Wireless power transmission secondary side voltage stability control method based on butterfly optimization

Abstract

The invention discloses a wireless power transmission secondary side voltage stability control method based on butterfly optimization. The method comprises the following steps: initializing a butterfly population and a position thereof; calculating the fitness value of each butterfly; updating the position of the butterfly according to the fitness value, and searching an optimal control parameter by updating the position of the butterfly; iteratively solving, continuously updating the position, and outputting an optimal solution when a termination condition is met; the optimized PID controller is used for controlling the secondary side voltage, so that the dynamic response time of the system can be reduced, and the secondary side voltage can reach a stable state more quickly; the system overshoot can be reduced, the control of the secondary side voltage is smoother, and the voltage fluctuation is reduced; the device for the wireless power transmission system comprises a processor and a memory connected with the processor; the response speed is high, the steady-state error is smaller, the system stability is higher, and the application prospect is wide; the stable output of the transmission system can be kept under different load change conditions, and the performance and efficiency of the wireless power transmission system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless power transmission, and in particular to a wireless power transmission secondary side voltage stabilization control method based on butterfly optimization. Background Art

[0002] Wireless power transmission technology is a technology that transmits electrical energy from the transmitter to the receiver through an electromagnetic field. It has the advantages of non-contact and high flexibility. It is widely used in the fields of wireless charging of electric vehicles and wireless power supply of household appliances. However, in wireless power transmission systems, the stability of the secondary voltage is one of the key factors affecting system performance. When adjusting the secondary voltage, the traditional PID controller has problems such as difficult parameter adjustment, slow response speed, and low control accuracy, which makes it difficult to meet the needs of high-performance wireless power transmission 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 algorithms. These algorithms can find the global optimal solution in a complex solution space by simulating the biological behavior in nature, and have good optimization performance. The Butterfly Optimization Algorithm (BOA) is an emerging intelligent optimization algorithm. By simulating the foraging behavior of butterflies in nature, it can perform global and local searches in the solution space. It has the advantages of fast convergence speed and high optimization accuracy.

[0003] In most PID controls and PID controls optimized by intelligent optimization algorithms, such as conventional PID controllers, the response speed is generally slow, especially for complex or nonlinear systems, the response time can be between 0.5 seconds and several seconds. The control accuracy is relatively low, especially when the system parameters change or the interference is large, the deviation may remain in the range of 5-10%. Its robustness is poor and it is easily affected by system changes and external interference, especially in nonlinear systems or when the load changes are large. The PID optimized by genetic algorithm (GA) has a certain improvement over conventional PID. The response time of the optimized PID controller in a complex system may be 0.2-2 seconds, the control accuracy is high, and the error can be reduced to 2-5%, but it still depends on the initial population setting and evolution strategy. The genetic algorithm provides a strong global optimization capability and improved robustness, but it is easy to fall into the local optimal solution for large-scale or high-dimensional problems. The PID optimized by particle swarm algorithm (PSO) has a relatively fast response speed after optimization, usually between 0.1-1 seconds, and is suitable for a variety of dynamic systems. The control accuracy can be further improved, and the error may be reduced to 1-3%. PSO has strong global convergence ability and good robustness, but its performance may degrade in high noise environments, and it is necessary to adjust the inertia weight and acceleration constant to adapt to changes. The response speed of PID optimized by the artificial bee colony algorithm (ABC) is generally between 0.2-1 seconds, which is similar to PSO, but may be slower when dealing with complex constraint problems. The control accuracy is comparable to PSO, with an error between 1-3%. It has strong robustness and can adapt well to changes in system parameters, but is not as efficient as PSO and GA when solving multi-modal optimization problems. Fuzzy logic control (FLC), whose response time depends on the complexity of the rule base and the degree of nonlinearity of the system, is usually in the range of 0.1-1 seconds. Since fuzzy logic is based on rules and experience, the control accuracy is usually low, with an error of up to 5-10%, especially under boundary conditions. It shows strong robustness when dealing with uncertainty and nonlinear problems, but may fail in overly complex systems. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the object of the present invention is to provide a wireless power transmission method based on a butterfly optimization algorithm, aiming to solve the technical problems of low efficiency and stability in the wireless power transmission system.

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

[0006] The wireless power transmission secondary side voltage stabilization control method based on butterfly optimization includes the following steps:

[0007] Step 1, the number of butterfly populations is initialized to 50, and the position of each butterfly represents a PID controller parameter combination, including a proportional coefficient (P) of 0.1-2.0, an integral coefficient (I) of 0.01-1.0, and a differential coefficient (D) of 0.001-0.1;

[0008] Step 2, calculate the fitness value. The fitness function is used to evaluate the pros and cons of the position of each butterfly individual. According to the secondary voltage stability index of the wireless power transmission system, the fitness value of each butterfly is calculated. 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 the squares of the voltage fluctuations.

