Self-adaptive control method of wireless charging system

Abstract

The invention discloses a self-adaptive control method of a wireless charging system, and belongs to the technical field of wireless power transmission. Comprising the following steps: traversing all particles in a particle swarm algorithm population during each iteration, and taking the position coordinate of each particle as a controller parameter; the controller parameters are applied to the wireless charging system, and real-time operation data output by the wireless charging system are obtained; constructing a circuit performance evaluation function according to the real-time operation data, and calculating a performance index of the wireless charging system; fuzzy processing is carried out on the performance indexes, and the membership degree of the performance indexes after fuzzy processing is calculated; fusing the membership degrees of the performance indexes after fuzzification processing to obtain a fitness value representing the performance quality degree of the controller parameters; and applying the global optimal controller parameter corresponding to the optimal fitness value to the wireless charging system to obtain an adaptive control result of the wireless charging system. According to the invention, the control parameter optimization process and the system operation state can form closed-loop coupling.

Description

Technical Field

[0001] This invention relates to the field of wireless power transmission technology, and in particular to an adaptive control method for a wireless charging system. Background Technology

[0002] Wireless power transfer technology, due to its advantages such as no physical contact, safe and reliable operation, and flexible structure, has broad application prospects in fields such as electric vehicle charging, consumer electronics power supply, and industrial automation. However, as application scenarios continue to expand and system operating environments become increasingly complex, factors such as load impedance variations, coupling coefficient shifts, and external disturbances are prevalent, causing wireless power transfer systems to exhibit significant parameter uncertainties. Under these conditions, maintaining fast, stable, and consistent dynamic response performance despite parameter variations becomes a key issue in control system design.

[0003] In wireless power transfer systems, the control algorithm directly determines the system's dynamic performance and steady-state characteristics. To balance multiple performance indicators such as settling time, overshoot, and steady-state error, a comprehensive evaluation function is typically constructed to optimize the control parameters. Since the trade-offs between performance indicators change under different operating conditions, control parameter tuning is essentially a multi-objective optimization problem, placing higher demands on the rationality and adaptability of the evaluation mechanism. However, existing technologies often employ a weighted evaluation method based on fixed weights set by human experience, linearly combining multiple performance indicators into a single objective function for optimization. This method implicitly assumes that the system operating conditions are relatively stable. When the load or coupling state changes, the original weight configuration may fail to reflect the current operating requirements, easily leading to a mismatch in evaluation criteria and causing the optimization result to deviate from the actual optimal solution.

[0004] In recent years, intelligent optimization algorithms such as particle swarm optimization have been widely applied in the field of control parameter tuning, achieving a global search of control parameters by constructing a fitness function. Although such methods have advantages in global optimization capabilities, most studies still use static evaluation functions, meaning that the performance index weights remain unchanged throughout the optimization process. When system parameters drift or dynamic characteristics change, although the optimization algorithm can search the parameter space, it cannot synchronously adjust the evaluation criteria, leading to an inconsistency between the optimization direction and the actual control objective. Furthermore, some methods improve adaptability by increasing model complexity or introducing online identification mechanisms, but these often have high computational complexity, making stable application in real-time control scenarios difficult.

[0005] In summary, existing control parameter optimization methods generally suffer from the problem of evaluation mechanisms being disconnected from system states, making it difficult to achieve dynamic trade-offs of multiple performance indicators under various operating conditions. In particular, under load changes and coupled fluctuations, the dynamic performance of the system is prone to significant degradation. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide an adaptive control method for a wireless charging system. By constructing a comprehensive evaluation mechanism that can be dynamically adjusted according to changes in system state, the optimization process of control parameters is coupled in a closed loop with the system operating state, thereby eliminating the limitation of manually fixed weights on the optimization results and improving the dynamic response capability of the wireless power transmission system under complex parameter perturbation conditions.

[0007] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0008] This invention provides an adaptive control method for a wireless charging system, comprising:

[0009] In each iteration, all particles in the particle swarm optimization (PSO) population are traversed, and the position coordinates of each particle are used as controller parameters. The fitness value, which characterizes the performance of the controller parameters, is used as the fitness value of the corresponding particle. The position of each particle in the PSO population is updated in the next iteration based on the fitness value of the particle in the current iteration.

[0010] The controller parameters are applied to the wireless charging system to obtain the real-time operating data output by the wireless charging system.

[0011] Based on the real-time operating data, a circuit performance evaluation function is constructed to calculate the performance indicators of the wireless charging system.

