An improved FCS-MPC-based wireless charging system grid-connected multi-objective control method, system, device and medium

Abstract

This invention discloses a grid-connected multi-objective control method, system, device, and medium for wireless charging systems based on an improved FCS-MPC, belonging to the field of grid-connected multi-objective control technology. The method includes processing the multi-port operating signals of the wireless charging system to form a control state representation; constructing a disturbance coupling representation based on the control state representation; introducing the disturbance coupling representation into a discrete prediction model to form candidate prediction states; constructing a cost function for each candidate switching state; adaptively correcting the initial weight coefficients to obtain updated cost function weight coefficients; and selecting candidate switching states that satisfy the target cost conditions from a finite set of candidate switching states as the target switching states, and sending them to the grid-side converter drive circuit. This invention reflects the impact of changes in the coupling state between the transmitting and receiving coils in the wireless charging system on the DC bus and grid-connected current, overcoming the shortcomings of single-objective control methods that cannot simultaneously address multiple operational requirements.

Description

Technical Field

[0001] This invention relates to the field of grid-connected multi-objective control technology, specifically to a grid-connected multi-objective control method, system, device, and medium for a wireless charging system based on an improved FCS-MPC. Background Technology

[0002] The grid connection stability of wireless charging systems is a crucial foundation for ensuring efficient interaction between mobile electrical devices such as electric vehicles, drones, and mobile robots and the power grid. Existing wireless charging grid-connected systems typically consist of a wireless power transmission unit, a DC bus, a grid-side converter, an LCL filter, and an AC power grid.

[0003] Finite Control Set Model Predictive Control (FCS-MPC) can directly predict the future state of the system based on the finite switching states of the converter and select the optimal switching vector through a cost function. Compared with traditional PI plus PWM control, FCS-MPC has advantages such as fast dynamic response, flexible constraint handling, and no need for additional modulation links. Therefore, applying FCS-MPC to the grid-connected control of wireless charging systems can, to some extent, improve the adaptability of traditional linear control methods in switching nonlinearities and fast dynamic processes.

[0004] Most existing FCS-MPC methods still focus on ordinary grid-connected converters or motor drive systems as the control objects, typically only considering current tracking error, switching losses, or a single power control objective. They rarely incorporate the unique characteristics of wireless charging systems, such as variations in magnetic coupling parameters, receiver load disturbances, DC bus energy fluctuations, LCL filter resonance, and weak grid frequency disturbances, into a unified prediction model and unified cost function. Therefore, when these existing methods are directly applied to grid-connected wireless charging scenarios, they still struggle to fully meet the requirements of multi-objective cooperative control under complex operating conditions. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention provides a grid-connected multi-target control method, system, device and medium for a wireless charging system based on an improved FCS-MPC.

[0006] Therefore, the technical problems solved by this invention are: how to address the following issues in existing wireless charging grid-connected control methods: First, the changes in mutual inductance, coupling coefficient, and equivalent input power disturbances on the wireless charging side are not incorporated into the grid-connected prediction model, leading to increased prediction errors of DC bus voltage and grid-connected current when wireless charging power fluctuates; second, although ordinary FCS-MPC can perform switch vector optimization, it often focuses on grid-connected current tracking as a single objective, without simultaneously considering DC bus energy balance, frequency support, LCL resonance suppression, and switch loss constraints; third, fixed weights are difficult to adapt to weak grid frequency disturbances, load abrupt changes, and harmonic distortion changes; and fourth, existing delay compensation methods do not simultaneously combine the actual switching state of the previous cycle and the power disturbances on the wireless charging side, resulting in inconsistencies between the prediction time and the switching execution time.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a multi-objective control method for grid-connected wireless charging systems based on improved FCS-MPC, comprising: performing sampling consistency processing and coordinate unification processing on the multi-port operating signals of the wireless charging system to form a control state representation; constructing a disturbance coupling representation based on the control state representation, the disturbance coupling representation being used to describe the power change and magnetic coupling state change on the wireless charging side; introducing the disturbance coupling representation into a discrete prediction model, performing delay compensation prediction in conjunction with the switch state of the previous sampling period, traversing a finite set of candidate switch states to predict candidate switch states, forming candidate prediction states; correcting the grid-connected active power reference based on the control state representation, and constructing a cost function for each candidate switch state by combining the candidate prediction states, multi-objective operating constraints, and initial weight coefficients; adaptively correcting the initial weight coefficients based on the control state representation to obtain updated cost function weight coefficients; selecting candidate switch states that satisfy the target cost conditions from the finite set of candidate switch states as target switch states, and directly sending the target switch states to the grid-side converter drive circuit.

[0008] As a preferred embodiment of the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC as described in this invention, the disturbance coupling characterization is constructed based on the control state characterization. The disturbance coupling characterization is used to describe the power change and magnetic coupling state change on the wireless charging side, including: extracting the operating state of the wireless charging side from the control state characterization; forming the equivalent input power of the wireless charging side based on the operating state of the wireless charging side; forming the power disturbance quantity based on the equivalent input power of the wireless charging side and the steady-state power reference; and forming the coupling coefficient based on the coil parameters and mutual inductance changes of the wireless charging side. The disturbance coupling characterization is composed of the equivalent input power of the wireless charging side, the power disturbance quantity, and the coupling coefficient.

[0009] This preferred scheme enables the discrete prediction model to reflect power fluctuations and magnetic coupling changes on the wireless charging side by determining the power disturbance and coupling coefficient.

[0010] As a preferred embodiment of the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC as described in this invention, the step of introducing disturbance coupling representation into the discrete prediction model, performing delay compensation prediction in conjunction with the switch state of the previous sampling period, and traversing a finite set of candidate switch states to predict candidate switch states and form candidate prediction states includes: integrating control state representation and disturbance coupling representation into the discrete prediction model; updating the disturbance input and time-varying parameters in the discrete prediction model based on the disturbance coupling representation; performing delay compensation prediction in conjunction with the switch state of the previous sampling period to form a delay compensation state; and traversing a finite set of candidate switch states, substituting the candidate switch states into the discrete prediction model updated based on the delay compensation state to form candidate prediction states.

