Wireless power transmission system multi-parameter collaborative optimization method based on VPSR-PSO algorithm

Abstract

The invention 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. Comprising the following steps: step 1, according to a primary side input voltage, a primary side input current, a primary side output current, a primary side compensation inductor, a primary side compensation inductor internal resistance, a primary side coil self-inductance internal resistance and a primary side coil self-inductance of a primary side of a system, calculating a primary side compensation voltage according to the primary side compensation inductor internal resistance, the primary side compensation inductor internal resistance and the primary side coil self-inductance; secondary side output current, secondary side coil self-inductance, primary side coil series capacitor, secondary side coil self-inductance internal resistance, secondary side coil series capacitor and mutual inductance between the primary side coil and the secondary side coil are arranged on the secondary side; 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; establishing constraint conditions of the target model; according to the method, the multi-target model for optimizing the transmission efficiency and the output power at the same time is constructed, and efficient search and balanced regulation and control are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless power transmission technology, and in particular to a multi-parameter collaborative optimization method for a wireless power transmission system based on a VPSR-PSO algorithm. BACKGROUND

[0002] Wireless power transmission technology is reshaping the underlying logic of energy supply, and its non-contact energy transmission mode has shown disruptive potential in dynamic charging of electric vehicles, power supply for implanted medical devices, and wireless of consumer electronics. In the field of new energy vehicles, dynamic wireless charging technology can significantly alleviate range anxiety and realize the ultimate vision of "charging while driving" with intelligent transportation systems. Wireless power supply for medical implant devices such as pacemakers avoids the risk of repeated surgery to replace batteries and greatly improves the quality of life for patients. The explosive growth of these application scenarios marks wireless power transmission as a key pillar of the energy internet revolution.

[0003] To improve system performance, researchers have introduced various intelligent algorithms for parameter optimization design. Genetic algorithms search for optimal solutions by simulating biological evolution mechanisms, but the convergence speed decreases dramatically when dealing with high-order systems; particle swarm optimization algorithms have parallel search advantages, but are prone to local optimal traps, resulting in limited transmission efficiency improvement; multi-objective optimization frameworks can balance transmission efficiency and power, but it is difficult to balance the strong coupling conflicts between parameters. The more fundamental problem is that inductance, load, and internal resistance are mutually restrictive, and single parameter adjustment often causes system resonance instability. Designers often need to try and error repeatedly, consuming a lot of computing resources and still difficult to break through performance. The above bottlenecks have seriously restricted the commercialization process of wireless charging products.

[0004] Therefore, the present application designs a multi-parameter collaborative optimization method for a wireless power transmission system based on a VPSR-PSO (Velocity Pausing Stagnation Reset Particle Swarm Optimization) algorithm to adapt to complex and variable dynamic environments and meet the needs of practical applications. SUMMARY

[0005] In view of the above-mentioned shortcomings of the prior art, the present application provides a multi-parameter collaborative optimization method for a wireless power transmission system based on a VPSR-PSO algorithm.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: The multi-parameter collaborative optimization method for a wireless power transmission system based on a VPSR-PSO algorithm 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 resistance, the primary side coil self-inductance resistance and the primary side coil self-inductance of the primary side of the system, the secondary side output current, the secondary side coil self-inductance, the primary side coil series capacitance, the secondary side coil self-inductance resistance and the secondary side coil series capacitance of the secondary side, the mutual inductance between the primary side coil and the secondary side coil, and the equivalent load resistance, a two-coil circuit model in full resonance state is constructed, and the output power and the transmission efficiency are obtained; Step 2: 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, a target model is constructed; and the constraint condition of the target model is established; Step 3: Taking the maximum of the target model as the optimization target, combining the constraint condition of the target model, the VPSR-PSO algorithm is used for optimization solution, and the Pareto frontier solution between the optimized output power and the transmission efficiency is obtained; Step 4: The primary side compensation inductance L P , the mutual inductance between the primary side coil and the secondary side coil M , and the equivalent load resistance R L are determined through the Pareto frontier solution, so as to determine the transmission efficiency and the output power, realize efficient search, and balance and control the transmission efficiency and the output power.

