Fuzzy PID Wireless Power Transfer Control Method, System, and Apparatus Based on Load Estimation

Abstract

This invention relates to the field of wireless power transfer technology, and more particularly to a fuzzy PID wireless power transfer control method, system, and apparatus based on load estimation. The method includes: measuring output voltage and output current to estimate load information of the wireless power transfer line; constructing an equivalent circuit model based on the load information to establish the relationship between the output active power of the full-bridge inverter and the load; monitoring load information in real time and identifying system interference and load changes according to a preset rate of change threshold to determine whether to execute fuzzy PID self-tuning control; when fuzzy PID self-tuning control is executed, establishing fuzzy control rules by fuzzifying the output error and error rate of change; obtaining the control quantity through fuzzy inference based on the fuzzy control rules, and generating corrected PID control parameters through a defuzzification method. This invention effectively solves the limitations of traditional PID control methods in wireless power transfer systems when facing nonlinearity, dynamic changes, and interference.

Description

Technical Field

[0001] This invention relates to the field of wireless power transfer technology, and in particular to a fuzzy PID wireless power transfer control method, system, and apparatus based on load estimation. Background Technology

[0002] Wireless power transfer systems (WPT) enable contactless power transfer, greatly improving security and flexibility. However, in complex applications such as wireless charging of electric vehicles or wireless power supply for drones, WPT systems face challenges such as load variations, environmental interference, and nonlinear characteristics.

[0003] In WPT systems, PID control is typically used to regulate output power and voltage. However, due to its linear characteristics, it often fails to provide ideal control performance when faced with time delays and dynamic changes. During wireless charging, the load fluctuates dramatically as the battery's state of charge changes. Traditional PID control methods may lead to power or voltage overshoot or oscillation, affecting charging efficiency and battery life. Existing self-tuning methods, such as the identification method, adjust parameters by inserting disturbance signals, but this can cause system instability, especially in wireless power transfer. While the rule-based method has some robustness, it cannot effectively distinguish the effects caused by disturbances and dynamic changes in the system under complex environments, often leading to unnecessary overshoot.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a fuzzy PID wireless power transfer control method, system, and device based on load estimation, which can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A fuzzy PID wireless power transfer control method based on load estimation, the method comprising:

[0008] Measure the output voltage and output current of the full-bridge inverter, establish the relationship between the output active power and the load, and estimate the load information of the wireless power transmission line;

[0009] The load information is monitored in real time, and system disturbances and load changes are identified based on a preset rate of change threshold to determine whether to execute fuzzy PID self-tuning control.

[0010] When the fuzzy PID self-tuning control is executed, the output error and error change rate are processed by fuzzification to establish fuzzy control rules;

[0011] The control quantity is obtained by fuzzy inference based on the fuzzy control rules, and the corrected PID control parameters are generated by defuzzification.

[0012] Furthermore, the output error and error rate of change are processed through fuzzification, including:

[0013] The output error and the error change rate are obtained, wherein the output error is the error between the target output and the actual output, and the error change rate is the time change rate of the error.

[0014] The output error and the rate of change of error are fuzzified to generate fuzzy sets for the output error and the rate of change of error, respectively.

[0015] Fuzzy inference is performed based on the fuzzy control rule base to map the fuzzy set into fuzzy control quantities, which are used to calculate the adjustment amount of the PID control parameters;

[0016] The results of the fuzzy inference are converted into actual control quantities through a defuzzification method, which are then used to correct the PID control parameters.

[0017] Further, the output error and the rate of change of error are fuzzified, including:

[0018] Select a suitable membership function type to perform fuzzification processing on the output error and the error change rate;

[0019] The interval of the membership function is defined based on the numerical range of the output error and the rate of change of the error;

[0020] Multiple membership functions are established for the output error and the error change rate, respectively, and the membership functions represent input values ​​with different degrees of fuzzification;

[0021] The membership values ​​of the output error and the rate of change of error are calculated based on the membership function, and a fuzzy set of the output error and the rate of change of error is generated.

[0022] Furthermore, fuzzy inference is performed based on the fuzzy control rule base, including:

[0023] Based on the fuzzy set of the output error and the error change rate, select a control rule corresponding to the current output state from the fuzzy control rule base;

[0024] The control rules are subjected to fuzzy inference, and the applicability of the control rules is determined based on the membership values ​​of the fuzzy sets.

