Wireless charging system maximum efficiency optimization method based on improved parrot algorithm

Abstract

The invention belongs to the technical field of LCC-S wireless charging, and particularly relates to an optimization method and system for realizing maximum efficiency of a wireless charging system, parameter optimization is performed on the system by using a proposed improved parrot algorithm, and maximum efficiency optimization is realized by designing a loose coupling transformer according to the optimized parameters. One side of the circuit structure is composed of a full-bridge inverter and an LCC topological structure and comprises four switching tubes, and a secondary side is composed of a rectification structure and a DC-DC converter. According to the method, the wireless charging system of the LCC-S topology is established, the improved parrot algorithm is combined with the wireless charging technology, the charging system with the maximum efficiency optimization is designed and developed, on one hand, the actual requirements of some special application occasions can be met, and the application field of the wireless charging technology is expanded; and on the other hand, energy conservation and environmental protection are achieved.

Description

Technical Field

[0001] The present application belongs to the field of LCC-S wireless charging technology, and specifically relates to a method for optimizing the maximum efficiency of a wireless charging system based on an improved parrot algorithm. Background Art

[0002] As the global energy crisis and environmental pollution become increasingly serious, electric vehicles (EVs) have gradually become the development trend of the automotive industry due to their clean and energy-saving characteristics. However, the traditional wired charging method has certain limitations in terms of convenience and safety, especially in public charging stations, home garages and other scenarios, where cables need to be frequently plugged and unplugged, which is both troublesome and unsafe. Wireless charging technology (Wireless Power Transfer, WPT) has emerged as the times require, providing a more convenient, efficient and safe charging method. Wireless charging technology is mainly based on the principles of electromagnetic induction and electromagnetic resonance, and realizes wireless energy transfer through the transmission of electromagnetic fields in space. Electromagnetic induction technology uses the magnetic field coupling between the primary coil and the secondary coil to transmit electrical energy from the transmitter to the receiver. It has the characteristics of high efficiency and low loss and is suitable for short-distance power transmission. Electromagnetic resonance technology achieves efficient power transmission over a larger distance by tuning the resonant frequency of the primary and secondary coils, which is particularly suitable for wireless charging of high-power electric vehicles. Compared with the traditional wired charging method, wireless charging technology improves user convenience, eliminates the cumbersome operation of plugging and unplugging cables, enhances user safety, avoids electrical contact problems and wear caused by wired connections, improves equipment durability, reduces mechanical wear, and prolongs service life. It has wide adaptability and can be flexibly applied in different environments, such as parking lots, garages, and public charging stations. Although wireless charging technology has shown great potential in the field of electric vehicles, its low transmission efficiency, low output power, standardization issues, and cost issues have affected the development and application of wireless charging technology for electric vehicles to a certain extent. In the WPT system, the magnetic coupling mechanism, as a key component of energy transmission, greatly affects the transmission efficiency of the system, but its shape, size, number of turns, coil radius, transmission distance between two coils, etc. are often affected by the actual space. Therefore, under limited conditions, it is necessary to accurately optimize it to achieve the best transmission efficiency. In order to optimize key parameters such as the number of turns, spacing, and transmission distance between two coils, the present invention adopts an advanced optimization algorithm. Through this algorithm, the system can efficiently search and adjust these parameter combinations within a preset range, so as to find the configuration with the highest transmission efficiency, which has important research significance and application value. Summary of the invention

[0003] The purpose of the present invention is to provide a method for optimizing the maximum efficiency of a wireless charging system based on an improved parrot algorithm, so as to solve the problem of the optimal efficiency method of coil structure design for wireless charging in a limited space proposed in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing the maximum efficiency of a wireless charging system based on an improved parrot algorithm comprises the following steps: S1. Establish the mutual inductance in the loosely coupled transformer of the LCC-S wireless charging system and the efficiency transfer adaptation function of the LCC-S; S2. Provide the parameter range of the number of turns, coil radius and transmission distance of the loosely coupled transformer according to actual needs; S3. According to the proposed improved Parrot algorithm, the adaptation function established in S1 and the parameter range given in S2 are optimized to calculate the parameters of the loosely coupled transformer at the maximum transmission efficiency.

