Multi-parameter collaborative optimization method for wireless power transmission system based on VPSR-PSO algorithm

Abstract

The application discloses a wireless power transmission system multi-parameter collaborative optimization method based on a VPSR-PSO algorithm, and belongs to the technical field of wireless power transmission, and comprises the following steps: step 1: according to the primary side input voltage, the primary side input current, the primary side output current, the primary side compensation inductance, the primary side compensation inductance internal resistance, the primary side coil self-induction internal resistance and the primary side coil self-induction of the system, the secondary side output current, the secondary side coil self-induction, the primary side coil series capacitor, the secondary side coil self-induction internal resistance and the secondary side coil series capacitor, and the mutual inductance between the primary side coil and the secondary side coil; step 2: constructing a target model according to the primary side compensation inductance, the mutual inductance between the primary side coil and the secondary side coil and the equivalent load resistance; and establishing the constraint condition of the target model; the application constructs a multi-objective model for simultaneously optimizing transmission efficiency and output power, and realizes efficient search and balanced regulation and control.

Description

Technical Field

[0001] This invention relates to the field of wireless power transmission technology, and more specifically to a multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm. Background Technology

[0002] Wireless power transfer technology is reshaping the underlying logic of energy supply, with its contactless energy transfer mode demonstrating disruptive potential in areas such as dynamic charging of electric vehicles, powering implantable medical devices, and the wireless transformation of consumer electronics. In the new energy vehicle sector, dynamic wireless charging technology can significantly alleviate range anxiety, enabling the ultimate vision of "charging while driving" in conjunction with intelligent transportation systems. Wireless power supply for implantable medical devices, such as pacemakers, avoids the risks of repeated battery replacements, greatly improving patients' quality of life. The explosive growth of these applications signifies that wireless power transfer has become a key pillar of the energy internet revolution.

[0003] To improve system performance, researchers have successively introduced various intelligent algorithms for parameter optimization design. Genetic algorithms search for optimal solutions by simulating biological evolution mechanisms, but their convergence speed drops sharply when dealing with high-order systems; particle swarm optimization, while having the advantage of parallel search, is prone to getting trapped in local optima, resulting in limited improvements in transmission efficiency; multi-objective optimization frameworks, while able to balance transmission efficiency and power, struggle to balance the strong coupling conflicts between parameters. More fundamentally, key parameters such as inductance, load, and internal resistance are mutually restrictive, and adjusting a single parameter often triggers system resonance instability. Designers often need to repeatedly try and fail, consuming significant computational resources without achieving performance breakthroughs. These bottlenecks severely restrict the commercialization of wireless charging products.

[0004] Based on this, the present invention designs a multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO (Velocity Pausing Stagnation ResetParticle Swarm Optimization) algorithm to adapt to complex and ever-changing dynamic environments and meet practical application requirements. Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention provides a multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A multi-parameter collaborative optimization method for wireless power transfer systems based on the VPSR-PSO algorithm includes the following steps:

[0008] Step 1: Based on the primary input voltage, primary input current, primary output current, primary compensation inductor, primary compensation inductor internal resistance, primary coil self-inductance and primary coil self-inductance of the primary side, the secondary output current, secondary coil self-inductance, primary coil series capacitance, secondary coil self-inductance and secondary coil series capacitance, mutual inductance between the primary and secondary coils, and equivalent load resistance, construct a two-coil circuit model under full resonance state, and obtain the output power and transmission efficiency.

[0009] Step 2: Construct the target model based on the primary-side compensation inductance, the mutual inductance between the primary-side coil and the secondary-side coil, and the equivalent load resistance; and establish the constraints of the target model.

[0010] Step 3: Maximize the target model as the optimization objective. Combine the constraints of the target model with the optimization solution using the VPSR-PSO algorithm to obtain the Pareto front solution between the optimized output power and transmission efficiency.

[0011] Step 4: Determine the primary-side compensation inductance using the Pareto front solution. L P Mutual inductance between the primary and secondary coils M Equivalent load resistance R L This allows for the determination of transmission efficiency and output power, enabling efficient searching and balanced regulation of transmission efficiency and output power.

