled-mode equations based on the discrete-time state averaging method to obtain the discrete-time state-averaged model of the multi-device wireless power transmission system includes:

[0015] Determine the averaged state variable; wherein the averaged state variable is the average value of the real or imaginary component of the magnitude over one resonance period;

[0016] Substituting the averaged state variables into the real-valued coupled-mode equation, we obtain the state-averaged mathematical model.

[0017] Based on the sampling sequence number and sampling period, the state averaging mathematical model is discretized to obtain the discrete-time state averaging model.

[0018] In one possible design, determining the averaged state variables includes:

[0019] A single resonant period is defined as the calculation interval, and the real and imaginary components of the modulus value are respectively integrated and averaged within the calculation interval.

[0020] The average value of the interval states obtained from the calculation is determined as the corresponding averaged state variable.

[0021] In one possible design, the equations of the discrete-time state-averaged model are expressed as:

[0022] ;

[0023] in, This represents the sampling time sequence number, which is a non-negative integer; The sampling period is a fixed time interval between two adjacent sampling moments. The discrete average state variable represents the real component of the magnitude value of the i-th module; Let represent the discrete average state variable of the imaginary component of the magnitude value of the i-th module.

[0024] In one possible design, the resonant circuit of the transmitting module includes a first compensation capacitor, a transmitting coil, an AC power supply, and a first loss resistor connected in series, and the resonant circuit of the receiving module includes a second compensation capacitor, a receiving coil, a second loss resistor, and a load resistor connected in series, and the resonant frequency of the resonant circuit of the transmitting module is the same as the resonant frequency of the resonant circuit of the receiving module.

[0025] One possible design also includes:

[0026] Based on the discrete-time state-averaged model, a disturbance is introduced at the stable operating point of the multi-device wireless power transmission system to obtain a small-signal model of the multi-device wireless power transmission system; wherein, the small-signal model is used to analyze the transient characteristics of the multi-device wireless power transmission system.

[0027] In one possible design, the equation of the small-signal model is expressed as:

[0028] ;

[0029] in, Represents the i-th module in the i-th position. The disturbance of the average state quantity of the real part of the magnitude value at each sampling time; Represents the i-th module in the i-th position. The disturbance of the average state quantity of the imaginary part of the magnitude value at each sampling time.

[0030] Secondly, this application provides a modeling apparatus for a magnetically coupled resonant multi-device wireless power transfer system, comprising:

[0031] An acquisition module is used to acquire a circuit model of a multi-device wireless power transmission system. The multi-device wireless power transmission system includes multiple transmitting modules and multiple receiving modules. Each transmitting module and each receiving module contains a resonant circuit, and magnetic coupling is achieved between any two transmitting modules, between any two receiving modules, and between the transmitting module and the receiving module through mutual inductance.

[0032] A coupled-mode equation establishment module is used to determine the coupled-mode equations of the multi-device wireless power transmission system based on the circuit model.

[0033] The equation decomposition module is used to decompose the magnitude value in the coupled mode equation into real and imaginary parts, and substitute the decomposed real and imaginary parts into the coupled mode equation to separate the real and imaginary parts of the coupled mode equation and obtain the real-valued coupled mode equation.

[0034] The discrete processing module is used to process the real-valued coupled-mode equations based on the discrete-time state averaging method to obtain the discrete-time state-averaged model of the multi-device wireless power transmission system.

[0035] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory to implement the method as described in any of the first aspects.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0039] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the first aspects.

[0040] This application provides a modeling method, apparatus, and equipment for a magnetically coupled resonant multi-device wireless power transmission system. The method constructs a circuit model containing multiple transmitting and receiving modules, with mutual inductance and magnetic coupling between any modules, and derives the corresponding coupled mode equations. This preserves the fully coupled topology of the multi-device cluster system, avoiding accuracy deviations caused by simplification of the coupling relationship, and accurately characterizing the impact of dynamic changes in mutual inductance on the power transfer process. By decomposing the magnitude values ​​in the coupled mode equations into real and imaginary parts and separating them to obtain real-valued coupled mode equations, the computational overhead of complex number solutions is eliminated, effectively reducing the computational complexity of modeling and solving, and improving the system's real-time analysis capabilities. Furthermore, the discrete-time state-average method is used to process the real-valued coupled mode equations to obtain a discrete-time state-averaged model, which can accurately characterize the transient response characteristics of the system. This effectively supports dynamic load adjustment, stability analysis, and fault diagnosis of multi-device wireless power transmission cluster systems, balancing modeling computational efficiency with the accuracy of describing dynamic characteristics and disturbance responses. It is suitable for the application requirements of clustered wireless power supply for various monitoring devices, sensors, and inspection equipment in power distribution systems. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] Figure 1 A schematic flowchart illustrating a modeling method for a magnetically coupled resonant multi-device wireless power transfer system provided in an embodiment of this application;

[0043] Figure 2 A circuit diagram of a magnetically coupled resonant multi-device wireless power transfer system provided in an embodiment of this application;

[0044] Figure 3 A waveform diagram of the amplitude values ​​of the transmitting module and the receiving module calculated by the discrete-time state averaging model provided in an embodiment of this application;

[0045] Figure 4 A waveform diagram of the amplitude values ​​of the actual transmitting and receiving modules of a magnetically coupled resonant multi-device wireless power transmission system provided in an embodiment of this application;

[0046] Figure 5 A schematic diagram of the structure of a modeling device for a magnetically coupled resonant multi-device wireless power transfer system provided in an embodiment of this application;

[0047] Figure 6 This is a structural example diagram of an electronic device provided in an embodiment of this application.

[0048] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0050] To clearly understand the technical solution of this application, the solutions of the prior art will be described in detail first.

[0051] Magnetic coupling resonant multi-device wireless power transfer technology belongs to the field of wireless power supply technology and is suitable for clustered power supply scenarios of multiple devices such as monitoring devices, environmental sensors, and inspection equipment in power distribution systems. These scenarios typically include multiple transmitting modules and multiple receiving modules, each with a resonant circuit, forming a magnetic coupling power transfer network through mutual inductance between the modules.

[0052] In existing technologies, a system model is typically established first based on circuit parameters such as coils, capacitors, and resistors at the transmitting and receiving ends. Then, coupled-mode equations or circuit simulations are used to analyze the energy transfer relationships between modules to evaluate power distribution, transmission efficiency, and operating status. For systems with a small number of devices, the above method can reflect the resonant energy transfer process to a certain extent.