[0005] Preferably, in S1, the mutual inductance adaptation function is established as follows (1): (1); in is the mutual inductance between the two coils of the loosely coupled transformer, is the magnetic permeability, , The number of turns of the transmitting coil and the number of turns of the receiving coil are divided into two parts. , are the radii of the transmitting coil and the receiving coil respectively, is the distance between the two coils.

[0006] Preferably, in step S1, the transmission efficiency adaptation function of the LCC-S wireless charging system is established as follows: (2); in , is the angular frequency of the system, is the system frequency, is the equivalent resistance of the secondary circuit, is the internal resistance of the transmitter compensation inductor, is the internal resistance of the primary coil, is the internal resistance of the secondary coil.

[0007] Preferably, in step S2, the efficiency to be optimized The improved Parrot algorithm combines the parameters in the system with the state variable data to obtain the unknown parameter turns of the system. , , coil radius , , transmission distance .

[0008] Preferably, in step S3, the improved parrot algorithm steps are: S31. Population initialization: Generate the initial population using the population initialization method of cat mapping and reverse learning strategy; S32. Fitness value calculation: Calculate the fitness value of each individual in the population. Each individual is a parameter , , , , The initial global optimal solution and average fitness value are determined by sorting. S33. Behavioral pattern updating: Parrot individuals update their positions through four behavioral patterns: foraging, staying, communicating, and fear of strangers. Adaptive switching factors are introduced into the communication behavior to disturb it. S34. Mutation operation: Use the adaptive Cauchy-Gaussian mixture mutation strategy to mutate individuals and generate new candidate solutions; S35. Update fitness value: calculate the new fitness value of the updated position and update the global optimal solution; S36. Record and save the best results to determine the population range for the next generation; S37. Iteration and Convergence: Update the number of iterations t = t +1, return to S32, until the maximum number of iterations or the convergence condition of the fitness value is reached.

[0009] Preferably, the Cat map is a two-dimensional reversible chaotic map, and its dynamic equation is as follows (3): (3); in Indicated in n The state variable of the step, mod1 means taking the decimal part of each element in the matrix multiplication result, and through the modulus operation, the result is always between the interval [0,1]; The specific steps of initializing the population based on the chaotic reverse learning strategy are: first, use the Cat chaotic sequence to generate N initial solutions , and then generate the corresponding inverse solution for each initial solution in the following way: (4); in, is a random number between [0,1]; For each initial solution The corresponding inverse solution; , Respectively represent all the initial solutions The minimum and maximum values ​​of the dimensional vector are obtained to obtain the corresponding N reverse solutions. The initial solution and the reverse solution are then merged to form 2N solutions and arranged in ascending order according to the fitness value. Finally, the top N solutions with the best fitness are selected as the initial population.

[0010] Preferably, the adaptive switching factor perturbs the position change of the unknown parameter particle. In the communication behavior stage of the parrot algorithm, the formula used to describe the behavior of the parrot in the population communication and the behavior of the parrot in information dissemination is as follows: (5); in, Indicates the updated position; is a random number between [0,1]; is the maximum number of iterations; is the average position of the population, is a random number on [0,1], Indicates the current iteration number; pass Factors are used to control the communication behavior of parrots in the population and the probability of parrots transmitting information. The factor formula is as follows: (6); Where: Indicates the current iteration number.