[0011] This preferred scheme uses coupled disturbance prediction, delay compensation prediction, and candidate state traversal to match the candidate predicted state with the actual switching execution time.

[0012] As a preferred embodiment of the multi-objective control method for grid-connected wireless charging systems based on improved FCS-MPC described in this invention, the step of modifying the grid-connected active power reference based on the control state representation, and constructing a cost function for each candidate switching state by combining candidate predicted states, multi-objective operating constraints, and initial weight coefficients, includes: extracting the grid-connected frequency state from the control state representation; modifying the power reference of the wireless charging system based on the grid-connected frequency state to form a grid-connected active power reference; configuring multi-objective operating constraints around the candidate predicted states; and constructing a cost function for each candidate switching state based on the grid-connected active power reference, multi-objective operating constraints, and initial weight coefficients.

[0013] This preferred solution constructs a cost function that includes frequency support and operational constraints, enabling the evaluation of candidate switch states to take into account both grid-connected control and frequency support requirements.

[0014] As a preferred embodiment of the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC as described in this invention, the step of performing sampling consistency processing and coordinate unification processing on the multi-port operating signals of the wireless charging system to form a control state representation includes: performing signal conditioning and analog-to-digital conversion on the multi-port operating signals to form a digital sampling signal; performing digital filtering on the digital sampling signal to form a filtered sampling signal; performing time-scale alignment on the filtered sampling signal to form a synchronous sampling signal; and performing coordinate transformation processing on the synchronous sampling signal to form a control state representation for use in discrete prediction model invocation.

[0015] This preferred solution performs unified preprocessing on multiple types of sampled signals, ensuring that the grid-connected side state input meets the synchronous calculation requirements of the discrete prediction model.

[0016] As a preferred embodiment of the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC as described in this invention, the step of adaptively correcting the initial weight coefficients based on the control state characterization to obtain updated cost function weight coefficients includes: the control state characterization includes grid-connected current tracking state, grid-connected point frequency state, DC bus voltage state, grid-connected current harmonic state, and changes in the switching state of the grid-side converter; determining the grid-connected current tracking error index based on the deviation between the grid-connected current reference value and the actual grid-connected current value, and correcting the weights of the grid-connected current tracking term; and based on the... Frequency deviation and frequency change rate are used to determine the frequency disturbance index and correct the weight of the frequency support term. The DC bus voltage deviation index is determined based on the deviation between the DC bus voltage and the DC bus voltage reference value, and the weight of the DC bus energy balance term is corrected. The harmonic distortion index is determined based on the total harmonic distortion rate of the grid-connected current, and the weight of the harmonic suppression term is corrected. The switching state change index is determined based on the number of times the switching state of the grid-side converter changes within a preset statistical window, and the weight of the switching loss constraint term is corrected. The corrected candidate weights are then subjected to amplitude limiting and normalization processing to obtain the updated cost function weight coefficients.

[0017] As a preferred embodiment of the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC as described in this invention, the step of selecting candidate switch states that satisfy the target cost condition from a finite set of candidate switch states as target switch states and directly sending the target switch states to the grid-side converter drive circuit includes: updating the cost function of each candidate switch state using a target weight coefficient; calculating the cost function value of each candidate switch state in the finite set of candidate switch states; determining the candidate switch state with the smallest cost function value as the target switch state that satisfies the target cost condition; and directly sending the target switch states to the grid-side converter drive circuit, so that the grid-side converter performs on / off actions according to the target switch states.

[0018] This invention provides a grid-connected multi-target control system for a wireless charging system based on an improved FCS-MPC.

[0019] To address the aforementioned technical problems, this invention provides the following technical solution: a grid-connected multi-objective control system for a wireless charging system based on an improved FCS-MPC, comprising: a signal preprocessing module, a disturbance coupling determination module, a predicted state generation module, a cost function construction module, a weight coefficient correction module, and a switch state execution module; the signal preprocessing module is used to perform sampling consistency processing and coordinate unification processing on the multi-port operating signals of the wireless charging system to form a control state representation; the disturbance coupling determination module is used to construct a disturbance coupling representation based on the control state representation, the disturbance coupling representation being used to describe the power change and magnetic coupling state change on the wireless charging side; the predicted state generation module is used to introduce the disturbance coupling representation into a discrete prediction model. Combining the switch state of the previous sampling period with the delay compensation prediction, the finite set of candidate switch states is traversed to predict candidate switch states, forming candidate predicted states; the cost function construction module is used to modify the grid-connected active power reference based on the control state characterization, and construct the cost function for each candidate switch state by combining the candidate predicted states, multi-objective operating constraints, and initial weight coefficients; the weight coefficient correction module is used to adaptively correct the initial weight coefficients based on the control state characterization to obtain the updated cost function weight coefficients; the switch state execution module is used to select the candidate switch state that meets the target cost condition from the finite set of candidate switch states as the target switch state, and directly send the target switch state to the grid-side converter drive circuit.

[0020] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for grid-connected multi-target control of a wireless charging system based on an improved FCS-MPC.

[0021] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for grid-connected multi-target control of a wireless charging system based on an improved FCS-MPC.

[0022] The beneficial effects of this invention are as follows: This invention introduces the equivalent input power disturbance, mutual inductance change and coupling coefficient change of the wireless charging side into the grid-connected discrete prediction model, so that the prediction model is no longer only for ordinary grid-connected converters, but can reflect the impact of the coupling state change of the transmitting coil and receiving coil in the wireless charging system on the DC bus and grid current, thereby improving the prediction accuracy of the model.

[0023] This invention employs a two-step delay compensation prediction mechanism based on the actual execution of the switching state in the previous cycle, and incorporates the power disturbance on the wireless charging side into the delay compensation process simultaneously, making the predicted state more consistent with the actual execution time of the optimal switching state, thereby reducing the impact of digital controller sampling, calculation, and drive delays on grid-connected control performance.