[0025] The fuzzy control quantity is obtained by combining the effects of multiple control rules through weighted averaging.

[0026] Calculate the fuzzy control quantity, and adjust the PID control parameters based on the fuzzy control quantity.

[0027] Furthermore, the relationship between the output active power of the full-bridge inverter and the load is established, including:

[0028] An equivalent circuit model is constructed based on the load information. The equivalent circuit model includes the output voltage, output current, and load impedance of the full-bridge inverter.

[0029] The mathematical relationship between the output active power and the load is derived based on the equivalent circuit model.

[0030] Using the fundamental frequency approximation simplification method, the mathematical expressions for the output active power of the full-bridge inverter and the load are obtained based on the mathematical relationship described above;

[0031] Based on the mathematical expression, the output voltage and output current at different time points are substituted to estimate the load change in real time.

[0032] Furthermore, the mathematical expressions for output active power and load are included, including:

[0033] The output active power The relationship with the load impedance is expressed as follows:

[0034] ;

[0035] in, The output current, and These are the load resistors on the primary and secondary sides, respectively. Angular frequency, For mutual intuition, The equivalent input impedance is generally expressed as... ,in Reference load impedance;

[0036] The output active power The relationship with the load impedance can be further expressed as:

[0037] .

[0038] Further, determining whether to execute fuzzy PID self-tuning control includes:

[0039] The load information is monitored in real time, and the real-time rate of change of the load is obtained by analyzing the changes in the output voltage and the output current.

[0040] Set a change rate threshold and compare it with the load change rate to determine whether the wireless power transmission line has been interfered with.

[0041] When the load change rate exceeds the change rate threshold, it is determined that the wireless power transmission line is being interfered with, and then the fuzzy PID self-tuning control is executed.

[0042] Furthermore, a fuzzy rule base is established, including:

[0043] Based on the operating characteristics and load variation patterns of the wireless power transmission line, a set of fuzzy control rules based on theoretical analysis is designed.

[0044] The fuzzy control rules are classified into multiple categories according to control requirements.

[0045] According to the applicable scope of the fuzzy control rules, a corresponding weight value is assigned to each rule, and the weight value is optimized based on historical data;

[0046] The weighted fuzzy control rules are organized into categories to generate the fuzzy rule library.

[0047] A fuzzy PID wireless power transfer control system based on load estimation, the system comprising:

[0048] The information acquisition module measures the output voltage and output current of the full-bridge inverter, establishes the relationship between the output active power and the load, and estimates the load information of the wireless power transmission line.

[0049] The control judgment module monitors load information in real time and identifies system disturbances and load changes based on a preset rate of change threshold, and determines whether to execute fuzzy PID self-tuning control.

[0050] The fuzzy processing module, when executing fuzzy PID self-tuning control, establishes fuzzy control rules by fuzzifying the output error and error change rate.

[0051] The parameter correction module obtains the control quantity by performing fuzzy inference based on the fuzzy control rules, and generates the corrected PID control parameters through the defuzzification method.

[0052] A fuzzy PID wireless power transfer control device based on load estimation is provided, the device being used to implement the fuzzy PID wireless power transfer control method based on load estimation.

[0053] The technical solution of this invention can achieve the following technical effects:

[0054] By combining fuzzy control and PID self-tuning technology, the stability, robustness, and adaptability of the wireless power transmission system are improved, effectively coping with changing working environments and load fluctuations, and ensuring efficient and stable power transmission.

[0055] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the fuzzy PID wireless power transfer control method based on load estimation.

[0058] Figure 2 This is a schematic diagram of the equivalent circuit model;

[0059] Figure 3 The block diagram for fuzzy PID control;

[0060] Figure 4 A schematic diagram of the active power output measurement circuit;

[0061] Figure 5 Schematic diagram of a wireless power transfer (SS) topology system;

[0062] Figure 6 This is the control flowchart of a fuzzy PID wireless power transfer control system based on load estimation. Detailed Implementation

[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0065] Example 1;

[0066] like Figure 1 As shown, this application provides a fuzzy PID wireless power transfer control method based on load estimation, the method comprising:

[0067] S10: Measure the output voltage and output current of the full-bridge inverter, establish the relationship between the output active power and the load, and estimate the load information of the wireless power transmission line.

