Wireless power transmission system parameter optimization method based on improved tuna swarm optimization algorithm

Abstract

A wireless electric energy transmission system parameter optimization method based on an improved tuna swarm optimization algorithm comprises the following steps: firstly, establishing a mathematical model of a wireless electric energy transmission system with a resonant topological structure of an LCC-S type, analyzing key parameters influencing output power and transmission efficiency, and establishing a nonlinear constraint condition of the improved tuna swarm optimization algorithm; selecting self-inductance, compensation inductance and a coupling coefficient as optimization variables for optimization; and calculating other electrical parameters of the wireless power transmission system through the output optimization value, and deriving coil parameters from the calculated electrical parameters to carry out coil design. Compared with a traditional method in optimal parameter design, the method has the advantages that the problems of high-order nonlinearity and local optimal solution can be solved, the calculation complexity is reduced, and the transmission efficiency of the system is effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless power transmission technology, specifically relating to a method for optimizing the parameters of a wireless power transmission system based on an improved tuna swarm optimization algorithm; used to improve system transmission efficiency, power, and stability. Background Technology

[0002] Wireless power transfer technology has been widely used in recent years in fields such as electric vehicles, medical implants, consumer electronics, mobile robots, underwater exploration equipment, transportation, and aerospace. Wireless power transfer is mainly divided into magnetically coupled inductive power transfer (WPT), magnetically coupled resonant (MCR) WPT, and microwave radiating power transfer (WPT). Among them, magnetically coupled resonant WPT is widely used due to its high transmission efficiency, long transmission distance, and high transmission power.

[0003] In MCR-WPT systems, the compensation circuit plays a crucial role in power transmission. Due to the limitations of low-order compensation, higher-order compensation topologies such as LCC, LCL, and CLC have been proposed. The dual-terminal LCC compensation circuit can effectively achieve constant current output independent of the load and has a higher degree of freedom in parameter design, but its component count and cost are too high, and its parameter design is complex.

[0004] The performance of wireless power transfer systems is highly complex and nonlinear, involving multiple variables such as coil parameters, compensation network parameters, operating frequency, load, and the relative positions of the coils. Traditional parameter design methods heavily rely on the selection of initial values, easily getting trapped in local optima and failing to find the global optimum, resulting in system performance falling far short of theoretical levels. Furthermore, traditional parameter design methods are time-consuming, incurring high labor and material costs, severely hindering the commercialization of the technology and its market competitiveness. Summary of the Invention

[0005] To address the computational complexity and inability to handle high-dimensional and nonlinear issues inherent in traditional parameter design methods, this technical solution provides a parameter optimization method for wireless power transfer systems based on an improved tuna swarm optimization algorithm. Specifically, it involves a parameter optimization method for an LCC-S type WPT system based on an improved tuna swarm optimization algorithm with multiple optimization strategies. The LCC-S type compensation circuit possesses inherent constant current output characteristics, along with high parameter design freedom, fewer components, and simpler parameter design. Furthermore, it optimizes key system parameters under nonlinear constraints to obtain the system's maximum transmission efficiency and maximum power. Finally, the remaining electrical parameters of the wireless power transfer system are solved using the optimized electrical parameters, and coil parameters are derived from all electrical parameters, leading to the design of the coupling coil. This effectively solves the aforementioned technical problems.

[0006] This invention is achieved through the following technical solution:

[0007] A method for optimizing parameters of a wireless power transfer system based on an improved tuna swarm optimization algorithm, comprising the following steps:

[0008] Step 1: Create a wireless power transfer system, which includes a DC input power supply, a high-frequency inverter circuit, an LCC-S type compensation circuit, a rectifier circuit, and a load resistor;

[0009] Step 2: Analyze the parameters affecting the output power and output efficiency in the wireless power transmission system, establish nonlinear constraints, and use the improved tuna swarm algorithm with multiple optimization strategies to optimize the parameters affecting the output power and output efficiency of the wireless power transmission system, and output the optimized optimal parameters.