[0024] This invention constructs a multi-objective cost function that includes grid-connected current tracking, DC bus energy balance, frequency support, LCL resonance or harmonic suppression, and switching loss constraints. This enables wireless charging systems to coordinate energy transmission, power quality, grid ancillary services, and equipment safety constraints within a unified evaluation framework, overcoming the shortcomings of traditional single-objective control methods that struggle to accommodate multiple operational needs.

[0025] This invention corrects the grid-connected active power reference based on the grid connection point frequency deviation and frequency change rate, enabling the wireless charging system to actively adjust the active power output when the grid frequency drops or is disturbed, and possesses frequency support characteristics similar to virtual inertia and damping, thereby improving the system's response capability to grid frequency disturbances.

[0026] This invention introduces an LCL resonance or harmonic suppression term into the cost function. By constraining the filter capacitor current, the change in grid current, or the harmonic distortion index, it reduces the grid current distortion caused by LCL filter resonance and improves the grid-connected power quality of the wireless charging system.

[0027] This invention employs a limit-normalization adaptive weight correction method, which dynamically adjusts the weights of each control target based on frequency deviation, frequency change rate, DC bus voltage deviation, and current harmonic distortion. This enables the controller to automatically change the priority of control targets according to different operating conditions, avoiding target conflicts caused by fixed weights under complex operating conditions.

[0028] In summary, this invention can improve grid-connected current tracking accuracy, reduce bus voltage fluctuations, enhance active frequency support capability, and improve grid-connected power quality under complex operating conditions such as changes in coupling parameters, sudden changes in receiver load, DC bus voltage fluctuations, grid frequency disturbances, and LCL filter resonance in wireless charging systems. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 The present invention provides an overall flowchart of a grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC, as an embodiment of the present invention.

[0031] Figure 2 This is a flowchart of a grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC, provided as an embodiment of the present invention.

[0032] Figure 3 The simulation verification waveform diagram shows a multi-objective control method for grid-connected wireless charging system based on improved FCS-MPC, which is provided as an embodiment of the present invention. Detailed Implementation

[0033] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0034] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a grid-connected multi-target control method for a wireless charging system based on an improved FCS-MPC, comprising: S1. Perform sampling consistency processing and coordinate unification processing on the multi-port operation signals of the wireless charging system to form a control state representation.

[0035] S2. Based on the control state characterization, a disturbance coupling characterization is constructed. The disturbance coupling characterization is used to describe the power change and magnetic coupling state change on the wireless charging side.

[0036] S3. Introduce the disturbance coupling characterization into the discrete prediction model, combine the switch state of the previous sampling period to perform delay compensation prediction, traverse the finite set of candidate switch states to predict candidate switch states, and form candidate prediction states.

[0037] S4. Based on the control state characterization, the grid-connected active power reference is corrected, and the cost function of each candidate switching state is constructed by combining the candidate predicted state, multi-objective operation constraints and initial weight coefficients.

[0038] S5. Adaptively correct the initial weight coefficients based on the control state characterization to obtain the updated cost function weight coefficients.

[0039] S6. Select the candidate switch state that satisfies the target cost condition from the finite set of candidate switch states as the target switch state, and send the target switch state directly to the grid-side converter drive circuit.

[0040] It should be noted that when the wireless charging system is running in grid-connected mode, changes in the load and coupling state on the wireless charging side will affect the grid-connected current, bus voltage, and grid-connected point frequency via the DC bus, thereby affecting the selection of candidate switch states.

[0041] Therefore, through steps S1-S6, the power disturbance and coupling coefficient of the wireless charging side are introduced into the discrete prediction model, and combined with delay compensation, frequency support, cost function and adaptive correction of weight coefficients, the target switching state is determined to achieve grid-connected predictive control of the wireless charging system.

[0042] Example 2, refer to Figure 2 and Figure 3 As one embodiment of the present invention, based on the previous embodiment, a grid-connected multi-target control method for a wireless charging system based on improved FCS-MPC is provided, comprising: Furthermore, in step S1, the multi-port operating signals of the wireless charging system undergo sampling consistency processing and coordinate unification processing to form a control state representation, including the following steps A1-A4: A1. Perform signal conditioning and analog-to-digital conversion on multi-port operating signals to form digital sampling signals.

[0043] A2. Perform digital filtering on the digital sampled signal to form a filtered sampled signal.

[0044] A3. Time-align the filtered sampling signal to form a synchronous sampling signal.

[0045] A4. Perform coordinate transformation on the synchronous sampling signal to form a control state representation for use in discrete prediction model invocation.

[0046] In this embodiment, the multi-port operating signals originate from the AC port of the grid-side converter, the DC bus side, and the wireless charging power transmission side of the wireless charging system. The multi-port operating signals include three-phase grid voltage, three-phase grid-connected current, converter-side inductor current, LCL filter capacitor voltage, DC bus voltage, wireless charging side input voltage, wireless charging side input current, and grid connection point frequency. After signal conditioning, analog-to-digital conversion, digital filtering, and time-scale alignment, the signals from different sampling channels form a synchronous sampling signal. Then, through coordinate transformation processing, the voltage and current quantities in the three-phase stationary coordinate system are converted into components in the two-phase stationary coordinate system, thus obtaining the control state characterization.

[0047] Specifically, voltage and current sampling units are arranged at the AC port of the grid-side converter, the DC bus side, and the wireless charging power transmission side of the wireless charging system to collect the three-phase grid voltage in real time. Three-phase grid-connected current Inductor current on the converter side LCL filter capacitor voltage DC bus voltage Wireless charging side input voltage Wireless charging side input current and grid connection frequency The acquired signals undergo conditioning circuitry and A / D conversion, followed by digital filtering, time-scale alignment, and coordinate transformation to convert the voltage and current quantities in the three-phase stationary coordinate system into those in the two-phase stationary coordinate system. The components below provide real-time state input for subsequent predictive modeling. For example... Figure 2 As shown.