[0053] However, in multi-device cluster scenarios, mutual inductance coupling may exist between any two transmitting modules, between any two receiving modules, and between transmitting and receiving modules, significantly increasing the number of system coupling relationships. In this case, solving the complex coupled modular equations involves a large computational load, making it difficult to maintain real-time performance. Furthermore, even after simplifying and fixing the coupling relationships, it becomes difficult to accurately characterize changes in mutual inductance, load fluctuations, and system transient responses. This further limits the system's ability to support dynamic load adjustments, stability analysis, and fault diagnosis. Especially when the number of devices increases and operating states change frequently, model accuracy and computational efficiency cannot simultaneously meet the requirements of engineering applications. Therefore, a modeling method that balances computational complexity and dynamic description capabilities is urgently needed.

[0054] Therefore, how to reduce the computational complexity of modeling and improve the accuracy of describing dynamic characteristics and disturbance responses in multi-device wireless power transfer systems has become an urgent technical problem to be solved. To address this problem, this application provides a modeling method for a magnetically coupled resonant multi-device wireless power transfer system. This method involves obtaining a circuit model containing multiple transmitting and receiving modules, determining the coupled-mode equations based on the circuit model, and decomposing the amplitude values ​​in the coupled-mode equations into real and imaginary parts to obtain real-valued coupled-mode equations. Then, the real-valued coupled-mode equations are processed using the discrete-time state-averaging method to obtain a discrete-time state-averaged model, thereby providing a more suitable modeling foundation for the analysis and control of multi-device wireless power transfer systems.

[0055] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0057] Figure 1 This is a flowchart illustrating a modeling method for a magnetically coupled resonant multi-device wireless power transfer system according to an embodiment of this application, as shown below. Figure 1 As shown, the execution subject of this embodiment is a modeling device for a magnetically coupled resonant multi-device wireless power transfer system. This device can be implemented through a computer program, or through a medium storing the relevant computer program, such as a USB flash drive and / or optical disc; alternatively, it can be implemented through a physical device integrating or installing the relevant computer program, such as a chip or electronic device. The electronic device may be a computer or a server, etc. The modeling method for a magnetically coupled resonant multi-device wireless power transfer system provided in this embodiment includes the following steps:

[0058] S201. Obtain the circuit model of the multi-device wireless power transmission system; the multi-device wireless power transmission system includes multiple transmitting modules and multiple receiving modules. Both the transmitting and receiving modules contain resonant circuits, and magnetic coupling is achieved between any two transmitting modules, between any two receiving modules, and between the transmitting and receiving modules through mutual inductance.

[0059] Specifically, in this application, the magnetically coupled resonant multi-device wireless power transmission system is used as the modeling object, which is composed of multiple transmitting modules and multiple receiving modules, with each transmitting module and each receiving module corresponding to an independent resonant circuit.

[0060] The circuit model is used to abstractly express the electrical composition of each module and the magnetic coupling relationship between modules, and it is the basis for subsequently determining the coupling mode equations.

[0061] The transmitting module injects energy into the space magnetic field, while the receiving module senses and outputs energy. The resonant circuit forms the resonant energy transfer characteristics of the system, and mutual inductance characterizes the magnetic coupling strength between any two modules, including the mutual inductance between transmitting modules, the mutual inductance between receiving modules, and the mutual inductance between the transmitting and receiving modules.

[0062] Optionally, in implementation, the number of transmitting modules *m* and the number of receiving modules *n* in the system are first determined, and all modules are uniformly numbered. Then, a mutual inductance parameter matrix is ​​established between the modules, where each element corresponds to the mutual inductance value between any two modules; when two modules do not have effective magnetic flux coupling, the corresponding mutual inductance value is recorded as zero. The mutual inductance parameters can be obtained through 3D electromagnetic simulation, prototype measurement, or analytical calculation based on the geometric position of the coils, and then stored in the parameter table of the modeling program.

[0063] Optionally, when establishing the circuit model, the resonant circuit of each module can be regarded as a node branch unit, and all branches can be combined into a multi-port coupled network according to the module number. The self-parameters of each branch are used to characterize the electrical characteristics of the module itself, and the coupling parameters between branches include mutual inductance values ​​and their sign and direction. If the winding direction of the coils is different, the polarity relationship of the induced voltage is reflected in the model through the positive and negative signs of the mutual inductance parameters.

[0064] Furthermore, to maintain consistency with subsequent coupled-mode modeling, it is necessary to clarify the correspondence between the state variables of each module in the circuit model. Specifically, the state variables of each module can be mapped to a unified state description framework.

[0065] It should be noted that the circuit model obtained through the above method not only includes structural information about multiple transmitters and receivers, but also information about the full mutual coupling between modules. Therefore, it can provide input for the subsequent unified derivation of the dynamic equations of a multi-device wireless power transfer system. Based on the above analysis, this step transforms the physical system into a parameterized and structured circuit representation, so that the mutual inductance relationship no longer depends on a fixed simplified structure, thus allowing the process to continue into the mathematical modeling process that includes all coupling relationships. It should be understood that the above example is merely illustrative and not limiting.

[0066] As an optional implementation, based on any of the above embodiments, the resonant circuit of the transmitting module includes a first compensation capacitor, a transmitting coil, an AC power supply, and a first loss resistor connected in series, and the resonant circuit of the receiving module includes a second compensation capacitor, a receiving coil, a second loss resistor, and a load resistor connected in series, and the resonant frequency of the resonant circuit of the transmitting module is the same as the resonant frequency of the resonant circuit of the receiving module.

[0067] Figure 2 A circuit diagram of a magnetically coupled resonant multi-device wireless power transfer system provided in an embodiment of this application is shown below. Figure 2 As shown, the Figure 2 The example includes one transmitting module and two receiving modules. Figure 2 middle, AC power supply for the transmitter module, The first compensation capacitor for the transmitting module, The first loss resistor of the transmitting module, For the transmitting coil of the transmitting module, This indicates the mutual inductance between the transmitting coil and the receiving coil of the first receiving module. For the second compensation capacitor of the first receiving module, For the receiving coil of the first receiving module, The first loss resistor of the first receiving module, For the load resistor of the first receiving module, This indicates the mutual inductance between the receiving coil of the first receiving module and the receiving coil of the second receiving module. For the second compensation capacitor of the second receiving module, For the first loss resistor of the second receiving module, For the load resistor of the second receiving module, For the receiving coil of the second receiving module, This indicates the mutual inductance between the transmitting coil and the receiving coil of the second receiving module.

