A Butterfly-Optimized Method for Stabilizing Second-Side Voltage in Wireless Power Transfer

Abstract

A butterfly-based optimization-based method for stabilizing the secondary voltage of wireless power transfer comprises the following steps: initializing the butterfly population and its positions; calculating the fitness value of each butterfly; updating the butterfly positions based on the fitness values, and searching for optimal control parameters by updating the butterfly positions; iteratively solving the problem, continuously updating the positions, and outputting the optimal solution when the termination condition is met; using an optimized PID controller to control the secondary voltage, which can reduce the dynamic response time of the system and enable the secondary voltage to reach a stable state more quickly; it can reduce system overshoot, making the control of the secondary voltage smoother and reducing voltage fluctuations; the device used in the wireless power transfer system includes a processor and a memory connected to the processor; it has fast response speed, smaller steady-state error and higher system stability, and has broad application prospects; it can maintain the stable output of the transmission system under different load changes, improving the performance and efficiency of the wireless power transfer system.

Description

Technical Field

[0001] This invention relates to the field of wireless power transfer technology, and particularly to a butterfly-optimized method for stabilizing and controlling the secondary voltage of wireless power transfer. Background Technology

[0002] Wireless power transfer technology is a technique that transmits electrical energy from a transmitter to a receiver using electromagnetic fields. It offers advantages such as being contactless and highly flexible, and is widely used in wireless charging of electric vehicles and wireless power supply for home appliances. However, in wireless power transfer systems, the stability of the secondary-side voltage is one of the key factors affecting system performance. Traditional PID controllers suffer from difficulties in parameter adjustment, slow response speed, and low control accuracy when regulating the secondary-side voltage, making it difficult to meet the requirements of high-performance wireless power transfer systems. In recent years, the application of intelligent optimization algorithms in control systems has gradually increased, such as genetic algorithms, particle swarm optimization algorithms, and ant colony optimization algorithms. These algorithms, by simulating the behavior of organisms in nature, can find the global optimum in a complex solution space, exhibiting good optimization performance. The Butterfly Optimization Algorithm (BOA) is an emerging intelligent optimization algorithm that, by simulating the foraging behavior of butterflies in nature, can perform global and local searches in the solution space, offering advantages such as fast convergence speed and high optimization accuracy.

[0003] In most PID control and PID control optimized by intelligent optimization algorithms, such as conventional PID controllers, the response speed is generally slow, especially for complex or nonlinear systems, with response times ranging from 0.5 seconds to several seconds. Control accuracy is relatively low, especially under conditions of significant system parameter variations or disturbances, where deviations may remain in the range of 5-10%. Its robustness is poor, easily affected by system changes and external disturbances, particularly performing poorly in nonlinear systems or under conditions of large load variations. PID optimized by genetic algorithms (GA) offers some improvement over conventional PID. The optimized PID controller in complex systems may have a response time of 0.2-2 seconds, with higher control accuracy and errors reduced to 2-5%, but it still depends on the initial population settings and evolutionary strategy. Genetic algorithms provide strong global optimization capabilities and improved robustness, but are prone to getting trapped in local optima in large-scale or high-dimensional problems. PID optimized by particle swarm optimization (PSO) has a relatively fast response speed, typically between 0.1-1 seconds, and is suitable for various dynamic systems. Control accuracy can be further improved, with errors potentially reduced to 1-3%. PSO (Programmable Logic Optimizer) exhibits strong global convergence and good robustness, but its performance may degrade in high-noise environments, requiring adjustments to inertia weights and acceleration constants to adapt to changes. PID control optimized by Artificial Bee Colony (ABC) typically has a response time between 0.2 and 1 second, similar to PSO, but may be slower when handling complex constraint problems. Its control accuracy is comparable to PSO, with errors between 1% and 3%. It possesses strong robustness and adapts well to changes in system parameters, but its efficiency is lower than PSO and GA when solving multimodal optimization problems. Fuzzy logic control (FLC) has a response time that depends on the complexity of the rule base and the degree of system nonlinearity, typically ranging from 0.1 to 1 second. Because fuzzy logic is rule-based and experience-based, its control accuracy is usually low, with errors reaching 5% to 10%, especially under boundary conditions. It demonstrates strong robustness in handling uncertainties and nonlinear problems, but may fail in overly complex systems. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the present invention aims to provide a wireless power transmission method based on the butterfly optimization algorithm, which is intended to solve the technical problems of low efficiency and stability in wireless power transmission systems.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The butterfly-optimized wireless power transfer secondary voltage stabilization control method includes the following steps:

[0007] Step 1: Initialize the butterfly population to 50. The position of each butterfly represents a combination of PID controller parameters, including a proportional coefficient (P) of 0.1-2.0, an integral coefficient (I) of 0.01-1.0, and a derivative coefficient (D) of 0.001-0.1.

[0008] Step 2: Calculate the fitness value. The fitness function is used to evaluate the quality of each butterfly's position. Based on the secondary voltage stability index of the wireless power transmission system, calculate the fitness value of each butterfly. The fitness value reflects the effect of the PID parameter combination corresponding to the butterfly in controlling the secondary voltage stability. The fitness function is the sum of squares of voltage fluctuations.

[0009] Step 3: Based on the fitness value, update the position of each butterfly using the update rules of the butterfly optimization algorithm; the update rules include local search and global search. Local search adjusts the parameters within a 10% range of the current butterfly position, while global search generates new parameter combinations randomly.

[0010] Step 4, optimize PID parameter combination

[0011] The position is continuously updated iteratively by calculating the fitness value. The position of the butterfly population is optimized through multiple iterations. After each iteration, it is determined whether the termination condition is met. The termination condition is that the preset number of iterations of 100 is reached, or the change in fitness value is less than the preset threshold of 0.001. If the termination condition is met, the optimal combination of PID parameters is output; otherwise, the iteration continues.