[0068] S20: Monitor load information in real time, identify system disturbances and load changes based on preset rate of change thresholds, and determine whether to execute fuzzy PID self-tuning control;

[0069] S30: When performing fuzzy PID self-tuning control, fuzzy control rules are established by processing the output error and error change rate through fuzzification.

[0070] S40: Obtain the control quantity by performing fuzzy inference based on the fuzzy control rules, and generate the corrected PID control parameters by defuzzification method.

[0071] Specifically, sensors are used to collect the output voltage and current of the full-bridge inverter in the wireless power transmission system in real time. The average active power is then obtained through an analog multiplier and low-pass filter. Based on the current operating mode, a load estimation algorithm (such as a power factor-based estimation method) is used to estimate the load variation trend and load information, including load impedance, current fluctuations, and power demand changes. Based on the estimated load information, a system is constructed... Figure 2The equivalent circuit model shown maps load changes to changes in the output voltage and current of the full-bridge inverter by calculating the relationship between the load and the transmission line. The equivalent circuit model describes the mathematical relationship between output active power and load through load characteristic modeling (such as using a circuit model with elements like parallel / series resistors, inductors, and capacitors). Based on the collected real-time load data, the system continuously monitors load information, especially voltage and current changes. According to a preset rate of change threshold, it identifies the presence of external interference (such as noise or external electromagnetic interference) or load changes (such as sudden load increases or decreases). If external interference or load changes exceed the threshold, fuzzy PID self-tuning control is triggered. After identifying interference or load changes, fuzzy PID self-tuning control is initiated. First, the output error and error rate of change are calculated, where the output error is the difference between the target output and the actual output, and the error rate of change is the rate of change of the error over time. The output error and error rate of change are then fuzzified to generate a fuzzy set, and the error and error rate of change are converted into fuzzy values ​​using a fuzzification method. For example, "large error" or "rapid error change" is transformed into different membership values ​​in a fuzzy set; based on the working characteristics and load change patterns, a set of fuzzy control rules is established. The fuzzy control rules adopt an "if-then" form, describing how to adjust the parameters of the PID controller under different error and error change rate conditions. For example, "if the error is large and the error changes rapidly, then increase the proportional gain and decrease the integral gain." Based on the above fuzzy control rules, the corresponding fuzzy control quantity is derived through a fuzzy inference process combined with the output fuzzy set. The inference process combines the output results of multiple rules through methods such as weighted average and maximum membership method to obtain the fuzzy control quantity; the fuzzy control quantity is converted into the actual PID control quantity through a defuzzification method. The defuzzification algorithm (such as the center of gravity method) is used to convert the fuzzy control quantity into specific PID parameter adjustment quantities. The adjusted PID control parameters (proportional, integral, and derivative gains) will be used to correct the PID controller settings, enabling dynamic adjustment of control parameters according to load changes and disturbances, maintaining system stability and response speed.

[0072] The technical solution of this invention, combined with fuzzy control and PID self-tuning technology, improves the stability, robustness and adaptability of wireless power transmission systems, effectively copes with changing working environments and load fluctuations, and ensures efficient and stable power transmission.

[0073] Furthermore, such as Figure 3 As shown, the output error and error change rate are processed through fuzzification, including:

[0074] Obtain the output error and the rate of change of error. The output error is the error between the target output and the actual output, and the rate of change of error is the time rate of change of error.

[0075] The output error and the rate of change of error are fuzzified to generate fuzzy sets of output error and the rate of change of error, respectively.

[0076] Fuzzy inference is performed based on the fuzzy control rule base to map fuzzy sets into fuzzy control quantities, which are used to calculate the adjustment amount of PID control parameters;

[0077] The results of fuzzy inference are converted into actual control quantities through defuzzification, which are then used to correct PID control parameters.