[0012] Furthermore, the specific two-coil circuit model is as follows:

[0013] Input impedance Z IN and secondary impedance Z 2 They can be represented as follows:

[0014]

[0015] Z R This is the reflection impedance from the secondary side to the primary side;

[0016] L P 、L 1 、L 2 、C P 、C 1 、C 2These are, respectively, the primary-side compensation inductor, the primary-side coil self-inductance, the secondary-side coil self-inductance, the primary-side compensation capacitor, the primary-side coil series capacitor, and the secondary-side coil series capacitor;

[0017] R P 、R 1 、R 2 、R L These are, respectively, the internal resistance of the primary-side compensating inductor, the internal resistance of the primary-side coil, the internal resistance of the secondary-side coil, and the equivalent load resistance;

[0018] Reflection impedance from secondary side to primary side Z R The specific calculations are as follows:

[0019]

[0020] M This refers to the mutual inductance between the primary and secondary coils.

[0021]

[0022] , , , These are the primary side input voltage, primary side input current, primary side output current, and secondary side output current, respectively.

[0023] The following conditions must be met in the fully resonant state:

[0024]

[0025] This refers to the system's operating angular frequency; This is the system's inherent resonant angular frequency;

[0026] Output power and transmission efficiency can be expressed as:

[0027]

[0028] For output power, For transmission efficiency, For input transmission efficiency.

[0029] Furthermore, optimize the target :

[0030]

[0031] Furthermore, the constraints of the target model are as follows:

[0032]

[0033] Furthermore, step 3 is detailed below:

[0034] Step 31: Determine the position and velocity of the particles in the initial state, and record the stagnation count value as 0;

[0035] Step 32: Calculate the initial output power and transmission efficiency by determining the particle positions under the initial state, and calculate the initial individual optimal solution. and the group optimal solution And save it to the initial Pareto archive;

[0036] Step 33: Calculate the nonlinear dynamic inertia weight. ;

[0037] Step 34: Determine if the particle has stopped. If the determination is no, proceed to step 35. If the determination is yes, reset the particle's speed, position, and stop count value, and then re-execute step 34.

[0038] Step 35: Determine a random value using a random function, and determine whether the random value is greater than or equal to the speed pause parameter. If the determination is yes, update the particle's velocity and execute step 36; if the determination is no, the velocity remains unchanged and execute step 36.

[0039] Step 36: Update the particle's position using velocity and determine whether the updated particle's position can dominate the individual's historical optimal solution. If the judgment is yes, then the individual's historical optimal solution. The update is successful and the stall count is cleared to zero. Then proceed to step 37. If the result is negative, increment the stall count by one and proceed to step 37.

[0040] Step 37, Update the population optimal solution Update the Pareto archive;

[0041] Step 38: Determine whether the Pareto archive has exceeded the number of iterations based on the updated Pareto archive. If yes, determine the Pareto front solution and end the optimization. If no, proceed to step 33.

[0042] Furthermore, nonlinear dynamic inertia weights The calculation method is as follows:

[0043]

[0044] in, For the number of iterations, It is the total number of iterations. It is a constant, usually set to 2.5.

[0045] Furthermore, the stagnation condition is determined by whether the stagnation count value is greater than the stagnation threshold. If the condition is met, the particle's velocity, position, and stagnation count value are reset, and step 34 is executed again.

[0046] Furthermore, the particle velocity update formula in step 35 is as follows:

[0047]

[0048]

[0049] Representing the The particle in the first The iteration speed; For the individual optimal solution; The optimal solution for the population; , Here is the acceleration constant; , It is a random number; Representing the The particle in the first Position information for the next iteration.

[0050] Furthermore, the particle position update formula in step 35 is as follows:

[0051] ;

[0052]

[0053] It is the dimension of the problem.