[0012] Step 5: Apply the optimized PID parameters to the PID controller of the wireless power transmission system to control the secondary voltage in real time. By adjusting the output of the PID controller, ensure that the secondary voltage is stable near the target value. Monitor the changes in the secondary voltage in the wireless power transmission system in real time and record indicators such as voltage fluctuation and response time. Based on the monitored changes in the secondary voltage, adjust the parameters of the PID controller using a feedback mechanism. By continuously optimizing the PID parameters, further improve the stability and control accuracy of the secondary voltage.

[0013] The initial butterfly population described in step 1 has sufficient diversity, which is ensured by randomly generating individuals. Initializing the butterfly population using chaotic mappings (such as Tent chaos or Circle chaos) increases the diversity of initial individuals, thereby improving the convergence speed and accuracy of the algorithm.

[0014] The PID controller parameter combination described in step 1 includes the proportional coefficient (P), integral coefficient (I), and derivative coefficient (D). The output formula of the PID controller is:

[0015]

[0016] In the formula, u(t) is the controller output; e(t) is the error, i.e., the difference between the set value and the actual value; K p It is the proportional coefficient, which determines the degree of response of the controller output to the current error; K i It is the integral coefficient, which determines the degree of response of the controller output to error accumulation; K d These are the differential coefficients, which determine the degree to which the controller output responds to the rate of change of the error.

[0017] The function of proportional control is to adjust the controller output according to the magnitude of the current error; the larger the error, the larger the controller output adjustment, thereby reducing the error. The function of integral control is to eliminate steady-state error, that is, when the system reaches steady state but the error still exists. By accumulating the error, integral control can compensate for historical errors. The function of derivative control is to predict the error trend and adjust the controller output accordingly, thus reducing or mitigating the system's over-response and oscillations. By optimizing the adjusted proportional (P), integral (I), and derivative (D) coefficients, precise control of the secondary voltage can be achieved.

[0018] The wireless power transmission system described in step 2 includes a transmitter and a receiver. The PID controller is used to control the secondary voltage of the receiver.

[0019] The transmitter transfers electrical energy to the receiver via an electromagnetic field; the power of the transmitter is P. t The power at the receiving end is P. r If the transmission efficiency is η, then:

[0020] P r =ηP t

[0021] The transmission efficiency between the transmitter and receiver is a key factor affecting system performance. Improving transmission efficiency can reduce energy loss and increase the power output of the receiver.

[0022] The electromagnetic field strength at the transmitting end is E, and the electromagnetic field strength at the receiving end is E. r If the distance between the transmitter and receiver is d, then:

[0023]

[0024] Electromagnetic field strength decreases with increasing distance. Therefore, when designing a wireless power transmission system, the distance between the transmitter and receiver must be considered to ensure that the receiver can receive sufficient electrical energy.

[0025] The fitness value mentioned in step 2 is:

[0026] The objective function is defined as the weighted sum of the system's steady-state error, overshoot, and settling-time performance indicators, with the sum of squared voltage fluctuations used as the fitness function.

[0027]

[0028] J is the fitness value; V ref It is the target voltage value; V i It is the actual voltage value at time i; n is the number of sampling points;

[0029] For each individual, i.e., each set of PID parameters, its fitness value is calculated according to the fitness function. Let the PID parameters of a certain individual be (K). p ,K i ,K d If the fitness value is 0, then its fitness value is calculated as follows:

[0030]

[0031] Among them (V) i (K p ,K i ,K d )) is in the PID parameter (K p ,K i ,K d The actual voltage value at the i-th time step;

[0032] To facilitate comparison and selection, the fitness values ​​of all individuals are normalized to fall within a fixed range. The normalization formula is as follows:

[0033]

[0034] J norm J is the normalized fitness value; J is the original fitness value; J min It is the smallest fitness value among all individuals; J max It is the highest fitness value among all individuals.

[0035] All individuals are sorted according to their fitness values, and individuals with higher fitness values ​​are selected for the next step. The higher the fitness value, the better the control effect of the PID parameters.

[0036] Step 3, updating the position of each butterfly, specifically includes:

[0037] Update the butterfly's position using the fragrance update formula:

[0038] X i (t+1)=X i (t)+r·I i (t)·exp(-a·Ii (t))

[0039] Among them, X i (t) represents the position of the i-th butterfly at time t; X i (t+1) represents the position of the i-th butterfly in generation t+1, i.e., the updated position; I i (t) represents the scent intensity of the i-th butterfly; r and a are control parameters, where r controls the step size and update amplitude, and a is used to adjust the effect of scent intensity on position update, which can adjust the decay rate of scent intensity, exp(-a·I i (t) is the exponential decay factor of aroma intensity, which increases with aroma intensity I. i As (t) increases, this factor decreases, thereby reducing the impact of aroma intensity on position updates.

[0040] Local search is performed based on the butterfly's current position and velocity, allowing it to search in its vicinity. This increases the algorithm's local search capability and helps find local optima. The position of the i-th butterfly at time t is X. i (t), velocity v i (t) represents the velocity of the i-th butterfly at time t. The position update formula at time (t+1) is:

[0041] X i (t+1)=X i (t)+v i (t)

[0042] The global search is based on the global optimum and the current butterfly position. By adjusting the butterfly's position to move closer to the global optimum, the algorithm's global search capability is enhanced, which helps in finding the global optimum solution. The global optimum position is X. g If (t), then the position update formula at time (t+1) is:

[0043] X i (t+1)=X i (t)+c·(X g (t)-X i (t))X g (t) represents the global optimal position; c is the adjustment coefficient.

[0044] The speed update is based on the butterfly's fitness value and position. The speed update formula combines the effects of inertia weight, individual optimal position, and global optimal position, allowing the butterfly to maintain a certain inertia during the search process while adjusting according to individual and global optimal information. This improves the convergence speed and accuracy of the algorithm. The individual optimal position of the i-th butterfly at time t is p. iIf (t), then the velocity update formula at time (t+1) is:

[0045] v i (t+1)=w·v i (t)+c1·r1·(p i (t)-X i (t))+c2·r2·(X g (t)-X i (t))

[0046] In the formula, w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) represents the optimal position of the i-th butterfly at time t.