[0007] Further, the two-coil circuit model is as follows: Input impedance Z IN and secondary side impedance Z 2 may be represented as

[0008] Z R is the reflection impedance from the secondary side to the primary side; L P 、L 1 、L 2 、C P 、C 1 、C 2 are the primary side compensation inductance, the primary side coil self-inductance, the secondary side coil self-inductance, the primary side compensation capacitance, the primary side coil series capacitance, and the secondary side coil series capacitance, respectively; R P 、R1 、R 2 、R L L1, L2, Lm, RL Zref Z R The specific calculation is as follows:

[0009] M M12

[0010] , , , Vi, Ii, Io, Io The following conditions are met under full resonance: ω ω0 The output power and transmission efficiency can be expressed as:

[0011] Pout η ηin

[0012] Further, the optimization objective :

[0013] Further, the constraint conditions of the target model are as follows:

[0014] Further, the specific steps of step 3 are as follows: Step 31, determine the position and speed of the particle in the initialization state, and record the stagnation count value as 0; Step 32, calculate the initial output power and transmission efficiency by calculating the position of the particle in the initialization state position, calculate the initial individual optimal solution and the group optimal solution , and store them in 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's 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.

[0015] Furthermore, nonlinear dynamic inertia weights The calculation method is as follows:

[0016] in, For the number of iterations, It is the total number of iterations. It is a constant, usually set to 2.5.

[0017] 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.

[0018] Furthermore, the particle velocity update formula in step 35 is as follows:

[0019]

[0020] Representing the The particle in the first The iteration speed; For the individual optimal solution; The optimal solution for the population; , to accelerate the constant; , is a random number; represent the position information of the th particle in the th iteration.

[0021] Further, the position update formula of the particle in step 35 is as follows: ;

[0022] is the dimension of the problem.

[0023] Compared with the prior art, the present application has the beneficial effects that: the VPSR-PSO algorithm of the present application performs multi-objective collaborative optimization on system parameters, so as to L P , M and R L key parameters as decision variables, a multi-objective model for simultaneously optimizing transmission efficiency and output power is constructed, efficient search and balanced regulation are realized, and compared with the traditional multi-objective optimization strategy, the present application has advantages in convergence performance and solution distribution quality, can provide an effective parameter optimization idea for improving the comprehensive performance of the WPT system, and has good generalization potential. At the same time, the dynamic inertia weight, probabilistic speed pause and stagnation reset strategies are combined to realize the reinforcement of global search ability, avoid falling into local optimum, and realize the overall improvement of system performance. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0025] Figure 1 is a MCRWPT two-coil system diagram of the present application; Figure 2 is an equivalent circuit diagram of the MCRWPT two-coil system diagram of the present application; Figure 3 is a schematic diagram of the change of efficiency with M and R L ; Figure 4 is a schematic diagram of the change of output power with M and R L ; Figure 5 the change of the efficiency with L P and M the change of the output power with Figure 6 the change of the efficiency with L P and M the change of the output power with Figure 7 the change of the efficiency with L P and R L the change of the output power with Figure 8 the change of the efficiency with L P and R L the change of the output power with Figure 9 the flow chart of the present application; Figure 10 the schematic diagram of the Pareto frontier solution of the specific case. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0027] Embodiment one: Please refer to Figure 1 , 2 , 9, in order to achieve the above object, the present application is realized by the following technical solutions: The wireless power transmission system multi-parameter collaborative optimization method based on VPSR-PSO algorithm comprises the following steps: Step 1: According to the primary side input voltage, primary side input current, primary side output current, primary side compensation inductance, primary side compensation inductance resistance, primary side coil self-inductance resistance and primary side coil self-inductance of the primary side of the system, the secondary side output current, secondary side coil self-inductance, primary side coil series capacitance, secondary side coil self-inductance resistance and secondary side coil series capacitance of the secondary side, mutual inductance between the primary side coil and the secondary side coil, and equivalent load resistance, a two-coil circuit model under full resonance state is constructed, and the output power and transmission efficiency are obtained; Step 2: build a target model according to the primary side compensation inductance, mutual inductance between the primary side coil and the secondary side coil, and equivalent load resistance; and establish the constraint condition of the target model; Step 3: take the maximum of the target model as the optimization target, combine the constraint condition of the target model, and optimize and solve through the VPSR-PSO algorithm to obtain the Pareto frontier solution between the optimized output power and transmission efficiency; Step 4: determine the primary side compensation inductance L P , mutual inductance between the primary side coil and the secondary side coil M , and equivalent load resistance R L , so as to determine the transmission efficiency and output power, realize efficient search, and balance and control the transmission efficiency and output power.