[0011] Preferably, in step S34, the steps of the adaptive Cauchy-Gaussian mixture mutation strategy are as follows: First, the parrot individuals with high fitness values ​​in the population are mutated, and then their position information before and after the mutation is recorded, and the individuals with better positions are sent to the next iteration. The formula is as follows: (7); in, Represents the individual position of the parrot after mutation, is the position before mutation, is the reference individual in the current parrot population The fitness value of For variant individuals The fitness value of Indicates the best individual in the parrot population when iteration reaches t The fitness value of and represent Cauchy distribution random variables and Gaussian distribution random variables respectively, where , , in the optimization process, as the number of iterations gradually increases, the dynamic parameter Reduce, will increase, so that the solution process will produce different mutation solutions with the number of iterations, helping the algorithm to jump out of the local optimum.

[0012] Preferably, the LCC-S wireless charging system includes a switch tube ( ), transmitter compensation inductor , compensation capacitor , self-inductance of the transmitting and receiving coils and , resonant compensation capacitor ,inductance 、Filter capacitor , coil internal resistance and ,load ,diode ,in is the mutual inductance between the two coils; Switching tube And switch tube Series, switch tube And switch tube Series, and the switch tube Connect in parallel to the switch Both ends, switch tube Connect in parallel to the switch Two ends; diode , Series, diode , Connect in series and then in parallel with the diode , A capacitor across a series circuit Connected in parallel at both ends of the Buck circuit, the load Connected in parallel with capacitor Both ends.

[0013] Preferably, PWM control is adopted, working in complementary mode with a duty cycle of 50%; when the switch tube and When turned on, the current flows through the transmitter compensation inductor and transmitter compensation capacitor 、 The LCC resonant network is composed of and When turned off, the current does not stop immediately, but flows through and The reverse parallel diode is connected to ensure the constant current freewheeling path; after the commutation is completed, and Shutdown, and The DC voltage is inverted into AC voltage through the full-bridge inverter circuit, which serves as the input voltage of the transmitting side resonant network. The transmitting side LCC structure uses resonance to transfer power from the transmitting end to the receiving end. In order to reduce the weight of the receiving end, the receiving end compensation circuit adopts a single capacitor. The resonant compensation form is composed of LCC-S topology; the AC voltage at the receiving end passes through the diode The full-bridge rectifier circuit is converted into a DC voltage, which is stepped down by a Buck circuit and then passed through a capacitor. The filtered output DC voltage is supplied to the load .

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present application provides a method for optimizing the maximum efficiency of a wireless charging system based on an improved parrot algorithm, which improves charging efficiency and is safer and more reliable. Introducing wireless charging technology into an electric vehicle charging system can eliminate the need for physical connectors in traditional charging methods and reduce wear and maintenance problems caused by plugging and unplugging cables. In traditional wired charging methods, charging connectors and sockets are prone to wear and aging, resulting in poor contact, which in turn causes risks such as overheating and overcharging. In wireless charging, car owners only need to park the vehicle above the charging plate to start charging, without having to deal with cumbersome cable connections, and the energy transmission process is not affected by environmental factors such as moisture, dust and temperature changes, so it is safer and more reliable than traditional plug-in charging. In addition, wireless charging can achieve higher flexibility and compatibility. It can be applied to electric vehicles of various types and models, without the need to configure different charging interfaces for different vehicles. In this way, car owners can enjoy the same convenient charging experience when charging at public charging stations or at home.

[0015] The improved Parrot algorithm optimizes the wireless charging system faster and more accurately than the traditional algorithm. By optimizing the parameters, engineers can adjust the parameters of the system's transceiver more accurately. This improvement not only improves the efficiency of the charging process and shortens the charging time, but also significantly improves the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they provide further detailed explanations but do not constitute a limitation of the present invention.

[0017] Figure 1 It is a circuit diagram principle diagram of the wireless charging system of the present invention.

[0018] Figure 2It is an overall block diagram of an optimization method for achieving maximum efficiency of a wireless charging system according to the present invention.

[0019] Figure 3 This is a diagram introducing the parameters of the loosely coupled transformer of the present invention.