[0009] Step 3, according to the fitness value, using the update rule of the butterfly optimization algorithm, update the position of each butterfly; the update rule includes local search and global search, the local search is performed by adjusting the parameters within 10% of the current butterfly position, and the global search is performed by randomly generating new parameter combinations;

[0010] Step 4: Optimize PID parameter combination

[0011] By calculating the fitness value, the iterative solution continuously updates the position, and optimizes the position of the butterfly population 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 reaches 100 times, or the change in the fitness value is less than the preset threshold value of 0.001; if the termination condition is met, the optimal PID parameter combination is output; otherwise, the iteration continues;

[0012] Step 5, applying 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 of the secondary voltage in the wireless power transmission system in real time, and record indicators such as voltage fluctuations and response time; according to the monitored changes in the secondary voltage, use the feedback mechanism to adjust the parameters of the PID controller; by continuously optimizing the PID parameters, further improve the stability and control accuracy of the secondary voltage.

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

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

[0015]

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

[0017] The role of proportional control is to adjust the output of the controller according to the size of the current error. The larger the error, the larger the controller output adjustment is, so as to reduce the error; the role of integral control is to eliminate steady-state error, that is, when the system reaches a steady state, the error still exists. By accumulating errors, integral control can compensate for historical errors; the role of differential control is to predict the trend of the error and adjust the output of the controller accordingly, which can reduce or slow down the excessive response and oscillation of the system. By optimizing the adjusted proportional coefficient (P), integral coefficient (I) and differential coefficient (D), accurate control of the secondary voltage can be achieved.

[0018] The wireless power transmission system described in step 2 includes a transmitting end and a receiving end, and the PID controller is used to control the secondary side voltage of the receiving end.

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

[0020] P r =ηP t

[0021] The transmission efficiency between the transmitter and the receiver is a key factor affecting system performance. Improving the 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 , the distance between the transmitter and the receiver is d, then:

[0023]

[0024] The strength of the electromagnetic field weakens as the distance increases. Therefore, when designing a wireless power transmission system, it is necessary to consider the distance between the transmitter and the receiver to ensure that the receiver can receive enough electrical energy.

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

[0026] The objective function is set as the weighted sum of the system's steady-state error, overshoot, and adjustment time performance indicators, and the square sum of voltage fluctuations is used as the fitness function:

[0027]

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

[0029] For each individual, that is, each set of PID parameters, its fitness value is calculated according to the fitness function. Suppose the PID parameter of an individual is (K p ,K i ,K d ), then its fitness value is calculated as follows:

[0030]

[0031] Where (V i (K p ,K i ,K d )) is the PID parameter (K p ,K i ,K d )The actual voltage value at the i-th moment;

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

[0033]

[0034] J norm is the normalized fitness value; J is the original fitness value; J min is the minimum fitness value among all individuals; J max is the maximum 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 group of PID parameters.

[0036] The updating of the position of each butterfly described in step 3 specifically includes:

[0037] Update the butterfly's position using the scent 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 ith butterfly in generation t; X i (t+1) represents the position of the ith butterfly in the t+1 generation, i.e., the updated position; I i (t) is the fragrance intensity of the ith butterfly; r and a are control parameters, r is used to control the step size and update amplitude, a is used to adjust the influence of fragrance intensity on position update, and can adjust the decay speed of fragrance intensity, exp(-a·I i (t)) is the exponential decay factor of aroma intensity, as the aroma intensity I i As (t) increases, this factor decreases, thereby reducing the effect of aroma intensity on position updates.

[0040] The local search is based on the current position and speed of the butterfly, which makes it search near the current position, increasing the local search capability of the algorithm and helping to find the local optimal solution. The position of the i-th butterfly at time t is X i (t), with a velocity of v i (t) represents the speed of the ith butterfly at time t, and 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 optimal position and the current position of the butterfly. By adjusting the position of the butterfly to make it closer to the global optimal position, the global search capability of the algorithm is increased, which helps to find the global optimal solution. The global optimal position is X g (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))

[0044] X g (t) represents the global optimal position; c is the adjustment coefficient,

[0045] The speed update is based on the fitness value and position of the butterfly. The speed update formula combines the influence of inertia weight, individual optimal position and global optimal position, so that the butterfly can maintain a certain inertia during the search process and adjust according to the individual and global optimal information, which improves the convergence speed and accuracy of the algorithm. The individual optimal position of the i-th butterfly at time t is pi (t), then the speed update formula at time (t+1) is:

[0046] v i (t+1)=w·v i (t)+c1·r1·(p i (t)-X i (t))+c2·r2·(X g (t)-X i (t))

[0047] Where w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) is the individual optimal position of the i-th butterfly at time t.

[0048] The method is applicable to various wireless power transmission systems, including but not limited to electric vehicle wireless charging systems, household appliance wireless power supply systems, etc.

[0049] The wireless power transmission system comprises:

[0050] a transmitting module configured to wirelessly transmit power according to optimization parameters obtained by a butterfly optimization algorithm;

[0051] a receiving module configured to receive the transmitted power and convert it into usable power;

[0052] A control module configured to apply a butterfly optimization algorithm to optimize power transmission efficiency, including:

[0053] Initialize a population of butterflies with random positions and velocities;

[0054] The fitness of each butterfly is assessed based on the efficiency of electrical energy transfer;

[0055] Update the position and velocity of the butterfly based on the known best position and the global best position;

[0056] The process is iterated until the convergence criteria are met.

[0057] A device for a wireless power transmission 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; and the instructions enable the at least one processor to execute the steps of a wireless power transmission method based on a butterfly optimization algorithm.