[0028] The two-coil circuit model is as follows: Input impedance Z IN and secondary side impedance Z 2 may be represented as

[0029] Z R is the reflection impedance from the secondary side to the primary side; L P 、L 1 、L 2 、C P 、C 1 、C 2 are the primary side compensation inductance, primary side coil self-inductance, secondary side coil self-inductance, primary side compensation capacitance, primary side coil series capacitance, and secondary side coil series capacitance, respectively; R P 、R 1 、R 2 、R L are the primary side compensation inductance internal resistance, primary side coil self-inductance internal resistance, secondary side coil self-inductance internal resistance, and equivalent load resistance, respectively; Reflection impedance from the secondary side to the primary side Z R The specific calculation is as follows:

[0030] M is the mutual inductance between the primary and secondary side coils;

[0031] is the primary side input voltage, the primary side input current, the primary side output current, and the secondary side output current, respectively; under full resonance, the following conditions are satisfied: is the system operating angular frequency; For is the system inherent resonance angular frequency; the output power and the transmission efficiency can be expressed as:

[0032] is the output power, is the transmission efficiency, is the input transmission efficiency.

[0033] Optimization objective : .

[0034] Optimization objective aims to maximize the system transmission efficiency η and the output power P OUT .

[0035] The constraint conditions of the target model are as follows:

[0036] The specific steps of step 3 are as follows: Step 31, determine the position and speed of the particle in the initialization state, and record the stagnation count value as 0; Step 32, calculate the initial output power and transmission efficiency by the position of the particle in the initialization state, calculate the initial individual optimal solution and the group optimal solution , and store them in the initial Pareto archive; Step 33, calculate the nonlinear dynamic inertia weight ; Step 34, judge whether it is stagnant, if the judgment is no, execute step 35, if the judgment is yes, reset the speed, position and stagnation count value of the particle, and execute step 34 again; ​​​Step 35, determine a random value by a random function, judge whether the random value is greater than or equal to , if the judgment is yes, update the speed of the particle, execute step 36, if the judgment is no, the speed is unchanged, execute step 36; Step 36, update the position of the particle by the speed, judge whether the updated position of the particle can dominate the individual historical optimal solution , if the judgment is yes, the individual historical optimal solution is updated successfully, and the stagnation count value is cleared, then execute step 37 again, if the judgment is no, the stagnation count value is added by one, then execute step 37 again; Step 37, update the group optimal solution , update the Pareto archive; Step 38, judge whether the Pareto archive exceeds the iteration number according to the updated Pareto archive, if the judgment is yes, determine the Pareto front solution and end the optimization, if the judgment is no, execute step 33 again.

[0037] Nonlinear dynamic inertia weight The calculation method is as follows:

[0038] Wherein, is the iteration number, is the total iteration number, is a constant, generally set to 2.5.

[0039] The judgment of whether the stagnation condition is whether the stagnation count value is greater than the stagnation threshold value, if the judgment is yes, reset the speed, position and stagnation count value of the particle, and execute step 34 again.