[0048] Furthermore, in step S2, a perturbation coupling characterization is constructed based on the control state characterization. The perturbation coupling characterization is used to describe the power change and magnetic coupling state change on the wireless charging side, including the following steps B1-B4: B1. Extract the wireless charging side operating state from the control state representation.

[0049] B2. The equivalent input power of the wireless charging side is formed based on the operating status of the wireless charging side.

[0050] B3. Power disturbance is generated based on the equivalent input power and steady-state power reference of the wireless charging side.

[0051] B4. The coupling coefficient is formed based on the coil parameters and mutual inductance changes of the wireless charging side. The disturbance coupling is characterized by the equivalent input power of the wireless charging side, the power disturbance, and the coupling coefficient.

[0052] In this embodiment, the operating state of the wireless charging side includes changes in the wireless charging side input voltage, wireless charging side input current, DC bus voltage, and load side current. The equivalent input power of the wireless charging side is determined by the wireless charging side input voltage and input current; the power disturbance is determined by the difference between the equivalent input power and the steady-state power reference; the coupling coefficient is determined by the wireless charging side coil parameters and mutual inductance changes, wherein the wireless charging side coil parameters include the primary coil inductance and the secondary coil inductance. Thus, the equivalent input power, power disturbance, and coupling coefficient of the wireless charging side constitute a disturbance coupling characterization, used to characterize changes in power and magnetic coupling on the wireless charging side.

[0053] Based on the input voltage of the wireless charging side Input current DC bus voltage And the load-side current change, to estimate the equivalent input power on the wireless charging side. and its disturbance amount .in, It can be represented as: in, Indicates the first The equivalent input power of the wireless charging side per sampling period, Indicates the input voltage on the wireless charging side. Indicates the input current on the wireless charging side. This is the index of the sampling period.

[0054] in, This represents the equivalent input power of the wireless charging side under steady-state operating conditions. This represents the disturbance quantity.

[0055] Based on the inductance of the primary coil in the wireless charging system Secondary coil inductance and mutual induction Estimate the wireless charging coupling coefficient : in, This is the wireless charging coupling coefficient, used to characterize the coupling state between the transmitting and receiving coils. It is affected by changes in receiver position, air gap, or load conditions. and It changes accordingly and is used as a model parameter or perturbation input in subsequent prediction calculations.

[0056] The obtained wireless charging equivalent input power Power disturbance and coupling coefficient This is not an independent computational resource, but rather used as a perturbation input and time-varying parameter in subsequent discrete prediction models. Input matrix via equivalent power perturbation on the wireless charging side Application to DC bus voltage prediction Enter parameter set It is used to correct the impact of power changes on the wireless charging side on the grid-connected side state prediction.

[0057] Furthermore, in step S3, the disturbance coupling characterization is introduced into the discrete prediction model, and the delay compensation prediction is performed in combination with the switch state of the previous sampling period. The finite set of candidate switch states is traversed to predict candidate switch states, forming candidate prediction states, including the following steps C1-C4: C1. Integrate the control state representation and disturbance coupling representation into the discrete prediction model.

[0058] C2. Update the perturbation input and time-varying parameters in the discrete prediction model based on perturbation coupling characterization.

[0059] C3. Combine the switch state of the previous sampling period to perform delay compensation prediction and form a delay compensation state.

[0060] C4. Traverse the finite set of candidate switch states, substitute the candidate switch states into the discrete prediction model updated based on delay compensation, and form candidate prediction states.

[0061] In this embodiment, the discrete prediction model is a physical prediction model established based on the grid-side converter topology, LCL filter parameters, and DC bus dynamics. The control state representation serves as the state input to the discrete prediction model. The equivalent input power of the wireless charging side in the disturbance coupling representation serves as the disturbance input for DC bus voltage prediction, and the coupling coefficient in the disturbance coupling representation serves as a time-varying parameter for updating the discrete prediction model. Delay compensation prediction utilizes the actual switching states executed in the previous sampling period to form delay compensation states. Then, it traverses a finite set of candidate switching states, substituting these candidate switching states into the updated discrete prediction model to obtain the candidate prediction state for each candidate switching state.

[0062] Specifically, the discrete prediction model of this invention is a physical model based on circuit topology and state-space equations, not a data-driven training model. The model comprises a three-layer structure: the first layer is the state acquisition layer, used to acquire grid-connected current, filter capacitor voltage, DC bus voltage, grid voltage, and power disturbances on the wireless charging side; the second layer is the parameter update layer, used to form a parameter set based on LCL filter parameters, grid equivalent impedance, DC bus parameters, and wireless charging coupling parameters. The third layer is the prediction calculation layer, used for different candidate switch states. The system state for the next control cycle is predicted under the action of the discrete prediction model. The input to the discrete prediction model includes the current state vector. Candidate switch status Grid voltage and equivalent input power of wireless charging side The discrete prediction model outputs include the cost function values ​​for the predicted grid-connected current, predicted DC bus voltage, predicted filter capacitor current, and corresponding candidate switch states.

[0063] Based on the grid-side converter topology, LCL filter parameters, and DC bus dynamics, a state-space model of the grid-connected side of the wireless charging system is established. The state vector is selected as follows: in, For the first The converter-side inductor current in each sampling cycle is Components on the axis, For the first The converter-side inductor current in each sampling cycle is Components on the axis, For the first The LCL filter capacitor voltage during each sampling period is Components on the axis, For the first The LCL filter capacitor voltage during each sampling period is Components on the axis, For the first The grid-side current in each sampling period is Components on the axis, For the first The grid-side current in each sampling period is Components on the axis, This is the DC bus voltage. This is a transpose.

[0064] Establish a discrete prediction model: in, The discrete state matrix is ​​related to the system parameters. For the input matrix related to the switching state of the grid-side converter, For the grid voltage input matrix, The equivalent power perturbation input matrix for wireless charging. For grid voltage vector, Candidate switch state The corresponding converter output voltage vector, It is a set of parameters that includes LCL filter parameters, DC bus parameters, grid equivalent impedance, and wireless charging coupling parameters.