[0058] The memory stores a computer program thereon; when the computer program is executed by the processor, the processor executes the steps of the wireless power transmission method based on the butterfly optimization algorithm;

[0059] The input device and the output device are both connected to the processor; the processor is also connected to the memory; the power management module supplies power to the memory, the processor, the input device and the output device.

[0060] The method for wireless power transmission using a wireless power transmission system comprises the following steps:

[0061] Obtaining initial parameters of the wireless power transfer system, including the distance between the transmitter and receiver, power level, and operating frequency;

[0062] A butterfly optimization algorithm is applied to optimize the power transmission efficiency, wherein the algorithm comprises: initializing a butterfly population with random positions and velocities;

[0063] The fitness of each butterfly is assessed based on the efficiency of electrical energy transfer;

[0064] Update the position and velocity of the butterfly based on the known best position and the global best position;

[0065] By calculating the fitness value, the iterative solution continuously updates the position, and optimizes the position of the butterfly population 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 reaches 100 times, or the change in the fitness value is less than the preset threshold value of 0.001; if the termination condition is met, the optimal PID parameter combination is output; otherwise, the iteration continues, and the process is iterated until the convergence standard is met;

[0066] The system parameters are adjusted according to the optimized PID parameter combination to achieve the maximum power transmission efficiency.

[0067] A system for a wireless power transmission secondary voltage stabilization PID control method based on a butterfly optimization algorithm includes a secondary voltage acquisition module 210, an error calculation module 220, a PID control module 230 and a wireless power transmission system 250; the output end of the secondary voltage acquisition module 210 is connected to the input end of the error calculation module 220; the output end of the error calculation module 220 is connected to the input end of the PID control module 230; the output end of the PID control module 230 is connected to the input end of the wireless power transmission system 250; and the control end of the PID control module 230 is connected to the output end of the butterfly optimization algorithm module.

[0068] The beneficial effects of the present invention are:

[0069] The present invention adopts PID optimized by butterfly optimization algorithm (BOA), and the optimized response time is usually 0.1-0.5 seconds, which is similar to PSO and is suitable for a wider range of system types. After global optimization, BOA can achieve higher control accuracy, and the error can be controlled within the range of less than 1-2%, which is significantly higher than conventional PID and fuzzy logic control. Because BOA can better balance global exploration and local development, it has strong robustness and can adapt to a variety of complex and nonlinear system changes, while being able to better avoid local optimal solution problems. Therefore, the PID control system optimized by butterfly optimization algorithm (BOA) has the advantages of fast response speed, high control accuracy, and strong robustness.

[0070] In the wireless power transmission system, the butterfly optimization algorithm is introduced to optimize the parameters of the PID controller, which can effectively improve the stability of the secondary voltage. Specifically, the butterfly optimization algorithm gradually finds the optimal PID parameter combination through steps such as initializing the population, calculating the fitness value, updating the position and speed, etc. The optimized PID controller can improve the response speed of the transmission system and the efficiency of wireless power transmission, reduce system overshoot and oscillation, and enable the system to maintain the stability of the secondary voltage under different working conditions. The present invention proposes a PID control method for the secondary voltage stability of wireless power transmission based on the butterfly optimization algorithm, which optimizes the parameters of the PID controller by introducing the butterfly optimization algorithm to achieve stable control of the secondary voltage. This method is not only suitable for wireless power transmission application scenarios such as electric vehicle wireless charging systems and household appliance wireless power supply systems, but can also be extended to other power systems that require precise control, and has broad application prospects.

[0071] The wireless power transmission method and system of the present invention utilizes the butterfly optimization algorithm to optimize the power transmission efficiency, effectively improving the stability and efficiency of the wireless power transmission system. By obtaining initial parameters and iteratively optimizing, a high power transmission efficiency is ensured, making it applicable to various wireless power transmission devices or scenarios.

[0072] The method of the present invention optimizes the proportional, integral and differential parameters of the PID controller by introducing a butterfly optimization algorithm (BOA), thereby achieving stable control of the secondary voltage in the wireless power transmission system. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flowchart of a method for controlling secondary side voltage stabilization of wireless power transmission based on butterfly optimization is provided in accordance with an embodiment of the present invention.

[0074] Figure 2 A structural block diagram of a wireless power transmission secondary side voltage stabilization control system based on butterfly optimization provided by an embodiment of the present invention.

[0075] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] The wireless power transmission secondary side voltage stabilization control method based on butterfly optimization includes the following steps:

[0078] Step 1, the number of butterfly populations is initialized to 50, and the position of each butterfly represents a PID controller parameter combination, including a proportional coefficient (P) of 0.1-2.0, an integral coefficient (I) of 0.01-1.0, and a differential coefficient (D) of 0.001-0.1;

[0079] Step 2, calculate the fitness value. The fitness function is used to evaluate the pros and cons of the position of each butterfly individual. According to the secondary voltage stability index of the wireless power transmission system, the fitness value of each butterfly is calculated. 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 the squares of the voltage fluctuations.