[0040] The speed update formula of the particle in step 35 is as follows:

[0041]

[0042] Represents the speed of the first particle in the first iteration; is the individual optimal solution; is the group optimal solution; , is the acceleration constant; , is a random number; represents the position information of the first particle in the first iteration, is the speed pause parameter.

[0043] 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.

[0044] Speed ​​pause parameters This allows for a more detailed particle search.

[0045] 、 This is to randomly select a value between [0,1].

[0046] The particle position update formula in step 35 is as follows: ;

[0047] It is the dimension of the problem.

[0048] 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.

[0049] Experimental examples, such as Figures 3-8 As shown: Figure 3 Demonstration system transmission efficiency η With mutual induction M load resistor R L The trend of change. When M When the mutual inductance is <20 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 After 20 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.

[0050] Figure 4 The system output power P OUT With mutual inductance M and load resistance R L The trend of change. The results show that, P OUT With M the overall increase in the total, but in M 40 μH interval increase slowed down, indicating that the system into the coupling saturation state. At the same time, R L The change of the output power significantly, there is an optimal load range: the initial increase R L can enhance the output power output, and too high due to impedance mismatch resulting in a decrease in output power.

[0051] Figure 5 The transmission efficiency η with mutual inductance M and primary side compensation inductance L P The trend of change. It can be observed that, with the increase of M the system transmission efficiency is overall improved, especially in L P larger more obvious. It is worth noting that, in M larger (such as 50-60 μH), with the increase of L P transmission efficiency curve from steep rapid rise to slow saturation, showing a "flat" structure, which shows that in the higher coupling strength, reasonable increase in primary side compensation inductance helps the system to resonate state, to achieve efficient energy transmission. Therefore, from the system optimization point of view, the appropriate L P and M combination, can effectively system transmission efficiency.

[0052] Figure 6 The output power P OUT with mutual inductance M and primary side compensation inductance L P The trend of change. It can be observed that the system output power presents a typical "ridge shape distribution", revealing that there is a joint parameter path can achieve the maximum output power transmission. When M > smaller, L P the adjustment role is strong; while M > 30 μH, coupling strong, the system to LP The sensitivity decreases. This phenomenon reflects a significant synergistic optimization relationship between the compensation parameters and mutual inductance.

[0053] 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.

[0054] 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.

[0055] Overall, Figures 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. 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.

[0056] Specific examples: like Figure 10As shown, the particle swarm population size is set to 100, the maximum iteration number is set to 200, the individual optimal solution is set to 2, the group optimal solution is set to 2, the velocity pause parameter is set to 0.3, the stagnation threshold is set to 10, and the obtained Pareto front solution is as shown in the following table:

[0057] The first three columns in the above table are L P , M, R L The three variables (parameters) are η and the output power P OUT The last two columns are transmission efficiency

[0058] The above examples are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for some of the technical features; and these modifications or replacements 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 application.

Claims

1. A multi-parameter collaborative optimization method for wireless power transmission system based on VPSR-PSO algorithm, characterized in that: The method comprises the following steps: Step 1: constructing a two-coil circuit model in a full resonance state according to a primary side input voltage, a primary side input current, a primary side output current, a primary side compensation inductance, a primary side compensation inductance internal resistance, a primary side coil self-inductance internal resistance, a primary side coil self-inductance of the system, a secondary side output current, a secondary side coil self-inductance, a primary side coil series capacitance, a secondary side coil self-inductance internal resistance, a secondary side coil series capacitance, a mutual inductance between the primary side coil and the secondary side coil, and an equivalent load resistance, and obtaining an output power and a transmission efficiency; 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 a constraint condition of the target model; Step 3: taking a maximum of the target model as an optimization target, combining the constraint condition of the target model, and performing optimization and solving through a VPSR-PSO algorithm to obtain a Pareto frontier solution between the optimized output power and the transmission efficiency; Step 4: Determine the primary side compensation inductance by Pareto frontier solution L P Mutual inductance between the primary side coil and the secondary side coil M Equivalent load resistance R L Determine the transmission efficiency and output power, realize efficient search, and balance the transmission efficiency and output power.