[0054] Compared with the prior art, the beneficial effects of this invention are as follows: the VPSR-PSO algorithm of this invention performs multi-objective collaborative optimization of system parameters, in order to... L P , M and R L Using key parameters as decision variables, a multi-objective model is constructed to simultaneously optimize transmission efficiency and output power, achieving efficient search and balanced control. Compared to traditional multi-objective optimization strategies, it has advantages in convergence performance and solution distribution quality, providing an effective parameter optimization approach to improve the overall performance of WPT systems, and possessing good potential for general application. Furthermore, it integrates three strategies—dynamic inertia weighting, probabilistic velocity pausing, and stagnation reset—to enhance global search capabilities, avoid getting trapped in local optima, and achieve an overall improvement in system performance. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a diagram of the MCRWPT two-coil system of the present invention;

[0057] Figure 2 This is the equivalent circuit diagram of the MCRWPT two-coil system of the present invention;

[0058] Figure 3 For efficiency M and R L A schematic diagram illustrating the changes;

[0059] Figure 4 For output power to vary M and R L A schematic diagram illustrating the changes;

[0060] Figure 5 For efficiency L P and M A schematic diagram illustrating the changes;

[0061] Figure 6 For output power to vary L P and M A schematic diagram illustrating the changes;

[0062] Figure 7 For efficiency L P and R L A schematic diagram illustrating the changes;

[0063] Figure 8 For output power to vary L P and R L A schematic diagram illustrating the changes;

[0064] Figure 9 This is a flowchart of the present invention;

[0065] Figure 10 This is a schematic diagram of the Pareto front solution for a specific case. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0067] Example 1:

[0068] Please see Figure 1 , 2 9. To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0069] A multi-parameter collaborative optimization method for wireless power transfer systems based on the VPSR-PSO algorithm includes the following steps:

[0070] Step 1: Based on the primary input voltage, primary input current, primary output current, primary compensation inductor, primary compensation inductor internal resistance, primary coil self-inductance and primary coil self-inductance of the primary side, the secondary output current, secondary coil self-inductance, primary coil series capacitance, secondary coil self-inductance and secondary coil series capacitance, mutual inductance between the primary and secondary coils, and equivalent load resistance, construct a two-coil circuit model under full resonance state, and obtain the output power and transmission efficiency.

[0071] Step 2: Construct the target model based on the primary-side compensation inductance, the mutual inductance between the primary-side coil and the secondary-side coil, and the equivalent load resistance; and establish the constraints of the target model.

[0072] Step 3: Maximize the target model as the optimization objective. Combine the constraints of the target model with the optimization solution using the VPSR-PSO algorithm to obtain the Pareto front solution between the optimized output power and transmission efficiency.

[0073] Step 4: Determine the primary-side compensation inductance using the Pareto front solution. L P Mutual inductance between the primary and secondary coils M Equivalent load resistance R L This allows for the determination of transmission efficiency and output power, enabling efficient searching and balanced regulation of transmission efficiency and output power.

[0074] The specific model of the two-coil circuit is as follows:

[0075] Input impedance Z IN and secondary impedanceZ 2 They can be represented as follows:

[0076]

[0077] Z R This is the reflection impedance from the secondary side to the primary side;

[0078] L P 、L 1 、L 2 、C P 、C 1 、C 2 These are, respectively, the primary-side compensation inductor, the primary-side coil self-inductance, the secondary-side coil self-inductance, the primary-side compensation capacitor, the primary-side coil series capacitor, and the secondary-side coil series capacitor;

[0079] R P 、R 1 、R 2 、R L These are, respectively, the internal resistance of the primary-side compensating inductor, the internal resistance of the primary-side coil, the internal resistance of the secondary-side coil, and the equivalent load resistance;

[0080] Reflection impedance from secondary side to primary side Z R The specific calculations are as follows:

[0081]

[0082] M This refers to the mutual inductance between the primary and secondary coils.

[0083]

[0084] , , , These are the primary side input voltage, primary side input current, primary side output current, and secondary side output current, respectively.

[0085] The following conditions must be met in the fully resonant state:

[0086]

[0087] This refers to the system's operating angular frequency; for The system's inherent resonant angular frequency;

[0088] Output power and transmission efficiency can be expressed as:

[0089]

[0090] For output power, For transmission efficiency, For input transmission efficiency.

[0091] Optimization Objective :

[0092] .

[0093] Optimization Objective Aimed at maximizing system transmission efficiency η With output power P OUT .