[0080] Step 3, according to the fitness value, using the update rule of the butterfly optimization algorithm, update the position of each butterfly; the update rule includes local search and global search, the local search is performed by adjusting the parameters within 10% of the current butterfly position, and the global search is performed by randomly generating new parameter combinations;

[0081] Step 4: Optimize PID parameter combination

[0082] By calculating the fitness value, the iterative solution continuously updates the position, and optimizes the position of the butterfly population 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 reaches 100 times, or the change in the fitness value is less than the preset threshold value of 0.001; if the termination condition is met, the optimal PID parameter combination is output; otherwise, the iteration continues;

[0083] Step 5, applying 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 of the secondary voltage in the wireless power transmission system in real time, and record indicators such as voltage fluctuations and response time; according to the monitored changes in the secondary voltage, use the feedback mechanism to adjust the parameters of the PID controller; by continuously optimizing the PID parameters, further improve the stability and control accuracy of the secondary voltage.

[0084] The initialized butterfly population described in step 1 has sufficient diversity, and the diversity is ensured by randomly generating individuals; the butterfly population is initialized using chaotic mapping (such as Tent chaos and Circle chaos) to increase the diversity of initial individuals and improve the convergence speed and accuracy of the algorithm.

[0085] The PID controller parameter combination described in step 1 includes a proportional coefficient (P), an integral coefficient (I) and a differential coefficient (D). The output formula of the PID controller is:

[0086]

[0087] Where u(t) is the output of the controller; e(t) is the error, that is, the difference between the set value and the actual value; K p is the proportional coefficient, which determines the degree of response of the controller output to the current error; K i is the integral coefficient, which determines the response degree of the controller output to the error accumulation; K d is the differential coefficient, which determines the response of the controller output to the rate of change of the error.

[0088] The role of proportional control is to adjust the output of the controller according to the current error. The larger the error, the larger the controller output adjustment is, thereby reducing the error; the role of integral control is to eliminate steady-state errors, that is, when the system reaches a steady state, the error still exists. By accumulating errors, integral control can compensate for historical errors; the role of differential control is to predict the trend of the error and adjust the output of the controller accordingly. This can reduce or slow down the excessive response and oscillation of the system. By optimizing the adjusted proportional coefficient (P), integral coefficient (I) and differential coefficient (D), accurate control of the secondary voltage can be achieved.

[0089] The wireless power transmission system described in step 2 includes a transmitting end and a receiving end, and the PID controller is used to control the secondary side voltage of the receiving end.

[0090] The transmitter transmits electrical energy to the receiver through an electromagnetic field. The power of the transmitter is P t , the power at the receiving end is P r , the transmission efficiency is η, then:

[0091] P r =ηP t

[0092] The transmission efficiency between the transmitter and the receiver is a key factor affecting system performance. Improving the transmission efficiency can reduce energy loss and increase the power output of the receiver.

[0093] The electromagnetic field strength at the transmitting end is E, and the electromagnetic field strength at the receiving end is E r , the distance between the transmitter and the receiver is d, then:

[0094]

[0095] The strength of the electromagnetic field weakens as the distance increases. Therefore, when designing a wireless power transmission system, it is necessary to consider the distance between the transmitter and the receiver to ensure that the receiver can receive enough electrical energy.

[0096] The fitness value described in step 2 is:

[0097] The objective function is set as the weighted sum of the system's steady-state error, overshoot, and adjustment time performance indicators, and the square sum of voltage fluctuations is used as the fitness function:

[0098]

[0099] J is the fitness value; V ref is the target voltage value; V i is the actual voltage value at the i-th moment; n is the number of sampling points;

[0100] For each individual, that is, each set of PID parameters, its fitness value is calculated according to the fitness function. Suppose the PID parameter of an individual is (K p ,K i ,K d ), then its fitness value is calculated as follows:

[0101]

[0102] Where (V i (K p ,K i ,K d )) is the PID parameter (K p ,K i ,K d )The actual voltage value at the i-th moment;

[0103] To facilitate comparison and selection, the fitness values ​​of all individuals are normalized so that they fall within a fixed range. The normalization formula is as follows:

[0104]

[0105] J norm is the normalized fitness value; J is the original fitness value; J min is the minimum fitness value among all individuals; J max is the maximum fitness value among all individuals,

[0106] 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 group of PID parameters.

[0107] The updating of the position of each butterfly described in step 3 specifically includes:

[0108] Update the butterfly's position using the scent update formula:

[0109] X i (t+1)=X i (t)+r·I i (t)·exp(-a·I i (t))

[0110] Among them, X i (t) represents the position of the ith butterfly in generation t; X i (t+1) represents the position of the ith butterfly in the t+1 generation, i.e., the updated position; I i (t) is the fragrance intensity of the ith butterfly; r and a are control parameters, r is used to control the step size and update amplitude, a is used to adjust the influence of fragrance intensity on position update, and can adjust the decay speed of fragrance intensity, exp(-a·I i (t)) is the exponential decay factor of aroma intensity, as the aroma intensity I i As (t) increases, this factor decreases, thereby reducing the effect of aroma intensity on position updates.