[0010] Step 3: Using the optimal parameters output in Step 1, calculate the remaining electrical parameters of the wireless power transmission system. Calculate the coil parameters and design the coupling coil based on all the electrical parameters of the wireless power transmission system obtained through optimization and calculation.

[0011] Furthermore, the steps for analyzing the parameters affecting output power and output efficiency in the wireless power transmission system described in step two include:

[0012] When a wireless power transmission system is in full-resonance mode, the system can achieve optimal transmission efficiency and maximum output power under the same input conditions; the full-resonance condition is:

[0013] In a fully resonant mode, the wireless power transfer system achieves optimal transmission efficiency and maximum output power under the same input conditions; the full resonance condition is:

[0014] ;

[0015] In the formula: and For the system's operating angular frequency and resonant frequency; L1, L p L s These are the primary-side compensating inductance, the primary-side coil self-inductance, and the secondary-side coil self-inductance, respectively; C p C s It consists of a series capacitor for the primary coil and a series capacitor for the secondary coil.

[0016] Furthermore, the output power and transmission efficiency of the wireless power transmission system described in step two are as follows:

[0017] ;

[0018] In the formula: P out P is the system output power. in Input power to the system; For the system's transmission efficiency; Uin I1 is the input voltage of the resonant network; I2 is the primary input current; R1 and R2 are the secondary input currents; R1 and R2 are the input voltages of the resonant network. p R s These represent the internal resistance of the primary-side compensating inductor and the internal resistance of the coupling coils on the primary and secondary sides, respectively; M and k are the mutual inductance and coupling coefficient, respectively; R L For load resistance;

[0019] Based on the above formulas for calculating output power and transmission power, when the coil internal resistance and load resistance R of the wireless power transmission system... L Under certain conditions, output power and transmission efficiency are mainly affected by the coupling coefficient k and the coil self-inductance L. p The influence of primary-side compensating inductor L1;

[0020] k, L p L1 is used as an optimization parameter to improve the tuna swarm algorithm:

[0021] ;

[0022] The objective functions are to maximize both transmission efficiency and output power.

[0023] ;

[0024] In the formula: w1 and w2 are the weighting coefficients for transmission efficiency and output power.

[0025] Furthermore, the specific steps for establishing nonlinear constraints and using the improved tuna swarm algorithm with multiple optimization strategies to optimize parameters, as described in step two, include:

[0026] Step 2.1: Set initial parameters, including population size, maximum number of iterations, upper and lower bounds of the solution space, initial temperature, and initial Lévy parameters;

[0027] Step 2.2: Initialize the tuna swarm population using the improved Tent chaotic mapping;

[0028] Step 2.3: Detect the current best individual, calculate the value of the fitness function (i.e., the objective function), and generate random numbers randp to distinguish the foraging methods of the tuna school;

[0029] Step 2.4: Iteratively update the solution using the Lévy flight introduced by spiral foraging, and obtain the final candidate solution. ;

[0030] Step 2.5: Introduce the simulated annealing Metropolis criterion to calculate the current solution. Move to the final candidate solution fitness changes And based on the fitness change and the probability P, the final candidate solution is selected as to whether to accept the new solution;

[0031] Step 2.6: Output the optimization target value and retain the best individual;

[0032] Step 2.7: Determine if the number of iterations is maximum. If yes, output the optimal individual; otherwise, return to step 2.3 to continue iterative updates.