[0020] Figure 4 It is a flow chart of the improved parrot algorithm of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the embodiments of the present invention and the drawings of the specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0022] In the description of the present invention, it should be understood that the terms "center", "vertical", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicating orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0023] A method for optimizing the maximum efficiency of a wireless charging system based on an improved Parrot algorithm is proposed to design the parameters of a loosely coupled transformer in an LCC-S wireless charging system. The design parameters include the following steps: Step 1: Establish the mutual inductance in the loosely coupled transformer of the LCC-S wireless charging system and the efficiency transfer adaptation function of the LCC-S; The mutual inductance adaptation function is established as follows: (1); in is the mutual inductance between the two coils of the loosely coupled transformer, is the magnetic permeability vacuum constant, , , The number of turns of the transmitting coil and the number of turns of the receiving coil are divided into two parts. , are the radii of the transmitting coil and the receiving coil respectively, is the distance between the two coils.

[0024] The transmission efficiency adaptation function of the LCC-S wireless charging system is established as follows: (2); in , is the angular frequency of the system, is the system frequency, is the equivalent resistance of the secondary circuit, is the internal resistance of the transmitter compensation inductor, is the internal resistance of the primary coil, is the internal resistance of the secondary coil.

[0025] Step 2: According to actual requirements, the parameter range of the number of turns, coil radius and transmission distance of the loosely coupled transformer is given; In step 2, the model of the wireless charging system is based on certain principles, such as the size restrictions of the receiving end and the transmitting end; the limitation of the transmission distance, so that it can optimally find the parameters of the system under a certain range of input conditions.

[0026] The designed parameter optimization method is based on the derivation of the system mathematical model, and the efficiency to be optimized is The improved Parrot algorithm combines the parameters in the system with the state variable data to obtain the unknown parameter turns of the system. , , coil radius , , transmission distance The improved parrot algorithm is improved from three aspects: using Cat mapping and reverse learning strategy to initialize the population, using adaptive switching factor strategy to allow parrots to communicate more among populations in the later iteration period, and using adaptive Gauss-Cauchy mixed mutation strategy to increase the diversity of the population.

[0027] Step 3, optimizing the adaptation function established in step 1 and the parameter range given in step 2 according to the proposed improved Parrot algorithm, and calculating the parameters of the loosely coupled transformer at the maximum transmission efficiency; like Figure 4 As shown, the flowchart of the improved parrot algorithm: S31. Population initialization: the size of the initial population, the maximum number of iterations, the upper and lower bounds of the search space, the dimension of the objective function, etc. The population initialization method using cat mapping and reverse learning strategy is used to generate the initial population; a. Cat mapping and reverse learning strategies were introduced in the initialization process of the improved Parrot algorithm: Using Cat Mapping to Enable Wireless Charging System with Unknown Parameters , , , , The highly diverse and unpredictable initial population of particles helps to search comprehensively in the search space. Cat mapping is a two-dimensional reversible chaotic mapping, and its dynamic equation is as follows (3): (3); in Represents the state variable at the nth step. Mod1 means taking the decimal part of each element in the matrix multiplication result. Through the modulus operation, the result is always between the interval [0,1].

[0028] The specific steps of initializing the population based on the chaotic reverse learning strategy are: first, use the Cat chaotic sequence to generate N initial solutions , and then generate the corresponding inverse solution for each initial solution in the following way: (4); in, is a random number between [0,1]; For each initial solution The corresponding inverse solution; , Respectively represent all the initial solutions The minimum and maximum values ​​of the dimensional vector are used to obtain the corresponding N reverse solutions, and then the initial solution and the reverse solution are merged to form 2N solutions and arranged in ascending order according to the fitness value. Finally, the top N solutions with the best fitness are selected as the initial population.

[0029] S32. Calculate the fitness value of each individual in the population. Each individual is a parameter , , , , The initial global optimal solution and average fitness value are determined by sorting.

[0030] S33. Parrot individuals update their positions through four behavioral modes: foraging, staying, communicating, and fear of strangers. An adaptive switching factor is introduced into the communication behavior to disturb it.