[0111] The local search is based on the current position and speed of the butterfly, which makes it search near the current position, increasing the local search capability of the algorithm and helping to find the local optimal solution. The position of the i-th butterfly at time t is X i (t), with a velocity of v i (t) represents the speed of the ith butterfly at time t, and the position update formula at time (t+1) is:

[0112] X i (t+1)=X i (t)+v i (t)

[0113] The global search is based on the global optimal position and the current position of the butterfly. By adjusting the position of the butterfly to make it closer to the global optimal position, the global search capability of the algorithm is increased, which helps to find the global optimal solution. The global optimal position is X g (t), then the position update formula at time (t+1) is:

[0114] X i (t+1)=X i (t)+c·(X g (t)-X i (t))

[0115] X g (t) represents the global optimal position; c is the adjustment coefficient,

[0116] The speed update is based on the fitness value and position of the butterfly. The speed update formula combines the influence of inertia weight, individual optimal position and global optimal position, so that the butterfly can maintain a certain inertia during the search process and adjust according to the individual and global optimal information, which improves the convergence speed and accuracy of the algorithm. The individual optimal position of the i-th butterfly at time t is p i (t), then the speed update formula at time (t+1) is:

[0117] v i (t+1)=w·v i (t)+c1·r1·(p i (t)-X i (t))+c2·r2·(X g (t)-X i (t))

[0118] Where w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) is the individual optimal position of the i-th butterfly at time t.

[0119] The method is applicable to various wireless power transmission systems, including but not limited to electric vehicle wireless charging systems, household appliance wireless power supply systems, etc.

[0120] The wireless power transmission system comprises:

[0121] a transmitting module configured to wirelessly transmit power according to optimization parameters obtained by a butterfly optimization algorithm;

[0122] a receiving module configured to receive the transmitted power and convert it into usable power;

[0123] A control module configured to apply a butterfly optimization algorithm to optimize power transmission efficiency, including:

[0124] Initialize a population of butterflies with random positions and velocities;

[0125] The fitness of each butterfly is assessed based on the efficiency of electrical energy transfer;

[0126] Update the position and velocity of the butterfly based on the known best position and the global best position;

[0127] The process is iterated until the convergence criteria are met.

[0128] A device for a wireless power transmission 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; and the instructions enable the at least one processor to execute the steps of a wireless power transmission method based on a butterfly optimization algorithm.

[0129] The storage medium stores a computer program thereon; when the computer program is executed by the processor, the processor executes the steps of the wireless power transmission method based on the butterfly optimization algorithm;

[0130] See also Figure 3 The input device 340 and the 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, the processor, the input device and the output device.

[0131] The method for wireless power transmission using a wireless power transmission system comprises the following steps:

[0132] Obtaining initial parameters of the wireless power transfer system, including the distance between the transmitter and receiver, power level, and operating frequency;

[0133] A butterfly optimization algorithm is applied to optimize the power transmission efficiency, wherein the algorithm comprises: initializing a butterfly population with random positions and velocities;

[0134] The fitness of each butterfly is assessed based on the efficiency of electrical energy transfer;

[0135] Update the position and velocity of the butterfly based on the known best position and the global best position;

[0136] By calculating the fitness value, the iterative solution continuously updates the position, and 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 is 100, or the change in the fitness value is less than the preset threshold of 0.001; if the termination condition is met, the optimal PID parameter combination is output; otherwise, the iteration continues. The process is iterated until the convergence standard is met;

[0137] The system parameters are adjusted according to the optimized PID parameter combination to achieve the maximum power transmission efficiency.

[0138] See also Figure 2 A system for a wireless power transmission secondary side voltage stabilization PID control method based on a 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 transmission system 250; the output end of the secondary side voltage acquisition module 210 is connected to the input end of the error calculation module 220; the output end of the error calculation module 220 is connected to the input end of the PID control module 230; the output end of the PID control module 230 is connected to the input end of the wireless power transmission system 250; and the control end of the PID control module 230 is connected to the output end of the butterfly optimization algorithm module.

[0139] See also Figure 1 , which shows a flow chart of the wireless power transmission secondary side voltage stabilization control method based on butterfly optimization of the present application.

[0140] like Figure 1 As shown, the method for controlling the secondary voltage stability of wireless power transmission based on butterfly optimization specifically includes the following steps:

[0141] Step S101, initializing the butterfly population. In this stage, an initial population including multiple butterfly individuals is generated. Each butterfly individual represents a possible solution, and its position is randomly initialized in the solution space.

[0142] First, initialize the parameters of the wireless power transmission system, including the distance between the transmitter and the receiver, the transmission power, the operating frequency, etc. During the initialization process, set the initial power of the transmitter to 100W, the initial voltage of the receiver to 12V, and the operating frequency to 100kHz.

[0143] The number of butterfly populations is initialized to 50, and the position of each butterfly represents a possible PID controller parameter combination, including a proportional coefficient (P) of 0.1-2.0, an integral coefficient (I) of 0.01-1.0, and a differential coefficient (D) of 0.001-0.1. The initial position and speed of each butterfly are randomly generated.

[0144] Step S102, calculating the fitness value, the fitness function is used to evaluate the quality of the position (i.e., control parameter) of each butterfly individual. The fitness value reflects the effect of the position on the secondary voltage stability of the wireless power transmission system;

[0145] According to the secondary voltage stability index of the wireless power transmission system, the fitness value of each butterfly is calculated. The fitness function is the sum of the squares of the voltage fluctuations, and the specific formula is as follows:

[0146]

[0147] Among them, J is the fitness value; V ref is the target voltage value; V i is the actual voltage value at the i-th moment; n is the number of sampling points;

[0148] Step S103, updating the butterfly position Based on the characteristics of the butterfly optimization algorithm, the optimal control parameters are searched by updating the butterfly position.