[0078] As a preferred embodiment of the above, the output error is the difference between the target output and the actual output, reflecting the deviation of the overall output; the error change rate is the rate of change of the error over time, representing the speed of the overall dynamic change; fuzzification processing is the process of converting precise numerical inputs into fuzzy sets. For the output error and the error change rate, fuzzification methods are used to convert them into membership values ​​in the fuzzy sets. The fuzzy sets can include fuzzy sets of output errors, such as "negative large (NB)", "negative small (NS)", "zero (Z)", "positive small (PS)", and "positive large (PB)"; and fuzzy sets of error change rates, such as "negative large change (NB)", "negative small change (NS)", "zero change (Z)", "positive small change (PS)", and "positive large change (PB)". Through membership functions, corresponding membership values ​​(between 0 and 1) are assigned to the output error and the error change rate, respectively. The system performs fuzzification (between 1 and 1); fuzzy inference is performed based on a preset fuzzy control rule base, which contains a series of "if-then" rules, such as "if the output error is positive and the error change rate is positive, then increase the PID proportional gain." Through fuzzy inference, the fuzzy set of output error and error change rate is mapped to fuzzy control quantity; the result of fuzzy inference is converted into actual control quantity through defuzzification methods, including the centroid method and the maximum membership method, which convert the fuzzy control quantity into specific PID parameter adjustment quantities; based on the defuzzified control quantity, the proportional, integral, and derivative gains of the PID controller are adjusted, thereby achieving dynamic adjustment of PID control parameters, and adaptive adjustment of PID control parameters based on real-time output error and change rate, maintaining the stability and response speed of the wireless power transmission system.

[0079] Furthermore, the output error and the rate of change of error are fuzzed, including:

[0080] Choose a suitable membership function type to fuzzify the output error and error rate of change;

[0081] Define the interval of the membership function based on the numerical range of the output error and the rate of change of error;

[0082] Multiple membership functions are established for the output error and the rate of change of error, respectively. The membership functions represent the input values ​​with different degrees of fuzzification.

[0083] The membership values ​​of the output error and the rate of change of error are calculated based on the membership function, and a fuzzy set of the output error and the rate of change of error is generated.

[0084] As a preferred embodiment of the above, a suitable membership function type is selected based on the actual application and control requirements of wireless power transmission. Membership function types include triangular membership functions, trapezoidal membership functions, and Gaussian membership functions, etc. These functions can effectively describe the fuzziness degree of output error and error rate of change. Triangular membership functions are suitable for situations where the input value changes relatively linearly and the amplitude of change is small. Trapezoidal membership functions are suitable for handling a wide range of fuzzy inputs and can smoothly transition between different fuzzy states. Gaussian membership functions are suitable for situations where the error is relatively stable, especially when the error change is relatively smooth. The interval of the membership function is defined according to the actual numerical range of the output error and error rate of change (e.g., error from negative to positive, rate of change from decreasing to increasing). For example, for output error, the interval may be defined as from "large negative error" to "large positive error," and for error rate of change, the interval may be defined as from "large negative change" to "large positive change." Each membership function interval corresponds to a specific input range, and the interval is determined according to different input ranges. The membership degrees are defined accordingly. Multiple membership functions are established for the output error and the rate of change of error, each corresponding to a different degree of fuzzification. For the output error, membership functions may include "negative large (NB)", "negative small (NS)", "zero (Z)", "positive small (PS)" and "positive large (PB)". For the rate of change of error, membership functions may include "negative large change (NB)", "negative small change (NS)", "zero change (Z)", "positive small change (PS)" and "positive large change (PB)". These membership functions represent the degree of fuzziness of different output values ​​and error rates of change, thus providing necessary input data for fuzzy inference. Based on the specific values ​​of the output error and the rate of change of error, their membership values ​​are calculated through the membership functions. The membership value represents the degree to which the input value belongs to a certain fuzzy set. Based on the calculated membership values, fuzzy sets of output error and rate of change of error are generated. Each fuzzy set contains information on the degree of fuzziness of the output. The fuzzy sets will be used in the subsequent fuzzy inference process to calculate the adjustment amount of the PID control parameters.

[0085] Furthermore, fuzzy inference based on the fuzzy control rule base includes:

[0086] Based on the fuzzy set of output error and error change rate, select control rules corresponding to the current input state from the fuzzy control rule base;

[0087] Fuzzy reasoning is performed on the control rules, and the applicability of the control rules is determined based on the membership values ​​of the fuzzy sets;

[0088] The fuzzy control quantity is obtained by combining the effects of multiple control rules through weighted averaging.