[0065] The parameter set include: in, For the converter-side filter inductor, For grid-side filter inductance, For filtering capacitors, , These are the equivalent resistances of the corresponding branches. The equivalent impedance of the power grid. For wireless charging mutual inductance, The wireless charging coupling coefficient is... Equivalent input power for wireless charging.

[0066] Considering the inherent delays caused by the digital controller's sampling, calculation, and drive output, this invention employs a two-step delay compensation prediction. First, it utilizes the current sampling state... The actual switching state executed in the previous sampling period. and the current equivalent input power of wireless charging. Predicting the first Periodic delay compensation status: Traversing the finite set of candidate switching states of the grid-side three-phase converter Set the state of each candidate switch Substituting into the prediction model, we obtain the predicted state for the (k+2)th period: in, This represents a finite set of candidate switching states for a three-phase two-level grid-side converter. It should be noted that... , , and These are all matrices in the discrete state-space model, not single coefficients. This is the system state matrix, used to describe the influence of the current state on the state of the next cycle; The control input matrix is ​​used to describe the impact of the converter output voltage corresponding to the candidate switch state on the system state. This is the grid voltage input matrix, used to describe the impact of grid voltage disturbances on the system state; This is the power perturbation input matrix for the wireless charging side, used to describe the impact of the equivalent input power on the DC bus and grid-connected side states. The matrix consists of the LCL filter continuous state equation, the DC bus energy balance equation, and the sampling period. It is obtained by discretization and can vary with the parameter set. Online updates include 8 basic switch states: This two-step delay compensation mechanism ensures that the predicted state is consistent with the actual switching execution time, reducing the impact of computational delay and power disturbances on the wireless charging side on the accuracy of optimal switch selection.

[0067] Furthermore, in step S4, the grid-connected active power reference is corrected based on the control state characterization. Combining the candidate predicted states, multi-objective operating constraints, and initial weighting coefficients, a cost function for each candidate switching state is constructed, including the following steps D1-D4: D1. Extract the grid connection frequency state from the control state representation.

[0068] D2. Based on the grid connection frequency state, correct the power reference of the wireless charging system to form a grid connection active power reference.

[0069] D3. Configure multi-objective operational constraints around the candidate prediction state.

[0070] D4. Based on the grid-connected active power reference, multi-objective operation constraints, and initial weight coefficients, construct the cost function for each candidate switch state.

[0071] In this embodiment, the grid connection frequency state includes the grid connection point frequency deviation and the frequency change rate. The controller corrects the wireless charging system power reference based on the grid connection frequency state to form a grid-connected active power reference. Multi-objective operational constraints include grid connection current tracking constraints, DC bus energy balance constraints, frequency support constraints, LCL filter operational constraints, and switching loss constraints. Initial weighting coefficients are used to configure the evaluation proportion of multi-objective operational constraints in the cost function, enabling each candidate switching state to be evaluated under the same cost function.

[0072] Specifically, the frequency of grid connection points is detected. Calculate frequency deviation and frequency change rate : in, The rated power grid frequency, The sampling period.

[0073] A grid-connected active power reference with virtual inertia and damping characteristics is generated based on frequency deviation and frequency change rate. in, This is the corrected reference for grid-connected active power. This is the power reference value for the wireless charging system. This is the virtual damping coefficient. This is the virtual inertia coefficient.

[0074] When the grid connection frequency drops, the controller increases the grid-connected active power support output based on the frequency deviation and rate of change. When the frequency recovers to within the allowable range, the controller gradually reduces the frequency support output, restoring the system to normal power control. The generated corrected grid-connected active power reference... Used to generate grid-connected current reference values, which are then further incorporated into the frequency support items. Therefore, frequency deviation and frequency change rate are not independent evaluation parameters, but rather jointly influence the selection of optimal switching state through the evaluation of modified power reference and cost function.

[0075] Perform multi-objective cost function construction and rolling optimization. For each candidate switch state... Construct a multi-objective cost function : in, Indicates the state of the candidate switch The corresponding total cost function value, Represents the first in the finite set of candidate switch states. Candidate switch states, For grid-connected current tracking, This is the DC bus energy balance term. For frequency support items, This is an LCL resonance or harmonic suppression term. This is a switching loss constraint term; , , , , These are the corresponding weighting coefficients, used to adjust the priority of different control objectives in the total cost function. This indicates the weight of the grid-connected current tracking term, which is used to adjust the priority of current tracking accuracy in the total cost function. Weights for the DC bus energy balance term; Weights for frequency-supported terms; For LCL resonance or harmonic suppression terms; This represents the weight of the switching loss constraint term, which is used to limit the number of switching state changes within adjacent sampling periods, thereby reducing switching losses and switching frequency fluctuations.

[0076] The grid-connected current tracking items are: in, , They represent the first Each sampling period , Reference value for shaft-connected grid current; , These represent the candidate switch states respectively. The predicted number under the action Each sampling period , Grid-connected current value. The grid-connected current tracking term is used to evaluate the deviation between the predicted grid-connected current and the reference grid-connected current.

[0077] The DC bus energy balance term is: in, This indicates the reference value for the DC bus voltage. Indicates the candidate switch state The predicted number under the action The DC bus voltage value for each sampling period. The DC bus energy balance term is used to evaluate the stability of the DC bus voltage.

[0078] The frequency support items are: in, Indicates the candidate switch state The predicted number under the action Active power connected to the grid in one sampling period; This represents the grid-connected active power reference value after correction based on the grid connection point frequency deviation and frequency change rate. The frequency support term is used to evaluate the degree to which candidate switch states meet the grid-connected active power support target.

[0079] The LCL resonance or harmonic suppression term is: in, Indicates the candidate switch state The predicted number under the action The LCL filter capacitor current is measured in sampling periods. The LCL resonance or harmonic suppression term is used to suppress excessive filter capacitor current and high-frequency distortion of grid current, thereby reducing the impact of LCL filter resonance on grid power quality.