[0012] Based on preset excellent performance indicators and membership functions, the performance indicators are fuzzified, and the membership degree of the fuzzified performance indicators is calculated.

[0013] The membership degrees of the fuzzified performance indicators are fused to obtain fitness values ​​that characterize the performance of the controller parameters.

[0014] Until the preset maximum number of iterations or the preset fitness value is reached, the globally optimal controller parameters corresponding to the optimal fitness value are applied to the wireless charging system to obtain the adaptive control result of the wireless charging system.

[0015] Optionally, update the position of each particle in the particle swarm optimization population using the following formula:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] in, They represent the first The next iteration, the... The second iteration The optimal fitness value of each particle; Indicates the first The second iteration The fitness value of each particle; They represent the first The next iteration, the... The optimal fitness value of the population in the next iteration; Indicates the number of particles in the population; This indicates taking the maximum value within the parentheses; They represent the first The next iteration, the... The second iteration The velocity of each particle; Indicates inertia weight; These represent individual learning factors and global learning factors, respectively. Represents a random number; They represent the first The next iteration, the... The second iteration The position of each particle.

[0021] Optionally, the performance indicators of the wireless charging system include overshoot, steady-state error, settling time, and stability.

[0022] Optionally, the formula for calculating the overshoot is:

[0023] ;

[0024] in, Indicates overshoot; These represent the maximum and minimum voltage values, respectively. Indicates the final voltage value; This represents the minimum value used to eliminate division by zero. This indicates taking the maximum value within the parentheses; This represents the average voltage after a step jump; This represents the average voltage before the step jump.

[0025] Optionally, the formula for calculating the steady-state error is:

[0026] ;

[0027] in, Indicates steady-state error; Indicates the final voltage value; Indicates the voltage reference value; This represents the minimum value used to eliminate division by zero. This indicates taking the maximum value within the parentheses.

[0028] Optionally, the formula for calculating the settling time is:

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] in, These represent the lower limit and upper limit of the allowable voltage, respectively. Indicates the final voltage value; Indicates the allowable error bandwidth; They represent Is the voltage at any given time within the allowable error bandwidth? Whether the voltage at any given time is within the allowable error bandwidth; express The output voltage of the wireless charging system at all times; Indicates a stable index; Indicates the total number of moments; Indicates time 0; Indicates time N; This indicates that the steady-state time does not exist; Indicates the steady-state time; Indicates the start time of the stabilization process; Indicates the start time of the step jump; These represent taking the maximum and minimum values ​​within the parentheses, respectively.

[0034] Optionally, the formula for calculating the stability index is:

[0035] ;

[0036] ;

[0037] ;

[0038] in, Indicates steady-state deviation; express The output voltage of the wireless charging system at all times; Indicates the final voltage value; This represents the set of time points corresponding to the steady-state window; The root mean square value represents the steady-state deviation; This represents the number of sampling points within the steady-state window; Indicates stability index; This represents the minimum value used to eliminate division by zero. This indicates taking the maximum value within the parentheses.

[0039] Optionally, the membership function is expressed as:

[0040] ;

[0041] in, Indicates the current performance metrics and its excellent performance indicators The membership function under.

[0042] Optionally, the performance index is fuzzified, and the membership degree of the fuzzified performance index is calculated, including:

[0043] Set the parameters in the performance metrics that are less than 0 to 0;

[0044] When a stable time does not exist, the membership degree of the stability index is multiplied by a penalty term to obtain the membership degree of the stable time.

[0045] When a stationary time exists, the membership degree of the stationary time is represented as:

[0046] ;

[0047] The membership degree of overshoot is represented as:

[0048] ;

[0049] The membership degree of steady-state error is expressed as:

[0050] ;

[0051] The membership degree of the stability index is expressed as:

[0052] ;

[0053] in, These represent the membership degrees of settling time, overshoot, steady-state error, and stability index, respectively. Indicates the current steady-state time and its excellent performance indicators Membership degree; Indicates the current overshoot. and its excellent performance indicators Membership degree; Indicates the current steady-state error and its excellent performance indicators Membership degree; Indicates the current stability index and its excellent performance indicators The degree of membership.

[0054] Optionally, the fitness value is represented as:

[0055] ;

[0056] in, Indicates the fitness value; These represent the membership degree of settling time, overshoot, steady-state error, and stability index, respectively.

