Multi-objective optimization design method, device and storage medium for wireless power transmission system

Abstract

The present invention provides a multi-objective optimization design method, device and storage medium for a wireless power transmission system, the method comprising: classifying independent variables corresponding to a coupling mechanism; optimizing the classified independent variables based on the classified independent variables, a first constraint condition, an optimization target and a first value range of the classified independent variables using an intelligent algorithm to obtain a first Pareto front result; and performing multi-objective optimization on the wireless power transmission system based on the first Pareto front result. The multi-objective optimization design method for a wireless power transmission system provided by the present invention optimizes the classified independent variables using an intelligent algorithm to obtain a first Pareto front result, thereby performing multi-objective optimization on the wireless power transmission system, achieving a reduction in design dimension and search space, being able to quickly locate near the optimal particle, reducing the number of finite element simulations of unavailable particles, achieving acceleration of the design process, and reducing the amount of calculation and time consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless power transmission systems, and in particular to a multi-objective optimization design method, device and storage medium for wireless power transmission systems. Background Art

[0002] The design of the coupling mechanism is a complex and multi-faceted issue. It is necessary to comprehensively consider factors such as system cost and space, and design the coupler structure and parameters to meet the requirements of magnetic leakage intensity, insulation and heat dissipation while taking into account the improvement of multi-dimensional characteristics such as transmission efficiency, power density, and anti-offset characteristics.

[0003] The essence of multi-objective optimization of coupling mechanisms is to find parameter combinations with specific characteristics in high-dimensional space. Since there are many variables affecting the performance of coupling mechanisms and their distribution range is wide, the multi-objective optimization process of wireless power transmission systems will require a lot of calculations and finite element simulations, resulting in a long optimization process. Summary of the invention

[0004] The present invention provides a multi-objective optimization design method, device and storage medium for a wireless power transmission system, which are used to solve the defect that the multi-objective optimization of the wireless power transmission system in the prior art is time-consuming, accelerate the design process, and reduce the amount of calculation and time consumption.

[0005] The present invention provides a multi-objective optimization design method for a wireless power transmission system, comprising:

[0006] Classify the independent variables corresponding to the coupling mechanism;

[0007] Based on the classified independent variable, the first constraint condition, the optimization target and the first value range of the classified independent variable, an intelligent algorithm is used to optimize the classified independent variable to obtain a first Pareto frontier result;

[0008] Based on the first Pareto front result, a multi-objective optimization is performed on the wireless power transmission system.

[0009] In some embodiments, the classifying the independent variables corresponding to the coupling mechanism includes:

[0010] Based on the independent variable, the second constraint condition, the optimization target and the second value range of the independent variable, an intelligent algorithm is used to optimize the independent variable to obtain a second Pareto frontier result;

[0011] Correlation analysis and hierarchical clustering analysis are performed based on the second Pareto front result to classify the independent variables.

[0012] In some embodiments, the first constraint includes the second constraint;

[0013] The first constraint condition also includes at least one of the following:

[0014] The ratio constraint of the self-inductance of the primary side of the coupling mechanism to the primary side series compensation inductance, the load quality factor constraint of the primary side inverter or the load quality factor constraint of the secondary side of the coupling mechanism.

[0015] In some embodiments, the second constraint condition includes at least one of the following:

[0016] Transmission efficiency constraint, coil current density constraint, core maximum magnetic induction intensity constraint, core loss density constraint, resonant capacitor voltage constraint, horizontal leakage magnetic intensity constraint or vertical leakage magnetic intensity constraint.

[0017] In some embodiments, performing correlation analysis and hierarchical clustering analysis based on the second Pareto front result to classify the independent variables includes:

[0018] Performing correlation analysis based on the second Pareto front result, and determining a first variable among the independent variables;

[0019] Performing a hierarchical cluster analysis on the second Pareto front result, and determining a second variable from the independent variables;

[0020] The independent variable is classified based on the first variable and the second variable.