[0149] According to the fitness value, the update rule of the butterfly optimization algorithm is used to update the position of each butterfly. The update rule includes local search and global search. The local search adjusts the parameters within 10% of the current butterfly position, and the global search generates new parameter combinations randomly.

[0150] According to the fitness value and position of the butterfly, its speed is updated. The speed update formula is as follows:

[0151] v i (t+1)=w·v i (t)+c1·r1·(p i (t)-X i (t))+c2·r2·(X g (t)-X i (t))

[0152] Where w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) is the individual optimal position of the i-th butterfly at time t;

[0153] Step S104, iterative solution continues to update the position, and optimizes the position of the butterfly population through multiple iterations, so that the population as a whole tends to the optimal solution. Each iteration requires recalculating the fitness value and adjusting the position of the butterfly based on the fitness value.

[0154] After each iteration, determine whether the termination condition is met. The termination condition can be reaching the preset number of iterations 100 times, or the change in the fitness value is less than the preset threshold 0.001. If the termination condition is met, the optimal PID parameter combination is output; otherwise, continue to iterate.

[0155] The butterfly optimization algorithm is used to gradually find the optimal PID parameter combination. The optimized PID parameters include a proportional coefficient (P) of 1.2, an integral coefficient (I) of 0.5, and a differential coefficient (D) of 0.01.

[0156] Step S105, outputting the optimal control parameters After the algorithm iteration is completed, the position of the butterfly individual with the highest fitness value is output, which is the optimal control parameter for the secondary side voltage stability control of the wireless power transmission system.

[0157] 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 be stable near the target value of 12V.

[0158] Real-time monitoring of the secondary voltage changes in the wireless power transmission system, recording indicators such as voltage fluctuations and response time. The monitoring results show that the optimized PID controller can effectively reduce voltage fluctuations and enhance the stable operation capability of the transmission system.

[0159] According to the monitored changes in the secondary voltage, the feedback mechanism is used to adjust the parameters of the PID controller. By continuously optimizing the PID parameters, the stability of the secondary voltage and the required control accuracy can be further improved.

[0160] The method of the present invention is applied to the wireless charging system of electric vehicles. First, the system parameters are initialized, the fitness value is calculated, the butterfly position and speed are updated, and the optimal PID parameter combination is found. The optimized PID controller can maintain the stability of the secondary voltage under different charging conditions and improve the charging efficiency and safety factor.

[0161] The method of the present invention is applied to the wireless power supply system of household appliances. First, the system parameters are initialized, the fitness value is calculated, the butterfly position and speed are updated, and the optimal PID parameter combination is found. The experimental results show that the optimized PID controller can maintain the stability of the secondary voltage under different power supply conditions, so that the power supply system can be reliably powered.

[0162] The optimized PID controller is tested for robustness under different working conditions. Through test analysis, the optimized PID controller can maintain good control effect under different loads, different ambient temperatures and other working conditions, and has strong robustness.

[0163] The transmission efficiency of the wireless power transmission system is optimized by the butterfly optimization algorithm. The optimized system parameters include the transmission power of 120W, the receiving voltage of 12V, and the operating frequency of 110kHz. The experimental results show that the transmission efficiency of the optimized system is improved by 15%.

[0164] The stability test of the optimized wireless power transmission system was carried out. Through analysis, it was found that the secondary voltage fluctuation of the optimized system was small during long-term operation.

[0165] The butterfly optimization algorithm is used to perform multi-objective optimization, including secondary voltage stability and transmission efficiency. By adjusting the weight coefficient, a balanced optimization of the two is achieved. The optimized system not only ensures the stability of the secondary voltage, but also improves the transmission efficiency.

[0166] The butterfly optimization algorithm is combined with the particle swarm optimization algorithm to perform hybrid optimization. The two algorithms complement each other to improve the optimization effect and convergence speed. The hybrid optimization algorithm can find the optimal PID parameter combination more quickly.

[0167] The hardware of wireless power transmission system based on butterfly optimization algorithm is designed and implemented. The hardware includes transmitting module, receiving module and control module. The butterfly optimization algorithm is embedded in the control module to realize real-time optimization and control of system parameters.

[0168] See also Figure 2 , which shows a structural block diagram of a wireless power transmission secondary side voltage stabilization control system based on butterfly optimization of the present application.

[0169] 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 .

[0170] Among them, the secondary voltage acquisition module 210 is used to collect the voltage value of the secondary side in the wireless power transmission system in real time. The collected voltage data will be used as the input of the control system to reflect the voltage state of the current load; the error calculation module 220, the collected voltage data is transmitted to the error calculation module, compared with the target voltage value, and the current voltage error is calculated. This error value represents the deviation between the actual voltage and the expected voltage, which is used for subsequent control adjustment; the PID control module 230, the error value is input into the PID control module, and the module processes the error according to the set proportional (P), integral (I) and differential (D) parameters to generate a control signal. The function of the PID control module is to reduce the error and stabilize the secondary voltage near the expected value; the butterfly optimization algorithm module 240, the module adjusts the PID control parameters in real time through the butterfly optimization algorithm. The butterfly optimization algorithm can automatically optimize the PID parameters when the voltage changes or the load fluctuates to adapt to different working conditions; the wireless power transmission system 250, the control signal finally output by the PID control module will affect the operation of the wireless power transmission system, so that the system can perform voltage stability control according to the adjusted parameters to achieve the optimization effect.