[0068] Optionally, in a specific implementation, the first compensation capacitor is connected in series with the transmitting coil, the AC power supply, and the first loss resistor to form the equivalent resonant branch of the transmitting module. Similarly, the second compensation capacitor is connected in series with the receiving coil, the second loss resistor, and the load resistor to form the equivalent resonant branch of the receiving module. The transmitting and receiving modules are configured with parameters based on their respective coil inductance, compensation capacitor values, and operating angular frequencies to ensure that the resonant frequencies on both sides remain consistent. The resonant frequency can be determined according to the series resonance relationship as the natural frequency of the corresponding inductor and capacitor after matching. By aligning the resonant frequencies on both sides, the alternating magnetic field output by the transmitting module can form a synchronous resonant response in the receiving module, thereby enabling the receiving module to obtain a stable energy output across the load resistor.

[0069] Optionally, during operation, the AC power supply drives the series resonant circuit of the transmitting module to generate an oscillating current. The transmitting coil forms a strong magnetic field in the resonant state, and the receiving coil induces a voltage at the same resonant frequency, which is then transmitted to the load resistor via the series circuit of the receiving module. The first and second loss resistors reflect the copper loss, dielectric loss, and additional equivalent loss on both sides, respectively, enabling the resonant circuit to characterize the actual energy attenuation characteristics during modeling. Since the transmitting and receiving modules have the same resonant frequency, the system maintains frequency matching during the coupled energy transfer process, allowing the circuit parameters to be directly used for the subsequent construction and real-valued processing of the coupled-mode equations.

[0070] With this structure, the resonance conditions of the transmitting and receiving modules are consistent, which facilitates the formation of a unified frequency reference in a multi-device magnetic coupling network, reduces modeling deviations caused by parameter mismatch, and allows loop losses and load effects to be equivalently incorporated into the model, thereby improving the accuracy of characterization of system energy transmission status, load changes, and mutual inductance coupling relationships.

[0071] S202. Determine the coupling mode equations of a multi-device wireless power transmission system based on the circuit model.

[0072] In this application, coupled-mode equations are used to characterize the energy exchange, loss, and external interaction relationships between each transmitting module and each receiving module. The state variables in the coupled-mode equations are represented by the mode amplitude values ​​corresponding to each module. These mode amplitude values ​​reflect the equivalent oscillation intensity of the module in the resonant state, which varies with time and is simultaneously affected by the parameters of the module itself and the mutual inductance coupling with other modules. After being derived from the circuit model, the coupled-mode equations can be uniformly written as matrix differential equations for subsequent real-valued processing.

[0073] Optionally, in practical implementation, continuous-time circuit equations can be established first based on the circuit relationships of each resonant circuit, and then the voltage and current variables can be mapped into complex modal variables in conjunction with the resonance condition. Taking the i-th module as an example, its dynamic term can be expressed as the derivative of the mode amplitude with respect to time. The self-term includes the module's own dynamic characteristics, and the coupling term includes the magnetic coupling relationship with other modules. For different types of modules, equations can be expressed under a unified framework. In this way, all modules can be expressed in the same set of coupled mode equations.

[0074] In terms of mathematical organization, all the magnitude values ​​of the system can be written as a state vector A, the dynamics of each module and their mutual inductance coupling relationships can be written as a system matrix C, and the external action can be written as an input vector Z. Then the system can be expressed as a complex state equation of dA / dt=CA+Z. Here, the diagonal elements of the system matrix C represent the intrinsic dynamics of a single module, and the off-diagonal elements represent the magnetic coupling strength between modules.

[0075] Since any two transmitting modules, any two receiving modules, and any transmitting module and any receiving module may be coupled in a multi-device cluster scenario, the matrix dimension will increase as the number of modules increases.

[0076] Optionally, in a specific implementation, the system is assumed to contain N modules, of which n are transmitting modules and m are receiving modules, and the resonant circuits of all modules operate at the same resonant angular frequency. Based on Kirchhoff's voltage law and coupled-mode theory, the coupled-mode equations of the system are derived as follows:

[0077]

[0078] in, Indicates the module number. t represents continuous time, which is the standard independent variable of the time-domain differential equation, and its unit is seconds; Let be the amplitude value of the i-th module; The complex modulus amplitude of the k-th module is given; j is the imaginary unit; N is the total number of all modules; k is the module number traversed in the summation, representing other modules that are magnetically coupled to the i-th module. This represents the resonant angular frequency of a multi-device wireless power transfer system. This represents the mutual inductance between the coil of the i-th module and the coil of the k-th module, and characterizes the magnetic coupling strength between the two coils. This represents the coil inductance of the i-th module; This represents the coil inductance of the k-th module; This represents the loss resistance of the i-th module; This represents the load resistance of the i-th receiving module; This represents the module type identifier coefficient, used to distinguish between transmitting and receiving modules. When the module is a transmitting module... =1, when the module is a receiving module. =0. This represents the effective value of the AC power supply voltage for the i-th transmitting module; This indicates the operating angular frequency of the AC power supply.

[0079] It should be noted that this coupled-mode equation uniformly characterizes the combined effects of module self-loss, magnetic coupling energy exchange between modules, and external excitation, and can be directly extended to cluster systems with any number of transmitting and receiving modules.

[0080] By directly constructing unified coupled-mode equations from the circuit model, the fragmented analysis of each coupled branch is avoided, allowing subsequent processing to be performed at the matrix level. Based on the above analysis, this step compresses the complex, multi-coupled physical network into a unified dynamic mathematical expression, preserving the system's dynamic characteristics and providing a clear interface for variables and parameters in the subsequent conversion of complex number operations to real number operations. It should be understood that the above example is merely illustrative and not limiting.

[0081] S203. Decompose the magnitude value in the coupled mode equation into real and imaginary parts, and substitute the decomposed real and imaginary parts into the coupled mode equation to separate the real and imaginary parts of the coupled mode equation, thus obtaining the real-valued coupled mode equation.

[0082] In this application, the modal amplitude is a complex state variable, containing phase and amplitude information. By decomposing the modal amplitude into real and imaginary parts, the original complex coupled modal equation can be transformed into an equivalent real-valued equation. The real part characterizes the changes in the modal state on the in-phase components, and the imaginary part characterizes the changes in the modal state on the quadrature components. Together, they constitute a set of real-valued state variables that completely correspond to the original complex state.