[0047] The method is applicable to various wireless power transmission systems, including but not limited to wireless charging systems for electric vehicles and wireless power supply systems for home appliances.

[0048] The wireless power transmission system includes:

[0049] A transmitting module configured to wirelessly transmit electrical energy based on optimized parameters obtained from a butterfly optimization algorithm;

[0050] A receiving module configured to receive transmitted electrical energy and convert it into usable electrical energy;

[0051] A control module configured to apply the butterfly optimization algorithm to optimize power transmission efficiency includes:

[0052] Initialize a butterfly population with random positions and velocities;

[0053] The fitness of each butterfly was assessed based on its power transmission efficiency;

[0054] Update the butterfly's position and speed based on the known best position and the global best position;

[0055] Iterate the process until the convergence criterion is met.

[0056] An apparatus for a wireless power transfer system includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor; the instructions enable the at least one processor to perform steps of a wireless power transfer method based on a butterfly optimization algorithm.

[0057] The memory stores a computer program; when the computer program is executed by the processor, it causes the processor to perform the steps of the wireless power transfer method based on the butterfly optimization algorithm.

[0058] Input and output devices are connected to the processor; the processor is also connected to the memory; the power management module supplies power to the memory, processor, input devices, and output devices.

[0059] A method for wireless power transfer using a wireless power transfer system includes the following steps:

[0060] Obtain the initial parameters of the wireless power transmission system, including the distance between the transmitter and receiver, power level, and operating frequency;

[0061] The butterfly optimization algorithm is applied to optimize power transmission efficiency, wherein the algorithm includes: initializing a butterfly population with random positions and velocities;

[0062] The fitness of each butterfly was assessed based on its power transmission efficiency;

[0063] Update the butterfly's position and speed based on the known best position and the global best position;

[0064] The position is continuously updated iteratively by calculating the fitness value. The position of the butterfly population is optimized through multiple iterations. After each iteration, it is determined whether the termination condition is met. The termination condition is that the preset number of iterations of 100 is reached, or the change in fitness value is less than the preset threshold of 0.001. If the termination condition is met, the optimal combination of PID parameters is output. Otherwise, the iteration continues until the convergence criterion is met.

[0065] The system parameters are adjusted based on the optimized PID parameter combination to achieve maximum power transmission efficiency.

[0066] A system for a PID control method for stabilizing the secondary-side voltage of wireless power transfer based on the butterfly optimization algorithm includes a secondary-side voltage acquisition module 210, an error calculation module 220, a PID control module 230, and a wireless power transfer system 250. The output terminal of the secondary-side voltage acquisition module 210 is connected to the input terminal of the error calculation module 220; the output terminal of the error calculation module 220 is connected to the input terminal of the PID control module 230; the output terminal of the PID control module 230 is connected to the input terminal of the wireless power transfer system 250; and the control terminal of the PID control module 230 is connected to the output terminal of the butterfly optimization algorithm module.

[0067] The beneficial effects of this invention are:

[0068] This invention employs a Butterfly Optimization Algorithm (BOA) to optimize the PID control. The optimized response time is typically 0.1-0.5 seconds, similar to PSO, making it suitable for a wider range of system types. After global optimization, BOA achieves higher control accuracy, with errors controlled to within 1-2%, significantly higher than conventional PID and fuzzy logic control. Because BOA effectively balances global exploration and local exploitation, it exhibits strong robustness, adapts to various complex and nonlinear system variations, and better avoids local optima problems. Therefore, the PID control system optimized using the Butterfly Optimization Algorithm (BOA) offers advantages such as fast response speed, high control accuracy, and strong robustness.

[0069] In wireless power transfer systems, introducing the butterfly optimization algorithm to optimize the parameters of the PID controller can effectively improve the stability of the secondary voltage. Specifically, the butterfly optimization algorithm finds the optimal combination of PID parameters step by step through steps such as initializing the population, calculating fitness values, and updating position and velocity. The optimized PID controller can improve the response speed and wireless power transfer efficiency of the transmission system, reduce system overshoot and oscillation, and maintain the stability of the secondary voltage under different operating conditions. This invention proposes a PID control method for stabilizing the secondary voltage of wireless power transfer based on the butterfly optimization algorithm. By introducing the butterfly optimization algorithm to optimize the parameters of the PID controller, stable control of the secondary voltage is achieved. This method is not only applicable to wireless power transfer applications such as wireless charging systems for electric vehicles and wireless power supply systems for home appliances, but can also be extended to other power systems requiring precise control, showing broad application prospects.

[0070] The wireless power transmission method and system of this invention utilize the butterfly optimization algorithm to optimize power transmission efficiency, effectively improving the stability and efficiency of the wireless power transmission system. By obtaining initial parameters and iteratively optimizing, high power transmission efficiency is ensured, making it applicable to various wireless power transmission devices or scenarios.

[0071] The method of this invention optimizes the proportional, integral, and derivative parameters of the PID controller by introducing the Butterfly Optimization Algorithm (BOA), thereby achieving stable control of the secondary voltage in a wireless power transmission system. Attached Figure Description

[0072] Figure 1 This is a flowchart of a butterfly-optimized wireless power transfer secondary voltage stabilization control method provided in an embodiment of the present invention.

[0073] Figure 2 This is a structural block diagram of a butterfly-optimized wireless power transfer secondary voltage stabilization control system according to an embodiment of the present invention.

[0074] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] The butterfly-optimized wireless power transfer secondary voltage stabilization control method includes the following steps:

[0077] Step 1: Initialize the butterfly population to 50. The position of each butterfly represents a combination of PID controller parameters, including a proportional coefficient (P) of 0.1-2.0, an integral coefficient (I) of 0.01-1.0, and a derivative coefficient (D) of 0.001-0.1.