[0033] Furthermore, the improved tuna swarm algorithm using multiple optimization strategies described in step two is operated as follows:

[0034] (1) Adding random variables to the Tent chaotic map yields an improved Tent chaotic map for population initialization, expressed as:

[0035] ;

[0036] In the formula: X i This represents the output of the i-th iteration; 2x i The current value of the linear square root in the Tent mapping from 0 to 0.5; A value between 0.5 and 1 can be symmetrically mapped to the interval between 0 and 0.5; rand(0,1) is a random number between 0 and 1; S is the population size; 1 / S is a small-amplitude perturbation that resists periodic degradation;

[0037] (2) Based on spiral foraging, the Lévy flight mechanism is introduced to generate the final candidate solution. The implementation method is as follows:

[0038] Assign a probability P to each individual tuna levy When randp is less than P levy Tuna schools feed in spiral patterns; when randp is greater than P levy If so, then the Levi flight will be executed; the final candidate solution for:

[0039] ;

[0040] In the formula: The current optimal individual; The current individual; t is the current iteration number; t max This represents the maximum number of iterations; Levy(x) is the Levy flight step size, which is equal to the random step size s multiplied by 0.01; 1 and 2 represents the weighting coefficient; It is the helical factor;

[0041] (3) Introducing the simulated annealing Metropolis criterion, the algorithm helps escape local optima by probabilistically accepting deteriorating solutions; first, calculate the solution from the current solution. Move to the final candidate solution fitness changes Then, a judgment is made, and the condition for the judgment is:

[0042] ;

[0043] In the formula: Let P be the individual at the latest updated position, where P is the probability that the current individual is replaced by the new individual; and exp is the exponential function. The current temperature; the temperature is according to = Cooling update performed; This is the cooling coefficient.

[0044] Furthermore, step three, which involves calculating coil parameters from electrical parameters and designing the coupling coil, specifically includes the following steps:

[0045] Step 3.1: The transmitting and receiving ends use identical planar circular coils, and the average radius r of the coil is determined by the optimized coupling coefficient k and the target transmission distance D. avg ;

[0046] Step 3.2: Set the initial number of turns N0 and the initial fill rate The initial number of turns N0 and the initial fill rate Substituting the formula into Wheeler's formula, we obtain the initial calculated inductance L0;

[0047] Step 3.3: From the initial fill rate and average radius r avg The initial inner diameter R of the coil was calculated. min0 and initial outer diameter R max0 ;

[0048] Step 3.4: Select the coil material, determine the wire diameter based on the skin depth, and select an appropriate turn spacing;

[0049] Step 3.5: Compare the calculated inductance L0 with the optimized inductance L P Adjust the number of coil turns N0 and the inner diameter of the coil R. min0 Repeat the above operation. The error between the calculated inductance and the optimized inductance is less than 1%, and the coupled coil that satisfies the optimal system transmission efficiency and output power is obtained.

[0050] Beneficial effects

[0051] The present invention proposes a parameter optimization method for wireless power transmission systems based on an improved tuna swarm optimization algorithm, which has the following advantages compared with existing technologies:

[0052] (1) This technical solution introduces the Levy flight mechanism into the original tuna swarm algorithm. By introducing occasional long-distance jumps, the Levy flight mechanism algorithm explores unknown areas far from the current population position, which enhances the global exploration capability of the algorithm. It also introduces the simulated annealing mechanism, which allows the algorithm to accept "worse" solutions with a certain probability. This means that even if a move causes a temporary decrease in efficiency or power, the algorithm has the opportunity to accept it, thereby jumping from one local optimum to another. The combination of the Levy flight mechanism and the simulated annealing mechanism greatly reduces the risk of the algorithm getting trapped in local optima.

[0053] (2) In the population initialization of this technical solution, random variables are added to the Tent chaotic map to obtain an improved Tent chaotic map, which overcomes the problems of small periodicity and unstable periodic points in the original Tent chaotic map. This makes the initial population cover a wider and more uniform solution space, providing a richer search starting point for the algorithm. The improved algorithm can perform a global search in a complex, multi-dimensional system parameter space and find the electrical parameters that achieve the global optimal performance of the wireless power transmission system.