[0083] Among them, the real-valued coupled-mode equation is a continuous-time state equation after decomposing all complex states. It retains the influence of inter-module coupling, loss, resonance deviation and input excitation on state evolution, but eliminates the complex-number operation form.

[0084] Optionally, in specific implementation, the magnitude values ​​of each module can be decomposed into real and imaginary state variables. Then, the magnitude values ​​of all modules are substituted into the coupled module equations obtained in S102, and both sides of the coupled module equations are expanded, collecting the real and imaginary terms. After substitution, the complex equations corresponding to each module are split into two real equations. One describes the relationship between the derivative of the real state variable and the real, imaginary, and input components of all modules; the other describes the relationship between the derivative of the imaginary state variable and the real, imaginary, and input components of all modules. External inputs can also be decomposed into corresponding components in the same way and incorporated into the corresponding real equations.

[0085] Furthermore, the state variables after decomposition of all modules can be combined into an expanded real-valued state vector, and the original complex system matrix and input vector can be mapped to the corresponding real-valued matrix and real-valued input vector, thus obtaining the corresponding real-valued state equation. The dimension of this real-valued coupled-module equation is usually twice that of the original complex model, but each step of the operation is transformed into real number addition, subtraction, multiplication, and subtraction operations, making it suitable for execution in digital processors, embedded controllers, or offline analysis platforms.

[0086] It should be noted that for multi-device wireless power transmission systems, increasing the number of modules will rapidly increase the cost of solving complex matrices, while real-valued matrices are easier to assemble, perform eigenvalue analysis, and perform numerical integration using standard linear algebra libraries.

[0087] Based on the above analysis, this step is crucial in resolving the difficulty of balancing computational complexity in modeling with accuracy in dynamic description. By separating the real and imaginary parts, the dynamic integrity of the original coupled model equations is maintained, while the implementation burden of solving in the complex domain is reduced, allowing the model to continue to be used for subsequent discrete-time state averaging and digital control modeling. It should be understood that the above example is merely illustrative and not limiting.

[0088] S204. Based on the discrete-time state averaging method, the real-valued coupled-mode equations are processed to obtain the discrete-time state-averaged model of the multi-device wireless power transmission system.

[0089] In this application, the discrete-time state averaging method is used to transform the continuous-time dynamic process described by the real-valued coupled-mode equations into a discrete-time average dynamic process based on the sampling time. The discrete-time state averaging model reflects the average change law of the state of each module within adjacent sampling intervals, and is suitable for digital controller design, dynamic power allocation analysis, and operating state prediction.

[0090] Optionally, in specific implementation, based on the real-valued coupled-mode equations obtained in S103, the state variables can be averaged over the discrete interval to obtain the corresponding state-averaged mathematical model; then, the state-averaged mathematical model can be discretized by combining the sampling time to obtain the discrete-time state-averaged model. The discretization process can be implemented in a way suitable for the discrete expression of continuous state equations, so that the obtained model can characterize the evolution relationship of the states of each module at discrete time.

[0091] Optionally, after the model is built, each state component in the discrete-time state-averaged model can correspond to the average change of the real or imaginary part of the magnitude value of a certain module, thus it can be directly used to describe the discrete energy exchange trend between multiple modules. When the mutual inductance between modules changes, the relevant coupling parameters in the model can be updated to reflect the new system dynamics.

[0092] Based on the above analysis, it can be seen that by using the discrete-time state averaging method, the continuous resonance process is transformed into an average state evolution process over a sampling period scale. This allows the model to retain system dynamic information while also being adaptable to digital control and real-time computation. It should be understood that the above example is merely illustrative and not limiting.

[0093] The modeling method for a magnetically coupled resonant multi-device wireless power transmission system provided in this application constructs a circuit model containing multiple transmitting and receiving modules, with mutual inductance and magnetic coupling between any modules, and derives the corresponding coupled mode equations. This preserves the fully coupled topology of the multi-device cluster system, avoiding accuracy deviations caused by simplification of the coupling relationship, and accurately characterizing the impact of dynamic changes in mutual inductance on the power transfer process. By decomposing the magnitude values ​​in the coupled mode equations into real and imaginary parts and separating them to obtain real-valued coupled mode equations, the computational overhead of complex number solutions is eliminated, effectively reducing the computational complexity of modeling and solving, and improving the system's real-time analysis capabilities. Furthermore, the discrete-time state averaging method is used to process the real-valued coupled mode equations to obtain a discrete-time state-averaged model, which can accurately characterize the transient response characteristics of the system. This effectively supports the dynamic load adjustment, stability analysis, and fault diagnosis of multi-device wireless power transmission cluster systems, balancing modeling computational efficiency with the accuracy of describing dynamic characteristics and disturbance responses. It is suitable for the application requirements of clustered wireless power supply for various monitoring devices, sensors, and inspection equipment in power distribution systems.

[0094] As an optional implementation, based on any of the above embodiments, the real-valued coupled-mode equation is expressed as:

[0095] .

[0096] in, Let be the real component of the modulus value of the i-th module; This is the imaginary component of the modulus value of the i-th module; This indicates the operating angular frequency of the AC power supply.

[0097] Optionally, in a specific implementation, the coil parameters, loss parameters, and load parameters of each module are first determined based on the circuit model of the multi-device wireless power transmission system, and the amplitude value of the i-th module is written in complex form. ,in, For the real part, This is the imaginary part. Then... Substituting these terms into the coupled module equations and performing algebraic simplification on the terms containing real and imaginary parts, the original complex relation is separated into terms relating to... and The real-valued relational expression is obtained, and the excitation terms that distinguish the transmitting and receiving modules are combined with the module type identification coefficient to obtain the real-valued coupled-mode equations that can be directly used for subsequent discrete-time state averaging. The summation terms in the equations traverse other modules that are magnetically coupled to the i-th module, and the energy exchange and damping effects between modules are described by mutual inductance, coil inductance, and loss resistance.

[0098] It should be noted that by decomposing the complex modulus amplitude into real and imaginary parts, this real-valued coupled-mode equation transforms the coupling relationship, which originally required complex number operations, into a real-number domain expression. This allows for a state description suitable for numerical computation while preserving the influence of magnetic coupling between modules, load variations, and excitation frequency. Based on this equation, a discrete-time state-averaged model can be directly constructed to characterize the dynamic evolution of a multi-device wireless power transfer system under different operating conditions.