[0057] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0058] This invention incorporates the construction process of the comprehensive evaluation function into the optimization closed loop. Through a fuzzy inference mechanism, it perceives the system's operating status in real time and adaptively generates performance index weights or fusion relationships. This allows the evaluation criteria to adjust dynamically with load changes and coupling drift, avoiding the optimization direction deviation problem caused by evaluation criterion mismatch under parameter uncertainty. It fundamentally improves the consistency of control performance, eliminating reliance on repeated manual trial and error or preset weight ratios for specific systems. Instead, it normalizes each performance index through a fuzzy membership function and automatically completes performance evaluation and parameter updates during the optimization process, achieving adaptive optimization of control parameters. By tightly coupling the dynamic evaluation mechanism with the particle swarm optimization algorithm, the adjustment of evaluation criteria and the search for control parameters are synchronized, achieving true end-to-end closed-loop optimization. This ensures that under complex parameter perturbation conditions, the optimization process always proceeds in a direction that meets current operating requirements, thereby significantly improving the dynamic performance and robustness of the wireless power transmission system across all operating conditions. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the adaptive control method of the wireless charging system of the present invention.

[0060] Figure 2 This is a schematic diagram of the wireless charging system of the present invention;

[0061] Figure 3 This is a comparative diagram of the adaptive control results of the present invention;

[0062] Figure 4 This diagram illustrates a comparison of the convergence speed of the fitness values ​​of the present invention and traditional methods. Detailed Implementation

[0063] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0064] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0065] Example 1:

[0066] This embodiment introduces an adaptive control method for a wireless charging system, including:

[0067] In each iteration, all particles in the particle swarm optimization (PSO) population are traversed, and the position coordinates of each particle are used as controller parameters. The fitness value, which characterizes the performance of the controller parameters, is used as the fitness value of the corresponding particle, and the position of each particle in the PSO population is updated.

[0068] The controller parameters are applied to the wireless charging system to obtain the real-time operating data output by the wireless charging system.

[0069] Based on the real-time operating data, a circuit performance evaluation function is constructed to calculate the performance indicators of the wireless charging system.

[0070] Based on preset excellent performance indicators and membership functions, the performance indicators are fuzzified, and the membership degree of the fuzzified performance indicators is calculated.

[0071] The membership degrees of the fuzzified performance indicators are fused to obtain fitness values ​​that characterize the performance of the controller parameters.

[0072] Until the preset maximum number of iterations or the preset fitness value is reached, the globally optimal controller parameters corresponding to the optimal fitness value are applied to the wireless charging system to obtain the adaptive control result of the wireless charging system.

[0073] In this embodiment, as Figure 1 As shown, an adaptive control method for a wireless charging system includes the following steps:

[0074] Step 1: Initialize the population and controller parameters and specify the performance index of the fuzzy algorithm, specifically:

[0075] Initialize each particle in the particle swarm optimization (PSO) population by setting the initial velocity of each particle. Initial position Initial fitness value The initial fitness value of each particle It is the initial optimal fitness value of each particle. Initialize controller parameters for each particle. Initialize controller parameters That is, the initial position of each particle. The coordinates; set the maximum number of iterations. Set voltage reference value .

[0076] Assign a set of excellent performance metrics to the fuzzy algorithm, including excellent overshoot, excellent steady-state error, excellent settling time, and excellent stability metrics.

[0077] Step 2: Run the system cyclically and collect computational performance evaluation parameters, specifically as follows:

[0078] In each iteration, the controller parameters are traversed and applied to the wireless charging system to obtain real-time operating data output by the system. By running the wireless charging system, the output voltage value is collected and recorded at each moment, denoted as . ,in An array to record time points. An array to record the voltage corresponding to a given time. and The data in each step corresponds one-to-one. If it is the first iteration, the controller parameters are the initial controller parameters from step 1; otherwise, the controller parameters are the controller parameters updated after the previous iteration. The update process for the controller parameters includes:

[0079] The particle swarm optimization (PSO) updates the controller parameters, using the fitness values ​​obtained from fuzzy inference in step 3, which characterize the performance of the controller parameters, as the evaluation criteria for the PSO algorithm. Specifically, the fitness values ​​characterizing the performance of the controller parameters are used as the fitness values ​​of the corresponding particles. Based on the fitness values ​​of the particles at the current iteration, the position of each particle in the PSO population is updated for the next iteration, which means updating the controller parameters for the next iteration.