[0094] The constraints of the target model are as follows:

[0095]

[0096] Step 3 is as follows:

[0097] Step 31: Determine the position and velocity of the particles in the initial state, and record the stagnation count value as 0;

[0098] Step 32: Calculate the initial output power and transmission efficiency by determining the particle positions under the initial state, and calculate the initial individual optimal solution. and the group optimal solution And save it to the initial Pareto archive;

[0099] Step 33: Calculate the nonlinear dynamic inertia weight. ;

[0100] Step 34: Determine if the particle has stopped. If the determination is no, proceed to step 35. If the determination is yes, reset the particle's speed, position, and stop count value, and then re-execute step 34.

[0101] Step 35: Determine a random value using a random function, and determine whether the random value is greater than or equal to the speed pause parameter. If the determination is yes, update the particle's velocity and execute step 36; if the determination is no, the velocity remains unchanged and execute step 36.

[0102] Step 36: Update the particle's position using velocity and determine whether the updated particle's position can dominate the individual's historical optimal solution. If the judgment is yes, then the individual's historical optimal solution. The update is successful and the stall count is cleared to zero. Then proceed to step 37. If the result is negative, increment the stall count by one and proceed to step 37.

[0103] Step 37, Update the population optimal solution Update the Pareto archive;

[0104] Step 38: Determine whether the Pareto archive has exceeded the number of iterations based on the updated Pareto archive. If yes, determine the Pareto front solution and end the optimization. If no, proceed to step 33.

[0105] Nonlinear dynamic inertia weight The calculation method is as follows:

[0106]

[0107] in, For the number of iterations, It is the total number of iterations. It is a constant, usually set to 2.5.

[0108] The condition for determining whether a particle is stuck is whether the stuck count value is greater than the stuck threshold. If the condition is met, the particle's velocity, position, and stuck count value are reset, and step 34 is executed again.

[0109] The particle velocity update formula in step 35 is as follows:

[0110]

[0111]

[0112] Representing the The particle in the first The iteration speed; For the individual optimal solution; The optimal solution for the population; , Here is the acceleration constant; , It is a random number; Representing the The particle in the first Position information for the next iteration. It is the speed pause parameter.

[0113] Speed ​​pause parameters A value of 0.3 indicates a 0.3 probability that the speed will remain unchanged, which generally falls within the range of 0-1.

[0114] Speed ​​pause parameters This allows for a more detailed particle search.

[0115] 、 This is to randomly select a value between [0,1].

[0116] The particle position update formula in step 35 is as follows:

[0117] ;

[0118]

[0119] It is the dimension of the problem.

[0120] This invention uses the VPSR-PSO algorithm to perform multi-objective collaborative optimization of system parameters, in order to L P , M and R L Using key parameters as decision variables, a multi-objective model is constructed to simultaneously optimize transmission efficiency and output power, achieving efficient search and balanced control. Compared to traditional multi-objective optimization strategies, it has advantages in convergence performance and solution distribution quality, providing an effective parameter optimization approach to improve the overall performance of WPT systems, and possessing good potential for general application. Furthermore, it integrates three strategies—dynamic inertia weighting, probabilistic velocity pausing, and stagnation reset—to enhance global search capabilities, avoid getting trapped in local optima, and achieve an overall improvement in system performance.

[0121] Experimental examples, such as Figure 3-8 As shown:

[0122] Figure 3 Demonstration system transmission efficiency η With mutual induction M load resistor R L The trend of change. When M <20 At μH, the transmission efficiency increases significantly with increasing mutual inductance, mainly due to the enhanced energy transfer capability resulting from improved magnetic coupling; while when M >20 After μH, the increase in transmission efficiency slows down and gradually saturates, indicating that the system has entered the strongly coupled region. R L The transmission efficiency increases significantly with increasing dimension, then tends to stabilize, reflecting that there is an optimal load range in the system to achieve the best transmission efficiency.

[0123] Figure 4 The system output power was demonstrated. POUT With mutual induction M With load resistance R L The trend of change. The results show that... P OUT Follow M Increase overall improvement, but M >40 The slowdown in the increase within the μH range indicates that the system has entered a coupling saturation state. Meanwhile, R L Changes in load significantly affect output power, and there exists an optimal load range: initially, appropriately increasing... R L It can increase the output power, but if it is too high, the output power will decrease due to impedance mismatch.