[0054] (3) This technical solution uses a multi-optimized and improved tuna swarm algorithm to optimize and calculate the electrical parameters of all wireless power transmission systems. When the target transmission distance of the coupling coil is determined, the coil parameters are calculated from the electrical parameters of the wireless power transmission system, and the design of the coupling coil is completed. The coil parameters designed by this method are the optimal solution for the combined transmission efficiency and output power of the wireless power transmission system under given constraints, ensuring that the final designed coil is perfectly matched with the specific LCC-S compensation topology, thus ensuring the optimal performance of the entire system. While optimizing the electrical performance target, the algorithm also simultaneously determines the optimal coupling coefficient and coil self-inductance value, and reversely derives the precise coil structure, realizing a precise closed-loop design of electrical and physical components. In addition, this method can shorten the design cycle, reduce the repeated experiments, parameter measurements, and modifications in traditional design, greatly improve design efficiency, and reduce time and material costs. Attached Figure Description

[0055] Figure 1 This is a circuit diagram of the LCC-S type wireless power transmission system in this invention.

[0056] Figure 2 This is a flowchart of the improved tuna swarm algorithm with multiple optimization strategies in this invention.

[0057] Figure 3 This is a schematic diagram of the coupling coil structure in this invention.

[0058] Figure 4 A comparison chart of transmission efficiency before and after optimization of the wireless power transmission system.

[0059] Figure 5 A comparison chart of output power before and after optimization of the wireless power transmission system. Detailed Implementation

[0060] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. The described embodiments are merely some embodiments of the present invention, and not all embodiments. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the design concept of the present invention should fall within the protection scope of the present invention.

[0061] Example 1:

[0062] A method for optimizing parameters of a wireless power transfer system based on an improved tuna swarm optimization algorithm, comprising the following steps:

[0063] Step 1: Create a wireless power transfer system, which includes a DC input power supply, a high-frequency inverter circuit, an LCC-S type compensation circuit, a rectifier circuit, and a load resistor; such as Figure 1 As shown.

[0064] Step 2: Analyze the parameters affecting the output power and output efficiency in the wireless power transmission system, establish nonlinear constraints, and use the improved tuna swarm algorithm with multiple optimization strategies to optimize the parameters affecting the output power and output efficiency of the wireless power transmission system, and output the optimized optimal parameters.

[0065] The specific steps for analyzing the parameters affecting output power and output efficiency in a wireless power transmission system include:

[0066] The rectifier circuit and load resistance R are equivalent to R L When the wireless power transmission system is in full resonant mode, the system can obtain the optimal transmission efficiency and maximum output power under the same input conditions;

[0067] The full resonance condition is:

[0068] ;

[0069] In the formula: and For the system's operating angular frequency and resonant frequency; L1, L p L s These are the primary-side compensating inductance, the primary-side coil self-inductance, and the secondary-side coil self-inductance, respectively; C p C s It consists of a series capacitor for the primary coil and a series capacitor for the secondary coil.

[0070] The reflection impedance Z generated at the receiver and transmitter of this system can be obtained using basic circuit theorems. rfor:

[0071] ;

[0072] In the formula: R L R is the load resistance; s The internal resistance of the receiving coil; is the system resonant frequency; M is the mutual inductance of the coils.

[0073] System transmitter circuit impedance Z in for:

[0074] ;

[0075] In the formula: R1 is the internal resistance of the compensating inductor; L1 is the compensating inductor; R p This is the internal resistance of the transmitting coil.

[0076] The primary output current I1 can be obtained from the parallel current division:

[0077] ;

[0078] In the formula: U in For input voltage; C p It is a capacitor connected in series with the primary coil.

[0079] The secondary side output current I2 is:

[0080] ;

[0081] Therefore, the output voltage U can be obtained. out for:

[0082] .