[0080] Calculate the individual historical best fitness value of each particle and the global best fitness value of the population, record the corresponding controller parameters, and then each particle is further analyzed based on the learning factor. By learning the individual's historical best fitness value and the global historical best fitness value, new controller parameters are generated, gradually approaching the optimal combination of controller parameters.

[0081] Among them, the The second iteration The optimal fitness value of each particle Represented as:

[0082] ;

[0083] in, Indicates the first The second iteration The optimal fitness value of each particle; Indicates the first The second iteration The fitness value of each particle; This indicates taking the maximum value within the parentheses.

[0084] No. The optimal fitness value of the next iteration population Represented as:

[0085] ;

[0086] in, Indicates the first The optimal fitness value of the population in the next iteration; This indicates the number of particles in the population.

[0087] Update controller parameters using spatial representation, the first... The second iteration The speed of each particle Represented as:

[0088] ;

[0089] No. The second iteration The position of each particle Represented as:

[0090] ;

[0091] in, Indicates inertia weight; These represent individual learning factors and global learning factors, respectively. This represents a random number, with a value range of [0,1]. They represent the first The next iteration, the... The second iteration The position of each particle.

[0092] Based on real-time operating data, a circuit performance evaluation function is constructed to calculate the performance indicators of the wireless charging system. The performance indicators of the wireless charging system include overshoot, steady-state error, settling time, and stability.

[0093] Overshoot The calculation formula is:

[0094] ;

[0095] in, These represent the maximum and minimum voltage values, respectively. Indicates the final voltage value; This represents the minimum value used to eliminate division by zero. This indicates taking the maximum value within the parentheses; This represents the average voltage after a step jump; This represents the average voltage before the step jump.

[0096] steady-state error The calculation formula is:

[0097] ;

[0098] in, This indicates the voltage reference value.

[0099] Stabilization time The calculation formula is:

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] in, These represent the lower limit and upper limit of the allowable voltage, respectively. Indicates the allowable error bandwidth; They represent Is the voltage at any given time within the allowable error bandwidth? Whether the voltage at any given time is within the allowable error bandwidth; express The output voltage of the wireless charging system at all times; This indicates a stable index; the earliest stable index is found. This ensures that the voltage remains within the allowable error bandwidth from that moment to the final moment. Indicates the total number of moments; Indicates time 0; Indicates time N; This indicates that the settling time does not exist and is an invalid value. Indicates the start time of the stabilization process; Indicates the start time of the step jump; This indicates taking the minimum value within the parentheses.

[0105] Stability Indicators The calculation formula is:

[0106] ;

[0107] ;

[0108] ;

[0109] in, Indicates steady-state deviation; express The output voltage of the wireless charging system at all times; This represents the set of time points corresponding to the steady-state window; The root mean square value represents the steady-state deviation; This represents the number of sampling points within the steady-state window.

[0110] Step 3: Use a fuzzy algorithm to evaluate and derive the fitness, specifically:

[0111] Based on the preset excellent performance indicators and membership functions in step 1, the performance indicators are fuzzified, and the membership degrees of the fuzzified performance indicators are calculated:

[0112] The membership function is expressed as:

[0113] ;

[0114] in, Indicates the current performance metrics and its excellent performance indicators The membership function under, It refers to the dividing line between good and bad.

[0115] when Approaching 0, Approaching 1, when , ,when Approaching 1, As the membership function approaches 0, it monotonically decreases. The definition of good or bad control.

[0116] Set the parameters in the performance metrics that are less than 0 to 0;

[0117] When a stable time does not exist, the membership degree of the stability index is multiplied by a penalty term to obtain the membership degree of the stable time.

[0118] When a stationary time exists, the membership degree of the stationary time Represented as:

[0119] ;

[0120] Membership of overshoot Represented as:

[0121] ;

[0122] Membership degree of steady-state error Represented as:

[0123] ;

[0124] Membership degree of stability index Represented as:

[0125] ;

[0126] in, Indicates the current steady-state time and its excellent performance indicators Membership degree; Indicates the current overshoot. and its excellent performance indicators Membership degree; Indicates the current steady-state error and its excellent performance indicators Membership degree; Indicates the current stability index and its excellent performance indicators The degree of membership.

[0127] The membership degrees of the fuzzy performance indicators are fused to obtain the fitness value that characterizes the performance of the controller parameters. That is, the fitness value with a range of (0,1) is obtained through the geometric mean function, and each particle in the population corresponds to a fitness value.