[0124] Figure 5 Demonstrated transmission efficiency η With mutual induction M With primary side compensation inductor L P The trend of change. It can be observed that, with... M With the increase in [unclear], the overall system transmission efficiency has been improved, especially in [unclear]. L P This is more pronounced when the size is larger. It is worth noting that, in... M Larger (e.g., 50–60) When μH), as L P As the inductance increases, the transmission efficiency surface changes from a steep, rapid rise to a slow saturation, exhibiting a "flat-top" structure. This indicates that under high coupling strength, appropriately increasing the primary-side compensation inductance helps the system approach a resonant state, achieving efficient energy transfer. Therefore, from a system optimization perspective, selecting a suitable inductance... L P and M Combining these methods can effectively improve system transmission efficiency.

[0125] Figure 6 Output power was demonstrated P OUT With mutual induction M With primary side compensation inductor L P The trend of change. It can be observed that the system output power exhibits a typical "ridge-shaped distribution," revealing the existence of a joint parameter path that achieves maximum output power transfer. When M When it is smaller, L P The regulatory effect is relatively strong; while M >>30 At μH, the coupling strengthens, and the system's response to... L PThe sensitivity decreases. This phenomenon reflects a significant synergistic optimization relationship between the compensation parameters and mutual inductance.

[0126] Figure 7 Further analysis η Follow L P and R L The combined variation characteristics of the transmission efficiency are shown. The results indicate that the transmission efficiency varies with... R L The increase first rises and then tends to stabilize, demonstrating a clear optimal load matching characteristic; while L P The impact is relatively small, but it still has an auxiliary regulating effect on transmission efficiency optimization.

[0127] Figure 8 This further characterizes the output power. P OUT Follow L P With load resistance R L The trend of change. It can be observed that when... L P At a low value and R L When properly matched, the system output power reaches its peak; this region represents the optimal output power transmission range for the LCC-S system. With... L P or R L As the inductance continues to increase, the output power drops rapidly, indicating that an excessively large compensation inductor will cause the system to deviate from its resonant state, reducing the system's energy transfer.

[0128] Overall, Figure 3-8 Revealed L P , M , R L The three factors exhibit a highly nonlinear synergistic effect; system performance is sensitive to the joint changes of multiple parameters, and different parameter combinations will lead to changes in transmission efficiency.

[0129] The output power performance differs significantly from the expected performance. This complex coupling characteristic highlights the limitations of traditional analytical or point-by-point scanning methods in obtaining optimal solutions, and verifies the necessity and effectiveness of intelligent optimization methods in parameter design.

[0130] Specific examples:

[0131] like Figure 10As shown, with the particle swarm population size set to 100, the maximum number of iterations to 200, the individual optimal solution to 2, the swarm optimal solution to 2, the velocity pausing parameter to 0.3, and the stagnation threshold to 10, the Pareto front solutions obtained from the test are shown in the table below:

[0132]

[0133] The first three columns in the table above are L P M, R L Three variables (parameters), the last two columns are transmission efficiency. η and output power P OUT (Optimization Objective) describes a portion of the frontier solutions obtained after using this application for optimization. In practical applications, parameters can be set according to specific circumstances (e.g., high efficiency and low power or low efficiency and high power).