[0083] With voltage and current satisfying ZPA and zero phase difference, zero-phase start-up is possible. Under these conditions, the output power and transmission efficiency of the WPT system are:

[0084] ;

[0085] In the formula: P out P is the system output power. in Input power to the system; For the system's transmission efficiency; L p L s U represents the self-inductance of the primary coil and the self-inductance of the secondary coil. in I1 is the input voltage of the resonant network; I2 is the primary input current; R1 and R2 are the secondary input currents; R1 and R2 are the input voltages of the resonant network. p R sThese represent the internal resistance of the primary-side compensating inductor and the internal resistance of the coupling coils on the primary and secondary sides, respectively; M and k are the mutual inductance and coupling coefficient, respectively; R L This is the load resistance.

[0086] Based on the above formulas for calculating output power and transmission power, when the coil internal resistance and load resistance R of the wireless power transmission system... L Under certain conditions, output power and transmission efficiency are mainly affected by the coupling coefficient k and the coil self-inductance L. p The influence of primary-side compensating inductor L1;

[0087] k, L p L1 is used as an optimization parameter to improve the tuna swarm algorithm:

[0088] ;

[0089] The objective functions are to maximize both transmission efficiency and output power.

[0090] ;

[0091] In the formula: w1 and w2 are the weighting coefficients for transmission efficiency and output power.

[0092] Establish nonlinear constraints, including:

[0093] (1) When the wireless power transmission system is in full resonance mode, the system can obtain the maximum transmission efficiency and output power under the same input conditions; according to the resonance condition, L P -L1 should be greater than 0.

[0094] (2) When the compensation inductance is too large, the resonant frequency will decrease, which may cause the system to deviate from the design frequency band and reduce efficiency. When the compensation inductance is too small, the resonant frequency will increase, which may cause high-frequency loss and switching loss. The coil self-inductance is high when it is too large and weak when it is too small. Therefore, setting L... 1max L pmax To compensate for the maximum value within the variable range of inductance. L 1min L pmin This is to compensate for the minimum value within the variable range of the inductance.

[0095] (3) The coil coupling coefficient in practical applications is generally 0 to 0.9, and the constraint conditions set for the transmission efficiency of the wireless power transmission system ensure its minimum performance requirements.

[0096] In summary, the nonlinear constraints are set as follows:

[0097] .

[0098] A modified tuna swarm algorithm using multiple optimization strategies is employed for parameter optimization, as shown in the attached figure. Figure 2 As shown, the specific operating steps include:

[0099] Step 2.1: Set initial parameters, including population size, maximum number of iterations, upper and lower limits of the solution space, initial temperature, and initial Lévy parameters.

[0100] Step 2.2: Use the improved Tent chaotic mapping to initialize the tuna swarm population.

[0101] An improved Tent chaotic map is obtained by adding random variables to the Tent chaotic map. This map is then used for population initialization, expressed as:

[0102] ;

[0103] In the formula: X i This represents the output of the i-th iteration; 2x i The current value of the linear square root in the Tent mapping from 0 to 0.5; The range 0.5 to 1 can be symmetrically mapped to the interval 0 to 0.5; rand(0,1) is a random number between 0 and 1; S is the population size; 1 / S is a small-amplitude perturbation that resists periodic degradation.

[0104] Step 2.3: Detect the current best individual, calculate the value of the fitness function (i.e., the objective function), and generate random numbers randp to distinguish the foraging methods of the tuna school.

[0105] Step 2.4: Iterative updates are performed by introducing Lévy flight into the spiral foraging process, and the final candidate solution is obtained. The implementation method is as follows:

[0106] Assign a probability P to each individual tuna levy When randp is less than P levy Tuna schools feed in spiral patterns; when randp is greater than P levy Then, Levy's flight will be executed. The final candidate solution for the tuna shoal population location. for:

[0107] ;

[0108] In the formula: The current optimal individual; The current individual; t is the current iteration number; t max This represents the maximum number of iterations; Levy(x) is the Levy flight step size, which is equal to the random step size s multiplied by 0.01; 1 and 2 represents the weighting coefficient; It is the helical factor.

[0109] Step 2.5: Introduce the simulated annealing Metropolis criterion to calculate the current solution. Move to the final candidate solution fitness changes The final candidate solution is selected based on the fitness change and the probability P, and it is then determined whether to accept the new solution.