[0078] Step 2: Calculate the fitness value. The fitness function is used to evaluate the quality of each butterfly's position. Based on the secondary voltage stability index of the wireless power transmission system, calculate the fitness value of each butterfly. The fitness value reflects the effect of the PID parameter combination corresponding to the butterfly in controlling the secondary voltage stability. The fitness function is the sum of squares of voltage fluctuations.

[0079] Step 3: Based on the fitness value, update the position of each butterfly using the update rules of the butterfly optimization algorithm; the update rules include local search and global search. Local search adjusts the parameters within a 10% range of the current butterfly position, while global search generates new parameter combinations randomly.

[0080] Step 4, optimize PID parameter combination

[0081] The position is continuously updated iteratively by calculating the fitness value. The position of the butterfly population is optimized through multiple iterations. After each iteration, it is determined whether the termination condition is met. The termination condition is that the preset number of iterations of 100 is reached, or the change in fitness value is less than the preset threshold of 0.001. If the termination condition is met, the optimal combination of PID parameters is output; otherwise, the iteration continues.

[0082] Step 5: Apply the optimized PID parameters to the PID controller of the wireless power transmission system to control the secondary voltage in real time. By adjusting the output of the PID controller, ensure that the secondary voltage is stable near the target value. Monitor the changes in the secondary voltage in the wireless power transmission system in real time and record indicators such as voltage fluctuation and response time. Based on the monitored changes in the secondary voltage, adjust the parameters of the PID controller using a feedback mechanism. By continuously optimizing the PID parameters, further improve the stability and control accuracy of the secondary voltage.

[0083] The initial butterfly population described in step 1 has sufficient diversity, which is ensured by randomly generating individuals; the butterfly population is initialized using chaotic mapping (such as Tent chaos, Circle chaos) to increase the diversity of the initial individuals and improve the convergence speed and accuracy of the algorithm.

[0084] The PID controller parameter combination described in step 1 includes the proportional coefficient (P), integral coefficient (I), and derivative coefficient (D). The output formula of the PID controller is:

[0085]

[0086] In the formula, u(t) is the controller output; e(t) is the error, i.e., the difference between the set value and the actual value; K p It is the proportional coefficient, which determines the degree of response of the controller output to the current error; K i It is the integral coefficient, which determines the degree of response of the controller output to error accumulation; K d These are the differential coefficients, which determine the degree to which the controller output responds to the rate of change of error.

[0087] The function of proportional control is to adjust the controller output based on the magnitude of the current error. The larger the error, the larger the controller output adjustment, thereby reducing the error. The function of integral control is to eliminate steady-state error, that is, the situation where the error still exists when the system reaches steady state. By accumulating the error, integral control can compensate for historical errors. The function of derivative control is to predict the trend of the error and adjust the controller output accordingly. This can reduce or mitigate the system's over-response and oscillations. By optimizing the adjusted proportional (P), integral (I), and derivative (D) coefficients, precise control of the secondary voltage can be achieved.

[0088] The wireless power transmission system described in step 2 includes a transmitter and a receiver. The PID controller is used to control the secondary voltage of the receiver.

[0089] The transmitter transfers electrical energy to the receiver via an electromagnetic field; the power of the transmitter is P. t The power at the receiving end is P. r If the transmission efficiency is η, then:

[0090] P r =ηP t

[0091] The transmission efficiency between the transmitter and receiver is a key factor affecting system performance. Improving transmission efficiency can reduce energy loss and increase the power output of the receiver.

[0092] The electromagnetic field strength at the transmitting end is E, and the electromagnetic field strength at the receiving end is E. r If the distance between the transmitter and receiver is d, then:

[0093]

[0094] Electromagnetic field strength decreases with increasing distance. Therefore, when designing a wireless power transmission system, the distance between the transmitter and receiver must be considered to ensure that the receiver can receive sufficient electrical energy.

[0095] The fitness value mentioned in step 2 is:

[0096] The objective function is defined as the weighted sum of the system's steady-state error, overshoot, and settling-time performance indicators, with the sum of squared voltage fluctuations used as the fitness function.

[0097]

[0098] J is the fitness value; V ref It is the target voltage value; V i It is the actual voltage value at time i; n is the number of sampling points;

[0099] For each individual, i.e., each set of PID parameters, its fitness value is calculated according to the fitness function. Let the PID parameters of a certain individual be (K). p ,K i ,K d If the fitness value is 0, then its fitness value is calculated as follows:

[0100]

[0101] Among them (V) i (K p ,K i ,K d )) is in the PID parameter (K p ,K i ,K d The actual voltage value at the i-th time step;

[0102] To facilitate comparison and selection, the fitness values ​​of all individuals are normalized to fall within a fixed range. The normalization formula is as follows:

[0103]

[0104] J norm J is the normalized fitness value; J is the original fitness value; J min It is the smallest fitness value among all individuals; J max It is the highest fitness value among all individuals.

[0105] All individuals are sorted according to their fitness values, and individuals with higher fitness values ​​are selected for the next step. The higher the fitness value, the better the control effect of the PID parameters.

[0106] Step 3, updating the position of each butterfly, specifically includes:

[0107] Update the butterfly's position using the fragrance update formula:

[0108] X i (t+1)=X i (t)+r·I i (t)·exp(-a·I i (t))

[0109] Among them, X i (t) represents the position of the i-th butterfly at time t; X i (t+1) represents the position of the i-th butterfly in generation t+1, i.e., the updated position; I i (t) represents the scent intensity of the i-th butterfly; r and a are control parameters, where r controls the step size and update amplitude, and a is used to adjust the effect of scent intensity on position update, which can adjust the decay rate of scent intensity, exp(-a·I i (t) is the exponential decay factor of aroma intensity, which increases with aroma intensity I. i As (t) increases, this factor decreases, thereby reducing the impact of aroma intensity on position updates.