[0080] The switching loss constraint is: in, Indicates the state of the candidate switch The Middle The switching status of the phase bridge arm, In , , These represent the a-phase, b-phase, and c-phase bridge arms of the three-phase grid-side converter, respectively. Indicates the number of the previous sampling period The actual switching state executed by the phase bridge arm. The switching loss constraint term is used to evaluate the number of switching state changes within adjacent sampling periods, in order to reduce switching losses and switching frequency fluctuations.

[0081] Through the aforementioned cost function, this invention can simultaneously evaluate grid-connected current tracking performance, DC bus stability, frequency active support capability, LCL resonance suppression effect, and switching loss level during the same rolling optimization process.

[0082] Furthermore, in step S5, the initial weight coefficients are adaptively corrected based on the control state characterization to obtain the updated cost function weight coefficients, including the following steps E1-E7: E1. Control status characterization includes grid-connected current tracking status, grid-connected point frequency status, DC bus voltage status, grid-connected current harmonic status, and grid-side converter switching status changes.

[0083] E2. Based on the deviation between the grid-connected current reference value and the actual grid-connected current value, determine the grid-connected current tracking error index and correct the weight of the grid-connected current tracking item.

[0084] E3. Determine the frequency disturbance index and adjust the weight of the frequency support item based on the frequency deviation and frequency change rate at the grid connection point.

[0085] E4. Based on the deviation between the DC bus voltage and the DC bus voltage reference value, determine the DC bus voltage deviation index and correct the weight of the DC bus energy balance term.

[0086] E5. Determine the harmonic distortion index based on the total harmonic distortion rate of the grid-connected current, and correct the weight of the harmonic suppression term.

[0087] E6. Based on the number of times the switching state of the grid-side converter changes within the preset statistical window, determine the switching state change index and correct the weight of the switching loss constraint item.

[0088] E7. Perform amplitude limiting and normalization processing on the corrected candidate weights to obtain the updated cost function weight coefficients.

[0089] To eliminate the differences in dimensions and numerical ranges among grid-connected current, DC bus voltage, grid frequency, harmonic distortion rate, and the number of times switch status changes occur, each operating status indicator is first normalized.

[0090] The grid-connected current tracking error index is expressed as follows: in, and They represent the first Each sampling period shaft and Reference value for grid-connected current of the shaft; and These represent the actual values ​​of the corresponding grid-connected current; This indicates the permissible grid-connected current tracking error limit. When When the current tracking error is within the allowable range, it indicates that the grid-connected current tracking error is within the allowable range.

[0091] The DC bus voltage deviation index is expressed as follows: in, Indicates the first The actual value of the DC bus voltage for each sampling period; This indicates the reference value for the DC bus voltage. This indicates the permissible DC bus voltage deviation limit.

[0092] The frequency deviation index at the grid connection point is expressed as: The frequency change rate index is expressed as: in, Indicates the frequency deviation at the grid connection point; Indicates the rate of change of the frequency of grid connection points; Indicates the permissible frequency deviation limit at the grid connection point; This indicates the permissible rate of change limit for frequency.

[0093] Harmonic distortion index is expressed as: in, Indicates the first Total harmonic distortion rate of grid-connected current within a preset statistical window with a sampling period as the cutoff time; This indicates the preset limit for total harmonic distortion (THD) of the grid-connected current.

[0094] The number of on / off state changes in the preset statistics window is represented as follows: The switch state change index is expressed as follows: in, This indicates the number of sampling periods included in the preset statistics window; Indicates the first The sampling period The actual switching state of the phase bridge arm; ; This indicates the maximum number of times the switch state can be changed within the statistics window.

[0095] Based on the above normalized state index, the candidate weights of each cost function sub-item are calculated respectively.

[0096] The formula for updating the candidate weights of the grid-connected current tracking item is: The formula for updating the candidate weights of the DC bus energy balance term is: The formula for updating the candidate weights of frequency support items is: The formula for updating the candidate weights of the harmonic suppression term is: The formula for updating the candidate weights of the switching loss constraint is: in, , , , and These represent the candidate weights of the grid-connected current tracking term, DC bus energy balance term, frequency support term, harmonic suppression term, and switching loss constraint term before limiting and normalization processing, respectively.

[0097] , , , and These represent the initial weights of the five cost function sub-items mentioned above; , , , , and These represent the adjustment coefficients for candidate weights as the degree of exceedance increases after the corresponding state index exceeds the allowable limit.

[0098] No. The limiting function for the candidate weights is expressed as: in, (y) represents the weighted value before the clipping process; and They represent the first The minimum and maximum allowed values ​​for candidate weights.

[0099] After the amplitude limiting process is completed, the weights of each candidate are normalized: in, Indicates the first The sampling period The final weights of the sub-items of the cost function, satisfying: The initial weights are determined based on the importance of each control objective under normal grid-connected operating conditions. Under normal grid-connected operating conditions, grid current tracking and DC bus voltage stability are the primary control objectives. and Prioritize setting frequency support, harmonic suppression, and switching loss constraints as auxiliary control objectives, and set corresponding parameters for each. , and .

[0100] Each adjustment coefficient is determined based on the allowable limit of the corresponding state index, the maximum state index under the most unfavorable design condition, and the maximum allowable value of the candidate weights. The adjustment coefficients for the grid-connected current tracking term, DC bus energy balance term, harmonic suppression term, and switching loss constraint term are expressed as follows: in, Indicates the first The maximum normalized value of the status index under the most unfavorable design conditions; This indicates the corresponding initial weight; This indicates the maximum allowed weight for the corresponding candidate.

[0101] The frequency support term is affected by both frequency deviation and frequency change rate, and its adjustment coefficient is expressed as: in, and These represent the maximum values ​​of the frequency deviation index and the frequency change rate index, respectively, under the most unfavorable frequency disturbance conditions in the design. This represents the allocation coefficients of frequency deviation and frequency change rate in the frequency support weight update, and .