[0171] The wireless power transmission system software based on the butterfly optimization algorithm was developed. The software includes functional modules such as parameter initialization, fitness value calculation, position and speed update, PID parameter optimization, etc. The automatic optimization and control of the system is achieved through the software.

[0172] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a power management module 310, a processor 320 and a memory 330. The electronic device may also include: an input device 340 and an output device 350. The processor 320, the memory 330, the input device 340 and the output device 350 may be connected via a bus or other means. Figure 3 In the example, the bus connection is used. The power management module 310 can provide the required power for each system device module. The memory 330 is the above-mentioned computer-readable storage medium. The processor 320 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 330, that is, the wireless power transmission secondary side voltage stabilization PID control method of the butterfly optimization algorithm in the above-mentioned method embodiment is implemented. The input device 340 can receive input digital or character information, and generate key signal input related to user settings and function control of secondary side voltage acquisition. The output device 340 may include devices such as optimization parameter output devices and display devices.

[0173] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0174] The method of the present invention is compared with some intelligent algorithm PID control methods. 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 obvious advantages.

[0175] The sensitivity analysis of the parameters of the butterfly optimization algorithm is carried out. The analysis results show that parameters such as population size, inertia weight, and learning factor have an impact on the optimization effect. By reasonably adjusting these parameters, the desired optimization effect can be further improved.

[0176] The complexity of the butterfly optimization algorithm is analyzed. The analysis results show that the time complexity and space complexity of the butterfly optimization algorithm are both low, and it is suitable for real-time control systems.

[0177] The real-time performance of the butterfly optimization algorithm is analyzed. The analysis results show that the butterfly optimization algorithm can complete parameter optimization in a shorter 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.

[0178] The energy loss of the wireless power transmission system is analyzed. Through analysis, the energy loss of the system after the butterfly optimization algorithm is optimized is reduced, and the overall efficiency of the system is improved. The energy loss of the optimized system is reduced by 10%-20% during the transmission process.

[0179] The optimized wireless power transmission system is tested for adaptability under different environmental conditions. The optimized system needs to maintain good working performance and have a certain degree of environmental adaptability under extreme environmental conditions such as high temperature, low temperature, and high humidity.

[0180] The butterfly optimization algorithm is used to optimize the multi-frequency working mode of the wireless power transmission system. By optimizing the system parameters at different frequencies, efficient transmission at multiple frequencies is achieved. The optimized system can improve the transmission efficiency at different frequencies.

[0181] The calculation speed of the butterfly optimization algorithm is improved by using hardware acceleration technology. By introducing a hardware accelerator in the control module, the algorithm is quickly calculated and optimized. The experimental results show that hardware acceleration technology can significantly improve the calculation speed of the algorithm and shorten the optimization time.

[0182] The optimized wireless power transmission system is integrated into practical applications. Through seamless integration with the existing system, stable control and efficient transmission of the secondary voltage are achieved. It has high practicality.

[0183] This paper discusses the future development direction of wireless power transmission system based on butterfly optimization algorithm. In the future, we can further study the improvement of optimization algorithm to improve the transmission efficiency of the system. At the same time, we can explore more application scenarios, such as industrial automation, smart home, etc., to expand the application scope of this technology.

Claims

1. A wireless power transmission secondary side voltage stabilization control method based on butterfly optimization, characterized in that: The following steps are involved: Step 1, the number of butterfly populations is initialized to 50, and the position of each butterfly represents a PID controller parameter combination, including a proportional coefficient (P) of 0.1-2.0, an integral coefficient (I) of 0.01-1.0, and a differential coefficient (D) of 0.001-0.1; Step 2: Calculate the fitness value of each butterfly according to the secondary voltage stability index of the wireless power transmission system. 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 square sum of the voltage fluctuations. Step 3, according to the fitness value, using the update rule of the butterfly optimization algorithm, update the position of each butterfly; the update rule includes local search and global search, the local search is performed by adjusting the parameters within 10% of the current butterfly position, and the global search is performed by randomly generating new parameter combinations; Step 4: Optimize PID parameter combination By calculating the fitness value, the iterative solution continuously updates the position, and 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 reaches 100 times, or the change in fitness value is less than the preset threshold value 0.001; if the termination condition is met, the optimal PID parameter combination is output; Otherwise, continue iterating; Step 5, applying 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 of the secondary voltage in the wireless power transmission system in real time, and record indicators such as voltage fluctuations and response time; according to the monitored changes in the secondary voltage, use the feedback mechanism to adjust the parameters of the PID controller; by continuously optimizing the PID parameters, further improve the stability and control accuracy of the secondary voltage.

2. According to claim 1, the method for controlling the secondary voltage stability of wireless power transmission based on butterfly optimization is characterized in that: The initialized butterfly population described in step 1 has sufficient diversity, and the diversity is ensured by randomly generating individuals; the butterfly population is initialized using chaotic mapping, the diversity of initial individuals is increased, and the convergence speed and accuracy of the algorithm are improved.