[0134] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm, characterized in that: Includes the following steps: Step 1: Based on the primary input voltage, primary input current, primary output current, primary compensation inductor, primary compensation inductor internal resistance, primary coil self-inductance and primary coil self-inductance of the primary side, the secondary output current, secondary coil self-inductance, primary coil series capacitance, secondary coil self-inductance and secondary coil series capacitance, mutual inductance between the primary and secondary coils, and equivalent load resistance, construct a two-coil circuit model under full resonance state, and obtain the output power and transmission efficiency. Step 2: Construct the target model based on the primary-side compensation inductance, the mutual inductance between the primary-side coil and the secondary-side coil, and the equivalent load resistance; and establish the constraints of the target model. Step 3: Maximize the target model as the optimization objective. Combine the constraints of the target model with the optimization solution using the VPSR-PSO algorithm to obtain the Pareto front solution between the optimized output power and transmission efficiency. Step 3 is as follows: Step 31: Determine the position and velocity of the particles in the initial state, and record the stagnation count value as 0; Step 32: Calculate the initial output power and transmission efficiency by determining the particle positions under the initial state, and calculate the initial individual optimal solution. and the group optimal solution And save it to the initial Pareto archive; Step 33: Calculate the nonlinear dynamic inertia weight. ; Step 34: Determine if the particle has stopped. If the determination is no, proceed to step 35. If the determination is yes, reset the particle's speed, position, and stop count value, and then re-execute step 34. Step 35: Determine a random value using a random function, and determine whether the random value is greater than or equal to the speed pause parameter. If the determination is yes, update the particle velocity and execute step 36; if the determination is no, the velocity remains unchanged and execute step 36. Step 36: Update the particle's position using velocity and determine whether the updated particle's position can dominate the individual's historical optimal solution. If the judgment is yes, then the individual's historical optimal solution. The update is successful and the stall count is cleared to zero. Then proceed to step 37. If the result is negative, increment the stall count by one and proceed to step 37. Step 37, Update the population optimal solution Update the Pareto archive; Step 38: Determine whether the Pareto archive has exceeded the number of iterations based on the updated Pareto archive. If yes, determine the Pareto front solution and end the optimization. If no, proceed to step 33. Step 4: Determine the primary-side compensation inductance using the Pareto front solution. L P Mutual inductance between the primary and secondary coils M Equivalent load resistance R L This allows for the determination of transmission efficiency and output power, enabling efficient searching and balanced regulation of transmission efficiency and output power.

2. The multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm according to claim 1, characterized in that, The specific model of the two-coil circuit is as follows: Input impedance Z IN and secondary impedance Z 2 They can be represented as ； Z R This is the reflection impedance from the secondary side to the primary side; L P 、L 1 、L 2 、C P 、C 1 、C 2 These are, respectively, the primary-side compensation inductor, the primary-side coil self-inductance, the secondary-side coil self-inductance, the primary-side compensation capacitor, the primary-side coil series capacitor, and the secondary-side coil series capacitor; R P 、R 1 、R 2 、R L These are, respectively, the internal resistance of the primary-side compensating inductor, the internal resistance of the primary-side coil, the internal resistance of the secondary-side coil, and the equivalent load resistance; Reflection impedance from secondary side to primary side Z R The specific calculations are as follows: ； M This refers to the mutual inductance between the primary and secondary coils. ； , , , These are the primary side input voltage, primary side input current, primary side output current, and secondary side output current, respectively. The following conditions must be met in the fully resonant state: ； This refers to the system's operating angular frequency; This is the system's inherent resonant angular frequency; Output power and transmission efficiency can be expressed as: ； For output power, For transmission efficiency, For input transmission efficiency.

3. The multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm according to claim 2, characterized in that, Optimization Objective : 。 4. The multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm according to claim 3, characterized in that, The constraints of the target model are as follows: 。 5. The multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm according to claim 4, characterized in that, Nonlinear dynamic inertia weight The calculation method is as follows: ； in, For the number of iterations, It is the total number of iterations. It is a constant, usually set to 2.

5.

6. The multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm according to claim 5, characterized in that, The condition for determining whether a particle is stuck is whether the stuck count value is greater than the stuck threshold. If the condition is met, the particle's velocity, position, and stuck count value are reset, and step 34 is executed again.

7. The multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm according to claim 6, characterized in that, The particle velocity update formula in step 35 is as follows: ； ； Representing the The particle in the first The iteration speed; For the individual optimal solution; The optimal solution for the population; , Here is the acceleration constant; , It is a random number; Representing the The particle in the first Position information for the next iteration.

8. The multi-parameter collaborative optimization method for wireless power transmission systems based on the VPSR-PSO algorithm according to claim 7, characterized in that, The particle position update formula in step 35 is as follows: ； ； It is the dimension of the problem.

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