[0031] b. Use adaptive switching factors to control parrot communication behavior: The adaptive switching factor perturbs the position change of the unknown parameter particle. In the communication behavior stage of the parrot algorithm, the formula used to describe the behavior of the parrot in the population and the behavior of the parrot in information dissemination is as follows: (5); in, Indicates the updated position; is a random number between [0,1]; is the maximum number of iterations; is the average position of the population, is a random number on [0,1], Indicates the current iteration number.

[0032] Since the formula describing information propagation behavior has a small variation force, when the population falls into a local optimum, it cannot effectively jump out of the local optimum. Factors are used to control the probability of these two behaviors, so that parrots can engage in more inter-population communication behaviors in the later stages of iteration and reduce inefficient information dissemination. The factor formula is as follows: (6); Where: Indicates the current iteration number.

[0033] S34. Use mixed Cauchy and Gaussian mutation strategies to mutate individuals and generate new candidate solutions; c. Mixed Cauchy and Gaussian mutation: During the iteration of the traditional parrot optimization algorithm, due to the rapid assimilation of parrot individuals, parrot individuals tend to aggregate, which may cause the population to aggregate near the current optimal solution, causing the algorithm to fall into the local optimal solution. Since Gaussian mutation has strong local development ability but weak perturbation ability, while Cauchy mutation has stronger perturbation ability than Gaussian mutation and has stronger search ability. Therefore, an adaptive Cauchy-Gaussian hybrid mutation strategy that combines the advantages of Cauchy mutation and Gaussian mutation is used, and the mutation operator is used to interfere with individuals to increase the diversity of the population, improve convergence accuracy and iteration speed, and jump out of the local optimal solution. First, the parrot individuals with high fitness values ​​in the population are mutated, and then their position information before and after the mutation is recorded, and the individuals with better positions are sent to the next iteration. The formula is as follows (7): (7); in, Represents the individual position of the parrot after mutation, is the position before mutation, is the reference individual in the current parrot population The fitness value of For variant individuals The fitness value of Indicates the best individual in the parrot population when iteration reaches t The fitness value of and represent Cauchy distribution random variables and Gaussian distribution random variables respectively, where , , in the optimization process, as the number of iterations gradually increases, the dynamic parameter Reduce, will increase, so that the solution process will produce different mutation solutions with the number of iterations, helping the algorithm to jump out of the local optimum.

[0034] S35. Calculate the new fitness value of the updated position and update the global optimal solution.

[0035] S36. Record and save the best results to determine the population range for the next generation.

[0036] S37. Update the number of iterations t=t+1, and return to S32 until the maximum number of iterations or the convergence condition of the fitness value is reached.

[0037] The system has significantly improved system efficiency and transmission reliability, providing strong support for the development of wireless charging technology.

[0038] The LCC-S wireless charging system includes: five switch tubes including , the circuit is also equipped with a transmitter compensation inductor , compensation capacitor , self-inductance of the transmitting and receiving coils and , resonant compensation capacitor ,inductance 、Filter capacitor , coil internal resistance and ,load ,diode ,in is the mutual inductance between the two coils.

[0039] Specifically, the switch And switch tube Series, switch tube And switch tube Series, and the switch tube Connect in parallel to the switch Both ends, switch tube Connect in parallel to the switch Two ends of the diode , Series, diode , Connect in series and then in parallel with the diode , A capacitor across a series circuit Connected in parallel at both ends of the Buck circuit, the load Connected in parallel with capacitor Both ends.