3. The method for controlling the secondary voltage stability of wireless power transmission based on butterfly optimization according to claim 1 is characterized in that: The PID controller parameter combination described in step 1 includes a proportional coefficient (P), an integral coefficient (I) and a differential coefficient (D). The output formula of the PID controller is: Where u(t) is the output of the controller; e(t) is the error, that is, the difference between the set value and the actual value; K p is the proportional coefficient, which determines the degree of response of the controller output to the current error; K i is the integral coefficient, which determines the response degree of the controller output to the error accumulation; K d is the differential coefficient, which determines the response of the controller output to the error change rate. The role of proportional control is to adjust the output of the controller according to the size of the current error. The larger the error, the larger the output adjustment of the controller, so as to reduce the error; the role of integral control is to eliminate steady-state error, that is, when the system reaches a steady state, the error still exists. By accumulating errors, integral control can compensate for historical errors; the role of differential control is to predict the trend of the error and adjust the output of the controller accordingly, which can reduce or slow down the excessive response and oscillation of the system. By optimizing the adjusted proportional coefficient (P), integral coefficient (I) and differential coefficient (D), precise control of the secondary voltage can be achieved.

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

5. The method for controlling the secondary voltage stability of wireless power transmission based on butterfly optimization according to claim 4 is 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 , the distance between the transmitter and the receiver is d, then: The strength of the electromagnetic field weakens as the distance increases. Therefore, when designing a wireless power transmission system, it is necessary to consider the distance between the transmitter and the receiver to ensure that the receiver can receive enough electrical energy.

6. The method for controlling the secondary voltage stability of wireless power transmission based on butterfly optimization according to claim 1, characterized in that: The fitness value described in step 2 is: The objective function is set as the weighted sum of the system's steady-state error, overshoot, and adjustment time performance indicators, and the square sum of voltage fluctuations is used as the fitness function: J is the fitness value; V ref is the target voltage value; V i is the actual voltage value at the i-th moment; n is the number of sampling points; For each individual, that is, each set of PID parameters, its fitness value is calculated according to the fitness function. Suppose the PID parameter of an individual is (K p ,K i ,K d ), then its fitness value is calculated as follows: Where (V i (K p ,K i ,K d )) is the PID parameter (K p ,K i ,K d )The actual voltage value at the i-th moment; To facilitate comparison and selection, the fitness values ​​of all individuals are normalized so that they fall within a fixed range. The normalization formula is as follows: J norm is the normalized fitness value; J is the original fitness value; J min is the minimum fitness value among all individuals; J max is the maximum 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 group of PID parameters.

7. The method according to claim 1, characterized in that The updating of the position of each butterfly described in step 3 specifically includes: Update the butterfly's position using the scent 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 ith butterfly in generation t; X i (t+1) represents the position of the ith butterfly in the t+1 generation, i.e., the updated position; I i (t) is the fragrance intensity of the ith butterfly; r and a are control parameters, r is used to control the step size and update amplitude, a is used to adjust the influence of fragrance intensity on position update, and can adjust the decay speed of fragrance intensity, exp(-a·I i (t)) is the exponential decay factor of aroma intensity, as the aroma intensity I i As (t) increases, this factor decreases, thereby reducing the effect of aroma intensity on position updates. The local search is based on the current position and speed of the butterfly, which makes it search near the current position, increasing the local search capability of the algorithm and helping to find the local optimal solution. The position of the i-th butterfly at time t is X i (t), with a velocity of v i (t) represents the speed of the ith butterfly at time t, and 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 optimal position and the current position of the butterfly. By adjusting the position of the butterfly to make it closer to the global optimal position, the global search capability of the algorithm is increased, which helps to find the global optimal solution. The global optimal position is X g (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 fitness value and position of the butterfly. The speed update formula combines the influence of inertia weight, individual optimal position and global optimal position, so that the butterfly can maintain a certain inertia during the search process and adjust according to the individual and global optimal information, which improves the convergence speed and accuracy of the algorithm. The individual optimal position of the i-th butterfly at time t is p i (t), then the speed 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)) Where w is the inertia weight; c1 and c2 are learning factors; r1 and r2 are random numbers; P i (t) is the individual optimal position of the i-th butterfly at time t.

8. The method for controlling the secondary voltage stability of wireless power transmission based on butterfly optimization according to claim 1, characterized in that: The wireless power transmission system comprises: a transmitting module configured to wirelessly transmit power according to optimization parameters obtained by a butterfly optimization algorithm; a receiving module configured to receive the transmitted power and convert it into usable power; A control module configured to apply a butterfly optimization algorithm to optimize power transmission efficiency, including: Initialize a population of butterflies with random positions and velocities; The fitness of each butterfly is assessed based on the efficiency of electrical energy transfer; Update the position and velocity of the butterfly based on the known best position and the global best position; The process is iterated until the convergence criteria are met.

9. A device for a wireless power transmission system, characterized in that The invention comprises at least one processor and a memory which is communicatively connected to the at least one processor; wherein the memory stores instructions which can be executed by the at least one processor; and the instructions enable the at least one processor to execute the steps of the wireless power transmission method based on the butterfly optimization algorithm.

10. The device for wireless power transmission system according to claim 9, characterized in that: The memory stores a computer program thereon; when the computer program is executed by the processor, the processor is caused to execute the steps of the wireless power transmission method based on the butterfly optimization algorithm; The input device (340) and the output device (350) are both connected to the processor (330); the processor (330) is also connected to the memory (320); and the power management module (310) supplies power to the memory, the processor, the input device, and the output device.

Images