[0099] The method provided in this application reduces the computational burden of complex number solutions by transforming the coupled module equations from complex form to real form, and enables the coupling relationships, load effects, and excitation characteristics of each module to be uniformly described under the same mathematical framework, thereby providing a more stable mathematical foundation for the modeling, state analysis, and subsequent control calculations of multi-device wireless power transmission systems.

[0100] As an optional implementation, based on any of the above embodiments, the real-valued coupled-mode equations are processed using the discrete-time state averaging method to obtain a discrete-time state-averaged model of the multi-device wireless power transmission system, including the following steps:

[0101] Determine the averaged state variable; where the averaged state variable is the average value of the real or imaginary component of the magnitude over one resonant period.

[0102] Substituting the averaged state variables into the real-valued coupled-mode equations yields the state-averaged mathematical model.

[0103] Based on the sampling sequence number and sampling period, the state averaging mathematical model is discretized to obtain the discrete-time state averaging model.

[0104] Optionally, in a specific implementation, for the real and imaginary components of the amplitude value of the i-th module, the periodic average value is first calculated using one resonant period as the integration interval. This average value is then substituted into the real-valued coupled-mode equation, transforming the continuous coupling relationship of the system into a state-averaged mathematical model represented by averaged state variables. Next, combining the sampling period and sampling number, this mathematical model is discretized at each sampling time, resulting in a discrete-time state-averaged model indexed by the sampling number. This approach is suitable for multi-device wireless power transmission systems with a large variation in the number of modules and complex mutual inductance coupling relationships, and it is compatible with the sampling update mechanism of the digital controller.

[0105] It should be noted that, because the averaged state variables extract the real or imaginary components within a resonant period, the resulting state-averaged mathematical model can suppress the influence of high-frequency oscillation terms on the modeling results. Furthermore, the discretization based on sampling sequence and sampling period allows the model to be directly used for online controller calculations and state prediction. Thus, while maintaining its ability to describe the dynamic evolution process, the system reduces the computational burden of directly solving continuous coupled equations, making the model more suitable for operational analysis, control parameter updates, and state assessment of multi-device wireless power transfer systems.

[0106] As an optional implementation, based on any of the above embodiments, determining the averaged state variables includes:

[0107] A single resonant period is defined as the calculation interval, and the real and imaginary components of the modulus are integrated and averaged within the calculation interval. The average value of the interval state obtained by the calculation is determined as the corresponding averaged state variable.

[0108] Optionally, in a specific implementation, within the operating cycle corresponding to the discrete-time state averaging model, the time series of the real and imaginary components of the amplitude value of the i-th module can be extracted. The start and end times of one cycle are calculated based on the resonant frequency, and the time range between the start and end points within that cycle is used as the calculation interval. Subsequently, interval integration is performed on the real and imaginary components respectively to obtain their respective interval areas, which are then normalized using the length of the calculation interval to obtain the average real and imaginary components of the module within that resonant cycle. These average values ​​are defined as averaged state variables and used as input to the subsequent state averaging mathematical model to characterize the equivalent state of the module under stable oscillation conditions. If there are multiple transmitting and receiving modules in the system, the same processing can be performed on each module to form an averaged state variable set corresponding to each module.

[0109] It should be noted that the processing method in this embodiment uses a single resonant period as the statistical scale, converting the rapidly fluctuating state variables within the period into smooth average values, so that the real and imaginary components are given consistent equivalent representations throughout a complete oscillation process. The resulting averaged state variables can directly reflect the system's operating state on the periodic scale and provide a unified variable basis for state evolution calculations in discrete-time state averaging models.

[0110] Optionally, in a specific implementation, the system resonance period is defined as... By performing an integral-average operation on the real-valued coupled-mode equations over a single resonant period, and eliminating the high-frequency oscillation components corresponding to the resonant frequency, a state-averaged mathematical model for the continuous domain is obtained:

[0111]

[0112] in, Let be the discrete average state variable of the real component of the magnitude value of the i-th module; Let be the discrete average state variable of the imaginary component of the magnitude value of the i-th module.

[0113] It should be noted that the state-averaged mathematical model filters out high-frequency resonant fluctuations and retains only the slow-varying dynamic characteristics of the system energy, which can more clearly reflect the impact of load changes and coupling changes on the overall energy distribution of the system.

[0114] The method provided in this application, after adopting the processing method provided in this embodiment, the state quantity is no longer directly affected by instantaneous fluctuations, but participates in the modeling in the form of periodic average, making the discrete-time state average model more stable in representing the oscillation characteristics of multi-device wireless power transmission systems, and can provide a continuous and calculable state basis for subsequent small-signal disturbance analysis, load change analysis and module coupling relationship analysis.

[0115] Optionally, in the specific implementation, a sampling time sequence number is introduced. With a fixed sampling period Let continuous time The forward difference method is used to discretize the continuous state averaging model, and the resulting discrete-time state averaging model is obtained after processing.

[0116] As an optional implementation, based on any of the above embodiments, the equation of the discrete-time state-averaged model is expressed as:

[0117] .

[0118] in, This represents the sampling time sequence number, which is a non-negative integer; The sampling period is a fixed time interval between two adjacent sampling moments. Represented as the first The discrete average state variable of the real component of the magnitude value of the i-th module at each sampling time, and is... arrive Averaged state variables over time; Represented as the first The discrete average state variable of the imaginary component of the magnitude value of the i-th module at each sampling time, and is... arrive Averaged state variables over time.

[0119] Optionally, in specific implementation, a discrete-time state vector containing all modules is first established based on the updated circuit model, and the real and imaginary components of the magnitude value of each module are used as constituent elements of the state vector. Then, the state variables are discretized at fixed intervals with the sampling period. For each sampling time, the real and imaginary components obtained by averaging the continuous states within the resonant period are mapped to discrete-time averaged state variables. These are then combined with inter-module mutual inductance, loss, and load terms to form a discrete-time state-averaged model suitable for digital computation. The coefficient matrix in this model can be determined by the updated mutual inductance matrix, coil parameters, load parameters, and operating angular frequency, and can be directly used for subsequent state estimation, operation analysis, and control calculations.

[0120] It should be noted that this discrete-time state-averaged model maps the continuous resonance process to a fixed sampling time, preserving the dynamic variation characteristics of the real and imaginary parts of the module amplitude values. It expresses the system state in a discrete form, facilitating rapid reconstruction after changes in the number of modules or updates to mutual inductance relationships. Therefore, the model can reduce the computational burden of continuously coupled solutions while maintaining dynamic descriptive capabilities, and makes the system's representation of topological and operational state changes more consistent.