[0128] fitness value Represented as:

[0129] .

[0130] In each iteration, each particle obtains its corresponding fitness value. This process is repeated until each particle obtains its corresponding fitness value in each iteration. The particle with the largest fitness value is selected as the optimal fitness value from all the fitness values ​​generated in all iterations.

[0131] Step 4: Is the termination condition met? Specifically:

[0132] When the fitness value calculated in step 3 Greater than 0.8 or the number of iterations is greater than When the termination condition is met.

[0133] When the fitness value calculated in step 3 Not greater than 0.8 or the number of iterations is not greater than If the termination condition is not met, return to step 2 to update the controller parameters for particle swarm optimization and continue the performance index calculation and control parameter optimization process.

[0134] Step 5: Obtain the globally optimal controller parameters in the population and substitute them into the wireless charging system, specifically:

[0135] When the termination condition is met, the global optimal controller parameters corresponding to the optimal fitness value are applied to the wireless charging system to obtain the adaptive control result of the wireless charging system.

[0136] This embodiment addresses the problem that traditional fixed-weight evaluation strategies are prone to failure and multiple performance indicators are difficult to coordinate under uncertain conditions such as load changes and coupling coefficient drift in wireless power transmission systems. It constructs a unified comprehensive performance evaluation model to systematically quantify dynamic and steady-state indicators such as settling time, stability index, overshoot, and steady-state error. Based on this, a fuzzy inference mechanism is introduced to perceive the system's operating state in real time and adaptively generate performance indicator weights, enabling the evaluation criteria to be dynamically adjusted according to changes in operating conditions. Furthermore, by combining a particle swarm optimization algorithm, closed-loop optimization of controller parameters is performed under the guidance of the dynamic evaluation mechanism, achieving synchronous matching between control parameters and system state.

[0137] This embodiment tightly couples the adaptive construction of evaluation criteria with the control parameter optimization process, ensuring that the optimization direction always aligns with the current system operating requirements. This fundamentally overcomes the performance degradation problem of traditional manual fixed-weight methods under parameter variations. This technical solution guarantees that the wireless power transmission system maintains a consistent control strategy and optimization logic under complex parameter perturbations and multi-condition operation, significantly improving the system's dynamic response performance and overall robustness. It possesses strong engineering promotion value and application prospects.

[0138] Example 2:

[0139] Based on Example 1, this example introduces an experimental example of an adaptive control method for a wireless charging system:

[0140] like Figure 2As shown, Vin is the input voltage of the wireless charging system; Vout is the output voltage of the wireless charging system; Q1 is a power switch driven by a pulse width modulation (PWM) signal; D1 is a freewheeling diode that provides a current loop for the inductor / load when the power switch Q1 is turned off; C is the output filter capacitor; RL is the resistance of the load; the PI controller is a proportional-integral controller that calculates the evaluation index by acquiring the output voltage of the wireless charging system, processes the evaluation index using a fuzzy algorithm to obtain the evaluation fitness, and processes the evaluation fitness using the Particle Swarm Optimization (PSO) algorithm in this embodiment to obtain PI parameters, which are then input into the PI controller.

[0141] Step 1: Initialization. Set the number of particles in the population to 20, the maximum number of iterations to 750, and the voltage reference value. The optimal performance metrics for the fuzzy algorithm are set to 750V and the following values:

[0142] ;

[0143] in, These represent excellent overshoot, excellent steady-state error, excellent settling time, and excellent stability index, respectively.

[0144] Step 2: Set the output filter capacitor C of the wireless charging system to 1200e-6F and the load resistance RL to 18.75 Ohms.

[0145] Step 3: Run the fuzzy particle swarm optimization algorithm, iterate to obtain the final result, input it into the wireless charging system, and record the waveform curve.

[0146] Step 4: Set the output filter capacitor C of the wireless charging system to 2400e-6F and the load resistance RL to 25 Ohms.

[0147] Step 5: Run the fuzzy particle swarm optimization algorithm, iterate to obtain the final result, input it into the wireless charging system, and record the waveform curve.

[0148] Compare the waveforms from steps 3 and 5, as follows: Figure 3 As shown, due to system differences, the capacitor in step 3 is smaller, resulting in a faster voltage rise; while the capacitor in step 5 is larger, resulting in a slower voltage rise. Even with changes in system parameters, excellent results can still be obtained without modifying the indicators.