[0110] Local search is performed based on the butterfly's current position and velocity, allowing it to search in its vicinity. This increases the algorithm's local search capability and helps find local optima. The position of the i-th butterfly at time t is X. i (t), velocity v i (t) represents the velocity of the i-th butterfly at time t. The position update formula at time (t+1) is:

[0111] X i (t+1)=X i (t)+v i (t)

[0112] The global search is based on the global optimum and the current butterfly position. By adjusting the butterfly's position to move closer to the global optimum, the algorithm's global search capability is enhanced, which helps in finding the global optimum solution. The global optimum position is X. g If (t), then the position update formula at time (t+1) is:

[0113] X i (t+1)=X i (t)+c·(X g (t)-X i (t))X g (t) represents the global optimal position; c is the adjustment coefficient.

[0114] The speed update is based on the butterfly's fitness value and position. The speed update formula combines the effects of inertia weight, individual optimal position, and global optimal position, allowing the butterfly to maintain a certain inertia during the search process while adjusting according to individual and global optimal information. This improves the convergence speed and accuracy of the algorithm. The individual optimal position of the i-th butterfly at time t is p. i If (t), then the velocity update formula at time (t+1) is:

[0115] v i (t+1)=w·v i (t)+c1·r1·(p i (t)-X i (t))+c2·r2·(X g (t)-X i (t))

[0116] In the formula, w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) represents the optimal position of the i-th butterfly at time t.

[0117] The method is applicable to various wireless power transmission systems, including but not limited to wireless charging systems for electric vehicles and wireless power supply systems for home appliances.

[0118] The wireless power transmission system includes:

[0119] A transmitting module configured to wirelessly transmit electrical energy based on optimized parameters obtained from a butterfly optimization algorithm;

[0120] A receiving module configured to receive transmitted electrical energy and convert it into usable electrical energy;

[0121] A control module configured to apply the butterfly optimization algorithm to optimize power transmission efficiency includes:

[0122] Initialize a butterfly population with random positions and velocities;

[0123] The fitness of each butterfly was assessed based on its power transmission efficiency;

[0124] Update the butterfly's position and speed based on the known best position and the global best position;

[0125] Iterate the process until the convergence criterion is met.

[0126] An apparatus for a wireless power transfer system includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor; the instructions enable the at least one processor to perform steps of a wireless power transfer method based on a butterfly optimization algorithm.

[0127] The storage medium stores a computer program; when the computer program is executed by the processor, it causes the processor to perform the steps of the wireless power transfer method based on the butterfly optimization algorithm.

[0128] See Figure 3 Input device 340 and output device 350 are both connected to processor 330; processor 330 is also connected to memory 320; power management module 310 supplies power to memory, processor, input devices and output devices.

[0129] A method for wireless power transfer using a wireless power transfer system includes the following steps:

[0130] Obtain the initial parameters of the wireless power transmission system, including the distance between the transmitter and receiver, power level, and operating frequency;

[0131] The butterfly optimization algorithm is applied to optimize power transmission efficiency, wherein the algorithm includes: initializing a butterfly population with random positions and velocities;

[0132] The fitness of each butterfly was assessed based on its power transmission efficiency;

[0133] Update the butterfly's position and speed based on the known best position and the global best position;

[0134] The butterfly population's position is continuously updated iteratively by calculating fitness values, optimizing the population's location through multiple iterations. After each iteration, a termination condition is checked: either the preset number of iterations (100) is reached, or the change in fitness value is less than a preset threshold of 0.001. If the termination condition is met, the optimal PID parameter combination is output; otherwise, the iteration continues. This process is repeated until the convergence criterion is met.

[0135] The system parameters are adjusted based on the optimized PID parameter combination to achieve maximum power transmission efficiency.

[0136] See Figure 2 A system for a PID control method for stabilizing the secondary-side voltage of wireless power transfer based on the butterfly optimization algorithm includes a secondary-side voltage acquisition module 210, an error calculation module 220, a PID control module 230, and a wireless power transfer system 250. The output terminal of the secondary-side voltage acquisition module 210 is connected to the input terminal of the error calculation module 220; the output terminal of the error calculation module 220 is connected to the input terminal of the PID control module 230; the output terminal of the PID control module 230 is connected to the input terminal of the wireless power transfer system 250; and the control terminal of the PID control module 230 is connected to the output terminal of the butterfly optimization algorithm module.

[0137] See Figure 1 The diagram shows a flowchart of the butterfly-optimized wireless power transfer secondary voltage stabilization control method of this application.

[0138] like Figure 1 As shown, the butterfly-optimized wireless power transfer secondary voltage stabilization control method specifically includes the following steps:

[0139] Step S101: Initialize the butterfly population. In this stage, an initial population containing multiple butterfly individuals is generated. Each butterfly individual represents a possible solution, and its position is randomly initialized in the solution space.

[0140] First, initialize the parameters of the wireless power transmission system, including the distance between the transmitter and receiver, transmission power, and operating frequency. During initialization, set the initial power of the transmitter to 100W, the initial voltage of the receiver to 12V, and the operating frequency to 100kHz.

[0141] The initial butterfly population is set to 50. The position of each butterfly represents a possible combination of PID controller parameters, including a proportional coefficient (P) of 0.1–2.0, an integral coefficient (I) of 0.01–1.0, and a derivative coefficient (D) of 0.001–0.1. The initial position and velocity of each butterfly are randomly generated.

[0142] Step S102: Calculate the fitness value. The fitness function is used to evaluate the merits of each butterfly's location (i.e., control parameter). The fitness value reflects the effect of that location on the stability of the secondary voltage of the wireless power transfer system.

[0143] The fitness value of each butterfly is calculated based on the secondary voltage stability index of the wireless power transmission system. The fitness function is the sum of squares of voltage fluctuations, and the specific formula is as follows:

[0144]

[0145] Where J is the fitness value; V ref It is the target voltage value; V i It is the actual voltage value at time i; n is the number of sampling points;

[0146] Step S103: Update the butterfly position. Based on the characteristics of the butterfly optimization algorithm, the optimal control parameters are searched by updating the butterfly position.