[0121] As an optional implementation, based on any of the above embodiments, it further includes:

[0122] Based on the discrete-time state-averaged model, a disturbance is introduced at the stable operating point of the multi-device wireless power transmission system to obtain a small-signal model of the multi-device wireless power transmission system; the small-signal model is used to analyze the transient characteristics of the multi-device wireless power transmission system.

[0123] Optionally, in a specific implementation, the discrete state quantities in the discrete-time state-averaged model can be represented as a superposition of steady-state and disturbance components. The disturbance components are then substituted into the corresponding state update relation, retaining first-order small terms while ignoring higher-order terms, thus obtaining a linear state equation describing the disturbance propagation law. For the mutual inductance coupling relationship between modules in a multi-device wireless power transmission system, the obtained small-signal model can characterize the influence of slight changes in the parameters of any module on the states of adjacent modules and output the disturbance state sequence at each sampling time for subsequent transient response calculation and stability assessment.

[0124] Optionally, when the small-signal model is in operation, it uses the discrete-time state-averaged model as the input basis, first determines the steady-state operating point of the system, then applies a small perturbation near the steady-state operating point and completes linearization, finally forming a perturbation model that can be recursively derived with the sampling time. This small-signal model is used to analyze the transient characteristics of multi-device wireless power transmission systems under conditions of load change, coupling change, or input fluctuation, and thereby obtains the short-time response process of the system state variables.

[0125] The method provided in this application, by adopting the above approach, can directly obtain a disturbance model that is easy to analyze while maintaining the expressive power of the discrete-time state average model. This enables the system's response to small changes to be described in a linear form, thereby improving the computability and characterizability of transient characteristic analysis and providing model support for subsequent stability judgment and dynamic control.

[0126] As an optional implementation, based on any of the above embodiments, the equation of the small-signal model is expressed as:

[0127] ;

[0128] in, Represents the i-th module in the i-th position. The disturbance of the average state quantity of the real part of the magnitude value at each sampling time; Represents the i-th module in the i-th position. The disturbance of the average state quantity of the imaginary part of the magnitude value at each sampling time.

[0129] Optionally, in a specific implementation, a stable operating point Introducing a small disturbance in the vicinity and substituting it into the discrete-time state-averaged model, if we assume... , , By neglecting the quadratic terms resulting from small multiplications, a low-frequency small-signal model can be obtained. Among these, Let be the discrete average steady-state value of the real component of the magnitude value of the i-th module; The discrete average steady-state value of the imaginary component of the magnitude of the i-th module; The steady-state effective value of the AC power supply voltage of the i-th transmitting module.

[0130] Optionally, in specific implementation, the real-mean and imaginary-mean state variables of each module at the steady-state operating point are first determined by the discrete-time state-average model. Then, external load fluctuations, mutual inductance changes, or input voltage disturbances are introduced as small disturbances, and the state variables after the disturbance are written as the sum of the steady-state value and the disturbance quantity. This expression is then substituted into the discrete-time state-average model, and the steady-state term and disturbance term are separated to obtain the small-signal model equation that reflects only the propagation relationship of small deviations. In this small-signal model equation, the sampling time sequence is recursively calculated according to the discrete sampling period, and the real-mean and imaginary disturbance quantities together constitute the state vector, which can characterize the influence of inter-module coupling changes on the system's transient response based on the updated circuit model and coupled-mode equations. In practical applications, this small-signal model can be used in conjunction with state estimation or stability analysis modules; this application does not limit its use in this regard.

[0131] By decomposing the average state variables into disturbance variables and establishing corresponding small-signal models, the system can directly describe the linearized dynamic response near the stable operating point, enabling a clear expression of the state change relationship at different sampling times. This improves the accuracy of characterizing the response process to transient disturbances, load changes, and topology changes, and provides a consistent mathematical basis for subsequent stability judgment and control parameter tuning.

[0132] To verify the accuracy and effectiveness of the above hybrid modeling method, a simulation comparison verification was carried out using a typical system with one transmitting module and two receiving modules as an example.

[0133] Optionally, in the specific implementation, a circuit-level simulation model and a discrete-time state-averaged mathematical model are built separately, with the same system parameters and excitation conditions set, and the dynamic response waveforms of the two are compared. Figure 3 The amplitude waveforms of the transmitter and receiver modules calculated by the discrete-time state-average model provided in this application embodiment are shown. The horizontal axis represents time, and the vertical axis represents the amplitude value. The waveforms exhibit a smooth envelope characteristic, reflecting the slow-changing dynamic process of system energy on a macroscopic scale. Figure 4 The waveform diagram shows the amplitude values ​​of the actual transmitting and receiving modules of the magnetically coupled resonant multi-device wireless power transmission system provided in the embodiments of this application. The horizontal axis represents time, and the vertical axis represents the amplitude value. The waveform has a small-amplitude high-frequency resonant oscillation during the rising process, reflecting the switching and resonant details in the actual circuit.

[0134] Will Figure 3 and Figure 4Comparison reveals that the waveform obtained from circuit simulation carries small-amplitude high-frequency resonant oscillations, while the waveform output by the discrete-time state-averaged model is a smooth, slowly varying envelope. Approximately 0.02 seconds after system startup, the amplitudes of both waveforms tend to converge, and the dynamic upward trend perfectly matches the steady-state value. It should be noted that the simulation results verify that the hybrid modeling method proposed in this scheme can accurately reproduce the steady-state operating characteristics and slowly varying transient processes of the system while filtering out high-frequency oscillations and reducing computational load. This provides reliable mathematical support for the analysis and control of multi-device wireless power transmission trunking systems.

[0135] Specifically, this application is applied to scenarios requiring clustered wireless power supply for multiple devices, including industrial automation equipment clusters, intelligent inspection robot formations, and distributed sensor networks. Multiple devices achieve power transmission through the principle of magnetic coupling resonance, and dynamically adjust energy distribution based on load changes or fault switching. In power facility inspection scenarios, multiple inspection robots autonomously navigate in complex environments, charge in real time, and monitor equipment status.