[0149] like Figure 4As shown, the fitness convergence speed of step 3 in this embodiment is compared with that of the existing traditional method after 750 iterations. This embodiment uses a fuzzy PSO algorithm, while the existing method uses a traditional PSO algorithm. Fitness is calculated using the fuzzy PSO method, specifically by evaluating the results of the traditional PSO algorithm using a fuzzy algorithm. It can be seen that at each time point, the fuzzy PSO algorithm in this embodiment is significantly superior to the existing traditional PSO algorithm in terms of either time or fitness. Comparing the time required to simultaneously reach 85% fitness, the fuzzy PSO algorithm in this embodiment reduces the time by 24.87% compared to the existing traditional PSO algorithm. Furthermore, the maximum fitness of the fuzzy PSO algorithm in this embodiment is 1.66% greater than that of the existing traditional PSO algorithm.

[0150] In this embodiment, the construction process of the comprehensive evaluation function is first incorporated into the optimization closed loop. A fuzzy inference mechanism is used to perceive the system's operating status in real time, adaptively generating performance index weights or fusion relationships. This allows the evaluation criteria to adjust dynamically with load changes and coupling drift. Compared to traditional methods that use fixed weights based on manual experience, this invention avoids the optimization direction shift caused by evaluation criterion mismatch under uncertain parameter conditions, fundamentally improving the consistency of control performance.

[0151] Then, instead of relying on repeated manual trial and error or pre-setting weight ratios for specific systems, the method normalizes each performance index through a fuzzy membership function and automatically completes performance evaluation and parameter updates during the optimization process, achieving adaptive optimization of control parameters. This mechanism significantly reduces the degree of manual intervention, making the method applicable to wireless power transmission systems with different structures and operating conditions, thus improving engineering applicability and robustness.

[0152] Secondly, multiple performance indicators, such as settling time, stability index, overshoot, and steady-state error, are introduced simultaneously to comprehensively measure the control effect from multiple dimensions, including dynamic response speed, steady-state accuracy, and system stability characteristics. Through a unified fuzzy evaluation and fusion mechanism, the performance bias problem caused by optimizing a single indicator is avoided, making the control parameter optimization results more comprehensive, balanced, and reliable.

[0153] Then, in the early stages of optimization, the system may fail to reach a steady state due to parameter non-convergence, resulting in the loss of some performance indicators (such as settling time). This invention introduces a backoff process based on a stability index into the evaluation mechanism, which can still distinguish the merits of different combinations of control parameters even when key indicators are missing, avoiding interference from invalid solutions to the optimization process, and improving the search efficiency and convergence stability of the particle swarm optimization algorithm in the early stages.

[0154] Next, in the fitness construction process, the geometric mean of membership degree is used to fuse multiple indicators, so that when a certain indicator deteriorates significantly, the overall fitness is effectively suppressed, thereby preventing solutions with severely unbalanced performance from entering subsequent iterations. At the same time, for parameter combinations with good balanced performance of each indicator, their fitness values ​​are more easily amplified, which is conducive to the optimization algorithm quickly focusing on the solution space region with good overall performance, improving optimization efficiency and final control effect.

[0155] Finally, the dynamic evaluation mechanism is tightly coupled with the particle swarm optimization algorithm, enabling the adjustment of evaluation criteria and the search for control parameters to proceed synchronously, achieving true end-to-end closed-loop optimization. This structure ensures that under complex parameter perturbation conditions, the optimization process always proceeds in a direction that meets current operational requirements, thereby significantly improving the dynamic performance and robustness of the wireless power transfer system across the entire operating range.

[0156] The specific functions of each module described above are explained in the relevant content of Embodiment 1 or 2, and will not be repeated here.

[0157] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0158] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0161] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. An adaptive control method for a wireless charging system, characterized in that, include: In each iteration, all particles in the particle swarm optimization population are traversed, and the position coordinates of each particle are used as controller parameters. The fitness value, which characterizes the performance of the controller parameters, is used as the fitness value of the corresponding particle. The position of each particle in the particle swarm algorithm population is updated in the next iteration based on the fitness value of the particle at the current iteration. The controller parameters are applied to the wireless charging system to obtain the real-time operating data output by the wireless charging system. Based on the real-time operating data, a circuit performance evaluation function is constructed to calculate the performance indicators of the wireless charging system. Based on preset excellent performance indicators and membership functions, the performance indicators are fuzzified, and the membership degree of the fuzzified performance indicators is calculated. The membership degrees of the fuzzified performance indicators are fused to obtain fitness values ​​that characterize the performance of the controller parameters. Until the preset maximum number of iterations or the preset fitness value is reached, the globally optimal controller parameters corresponding to the optimal fitness value are applied to the wireless charging system to obtain the adaptive control result of the wireless charging system.