[0089] Calculate the fuzzy control quantity and adjust the PID control parameters based on the fuzzy control quantity.

[0090] As a preferred embodiment of the above embodiments, during the fuzzy inference process, a control rule matching the current state is selected based on the fuzzy sets of the output error and the rate of change of error calculated in real time. For example, if the fuzzy set of the output error is "negatively large" and the fuzzy set of the rate of change of error is "positively small change", then a relevant rule is selected from the fuzzy control rule base, such as: "If the error is negatively large and the rate of change is positively small change, then increase the PID proportional gain". Fuzzy inference is performed on the selected control rule. The fuzzy inference determines the applicability of each control rule by analyzing the membership values ​​of the output error and the rate of change of error. The applicability is determined by comparing the membership values ​​with... The decision is made by combining the conditions of the rules. If the conditions of multiple control rules are met, the applicability of all selected rules is calculated, and their effects are combined into a fuzzy control quantity using a weighted average method. The influence of each control rule is weighted by its applicability. For example, if the applicability of the "proportional gain increase" rule is 0.7, and the applicability of the "integral gain decrease" rule is 0.3, the effects of these two rules are combined by weighted averaging to obtain a comprehensive fuzzy control quantity. The fuzzy control quantity obtained by weighted averaging represents the degree to which the PID parameters need to be adjusted. This fuzzy control quantity is then converted into an actual control quantity, and the parameters of the PID controller are adjusted.

[0091] Furthermore, such as Figure 2 As shown, the relationship between the output active power of the full-bridge inverter and the load is established, including:

[0092] An equivalent circuit model is constructed based on load information. The equivalent circuit model includes the output voltage, output current and load impedance of the full-bridge inverter.

[0093] The mathematical relationship between the output active power of the full-bridge inverter and the load is derived based on the equivalent circuit model.

[0094] Using the fundamental frequency approximation simplification method, the mathematical expressions for output active power and load are obtained based on mathematical relationships;

[0095] Based on the mathematical expression, the output voltage and output current at different time points are substituted to estimate the load changes in real time.

[0096] As a preferred embodiment of the above embodiments, in the wireless power transmission system, an equivalent circuit model is constructed based on the working principle. The equivalent circuit model includes output voltage, output current, and load impedance. Analyzing the equivalent circuit model, a certain relationship exists between the output active power and the load impedance. Based on the output active power and load impedance, it can be deduced how the active power changes with the load. The process of obtaining the output active power is as follows: Figure 4 As shown; to simplify the calculation, the fundamental frequency approximation method is adopted. According to the fundamental frequency approximation method, the influence of higher harmonics and the power loss of the rectifier are ignored. The input voltage and current of the rectifier module are shown. effective value and equivalent input impedance The approximate equivalent is:

[0097] ;

[0098] According to Kirchhoff's equations, the currents on the primary and secondary sides can be obtained as follows:

[0099] ;

[0100] in The impedances measured at the first and second times are respectively. , ,in Angular frequency, For mutual intuition, , These are the load resistors on the primary and secondary sides, respectively. , For capacitors, , For inductance, for primary side impedance It can also be represented as When the circuit is in full resonance, the secondary side loss is low and can be approximately ignored. When the resonant network parameters are known, the load resistance at this moment can be estimated by sampling the output current and voltage of the inverter. By monitoring the output voltage and current in real time, the load change can be estimated in real time based on the derived mathematical relationship. The load change will cause voltage and current fluctuations. By calculating these fluctuations, the load change trend can be inferred. The control strategy can be adjusted in a timely manner according to the load change to ensure the stability and efficiency of wireless power transmission.

[0101] Furthermore, such as Figure 5 As shown, the mathematical expressions for output active power and load include:

[0102] Output active power The relationship with the load impedance is expressed as follows:

[0103] ;

[0104] in, For output current, and These are the load resistors on the primary and secondary sides, respectively. Angular frequency, For mutual intuition, The equivalent input impedance is generally expressed as... ,in Reference load impedance;

[0105] Output active power The relationship with the load impedance can be further expressed as:

[0106] .