[0136] For example, consider a cluster of inspection robots in an industrial facility. The cluster consists of one central transmitting module and three mobile receiving modules, each deployed on one of the three inspection robots. The inspection robots move autonomously within the factory area and collect equipment data in real time, obtaining power from the central transmitting module via wireless power transmission technology. Due to the dynamic changes in the robot's movement path, the mutual inductance value (i.e., the magnetic field coupling strength) between the modules fluctuates in real time, leading to uneven energy distribution or decreased transmission efficiency. Therefore, the hybrid modeling method provided in this application can be used to dynamically analyze system characteristics and optimize energy distribution strategies.

[0137] Figure 5 A schematic diagram of the structure of a modeling device for a magnetically coupled resonant multi-device wireless power transfer system provided in an embodiment of this application is shown below. Figure 5 As shown, the modeling device for the magnetically coupled resonant multi-device wireless power transfer system provided in this embodiment is located in an electronic device. The modeling device 40 for the magnetically coupled resonant multi-device wireless power transfer system provided in this embodiment includes: an acquisition module 41, a coupled mode equation establishment module 42, an equation decomposition module 43, and a discretization processing module 44.

[0138] Specifically, the acquisition module 41 is used to acquire the circuit model of the multi-device wireless power transmission system; the multi-device wireless power transmission system includes multiple transmitting modules and multiple receiving modules, each of which contains a resonant circuit, and magnetic coupling is achieved between any two transmitting modules, between any two receiving modules, and between the transmitting and receiving modules through mutual inductance; the coupled mode equation establishment module 42 is used to determine the coupled mode equation of the multi-device wireless power transmission system based on the circuit model; the equation decomposition module 43 is used to decompose the amplitude values ​​in the coupled mode equation into real and imaginary parts, and substitute the decomposed real and imaginary parts into the coupled mode equation to separate the real and imaginary parts of the coupled mode equation to obtain the real-valued coupled mode equation; the discretization processing module 44 is used to process the real-valued coupled mode equation based on the discrete-time state averaging method to obtain the discrete-time state-averaged model of the multi-device wireless power transmission system.

[0139] It should be noted that by extracting the circuit parameters and mutual inductance relationships of multiple transmitters and receivers through module 41, the clustered magnetically coupled power transfer network can be fully characterized. The coupled mode equation establishment module 42 constructs system-level coupling relationships based on this, enabling a unified description of the energy exchange paths between each resonant circuit. The equation decomposition module 43 transforms the complex mode amplitude values ​​into real and imaginary parts, thereby reducing the computational burden of complex solution and making the model more suitable for numerical calculation and control implementation. The discrete processing module 44 further combines the discrete-time state averaging method to obtain a discrete-time state-averaged model, thus taking into account the system's dynamic characteristics, disturbance response, and computational efficiency. Therefore, it can more accurately support the power allocation analysis, stability assessment, and control of multi-device wireless power supply systems.

[0140] Alternatively, the real-valued coupled-mode equations can be expressed as:

[0141] ;

[0142] in, Indicates the module number. N is the total number of all modules; Let be the real component of the modulus value of the i-th module; is the imaginary part of the modulus value of the i-th module; k is the module number of the summation traversal, representing other modules that are magnetically coupled to the i-th module; This represents the resonant angular frequency of a multi-device wireless power transfer system. This represents the mutual inductance between the coil of the i-th module and the coil of the k-th module, and characterizes the magnetic coupling strength between the two coils. This represents the coil inductance of the i-th module; This represents the coil inductance of the k-th module; This represents the loss resistance of the i-th module; This represents the load resistance of the i-th receiving module; This represents the module type identifier coefficient, used to distinguish between the transmitting module and the receiving module; This represents the effective value of the AC power supply voltage for the i-th transmitting module; This indicates the operating angular frequency of the AC power supply.

[0143] Optionally, the discrete processing module 44, when processing the real-valued coupled-mode equations based on the discrete-time state averaging method to obtain the discrete-time state-averaged model of the multi-device wireless power transmission system, specifically performs the following: determining the averaged state variables; wherein, the averaged state variables are the average values ​​of the real or imaginary components of the mode amplitude over one resonant period; substituting the averaged state variables into the real-valued coupled-mode equations to obtain the state-averaged mathematical model; and discretizing the state-averaged mathematical model based on the sampling sequence number and sampling period to obtain the discrete-time state-averaged model.

[0144] Optionally, the discrete processing module 44, when determining the averaged state variable, is specifically used to: determine a single resonance period as the calculation interval, perform integral averaging operations on the real and imaginary components of the modulus amplitude value within the calculation interval respectively; and determine the calculated interval state average value as the corresponding averaged state variable.

[0145] Alternatively, the equations for the discrete-time state-averaged model can be expressed as:

[0146] ;

[0147] in, This represents the sampling time sequence number, which is a non-negative integer; The sampling period is a fixed time interval between two adjacent sampling moments. The discrete average state variable represents the real component of the magnitude value of the i-th module; Let represent the discrete average state variable of the imaginary component of the magnitude value of the i-th module.

[0148] Optionally, the resonant circuit of the transmitting module includes a first compensation capacitor, a transmitting coil, an AC power supply, and a first loss resistor connected in series, and the resonant circuit of the receiving module includes a second compensation capacitor, a receiving coil, a second loss resistor, and a load resistor connected in series, and the resonant frequency of the resonant circuit of the transmitting module is the same as the resonant frequency of the resonant circuit of the receiving module.

[0149] Optionally, the discrete processing module 44 is further configured to: introduce a disturbance at the stable operating point of the multi-device wireless power transmission system based on the discrete-time state-average model to obtain a small-signal model of the multi-device wireless power transmission system; wherein the small-signal model is used to analyze the transient characteristics of the multi-device wireless power transmission system.

[0150] Alternatively, the equation for the small-signal model can be expressed as:

[0151] ;

[0152] in, Represents the i-th module in the i-th position. The disturbance of the average state quantity of the real part of the magnitude value at each sampling time; Represents the i-th module in the i-th position. The disturbance of the average state quantity of the imaginary part of the magnitude value at each sampling time.

[0153] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, as shown below. Figure 6 As shown, the electronic device 50 provided in this embodiment includes: a processor 51 and a memory 52 communicatively connected to the processor 51.

[0154] The memory 52 stores computer-executable instructions; the processor 51 executes the computer-executable instructions stored in the memory 52 to implement the method provided in any of the above embodiments.