2. The adaptive control method for the wireless charging system according to claim 1, characterized in that, The formula for updating the position of each particle in the particle swarm optimization (PSO) population is: ； ； ； ； in, They represent the first The next iteration, the... The second iteration The optimal fitness value of each particle; Indicates the first The second iteration The fitness value of each particle; They represent the first The next iteration, the... The optimal fitness value of the population in the next iteration; Indicates the number of particles in the population; This indicates taking the maximum value within the parentheses; They represent the first The next iteration, the... The second iteration The velocity of each particle; Indicates inertia weight; These represent individual learning factors and global learning factors, respectively. Represents a random number; They represent the first The next iteration, the... The second iteration The position of each particle.

3. The adaptive control method for the wireless charging system according to claim 1, characterized in that, The performance indicators of the wireless charging system include overshoot, steady-state error, settling time, and stability.

4. The adaptive control method for the wireless charging system according to claim 3, characterized in that, The formula for calculating the overshoot is: ； in, Indicates overshoot; These represent the maximum voltage value and the minimum voltage value, respectively. Indicates the final voltage value; This represents the minimum value used to eliminate division by zero. This indicates taking the maximum value within the parentheses; This represents the average voltage after a step jump; This represents the average voltage before the step jump.

5. The adaptive control method for the wireless charging system according to claim 3, characterized in that, The formula for calculating the steady-state error is: ； in, Indicates steady-state error; Indicates the final voltage value; Indicates the voltage reference value; This represents the minimum value used to eliminate division by zero. This indicates taking the maximum value within the parentheses.

6. The adaptive control method for the wireless charging system according to claim 3, characterized in that, The formula for calculating the settling time is: ； ； ； ； in, These represent the lower limit and upper limit of the allowable voltage, respectively. Indicates the final voltage value; Indicates the allowable error bandwidth; They represent Is the voltage at any given time within the allowable error bandwidth? Whether the voltage at any given time is within the allowable error bandwidth; express The output voltage of the wireless charging system at all times; Indicates a stable index; Indicates the total number of moments; Indicates time 0; Indicates time N; This indicates that the steady-state time does not exist; Indicates the steady-state time; Indicates the start time of the stabilization process; Indicates the start time of the step jump; These represent taking the maximum and minimum values ​​within the parentheses, respectively.

7. The adaptive control method for the wireless charging system according to claim 3, characterized in that, The formula for calculating the stability index is: ； ； ； in, Indicates steady-state deviation; express The output voltage of the wireless charging system at all times; Indicates the final voltage value; This represents the set of time points corresponding to the steady-state window; The root mean square value represents the steady-state deviation; This represents the number of sampling points within the steady-state window; Indicates stability index; This represents the minimum value used to eliminate division by zero. This indicates taking the maximum value within the parentheses.

8. The adaptive control method for the wireless charging system according to claim 1, characterized in that, The membership function is expressed as: ； in, Indicates the current performance metrics and its boundary value The membership function under.

9. The adaptive control method for the wireless charging system according to claim 3, characterized in that, The performance indicators are fuzzified, and the membership degrees of the fuzzified performance indicators are calculated, including: Set the parameters in the performance metrics that are less than 0 to 0; When a stable time does not exist, the membership degree of the stability index is multiplied by a penalty term to obtain the membership degree of the stable time. When a stationary time exists, the membership degree of the stationary time is represented as: ； The membership degree of overshoot is represented as: ； The membership degree of steady-state error is expressed as: ； The membership degree of the stability index is expressed as: ； in, These represent the membership degrees of settling time, overshoot, steady-state error, and stability index, respectively. Indicates the current steady-state time and its excellent performance indicators Membership degree; Indicates the current overshoot. and its boundary value Membership degree; Indicates the current steady-state error and its excellent performance indicators Membership degree; Indicates the current stability index and its excellent performance indicators The degree of membership.

10. The adaptive control method for the wireless charging system according to claim 1, characterized in that, The fitness value is expressed as: ； in, Indicates the fitness value; These represent the membership degree of settling time, overshoot, steady-state error, and stability index, respectively.

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