[0102] The most unfavorable operating conditions include sudden load changes, a decrease in the wireless charging coupling coefficient, a frequency drop at the grid connection point, changes in the grid equivalent impedance, and deviations in LCL filter parameters. The maximum values ​​of each state index under these most unfavorable conditions are obtained through simulation or experimentation, and the adjustment coefficients are determined based on the calculated relationships.

[0103] When any normalized state index is not greater than 1, the corresponding candidate weight remains the same as the initial weight; when the normalized state index is greater than 1, the corresponding candidate weight increases with the degree to which the state index exceeds the limit. Therefore, the weight of the grid-connected current tracking term is increased when the grid-connected current tracking error increases; the weight of the DC bus energy balance term is increased when the DC bus voltage deviation increases; the weight of the frequency support term is increased when the grid connection point frequency deviation or frequency change rate increases; the weight of the harmonic suppression term is increased when the grid-connected current harmonic distortion increases; and the weight of the switching loss constraint term is increased when the number of switching state changes exceeds the allowable upper limit.

[0104] Furthermore, in step S6, a candidate switch state that satisfies the target cost condition is selected from the finite set of candidate switch states as the target switch state, and the target switch state is directly sent to the grid-side converter drive circuit, including the following steps F1-F4: F1 is the cost function that updates the state of each candidate switch using the target weight coefficient.

[0105] F2. Calculate the cost function value for each candidate switch state in the finite set of candidate switch states.

[0106] F3. The candidate switch state with the smallest cost function value is determined as the target switch state that satisfies the target cost condition.

[0107] F4. Directly send the target switch state to the grid-side converter drive circuit, so that the grid-side converter performs on / off actions according to the target switch state.

[0108] In this embodiment, the target cost condition is that the cost function value of the candidate switching state is minimized. The controller updates the cost function of each candidate switching state using the target weight coefficient, and calculates the cost function value of each candidate switching state in the finite set of candidate switching states. The candidate switching state with the minimum cost function value is determined as the target switching state. Subsequently, the controller directly sends the target switching state to the grid-side converter drive circuit, causing the grid-side converter to perform switching actions according to the target switching state, and repeats the above-mentioned grid-connected predictive control process in the next sampling period.

[0109] Specifically, the cost function value corresponding to each candidate switch state in the candidate switch state set Ω is calculated, and the switch state that minimizes Jj is selected as the optimal switch state: Optimal switching state The system directly sends signals to the drive circuits of the power switching devices in the grid-side converter to control the on / off operation of IGBTs or MOSFETs. This process eliminates the need for PWM modulation, reducing modulation delay and improving dynamic response speed. After execution, the system enters the next sampling cycle, continues to collect state data, and repeats the above steps to achieve multi-objective rolling optimization control of the wireless charging system connected to the grid.

[0110] like Figure 3 The figure shown is a simulation result diagram of grid-connected current tracking and vector selection. The upper and middle parts are used to show the tracking of the reference current and the actual current, and the lower part is used to show the change of the optimal vector number at different sampling times, which is used to illustrate that the present invention can select the target switch state through rolling optimization.

[0111] Example 3 is an embodiment of the present invention. This embodiment provides a grid-connected multi-objective control system for a wireless charging system based on an improved FCS-MPC, including a signal preprocessing module, a disturbance coupling determination module, a predicted state generation module, a cost function construction module, a weight coefficient correction module, and a switch state execution module.

[0112] The signal preprocessing module is used to perform sampling consistency processing and coordinate unification processing on the multi-port operating signals of the wireless charging system to form a control state representation.

[0113] The disturbance coupling determination module is used to construct a disturbance coupling characterization based on the control state characterization. The disturbance coupling characterization is used to describe the power change and magnetic coupling state change on the wireless charging side.

[0114] The prediction state generation module is used to introduce the disturbance coupling characterization into the discrete prediction model, perform delay compensation prediction by combining the switch state of the previous sampling period, traverse the finite set of candidate switch states to predict candidate switch states, and form candidate prediction states.

[0115] The cost function construction module is used to modify the grid-connected active power reference based on the control state characterization, and to construct the cost function for each candidate switching state by combining the candidate predicted state, multi-objective operation constraints and initial weight coefficients.

[0116] The weight coefficient correction module is used to adaptively correct the initial weight coefficients based on the control state characterization, so as to obtain the updated cost function weight coefficients.

[0117] The switch state execution module is used to select the candidate switch state that meets the target cost condition from the finite set of candidate switch states as the target switch state, and directly send the target switch state to the grid-side converter drive circuit.

[0118] This embodiment also provides an electronic device applicable to a grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC as proposed in the above embodiment.

[0119] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a grid-connected multi-target control method for a wireless charging system based on an improved FCS-MPC, as proposed in the above embodiment.

[0120] The storage medium proposed in this embodiment and the method for implementing a grid-connected multi-target control of a wireless charging system based on an improved FCS-MPC proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0121] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC, characterized in that, include: The multi-port operating signals of the wireless charging system are subjected to sampling consistency processing and coordinate unification processing to form a control state representation. A perturbation coupling characterization is constructed based on the control state characterization. The perturbation coupling characterization is used to describe the power change and magnetic coupling state change on the wireless charging side. The disturbance coupling characterization is introduced into the discrete prediction model. Delay compensation prediction is performed by combining the switch state of the previous sampling period. The candidate switch state prediction is performed by traversing the finite set of candidate switch states to form candidate prediction states. Based on the control state characterization, the grid-connected active power reference is corrected, and the cost function of each candidate switching state is constructed by combining the candidate predicted state, multi-objective operation constraints and initial weight coefficients. The initial weight coefficients are adaptively corrected based on the control state characterization to obtain the updated cost function weight coefficients; From a finite set of candidate switch states, select the candidate switch state that satisfies the target cost condition as the target switch state, and directly send the target switch state to the grid-side converter drive circuit.