[0107] As a preferred embodiment of the above, the relationship between the output active power of the full-bridge inverter and the load shows that the magnitude of the load impedance directly affects the output power. In wireless power transmission, the change in load resistance affects the power transmission efficiency of the entire circuit. By establishing a mathematical relationship between load and power, the power output under different load conditions can be predicted. In wireless power transmission, the load resistance on the primary side and the secondary side correspond to the load at the power supply end and the receiving end, respectively. The load resistance on the primary side represents the resistance at the transmitting end, while the load resistance on the secondary side represents the resistance at the receiving end. The change in load resistance affects the current flow and the power transmission efficiency. Angular frequency reflects the rate of change of alternating current; mutual inductance describes the degree of magnetic coupling between the transmitter and receiver; the magnitude of mutual inductance directly determines the efficiency of power transmission. Higher mutual inductance results in higher energy transmission efficiency, and vice versa. Equivalent input impedance is a parameter describing the resistance characteristics between the input and output terminals, and it is influenced by factors such as primary-side resistance, secondary-side resistance, mutual inductance, and angular frequency. Based on a comprehensive consideration of primary and secondary load resistance, angular frequency, and mutual inductance, a more precise relationship between output active power and load is further derived, thereby adjusting parameters to optimize power output and improve energy transmission efficiency.

[0108] Furthermore, such as Figure 6 As shown, determining whether to execute fuzzy PID self-tuning control includes:

[0109] Real-time monitoring of load information; by analyzing changes in output voltage and output current, the real-time rate of change of the load can be obtained.

[0110] Set a rate of change threshold and compare it with the load rate of change to determine whether the wireless power transmission line is being interfered with.

[0111] When the load change rate exceeds the change rate threshold, it is determined that the wireless power transmission line is being interfered with, and fuzzy PID self-tuning control is executed.

[0112] As a preferred embodiment of the above, load information in the wireless power transmission system is monitored in real time using sensors or measuring devices. Load information typically includes output voltage and output current. By measuring load information, dynamic information about load changes can be obtained, providing a basis for subsequent interference assessment. Based on the real-time monitored changes in output voltage and output current, the real-time rate of change of the load is calculated to understand the load fluctuation. The load rate of change refers to the rate of change of the load, reflecting the degree of change of the load per unit time. By analyzing the trends of voltage and current changes, the load changes can be estimated, and its real-time rate of change can be obtained. To determine whether interference has occurred, a rate of change threshold needs to be set. The rate of change threshold is determined based on the system's normal operating range and historical data experience. This reflects the fluctuation range of the load under normal operating conditions. When the rate of change of the load exceeds the rate of change threshold, the load change is considered abnormal, possibly due to external interference. The real-time calculated rate of change of the load is compared with the rate of change threshold. If the real-time rate of change exceeds the set threshold, it indicates that the load change is abnormal. When the rate of change of the load exceeds the set rate of change threshold, it is determined that the wireless power transmission line is interfered with. At this time, the fuzzy PID self-tuning control mechanism is activated. The fuzzy PID self-tuning control adjusts the PID parameters to adapt to load changes and external interference, restoring the stability and performance of the system. When executing the fuzzy PID self-tuning control, the proportional, integral, and derivative parameters of the PID controller are dynamically adjusted to cope with load changes and ensure the stable operation of the wireless power transmission system.

[0113] Furthermore, establishing a fuzzy rule base includes:

[0114] Based on the operating characteristics and load variation patterns of wireless power transmission lines, a set of fuzzy control rules based on theoretical analysis is designed.

[0115] The fuzzy control rules are classified into multiple categories according to control requirements.

[0116] Based on the applicable scope of the fuzzy control rules, a corresponding weight value is assigned to each rule, and the weight value is optimized based on historical data;

[0117] The weighted fuzzy control rules are organized into categories to generate a fuzzy rule library.