[0040] like Figure 1 As shown in the figure, the circuit schematic of the wireless charging system. In the wireless charging system, the transmitter is connected to the resonant network through a full-bridge circuit, and the input voltage is provided by the power supply. The full-bridge inverter consists of a switch tube ( , , , ) and uses PWM control, working in complementary mode with a duty cycle of 50%. and When turned on, the current flows through the transmitter compensation inductor and transmitter compensation capacitor 、 The LCC resonant network is composed of and When turned off, the current does not stop immediately, but flows through and The reverse parallel diode is connected to ensure the constant current freewheeling path; after the commutation is completed, and Shutdown, and The DC voltage is inverted into AC voltage through the full-bridge inverter circuit, which serves as the input voltage of the transmitting side resonant network. The transmitting side LCC structure uses resonance to transfer power from the transmitting end to the receiving end. In order to reduce the weight of the receiving end, the receiving end compensation loop adopts a single capacitor. The resonant compensation form is composed of the LCC-S topology. The AC voltage at the receiving end passes through the diode The full-bridge rectifier circuit is converted into a DC voltage, which is stepped down by a Buck circuit and then passed through a capacitor. The filtered output DC voltage is supplied to the load .

[0041] like Figure 2 As shown in the figure, the overall block diagram of a method for optimizing the maximum efficiency of a wireless charging system based on the improved Parrot algorithm is shown in the figure. The parameter turns are given according to the actual conditions and design requirements. , , coil radius , , transmission distance The parameter range is set, and the unknown parameters are optimized by the improved Parrot algorithm. The parameters when the optimal result is optimized are the parameters when the system has the maximum transmission efficiency. The loosely coupled transformer is designed according to the optimized parameters.

[0042] like Figure 3 As shown, the loosely coupled transformer model diagram. , The number of turns of the transmitting coil and the number of turns of the receiving coil are divided into two parts. , are the radii of the transmitting coil and the receiving coil respectively, is the distance between the two coils.

[0043] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing the maximum efficiency of a wireless charging system based on an improved parrot algorithm, characterized in that: The following steps are involved: S1. Establish the mutual inductance in the loosely coupled transformer of the LCC-S wireless charging system and the efficiency transfer adaptation function of the LCC-S; S2. Provide the parameter range of the number of turns, coil radius and transmission distance of the loosely coupled transformer according to actual needs; S3. According to the proposed improved Parrot algorithm, the adaptation function established in S1 and the parameter range given in S2 are optimized to calculate the parameters of the loosely coupled transformer at the maximum transmission efficiency.

2. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 1, characterized in that: In S1, the mutual inductance adaptation function is established as follows (1): （1）； in is the mutual inductance between the two coils of the loosely coupled transformer, is the magnetic permeability, , The number of turns of the transmitting coil and the number of turns of the receiving coil are divided into two parts. , are the radii of the transmitting coil and the receiving coil respectively, is the distance between the two coils.

3. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 2, characterized in that: In step S1, the transmission efficiency adaptation function of the LCC-S wireless charging system is established as follows: （2）； in , is the angular frequency of the system, is the system frequency, is the equivalent resistance of the secondary circuit, is the internal resistance of the transmitter compensation inductor, is the internal resistance of the primary coil, is the internal resistance of the secondary coil, is the compensation inductance of the transmitter.

4. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 3 is characterized in that: In step S2, the efficiency to be optimized The improved Parrot algorithm combines the parameters in the state variable data to obtain the unknown parameters of the system, the number of turns of the transmitting coil and the number of turns of the receiving coil , , the number of turns of the transmitting coil and the radius of the receiving coil , , transmission distance .

5. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 4, characterized in that: In step S3, the improved parrot algorithm steps are: S31. Population initialization: Generate the initial population using the population initialization method of cat mapping and reverse learning strategy; S32. Fitness value calculation: Calculate the fitness value of each individual in the population. Each individual is a parameter , , , , The initial global optimal solution and average fitness value are determined by sorting. S33. Behavioral pattern updating: Parrot individuals update their positions through four behavioral patterns: foraging, staying, communicating, and fear of strangers. Adaptive switching factors are introduced into the communication behavior to disturb it. S34. Mutation operation: Use the adaptive Cauchy-Gaussian mixture mutation strategy to mutate individuals and generate new candidate solutions; S35. Update fitness value: calculate the new fitness value of the updated position and update the global optimal solution; S36. Record and save the best results to determine the population range for the next generation; S37. Iteration and Convergence: Update the number of iterations t = t +1, return to S32, until the maximum number of iterations or the convergence condition of the fitness value is reached.

6. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 4, characterized in that: Cat map is a two-dimensional reversible chaotic map, and its dynamic equation is as follows (3): （3）； in Indicated in n The state variable of the step, mod1 means taking the decimal part of each element in the matrix multiplication result, and through the modulus operation, the result is always between the interval [0,1]; The specific steps of initializing the population based on the chaotic reverse learning strategy are: first, use the Cat chaotic sequence to generate N initial solutions , and then generate the corresponding inverse solution for each initial solution in the following way: （4）； in, is a random number between [0,1]; For each initial solution The corresponding inverse solution; , Respectively represent all the initial solutions The minimum and maximum values ​​of the dimensional vector are obtained to obtain the corresponding N reverse solutions. The initial solution and the reverse solution are then merged to form 2N solutions and arranged in ascending order according to the fitness value. Finally, the top N solutions with the best fitness are selected as the initial population.

7. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 4, characterized in that: The adaptive switching factor perturbs the position change of the unknown parameter particle. In the communication behavior stage of the parrot algorithm, the formula used to describe the behavior of the parrot in the population and the behavior of the parrot in information dissemination is as follows: （5）； in, Indicates the updated position; is a random number between [0,1]; is the maximum number of iterations; is the average position of the population, is a random number on [0,1], Indicates the current iteration number; pass Factors are used to control the communication behavior of parrots in the population and the probability of parrots transmitting information. The factor formula is as follows: （6）； In the formula, Indicates the current iteration number.

8. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 4, characterized in that: In step S34, the steps of the adaptive Cauchy-Gaussian mixture mutation strategy are as follows: First, the parrot individuals with high fitness values ​​in the population are mutated, and then their position information before and after the mutation is recorded, and the individuals with better positions are sent to the next iteration. The formula is as follows: （7）； in, Represents the individual position of the parrot after mutation, is the position before mutation, is the reference individual in the current parrot population The fitness value of For variant individuals The fitness value of Indicates the best individual in the parrot population when iteration reaches t The fitness value of and represent Cauchy distribution random variables and Gaussian distribution random variables respectively, where , .

9. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 1, characterized in that: The LCC-S wireless charging system includes a switch tube ( ), transmitter compensation inductor , compensation capacitor , self-inductance of the transmitting and receiving coils and , resonant compensation capacitor ,inductance 、Filter capacitor , coil internal resistance and ,load ,diode ,in is the mutual inductance between the two coils; the switch tube And switch tube Series, switch tube And switch tube Series, and the switch tube Connect in parallel to the switch Both ends, switch tube Connect in parallel to the switch Two ends; diode , Series, diode , Connect in series and then in parallel with the diode , A capacitor across a series circuit Connected in parallel at both ends of the Buck circuit, the load Connected in parallel with capacitor Both ends.

10. The maximum efficiency optimization method for a wireless charging system based on an improved parrot algorithm according to claim 9, characterized in that: Using PWM control, working in complementary mode with a duty cycle of 50%; when the switch and When turned on, the current flows through the transmitter compensation inductor and transmitter compensation capacitor 、 The LCC resonant network is composed of and When turned off, the current does not stop immediately, but flows through and The reverse parallel diode is connected to ensure the constant current freewheeling path; after the commutation is completed, and Shutdown, and The DC voltage is inverted into AC voltage through the full-bridge inverter circuit, which serves as the input voltage of the transmitting side resonant network. The transmitting side LCC structure uses resonance to transfer power from the transmitting end to the receiving end. In order to reduce the weight of the receiving end, the receiving end compensation circuit adopts a single capacitor. The resonant compensation form is composed of LCC-S topology; the AC voltage at the receiving end passes through the diode The full-bridge rectifier circuit is converted into a DC voltage, which is stepped down by a Buck circuit and then passed through a capacitor. The filtered output DC voltage is supplied to the load .

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