2. The grid-connected multi-objective control method for a wireless charging system based on improved FCS-MPC as described in claim 1, characterized in that, The disturbance coupling characterization constructed based on the control state characterization is used to describe the power change and magnetic coupling state change on the wireless charging side, including: Extract the wireless charging side operating state from the control state representation; The equivalent input power of the wireless charging side is determined based on the operating status of the wireless charging side. Power perturbation is generated based on the equivalent input power and steady-state power reference of the wireless charging side; The coupling coefficient is formed based on the coil parameters and mutual inductance changes on the wireless charging side. The disturbance coupling is characterized by the equivalent input power, power disturbance, and coupling coefficient on the wireless charging side.

3. The grid-connected multi-objective control method for a wireless charging system based on improved FCS-MPC as described in claim 2, characterized in that, The process of introducing disturbance coupling characterization into the discrete prediction model, performing delay compensation prediction based on the switch state of the previous sampling period, and traversing a finite set of candidate switch states to predict candidate switch states, thereby forming candidate prediction states, includes: Integrate the control state representation and the disturbance coupling representation into the discrete prediction model; Update the perturbation input and time-varying parameters in the discrete prediction model based on perturbation coupling characterization; Combine the switch state of the previous sampling period to perform delay compensation prediction and form a delay compensation state; Traverse the finite set of candidate switch states, substitute the candidate switch states into the discrete prediction model updated based on delay compensation, and form candidate prediction states.

4. The grid-connected multi-objective control method for a wireless charging system based on improved FCS-MPC as described in claim 3, characterized in that, The reference for grid-connected active power, based on the control state characterization and modified, combines candidate predicted states, multi-objective operating constraints, and initial weighting coefficients to construct a cost function for each candidate switching state, including: Extract grid connection frequency status from control state representation; Based on the grid-connected frequency state, the power reference of the wireless charging system is corrected to form a grid-connected active power reference. Configure multi-objective operational constraints around candidate prediction states; Based on the grid-connected active power reference, multi-objective operation constraints, and initial weighting coefficients, a cost function for each candidate switch state is constructed.

5. The grid-connected multi-objective control method for a wireless charging system based on improved FCS-MPC as described in claim 4, characterized in that, The process of performing sampling consistency processing and coordinate unification processing on the multi-port operating signals of the wireless charging system to form a control state representation includes: Perform signal conditioning and analog-to-digital conversion on multi-port operating signals to form digital sampling signals; The digital sampled signal is digitally filtered to form a filtered sampled signal; The filtered sampling signal is time-aligned to form a synchronous sampling signal; The synchronously sampled signal is subjected to coordinate transformation to form a control state representation for use in discrete prediction model invocation.

6. The grid-connected multi-objective control method for a wireless charging system based on improved FCS-MPC as described in claim 5, characterized in that, The adaptive correction of the initial weight coefficients based on the control state characterization to obtain the updated cost function weight coefficients includes: The control status characterization includes grid-connected current tracking status, grid-connected point frequency status, DC bus voltage status, grid-connected current harmonic status, and grid-side converter switching status changes. Based on the deviation between the reference value and the actual value of the grid-connected current, the grid-connected current tracking error index is determined, and the weight of the grid-connected current tracking item is corrected. Based on the frequency deviation and frequency change rate at the grid connection point, determine the frequency disturbance index and adjust the weight of the frequency support item. Based on the deviation between the DC bus voltage and the DC bus voltage reference value, determine the DC bus voltage deviation index and correct the weight of the DC bus energy balance term; Based on the total harmonic distortion rate of the grid-connected current, determine the harmonic distortion index and correct the weight of the harmonic suppression term; Based on the number of times the switching status of the converter on the intranet side changes within the preset statistical window, determine the switching status change index and correct the weight of the switching loss constraint term. The corrected candidate weights are subjected to amplitude limiting and normalization to obtain the updated cost function weight coefficients.

7. The grid-connected multi-objective control method for a wireless charging system based on improved FCS-MPC as described in claim 6, characterized in that, The step of selecting candidate switch states that satisfy the target cost condition from a finite set of candidate switch states as the target switch state, and directly sending the target switch state to the grid-side converter drive circuit, includes: The cost function that updates the state of each candidate switch using the target weight coefficient is used. Calculate the cost function value for each candidate switch state in a finite set of candidate switch states; The candidate switch state with the minimum cost function value is determined as the target switch state that satisfies the target cost condition. The target switch state is directly sent to the grid-side converter drive circuit, so that the grid-side converter performs on / off actions according to the target switch state.

8. A grid-connected multi-objective control system for a wireless charging system based on an improved FCS-MPC, employing the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC as described in any one of claims 1 to 7, characterized in that, include: The module includes a signal preprocessing module, a disturbance coupling determination module, a prediction state generation module, a cost function construction module, a weight coefficient correction module, and a switch state execution module. The signal preprocessing module is used to perform sampling consistency processing and coordinate unification processing on the multi-port operating signals of the wireless charging system to form a control state representation. The disturbance coupling determination module is used to construct a disturbance coupling characterization based on the control state characterization. The disturbance coupling characterization is used to describe the power change and magnetic coupling state change on the wireless charging side. The prediction state generation module is used to introduce the disturbance coupling characterization into the discrete prediction model, perform delay compensation prediction by combining the switch state of the previous sampling period, traverse the finite set of candidate switch states to predict candidate switch states, and form candidate prediction states. The cost function construction module is used to modify the grid-connected active power reference based on the control state characterization, and to construct the cost function for each candidate switching state by combining the candidate predicted state, multi-objective operation constraints and initial weight coefficients. The weight coefficient correction module is used to adaptively correct the initial weight coefficients based on the control state characterization to obtain the updated cost function weight coefficients. The switch state execution module is used to select candidate switch states that meet the target cost conditions from a finite set of candidate switch states as target switch states, and directly send the target switch states to the grid-side converter drive circuit.

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 grid-connected multi-target control method for a wireless charging system based on an improved FCS-MPC, as described in 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 the processor, it implements the steps of the grid-connected multi-objective control method for a wireless charging system based on an improved FCS-MPC, as described in any one of claims 1 to 7.

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