[0021] The present invention also provides a multi-objective optimization design device for a wireless power transmission system, comprising:

[0022] A classification module, used to classify the independent variables corresponding to the coupling mechanism;

[0023] A first optimization module is used to optimize the classified independent variables using an intelligent algorithm based on the classified independent variables, the first constraint condition, the optimization target and the first value range of the classified independent variables to obtain a first Pareto frontier result;

[0024] The second optimization module is used to perform multi-objective optimization on the wireless power transmission system based on the first Pareto front result.

[0025] In some embodiments, the classification module is used to:

[0026] Based on the independent variable, the second constraint condition, the optimization target and the second value range of the independent variable, an intelligent algorithm is used to optimize the independent variable to obtain a second Pareto frontier result;

[0027] Correlation analysis and hierarchical clustering analysis are performed based on the second Pareto front result to classify the independent variables.

[0028] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the program, it implements any of the multi-objective optimization design methods for wireless power transmission systems described above.

[0029] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the multi-objective optimization design method for a wireless power transmission system as described in any one of the above is implemented.

[0030] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the multi-objective optimization design method for a wireless power transmission system as described above is implemented.

[0031] The multi-objective optimization design method, device and storage medium of the wireless power transmission system provided by the present invention classify the independent variables corresponding to the coupling mechanism, and use an intelligent algorithm to optimize the classified independent variables to obtain the first Pareto frontier result, thereby performing multi-objective optimization on the wireless power transmission system, reducing the design dimension and search space, and can quickly locate the vicinity of the optimal particle, and reduce the number of finite element simulations of unavailable particles, thereby accelerating the design process and reducing the amount of calculation and time consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 This is one of the flow charts of the multi-objective optimization design method for a wireless power transmission system provided by an embodiment of the present invention;

[0034] Figure 2 It is a flowchart of a multi-objective optimization method based on a particle swarm algorithm provided in an embodiment of the present invention;

[0035] Figure 3 is a schematic diagram of optimization results of DD-type coupling mechanisms of different power levels provided by an embodiment of the present invention;

[0036] Figure 4 The Q at different power levels provided by the embodiment of the present invention in , L PR , Q O Distribution diagram;

[0037] Figure 5 This is a second flow chart of a multi-objective optimization design method for a wireless power transmission system provided by an embodiment of the present invention;

[0038] Figure 6 is a schematic diagram comparing the generation of Pareto frontier particles and the total simulation time provided by an embodiment of the present invention;

[0039] Figure 7 It is a schematic diagram of multi-objective optimization simulation results when the independent variables and their ranges are different provided by an embodiment of the present invention;

[0040] Figure 8 It is a structural schematic diagram of a multi-objective optimization design device for a wireless power transmission system provided by an embodiment of the present invention;

[0041] Fig. 9 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The terms "first", "second", etc. in the specification and claims of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable under appropriate circumstances, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0044] Figure 1 FIG. 1 is one of the flow charts of the multi-objective optimization design method for a wireless power transmission system provided by an embodiment of the present invention. Figure 1 As shown, the multi-objective optimization design method of the wireless power transmission system provided by the embodiment of the present invention includes:

[0045] Step 101, classifying the independent variables corresponding to the coupling mechanism;

[0046] Step 102: Based on the classified independent variable, the first constraint condition, the optimization target and the first value range of the classified independent variable, an intelligent algorithm is used to optimize the classified independent variable to obtain a first Pareto front result;

[0047] Step 103: Based on the first Pareto front result, perform multi-objective optimization on the wireless power transmission system.

[0048] It should be noted that the execution subject of the multi-objective optimization design method for wireless power transmission system provided by the present invention can be an electronic device, a component in an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine or a self-service machine, etc., which is not specifically limited by the present invention.

[0049] In step 101, the independent variables corresponding to the coupling mechanism are classified.

[0050] The independent variables corresponding to the coupling mechanism are parameters that may affect the performance of the coupling coil, including parameters such as the coil shape and size. There are many variables that affect the performance of the coupling mechanism, and their distribution range is wide. The multi-objective optimization process of the system will require a lot of calculations and finite element simulations, resulting in a long optimization process.

[0051] Therefore, in the embodiment of the present invention, the independent variables can be classified according to the degree of influence of the independent variables on the performance of the coupling mechanism, that is, according to the importance of the independent variables, and the value range of the independent variables can be set according to the importance.