2. The method of claim 1, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transfer system is characterized in that, The two-coil circuit model is as follows: Input impedance Z IN And secondary side impedance Z 2 Can be expressed as ； Z R is the reflected impedance from the secondary to the primary L P 、L 1 、L 2 、C P 、C 1 、C 2 L1, L2, L3, C1, C2, C3 R P 、R 1 、R 2 、R L L1, L2, L3, RL are the primary side compensation inductance resistance, the primary side coil self-inductance resistance, the secondary side coil self-inductance resistance, the equivalent load resistance, respectively. Secondary-to-primary reflected impedance Z R The specific calculation is as follows: ； M is the mutual inductance between the primary and secondary coils; ； , , , are a primary-side input voltage, a primary-side input current, a primary-side output current, a secondary-side output current, respectively; The following conditions are met in the full resonance state: ； is the system operating angular frequency; is the system natural angular frequency; The output power and the transmission efficiency can be expressed as: ； for output power, for transmission efficiency, for input transmission efficiency.

3. The method of claim 2, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transfer system is characterized in that, Optimization objectives : 。 4. The method of claim 3, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transfer system is characterized by, The constraint condition of the target model is as follows: 。 5. The method of claim 2, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transfer system is characterized by, The specific step 3 is as follows: Step 31: determining a position and a speed of a particle in an initialization state, and recording a stagnation count value as 0; Step 32, calculate initial output power and transmission efficiency by initializing the position of the particle at the state position, calculate initial individual optimal solution and population optimal solution and store in initial Pareto archive; Step 33, compute non-linear dynamic inertia weight ; Step 34: judging whether to stagnate, if the judgment is no, executing step 35, if the judgment is yes, resetting the speed, the position and the stagnation count value of the particle, and re-executing step 34; Step 35, determining a random value by a random function, judging whether the random value is greater than or equal to the speed pause parameter If the judgment is yes, updating the particle speed, and executing step 36; if the judgment is no, the speed remains unchanged, and executing step 36; Step 36, update the position of the particle by velocity, judge whether the updated position of the particle can dominate the individual historical optimal solution , if yes, the individual historical optimal solution is updated successfully, and the stagnation count value is cleared, then step 37 is executed again, if no, the stagnation count value is added by one, then step 37 is executed again; Step 37, update population best solution , update Pareto archive; Step 38: judging whether the Pareto archive exceeds an iteration number according to the updated Pareto archive, if the judgment is yes, determining a Pareto frontier solution, and ending optimization, if the judgment is no, re-executing step 33.

6. The method of claim 5, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transfer system is characterized by, Non-linear dynamic inertia weight The calculation method is as follows: ； wherein, is the number of iterations, is the total number of iterations, is a constant, typically set to 2.

5.

7. The method of claim 6, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transmission system is characterized in that, The condition for judging whether to stagnate is whether the stagnation count value is greater than a stagnation threshold value, if the judgment is yes, resetting the speed, the position and the stagnation count value of the particle, and re-executing step 34.

8. The method of claim 7, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transmission system is characterized in that, The speed updating formula of the particle in step 35 is as follows: ； ； represents the position of the jth particle at the ith iteration; represents the position of the jth particle at the ith iteration; represents the velocity of the jth particle at the ith iteration; represents the individual best solution; represents the global best solution; , represents the acceleration constant; , represents the random number; represents the position of the jth particle at the ith iteration; represents the position of the jth particle at the ith iteration; represents the position of the jth particle at the ith iteration; 9. The method of claim 8, wherein the VPSR-PSO algorithm-based multi-parameter collaborative optimization method for wireless power transmission system is characterized in that, The position updating formula of the particle in step 35 is as follows: ； ； is the dimension of the problem.

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