[0147] Based on the fitness value, the position of each butterfly is updated using the update rules of the butterfly optimization algorithm. The update rules include local search and global search. Local search adjusts parameters within a 10% range of the current butterfly's position, while global search randomly generates new parameter combinations.

[0148] The butterfly's speed is updated based on its fitness value and location. The speed update formula is as follows:

[0149] v i (t+1)=w·v i (t)+c1·r1·(p i (t)-X i (t))+c2·r2·(X g (t)-X i (t))

[0150] In the formula, w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) represents the optimal position of the i-th butterfly at time t;

[0151] Step S104 involves iteratively solving and continuously updating the positions of the butterfly population. Through multiple iterations, the positions of the butterfly population are optimized until the overall population approaches the optimal solution. Each iteration requires recalculating the fitness value and adjusting the butterfly positions based on that value.

[0152] After each iteration, determine if the termination condition is met. The termination condition can be reaching a preset number of iterations (100) or the change in the fitness value being less than a preset threshold of 0.001. If the termination condition is met, output the optimal PID parameter combination; otherwise, continue iterating.

[0153] The optimal combination of PID parameters was found step by step using the butterfly optimization algorithm. The optimized PID parameters include a proportional coefficient (P) of 1.2, an integral coefficient (I) of 0.5, and a derivative coefficient (D) of 0.01.

[0154] Step S105: Output the optimal control parameters. After the algorithm iteration is completed, output the position of the butterfly with the highest fitness value, which is the optimal control parameter for the secondary voltage stability control of the wireless power transmission system.

[0155] The optimized PID parameters are applied to the PID controller of the wireless power transmission system to control the secondary voltage in real time. By adjusting the output of the PID controller, the secondary voltage is ensured to stabilize around the target value of 12V.

[0156] The changes in the secondary voltage of the wireless power transmission system were monitored in real time, and indicators such as voltage fluctuations and response time were recorded. Monitoring results show that the optimized PID controller can effectively reduce voltage fluctuations and enhance the stable operation of the transmission system.

[0157] Based on the monitored changes in the secondary voltage, the parameters of the PID controller are adjusted using a feedback mechanism. By continuously optimizing the PID parameters, the stability of the secondary voltage and the required control accuracy are further improved.

[0158] The method of this invention is applied to a wireless charging system for electric vehicles. First, system parameters are initialized, fitness values ​​are calculated, butterfly position and speed are updated, and the optimal combination of PID parameters is found. The optimized PID controller can maintain stable secondary voltage under different charging conditions, improving charging efficiency and safety.

[0159] The method of this invention is applied to a wireless power supply system for home appliances. First, system parameters are initialized, fitness values ​​are calculated, butterfly position and speed are updated, and the optimal combination of PID parameters is found. Experimental results show that the optimized PID controller can maintain stable secondary voltage under different power supply conditions, ensuring reliable power supply.

[0160] The robustness of the optimized PID controller was tested under different operating conditions. The test results show that the optimized PID controller maintains good control performance under different loads and ambient temperatures, demonstrating strong robustness.

[0161] The transmission efficiency of a wireless power transmission system was optimized using the butterfly optimization algorithm. The optimized system parameters included a transmit power of 120W, a receiver voltage of 12V, and an operating frequency of 110kHz. Experimental results show that the optimized system improved transmission efficiency by 15%.

[0162] Stability tests were conducted on the optimized wireless power transfer system. Analysis showed that the optimized system exhibited smaller secondary voltage fluctuations during long-term operation.

[0163] The butterfly optimization algorithm was used for multi-objective optimization, including secondary-side voltage stability and transmission efficiency. By adjusting the weighting coefficients, a balanced optimization of these two objectives was achieved. The optimized system improved transmission efficiency while maintaining secondary-side voltage stability.

[0164] By combining the butterfly optimization algorithm with the particle swarm optimization algorithm, a hybrid optimization approach is adopted. The complementary advantages of the two algorithms improve both optimization performance and convergence speed. This hybrid optimization algorithm can find the optimal combination of PID parameters more quickly.

[0165] A wireless power transfer system hardware based on the butterfly optimization algorithm was designed and implemented. The hardware includes a transmitting module, a receiving module, and a control module. The control module embeds the butterfly optimization algorithm to achieve real-time optimization and control of system parameters.

[0166] Please see Figure 2 The diagram shows a structural block diagram of a butterfly-optimized wireless power transfer secondary voltage stabilization control system according to this application.

[0167] like Figure 2 As shown, the secondary voltage optimization control system 200 includes a secondary voltage acquisition module 210, an error calculation module 220, a PID control module 230, a butterfly optimization algorithm module 240, and a wireless power transmission system 250.

[0168] The system includes a secondary-side voltage acquisition module 210, which acquires the voltage value of the secondary side of the wireless power transmission system in real time. The acquired voltage data serves as input to the control system, reflecting the current load voltage state. An error calculation module 220 receives the acquired voltage data, compares it with the target voltage value, and calculates the current voltage error. This error value represents the deviation between the actual voltage and the desired voltage, used for subsequent control adjustments. A PID control module 230 receives the error value and processes the error according to the set proportional (P), integral (I), and derivative (D) parameters to generate a control signal. The PID control module reduces the error and stabilizes the secondary-side voltage near the desired value. A butterfly optimization algorithm module 240 adjusts the PID control parameters in real time using the butterfly optimization algorithm. The butterfly optimization algorithm automatically optimizes the PID parameters to adapt to different operating conditions when the voltage changes or the load fluctuates. Finally, the control signal output by the PID control module affects the operation of the wireless power transmission system, enabling the system to perform voltage stabilization control based on the adjusted parameters, achieving an optimized effect.

[0169] Software for a wireless power transfer system based on the butterfly optimization algorithm was developed. The software includes modules for parameter initialization, fitness calculation, position and velocity updates, and PID parameter optimization. The software enables automatic optimization and control of the system.