[0155] Optionally, the electronic device 50 can be deployed in the control terminal, analysis terminal, or edge node of a multi-device wireless power transmission system. The processor 51 calls instructions from the memory 52 to acquire and process a circuit model containing multiple transmitting modules and multiple receiving modules, and determines the coupled mode equations accordingly. The mode amplitude is then decomposed into real and imaginary parts to form real-valued coupled mode equations. Furthermore, the discrete-time state averaging method is combined to obtain a discrete-time state-averaged model, thereby transforming the original complex domain solution into a modeling process more suitable for the processor 51 to execute, thus reducing the computational burden in multi-module coupled scenarios. Based on this hardware and instruction collaborative implementation, the model results can be quickly updated when mutual inductance changes, load fluctuations, and operating state switching occur, thereby improving the accuracy of describing the system's dynamic characteristics, disturbance response, and transient processes. Therefore, it can better support engineering applications such as power distribution analysis, stability assessment, and fault diagnosis.

[0156] The program may include program code, which includes computer-executable instructions. Memory 52 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device.

[0157] In this embodiment, the memory 52 and the processor 51 are connected via a bus. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single straight line, but this does not mean that there is only one bus or one type of bus.

[0158] This application also provides a computer-readable storage medium, including computer-executable instructions stored in the computer-readable storage medium, which, when executed by a processor, are used to implement the method provided in any of the above embodiments.

[0159] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in any of the above embodiments.

[0160] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0161] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0162] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0163] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0164] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0165] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0166] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0167] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A modeling method for a magnetically coupled resonant multi-device wireless power transfer system, characterized in that, include: Obtain a circuit model of a multi-device wireless power transmission system; the multi-device wireless power transmission system includes multiple transmitting modules and multiple receiving modules, each of the transmitting and receiving modules contains a resonant circuit, and magnetic coupling is achieved between any two transmitting modules, between any two receiving modules, and between the transmitting module and the receiving module through mutual inductance; Based on the circuit model, the coupling mode equations of the multi-device wireless power transmission system are determined. The magnitude value in the coupled mode equation is decomposed into real and imaginary parts, and the decomposed real and imaginary parts are substituted into the coupled mode equation to separate the real and imaginary parts of the coupled mode equation, thus obtaining the real-valued coupled mode equation; The discrete-time state-average model of the multi-device wireless power transmission system is obtained by processing the real-valued coupled-mode equations using the discrete-time state-average method.

2. The method according to claim 1, characterized in that, The real-valued coupled mode equation is expressed as: ； in, Indicates the module number. N is the total number of all modules; Let be the real component of the modulus value of the i-th module; is the imaginary part of the modulus value of the i-th module; k is the module number of the summation traversal, representing other modules that are magnetically coupled to the i-th module; This represents the resonant angular frequency of a multi-device wireless power transfer system. This represents the mutual inductance between the coil of the i-th module and the coil of the k-th module, and characterizes the magnetic coupling strength between the two coils. This represents the coil inductance of the i-th module; This represents the coil inductance of the k-th module; This represents the loss resistance of the i-th module; This represents the load resistance of the i-th receiving module; This represents the module type identifier coefficient, used to distinguish between the transmitting module and the receiving module; This represents the effective value of the AC power supply voltage for the i-th transmitting module; This indicates the operating angular frequency of the AC power supply.

3. The method according to claim 1, characterized in that, The discrete-time state-average method is used to process the real-valued coupled-mode equations to obtain the discrete-time state-average model of the multi-device wireless power transmission system, including: Determine the averaged state variable; wherein the averaged state variable is the average value of the real or imaginary component of the magnitude over one resonance period; Substituting the averaged state variables into the real-valued coupled-mode equation, we obtain the state-averaged mathematical model. Based on the sampling sequence number and sampling period, the state averaging mathematical model is discretized to obtain the discrete-time state averaging model.

4. The method according to claim 3, characterized in that, The determination of the averaged state variables includes: A single resonant period is defined as the calculation interval, and the real and imaginary components of the modulus value are respectively integrated and averaged within the calculation interval. The average value of the interval states obtained from the calculation is determined as the corresponding averaged state variable.

5. The method according to claim 1, characterized in that, The equation for the discrete-time state-averaged model is expressed as follows: ； in, This represents the sampling time sequence number, which is a non-negative integer; The sampling period is a fixed time interval between two adjacent sampling moments. The discrete average state variable represents the real component of the magnitude value of the i-th module; Let represent the discrete average state variable of the imaginary component of the magnitude value of the i-th module.

6. The method according to claim 1, characterized in that, The resonant circuit of the transmitting module includes a first compensation capacitor, a transmitting coil, an AC power supply, and a first loss resistor connected in series. The resonant circuit of the receiving module includes a second compensation capacitor, a receiving coil, a second loss resistor, and a load resistor connected in series. The resonant frequency of the resonant circuit of the transmitting module is the same as the resonant frequency of the resonant circuit of the receiving module.

7. The method according to any one of claims 1-6, characterized in that, Also includes: Based on the discrete-time state-averaged model, a disturbance is introduced at the stable operating point of the multi-device wireless power transmission system to obtain a small-signal model of the multi-device wireless power transmission system; wherein, the small-signal model is used to analyze the transient characteristics of the multi-device wireless power transmission system.

8. The method according to claim 7, characterized in that, The equation for the small-signal model is expressed as: ； in, Represents the i-th module in the i-th position. The disturbance of the average state quantity of the real part of the magnitude value at each sampling time; Represents the i-th module in the i-th position. The disturbance of the average state quantity of the imaginary part of the magnitude value at each sampling time.

9. A modeling device for a magnetically coupled resonant multi-device wireless power transfer system, characterized in that, include: An acquisition module is used to acquire a circuit model of a multi-device wireless power transmission system. The multi-device wireless power transmission system includes multiple transmitting modules and multiple receiving modules. Each transmitting module and each receiving module contains a resonant circuit, and magnetic coupling is achieved between any two transmitting modules, between any two receiving modules, and between the transmitting module and the receiving module through mutual inductance. A coupled-mode equation establishment module is used to determine the coupled-mode equations of the multi-device wireless power transmission system based on the circuit model. The equation decomposition module is used to decompose the magnitude value in the coupled mode equation into real and imaginary parts, and substitute the decomposed real and imaginary parts into the coupled mode equation to separate the real and imaginary parts of the coupled mode equation and obtain the real-valued coupled mode equation. The discrete processing module is used to process the real-valued coupled-mode equations based on the discrete-time state averaging method to obtain the discrete-time state-averaged model of the multi-device wireless power transmission system.

10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-8.

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