[0110] The simulated annealing Metropolis criterion is introduced, which helps the algorithm escape local optima by probabilistically accepting deteriorating solutions.

[0111] First calculate from the current solution Move to the final candidate solution fitness changes Then, a judgment is made, and the condition for the judgment is:

[0112] ;

[0113] In the formula: Let P be the individual at the latest updated position, where P is the probability that the current individual is replaced by the new individual; and exp is the exponential function. The current temperature; the temperature is according to = Cooling update performed; This is the cooling coefficient.

[0114] Step 2.6: Output the optimization target value and retain the best individual.

[0115] Step 2.7: Determine if the number of iterations is maximum. If yes, output the optimal individual; otherwise, return to step 2.3 to continue iterative updates.

[0116] In the early stages of the algorithm, the simulated annealing temperature is very high, and the probability of accepting a deteriorated solution is high. At the same time, the Lévy flight performs a large-scale search, and the algorithm focuses on global search. In the later stages of the algorithm, the temperature decreases, the probability of accepting a deteriorated solution decreases, and the algorithm behavior becomes more and more like the traditional tuna swarm algorithm, which performs local development near the high-quality solutions that have been discovered and finely tunes the parameters to obtain maximum power and efficiency.

[0117] Step 3: Using the optimal parameters output in Step 2, calculate the remaining electrical parameters of the wireless power transmission system. Calculate the coil parameters and design the coupling coil based on all the optimized and calculated electrical parameters of the wireless power transmission system. The steps for designing the coupling coil include:

[0118] Step 3.1: The transmitting and receiving ends use identical planar circular coils. For two identical and coaxial parallel planar circular coils, the average radius r of the coil is determined by the optimized coupling coefficient k and the target transmission distance D. avg Its coupling coefficient k and average radius r avg The approximate relationship between the transmission distance D and the distance is:

[0119] ;

[0120] Given the optimized coupling coefficient k and the set target transmission distance D, the average radii of the transmitting and receiving coils can be derived.

[0121] Step 3.2: Set the initial number of turns N0 and the initial fill rate The initial number of turns N0 and the initial fill rate Substituting the formula into Wheeler's formula, we obtain the initial calculated inductance L0;

[0122] Wheeler's formula:

[0123] ;

[0124] In the formula: L is the self-inductance of the coil; r is the permeability of free space; N is the number of turns of the coil; avg The average radius of the coil; This represents the fill rate.

[0125] Step 3.3: From the initial fill rate and average radius r avg The initial inner diameter R of the coil was calculated. min0 and initial outer diameter R max0 .

[0126] =(R max -R min ) / (R max +R min ) ;

[0127] r avg =(R max +R min ) / 2;

[0128] In the formula: R max R is the radius of the outermost layer of the coil; min is the radius of the innermost layer of the coil.

[0129] Step 3.4: Select the coil material, determine the wire diameter based on the skin depth, and select an appropriate turn spacing;

[0130] The material selected is copper Litz wire, based on the skin depth. Determine the diameter of each individual wire strand, ensuring that the diameter of each strand is much smaller than the skin depth at the system's operating frequency; skin depth The calculation formula is:

[0131] ;

[0132] In the formula: ρ is the resistivity of the copper conductor; f is the system operating frequency.

[0133] Select the diameter d of the single-strand Litz wire l 0.5 And select the one closest to d from the common Leeds wire single strand diameter standard specifications. l Specifications. Turn spacing should be selected as 0.5-1 times d. l It has good heat dissipation capabilities.

[0134] Step 3.5: Compare the calculated inductance L0 with the optimized inductance L P And by adjusting the initial number of turns N0 and the initial inner diameter R of the coil min0 This makes the calculated inductance L0 infinitely close to the optimized inductance L. p .