[0170] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a power management module 310, a processor 330, and a memory 320. The electronic device may also include an input device 340 and an output device 350. The processor 320, memory 330, input device 340, and output device 350 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the power management module 310 provides the necessary power to each system device module. The memory 330 is the aforementioned computer-readable storage medium. The processor 320 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 330, thereby implementing the wireless power transfer secondary-side voltage stabilization PID control method of the butterfly optimization algorithm in the above-described method embodiment. The input device 340 can receive input digital or character information and generate key signal inputs related to user settings and function control for secondary-side voltage acquisition. The output device 350 may include devices such as optimization parameter output devices and display devices.

[0171] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0172] The method of this invention was compared with some intelligent algorithm PID control methods in an experiment. The experimental results show that the PID control method based on the butterfly optimization algorithm is superior to other methods in terms of response speed, control accuracy, and robustness, and has significant advantages.

[0173] Sensitivity analysis was performed on the parameters of the butterfly optimization algorithm. The results show that parameters such as population size, inertia weight, and learning factor affect the optimization effect. By reasonably adjusting these parameters, the desired optimization effect can be further improved.

[0174] The complexity of the butterfly optimization algorithm is analyzed. The results show that the butterfly optimization algorithm has low time and space complexity, making it suitable for real-time control systems.

[0175] The real-time performance of the butterfly optimization algorithm is analyzed. The results show that the butterfly optimization algorithm can complete parameter optimization in a short time and is suitable for real-time control systems. Through the optimized PID controller, real-time control of the secondary voltage can be achieved, improving the control response speed of the transmission system.

[0176] Energy loss in a wireless power transmission system was analyzed. The analysis showed that the system optimized using the butterfly optimization algorithm exhibited reduced energy loss and improved overall system efficiency. The optimized system reduced energy loss by 10%-20% during transmission.

[0177] The optimized wireless power transfer system underwent adaptability testing under different environmental conditions. The optimized system needed to maintain good performance and demonstrate a certain degree of environmental adaptability under extreme environmental conditions such as high temperature, low temperature, and high humidity.

[0178] The butterfly optimization algorithm was used to optimize the multi-frequency operating modes of a wireless power transmission system. By optimizing system parameters at different frequencies, high-efficiency transmission at multiple frequencies was achieved. The optimized system showed improved transmission efficiency at all frequencies.

[0179] Hardware acceleration technology was used to improve the computation speed of the butterfly optimization algorithm. By introducing a hardware accelerator into the control module, rapid computation and optimization of the algorithm were achieved. Experimental results show that hardware acceleration technology can significantly improve the computation speed of the algorithm and shorten the optimization time.

[0180] The optimized wireless power transfer system is integrated into practical applications. Through seamless integration with existing systems, stable control and efficient transmission of the secondary voltage are achieved. It possesses high practicality.

[0181] This paper explores the future development direction of wireless power transmission systems based on the butterfly optimization algorithm. Future research could focus on improving the optimization algorithm to enhance system transmission efficiency. Simultaneously, it could explore more application scenarios, such as industrial automation and smart homes, to expand the application scope of this technology.

Claims

1. A butterfly-optimized method for stabilizing the secondary voltage of wireless power transfer, characterized in that, Includes the following steps: Step 1: Initialize the butterfly population to 50. The position of each butterfly represents a combination of PID controller parameters, including a proportional coefficient (P) of 0.1-2.0, an integral coefficient (I) of 0.01-1.0, and a derivative coefficient (D) of 0.001-0.

1. Step 2: Calculate the fitness value of each butterfly based on the secondary voltage stability index of the wireless power transmission system. The fitness value reflects the effect of the corresponding PID parameter combination on controlling the secondary voltage stability. The fitness function is the sum of squares of voltage fluctuations. Step 3: Based on the fitness value, update the position of each butterfly using the update rules of the butterfly optimization algorithm; the update rules include local search and global search. Local search adjusts the parameters within a 10% range of the current butterfly position, while global search generates new parameter combinations randomly. Step 4, optimize PID parameter combination By calculating the fitness value, the position is continuously updated iteratively. The position of the butterfly population is optimized through multiple iterations. After each iteration, it is determined whether the termination condition is met. The termination condition is reaching the preset number of iterations (100) or the change in fitness value is less than the preset threshold (0.001). If the termination condition is met, the optimal combination of PID parameters is output. Otherwise, continue iterating; Step 5: Apply the optimized PID parameters to the PID controller of the wireless power transmission system to control the secondary voltage in real time. By adjusting the output of the PID controller, ensure that the secondary voltage is stable near the target value. Monitor the changes in the secondary voltage in the wireless power transmission system in real time and record indicators such as voltage fluctuation and response time. Based on the monitored changes in the secondary voltage, adjust the parameters of the PID controller using a feedback mechanism. By continuously optimizing the PID parameters, further improve the stability and control accuracy of the secondary voltage.

2. The wireless power transfer secondary-side voltage stabilization control method based on butterfly optimization according to claim 1, characterized in that, The initial butterfly population described in step 1 has sufficient diversity, which is ensured by randomly generating individuals; the butterfly population is initialized using chaotic mapping to increase the diversity of the initial individuals and improve the convergence speed and accuracy of the algorithm.

3. The wireless power transfer secondary-side voltage stabilization control method based on butterfly optimization according to claim 1, characterized in that, The PID controller parameter combination described in step 1 includes the proportional coefficient (P), integral coefficient (I), and derivative coefficient (D). The output formula of the PID controller is: In the formula, u(t) is the controller output; e(t) is the error, i.e., the difference between the set value and the actual value; K p It is the proportional coefficient, which determines the degree of response of the controller output to the current error; K i It is the integral coefficient, which determines the degree of response of the controller output to error accumulation; K d These are the differential coefficients, which determine the degree to which the controller output responds to the rate of change of the error. The function of proportional control is to adjust the controller output according to the magnitude of the current error. The larger the error, the larger the controller output adjustment, thereby reducing the error. The function of integral control is to eliminate steady-state error, that is, when the system reaches steady state, the error still exists. By accumulating the error, integral control can compensate for historical errors. The function of derivative control is to predict the error trend and adjust the controller output accordingly. This can reduce or mitigate the system's over-response and oscillation. By optimizing the adjusted proportional coefficient (P), integral coefficient (I), and derivative coefficient (D), precise control of the secondary voltage can be achieved.