[0118] As a preferred embodiment of the above, based on the operating characteristics and load variation patterns of wireless power transmission, the typical performance of the system under different loads and operating conditions is analyzed and identified. For example, when the load fluctuates significantly, it may be necessary to increase the proportional gain of the PID controller; when the load changes slowly, it may be necessary to adjust the integral gain, etc. Based on theoretical analysis, a set of fuzzy control rules is designed. These rules adopt an "if-then" format, specifying how to adjust the parameters of the PID controller under specific conditions. For example, "if the error is large positive and the error change rate is small negative, then reduce the proportional gain and increase the integral gain." The designed fuzzy control rules are classified according to control requirements, based on different control objectives, such as stability control, response speed control, power regulation, etc. The rules under each category will solve specific types of problems, such as... Rules under the "Stability Control" category may focus on reducing system oscillations, while rules under the "Response Speed ​​Control" category may adjust for the smoothness of system response. An applicability analysis is performed on each fuzzy control rule to determine its effectiveness under different operating conditions. Based on the rule's applicability, a weight value is assigned to each fuzzy control rule. This weight value is optimized based on historical data, which helps determine which rules were more effective in past control processes. Based on this, an appropriate weight value is assigned to each rule, thus prioritizing more effective rules in fuzzy inference. The categorized and weighted fuzzy control rules are then organized by category to generate a final fuzzy rule library. This library contains all necessary control rules and their corresponding weight values, providing accurate decision support in practical applications.

[0119] Example 2;

[0120] Based on the same inventive concept as the fuzzy PID wireless power transfer control method based on load estimation in the foregoing embodiments, the present invention also provides a fuzzy PID wireless power transfer control system based on load estimation, the system comprising:

[0121] The information acquisition module measures the output voltage and output current of the full-bridge inverter, establishes the relationship between the output active power and the load, and estimates the load information of the wireless power transmission line.

[0122] The control judgment module monitors load information in real time and identifies system disturbances and load changes based on a preset rate of change threshold, and determines whether to execute fuzzy PID self-tuning control.

[0123] The fuzzy processing module, when executing fuzzy PID self-tuning control, establishes fuzzy control rules by fuzzifying the output error and error change rate.

[0124] The parameter correction module obtains the control quantity by performing fuzzy inference based on the fuzzy control rules, and generates the corrected PID control parameters through the defuzzification method.

[0125] The adjustment system described above in this invention can effectively implement a fuzzy PID wireless power transfer control method based on load estimation, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0126] Example 3;

[0127] Based on the same inventive concept as the fuzzy PID wireless power transfer control method based on load estimation in the foregoing embodiments, the present invention also provides a fuzzy PID wireless power transfer control device based on load estimation, for implementing the fuzzy PID wireless power transfer control method based on load estimation.

[0128] The device described above in this invention can effectively implement the fuzzy PID wireless power transfer control method based on load estimation, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0129] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A fuzzy PID wireless power transfer control method based on load estimation, characterized in that, The method comprises: Measure the output voltage and output current of the full-bridge inverter, establish the relationship between the output active power and the load, and estimate the load information of the wireless power transmission line; The load information is monitored in real time, and system disturbances and load changes are identified based on a preset rate of change threshold. It is then determined whether to execute fuzzy PID self-tuning control. The rate of change threshold is determined based on the normal operating range of the system and historical data experience, and usually reflects the fluctuation range of the load under normal operating conditions. When the fuzzy PID self-tuning control is executed, the output error and the rate of change of error are processed by fuzzification to establish fuzzy control rules. The output error is the error between the target output and the actual output. The control quantity is obtained by fuzzy inference based on the fuzzy control rules, and the corrected PID control parameters are generated by defuzzification.

2. The fuzzy PID wireless power transfer control method based on load estimation according to claim 1, characterized in that, The output error and error rate of change are processed by fuzzification, including: The output error and the error change rate are obtained, wherein the output error is the error between the target output and the actual output, and the error change rate is the time change rate of the error. The output error and the rate of change of error are fuzzified to generate fuzzy sets for the output error and the rate of change of error, respectively. Fuzzy inference is performed based on the fuzzy control rule base to map the fuzzy set into fuzzy control quantities, which are used to calculate the adjustment amount of the PID control parameters; The results of the fuzzy inference are converted into actual control quantities through a defuzzification method, which are then used to correct the PID control parameters.