[0135] The average radius r is always satisfied throughout the process. avg Under the unchanged condition, then compare L0 with L P :

[0136] If L0 <L p Then increase the number of turns N or decrease the inner diameter of the coil R. min ;

[0137] If L0>L p Then reduce the number of turns N or increase the inner diameter R of the coil. min ;

[0138] Repeat the above operation until |L0-L P | / L P <1%, to obtain a coupling coil that satisfies the optimal system transmission efficiency and output power, such as Figure 3 As shown.

[0139] Depend on Figure 4 and Figure 5It can be seen that, under the same conditions, the output power after using the improved tuna swarm optimization algorithm reaches about 28W and the transmission efficiency reaches about 85%, while the output power without algorithm optimization is only about 20W and the transmission efficiency is only about 65%. It is clear that the transmission efficiency and output power after using the improved tuna swarm optimization algorithm can reach a higher level overall. The optimized output power curve reaches the high power plateau region earlier, showing excellent global search ability and convergence ability.

[0140] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered by the present invention.

Claims

1. A method for optimizing parameters of a wireless power transmission system based on an improved tuna swarm optimization algorithm, characterized in that: Including the following steps: Step 1: Create a wireless power transfer system, which includes a DC input power supply, a high-frequency inverter circuit, an LCC-S type compensation circuit, a rectifier circuit, and a load resistor; Step 2: Analyze the parameters affecting the output power and output efficiency in the wireless power transmission system, establish nonlinear constraints, and use the improved tuna swarm algorithm with multiple optimization strategies to optimize the parameters affecting the output power and output efficiency of the wireless power transmission system, and output the optimized optimal parameters. Step 3: Using the optimal parameters output in Step 1, calculate the remaining electrical parameters of the wireless power transmission system. Calculate the coil parameters and design the coupling coil based on all the electrical parameters of the wireless power transmission system obtained through optimization and calculation.

2. The method for optimizing parameters of a wireless power transmission system based on an improved tuna swarm optimization algorithm according to claim 1, characterized in that: Step two describes the analysis of parameters affecting output power and output efficiency in a wireless power transmission system. The operational steps include: When a wireless power transmission system is in full-resonance mode, the system can achieve optimal transmission efficiency and maximum output power under the same input conditions; the full-resonance condition is: ； In the formula: and Here are the system's operating angular frequency and resonant frequency; L1, L p L s These are the primary-side compensating inductance, the primary-side coil self-inductance, and the secondary-side coil self-inductance, respectively; C p C s It consists of a series capacitor for the primary coil and a series capacitor for the secondary coil.

3. The method for optimizing parameters of a wireless power transmission system based on an improved tuna swarm optimization algorithm according to claim 2, characterized in that: The output power and transmission efficiency of the wireless power transmission system described in step two are as follows: ； In the formula: P out P is the system output power. in Input power to the system; For the system's transmission efficiency; U in I1 is the input voltage of the resonant network; I2 is the primary input current; R1 and R2 are the secondary input currents; R1 and R2 are the input voltages of the resonant network. p R s These represent the internal resistance of the primary-side compensating inductor and the internal resistance of the coupling coils on the primary and secondary sides, respectively; M and k are the mutual inductance and coupling coefficient, respectively; R L For load resistance; Based on the above formulas for calculating output power and transmission power, when the coil internal resistance and load resistance R of the wireless power transmission system... L Under certain conditions, output power and transmission efficiency are mainly affected by the coupling coefficient k and the coil self-inductance L. p The influence of primary-side compensating inductor L1; k, L p L1 is used as an optimization parameter to improve the tuna swarm algorithm: ； The objective functions are to maximize both transmission efficiency and output power. ； In the formula: w1 and w2 are the weighting coefficients for transmission efficiency and output power.