4. The wireless power transfer secondary-side voltage stabilization control method based on butterfly optimization according to claim 1, characterized in that, The wireless power transmission system described in step 2 includes a transmitter and a receiver. The PID controller is used to control the secondary voltage of the receiver. The transmitter transfers electrical energy to the receiver via an electromagnetic field; the power of the transmitter is P. t The power at the receiving end is P. r If the transmission efficiency is η, then: P r =ηP t The transmission efficiency between the transmitter and receiver is a key factor affecting system performance. Improving transmission efficiency can reduce energy loss and increase the power output of the receiver.

5. The wireless power transfer secondary-side voltage stabilization control method based on butterfly optimization according to claim 4, characterized in that, The electromagnetic field strength at the transmitting end is E, and the electromagnetic field strength at the receiving end is E. r If the distance between the transmitter and receiver is d, then: Electromagnetic field strength decreases with increasing distance. Therefore, when designing a wireless power transmission system, the distance between the transmitter and receiver must be considered to ensure that the receiver can receive sufficient electrical energy.

6. The method for stabilizing and controlling the secondary voltage of wireless power transfer based on butterfly optimization according to claim 1, characterized in that, The fitness value mentioned in step 2 is: The objective function is defined as the weighted sum of the system's steady-state error, overshoot, and settling-time performance indicators, with the sum of squared voltage fluctuations used as the fitness function. J is the fitness value; V ref It is the target voltage value; V i It is the actual voltage value at time i; n is the number of sampling points; For each individual, i.e., each set of PID parameters, its fitness value is calculated according to the fitness function. Let the PID parameters of a certain individual be (K). p ,K i ,K d If the fitness value is 0, then its fitness value is calculated as follows: Among them (V) i (K p ,K i ,K d )) is in the PID parameter (K p ,K i ,K d The actual voltage value at the i-th time step; To facilitate comparison and selection, the fitness values ​​of all individuals are normalized to fall within a fixed range. The normalization formula is as follows: J norm J is the normalized fitness value; J is the original fitness value; J min It is the smallest fitness value among all individuals; J max It is the highest fitness value among all individuals. All individuals are sorted according to their fitness values, and individuals with higher fitness values ​​are selected for the next step. The higher the fitness value, the better the control effect of the PID parameters.

7. The method according to claim 1, characterized in that, Step 3, updating the position of each butterfly, specifically includes: Update the butterfly's position using the fragrance update formula: X i (t+1)=X i (t)+r·I i (t)·exp(-a·I i (t)) Among them, X i (t) represents the position of the i-th butterfly at time t; X i (t+1) represents the position of the i-th butterfly in generation t+1, i.e., the updated position; I i (t) represents the scent intensity of the i-th butterfly; r and a are control parameters, where r controls the step size and update amplitude, and a is used to adjust the effect of scent intensity on position update, which can adjust the decay rate of scent intensity, exp(-a·I i (t) is the exponential decay factor of aroma intensity, which increases with aroma intensity I. i As (t) increases, this factor decreases, thereby reducing the impact of aroma intensity on position updates. Local search is performed based on the butterfly's current position and velocity, allowing it to search in its vicinity. This increases the algorithm's local search capability and helps find local optima. The position of the i-th butterfly at time t is X. i (t), velocity v i (t) represents the velocity of the i-th butterfly at time t. The position update formula at time (t+1) is: X i (t+1)=X i (t)+v i (t) The global search is based on the global optimum and the current butterfly position. By adjusting the butterfly's position to move closer to the global optimum, the algorithm's global search capability is enhanced, which helps in finding the global optimum solution. The global optimum position is X. g If (t), then the position update formula at time (t+1) is: X i (t+1)=X i (t)+c·(X g (t)-X i (t)) X g (t) represents the global optimal position; c is the adjustment coefficient. The speed update is based on the butterfly's fitness value and position. The speed update formula combines the effects of inertia weight, individual optimal position, and global optimal position, allowing the butterfly to maintain a certain inertia during the search process while adjusting according to individual and global optimal information. This improves the convergence speed and accuracy of the algorithm. The individual optimal position of the i-th butterfly at time t is p. i If (t), then the velocity update formula at time (t+1) is: v i (t+1)=w·v i (t)+c1·r1·(p i (t)-X i (t))+c2·r2·(X g (t)-X i (t)) In the formula, w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) represents the optimal position of the i-th butterfly at time t.

8. The wireless power transfer secondary-side voltage stabilization control method based on butterfly optimization according to claim 1, characterized in that, The wireless power transmission system includes: A transmitting module configured to wirelessly transmit electrical energy based on optimized parameters obtained from a butterfly optimization algorithm; A receiving module configured to receive transmitted electrical energy and convert it into usable electrical energy; A control module configured to apply the butterfly optimization algorithm to optimize power transmission efficiency includes: Initialize a butterfly population with random positions and velocities; The fitness of each butterfly was assessed based on its power transmission efficiency; Update the butterfly's position and speed based on the known best position and the global best position; Iterate the process until the convergence criterion is met.

9. An apparatus for use in the butterfly-optimized wireless power transfer secondary voltage stabilization control method according to any one of claims 1-8, characterized in that, The device includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor; the instructions enable the at least one processor to perform steps of a wireless power transfer method based on a butterfly optimization algorithm.

10. The apparatus for a butterfly-optimized wireless power transfer secondary-side voltage stabilization control method according to claim 9, characterized in that, The memory stores a computer program; when the computer program is executed by the processor, it causes the processor to perform the steps of the wireless power transfer method based on the butterfly optimization algorithm. The input device (340) and output device (350) are both connected to the processor (330); the processor (330) is also connected to the memory (320); the power management module (310) supplies power to the memory, processor, input device and output device.

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