3. The fuzzy PID wireless power transfer control method based on load estimation according to claim 2, characterized in that, The output error and the rate of change of error are fuzzed, including: Select a suitable membership function type to perform fuzzification processing on the output error and the error change rate; The interval of the membership function is defined based on the numerical range of the output error and the rate of change of the error; Multiple membership functions are established for the output error and the error change rate, respectively, and the membership functions represent input values ​​with different degrees of fuzzification; The membership values ​​of the output error and the rate of change of error are calculated based on the membership function, and a fuzzy set of the output error and the rate of change of error is generated.

4. The fuzzy PID wireless power transfer control method based on load estimation according to claim 2, characterized in that, Fuzzy inference is performed based on a fuzzy control rule base, including: Based on the fuzzy set of the output error and the error change rate, select a control rule corresponding to the current output state from the fuzzy control rule base; The control rules are subjected to fuzzy inference, and the applicability of the control rules is determined based on the membership values ​​of the fuzzy sets. The fuzzy control quantity is obtained by combining the effects of multiple control rules through weighted averaging. Calculate the fuzzy control quantity, and adjust the PID control parameters based on the fuzzy control quantity.

5. The fuzzy PID wireless power transfer control method based on load estimation according to claim 1, characterized in that, Establish the relationship between the output active power of the full-bridge inverter and the load, including: An equivalent circuit model is constructed based on the load information. The equivalent circuit model includes the output voltage, output current, and load impedance of the full-bridge inverter. Based on the equivalent circuit model, the mathematical relationship between the output active power of the full-bridge inverter and the load is derived. Using the fundamental frequency approximation simplification method, the mathematical expressions for output active power and load are obtained based on the aforementioned mathematical relationship; Based on the mathematical expression, the output voltage and output current at different time points are substituted to estimate the load change in real time.

6. The fuzzy PID wireless power transfer control method based on load estimation according to claim 5, characterized in that, The mathematical expressions for the output active power of a full-bridge inverter in relation to the load include: The output active power The relationship with the load impedance is expressed as follows: ； in, The output current, and These are the load resistors on the primary and secondary sides, respectively. Angular frequency, For mutual intuition, The equivalent input impedance is generally expressed as... ,in Reference load impedance; The output active power The relationship with the load impedance can be further expressed as: 。 7. The fuzzy PID wireless power transfer control method based on load estimation according to claim 1, characterized in that, Determining whether to execute fuzzy PID self-tuning control includes: The load information is monitored in real time, and the real-time rate of change of the load is obtained by analyzing the changes in the output voltage and the output current. Set a change rate threshold and compare it with the load change rate to determine whether the wireless power transmission line has been interfered with. When the load change rate exceeds the change rate threshold, it is determined that the wireless power transmission line is being interfered with, and then the fuzzy PID self-tuning control is executed.

8. The fuzzy PID wireless power transfer control method based on load estimation according to claim 1, characterized in that, Establish a fuzzy rule base, including: Based on the operating characteristics and load variation patterns of the wireless power transmission line, a set of fuzzy control rules based on theoretical analysis is designed. The fuzzy control rules are classified into multiple categories according to control requirements. According to the applicable scope of the fuzzy control rules, a corresponding weight value is assigned to each rule, and the weight value is optimized based on historical data; The weighted fuzzy control rules are organized into categories to generate the fuzzy rule library.

9. A fuzzy PID wireless power transfer control system based on load estimation, characterized in that, The system includes: The information acquisition module measures the output voltage and output current of the full-bridge inverter, establishes the relationship between the output active power and the load, and estimates the load information of the wireless power transmission line. The control and judgment module monitors load information in real time and identifies system disturbances and load changes based on a preset rate of change threshold, determining whether to execute fuzzy PID self-tuning control. The rate of change threshold is determined based on the system's normal operating range and historical data experience, and usually reflects the fluctuation range of the load under normal operating conditions. The fuzzy processing module, when executing fuzzy PID self-tuning control, establishes fuzzy control rules by fuzzifying the output error and error change rate. The output error is the error between the target output and the actual output. The parameter correction module obtains the control quantity by performing fuzzy inference based on the fuzzy control rules, and generates the corrected PID control parameters through the defuzzification method.

10. A fuzzy PID wireless power transfer control device based on load estimation, characterized in that, The apparatus is used to implement the method of any one of claims 1-8.

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