4. The method for optimizing parameters of a wireless power transmission system based on an improved tuna swarm optimization algorithm according to claim 1, characterized in that: Step two involves establishing nonlinear constraints and using a multi-optimization strategy to improve the tuna swarm algorithm for parameter optimization. The specific steps include: Step 2.1: Set initial parameters, including population size, maximum number of iterations, upper and lower bounds of the solution space, initial temperature, and initial Lévy parameters; Step 2.2: Initialize the tuna swarm population using the improved Tent chaotic mapping; Step 2.3: Detect the current best individual, calculate the value of the fitness function (i.e., the objective function), and generate random numbers randp to distinguish the foraging methods of the tuna school; Step 2.4: Iteratively update the solution using the Lévy flight introduced by spiral foraging, and obtain the final candidate solution. ; Step 2.5: Introduce the simulated annealing Metropolis criterion to calculate the current solution. Move to the final candidate solution fitness changes And based on the fitness change and the probability P, the final candidate solution is selected as to whether to accept the new solution; Step 2.6: Output the optimization target value and retain the best individual; Step 2.7: Determine if the number of iterations is maximum. If yes, output the optimal individual; otherwise, return to step 2.3 to continue iterative updates.

5. A method for optimizing the parameters of a wireless power transmission system based on an improved tuna swarm optimization algorithm according to claim 1 or 4, characterized in that: The improved tuna swarm algorithm using multiple optimization strategies described in step two is operated as follows: (1) Adding random variables to the Tent chaotic map yields an improved Tent chaotic map for population initialization, expressed as: ； In the formula: X i This represents the output of the i-th iteration; 2x i The current value of the linear square root in the Tent mapping from 0 to 0.5; A value between 0.5 and 1 can be symmetrically mapped to the interval between 0 and 0.5; rand(0,1) is a random number between 0 and 1; S is the population size; 1 / S is a small-amplitude perturbation that resists periodic degradation; (2) Based on spiral foraging, the Lévy flight mechanism is introduced to generate the final candidate solution. The implementation method is as follows: Assign a probability P to each individual tuna levy When randp is less than P levy Tuna schools feed in spiral patterns; when randp is greater than P levy If so, then the Levi flight will be executed; the final candidate solution for: ； In the formula: The current optimal individual; For the current individual; t is the current iteration number; t max This represents the maximum number of iterations; Levy(x) is the Levy flight step size, which is equal to the random step size s multiplied by 0.01; 1 and 2 represents the weighting coefficient; It is the helical factor; (3) Introducing the simulated annealing Metropolis criterion, the algorithm helps escape local optima by probabilistically accepting deteriorating solutions; first, calculate the solution from the current solution. Move to the final candidate solution fitness changes Then, a judgment is made, and the condition for the judgment is:

6. In the formula: Let P be the individual at the latest updated position, where P is the probability that the current individual is replaced by the new individual; and exp is the exponential function. The current temperature; the temperature is according to = Cooling update performed; This is the cooling coefficient.

7. The method for optimizing parameters of a wireless power transmission system based on an improved tuna swarm optimization algorithm according to claim 1, characterized in that: Step three, which involves calculating coil parameters from electrical parameters and designing the coupling coil, specifically includes the following steps: Step 3.1: The transmitting and receiving ends use identical planar circular coils, and the average radius r of the coil is determined by the optimized coupling coefficient k and the target transmission distance D. avg ; Step 3.2: Set the initial number of turns N0 and the initial fill rate The initial number of turns N0 and the initial fill rate Substituting Wheeler's formula, we obtain the initial calculated inductance L0; Step 3.3: From the initial fill rate and average radius r avg The initial inner diameter R of the coil was calculated. min0 and initial outer diameter R max0 ; Step 3.4: Select the coil material, determine the wire diameter based on the skin depth, and select an appropriate turn spacing; Step 3.5: Compare the calculated inductance L0 with the optimized inductance L P Adjust the number of coil turns N0 and the inner diameter of the coil R. min0 Repeat the above operation. The error between the calculated inductance and the optimized inductance is less than 1%, and the coupled coil that satisfies the optimal system transmission efficiency and output power is obtained.

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