[0052] Optionally, the embodiment of the present invention can carry out multi-objective optimization of the coupling mechanism based on an intelligent algorithm, and analyze the distribution law of the optimization results, so as to classify the independent variables. The intelligent algorithm can include an ant colony algorithm, a particle swarm algorithm, a genetic algorithm, and the like.

[0053] In some embodiments, the classifying the independent variables corresponding to the coupling mechanism includes:

[0054] Based on the independent variable, the second constraint condition, the optimization target and the second value range of the independent variable, an intelligent algorithm is used to optimize the independent variable to obtain a second Pareto frontier result;

[0055] Correlation analysis and hierarchical clustering analysis are performed based on the second Pareto front result to classify the independent variables.

[0056] The premise for the stable and safe operation of the WPT system is to meet the limitations of the thermal management system and insulation capacity. Therefore, when designing the system, parameters such as system efficiency, current density of the coupling coil, loss density of the magnetic core, and terminal voltage of the resonant element are limited as the second constraint.

[0057] In some embodiments, the second constraint condition includes at least one of the following: transmission efficiency constraint, coil current density constraint, core maximum magnetic induction intensity constraint, core loss density constraint, resonant capacitor voltage constraint, horizontal leakage magnetic intensity constraint or vertical leakage magnetic intensity constraint.

[0058] The optimization objectives include transmission efficiency η, weight power density G den , the area power density A at the receiving end den , Efficiency η in the offset state mis , tolerance ψ, etc.

[0059] According to the independent variable corresponding to the coupling mechanism, the second constraint condition, the optimization target and the second value range of the independent variable, an intelligent algorithm can be used to optimize the independent variable to obtain the second Pareto frontier result.

[0060] Optionally, in the embodiment of the present invention, a particle swarm algorithm is used as an example. Figure 2 is a flow chart of a multi-objective optimization method based on a particle swarm algorithm provided in an embodiment of the present invention, such as Figure 2 As shown, the multi-objective optimization method based on particle swarm algorithm provided by the embodiment of the present invention includes:

[0061] Parameter initialization;

[0062] Randomly generate available particles;

[0063] Step 1: obtain the self-inductance and mutual inductance of the coupling mechanism through finite element simulation;

[0064] Step 1.1, obtain the self-inductance and mutual inductance of the primary coil;

[0065] Step 1.2, obtain the self-inductance of the secondary coil;

[0066] Step 2: Calculate the efficiency of the secondary rectifier, the current of each link of the circuit, etc.

[0067] Determine particle availability based on current density, capacitance and voltage;

[0068] Step 3: Perform finite element simulation based on the calculated current to obtain core loss, shielding loss, and extract coil magnetic field information;

[0069] Step 4, post-processing: calculate the loss, efficiency, weight, etc. of each part;

[0070] Determine particle availability based on magnetic flux leakage, magnetic core magnetic induction intensity, etc.;

[0071] Step 5: finite element simulation to obtain parameters such as the coupling mechanism at the offset position;

[0072] Step 6, post-processing: calculate the system loss and efficiency during offset;

[0073] Step 7: Filter particles that meet the conditions, update particles on the Pareto front, and select the next generation of available particles based on the particles on the Pareto front, and determine whether the optimization is completed:

[0074] If yes, the optimization is completed;

[0075] If no, repeat steps 1 to 7.

[0076] Optionally, in the embodiment of the present invention, the system parameters are as shown in Table 1.

[0077] Table 1

[0078]

[0079] In the optimization target of the coupling mechanism, the system weight power density is the ratio of the system output power to the weight of the coupling mechanism. Since the weight of the compensation capacitor and the converter is difficult to evaluate directly, only the weight of the coupling mechanism itself is considered when calculating the weight power density.

[0080] In practical applications, the secondary side of the coupling mechanism needs to be installed inside a mobile electrical device, where the space is often small. Therefore, this embodiment only focuses on the area power density of the secondary side of the coupling mechanism, that is, the ratio of the output power to the area of ​​the secondary side of the coupling mechanism.

[0081] The tolerance also takes into account the deviation of mutual inductance and primary compensation inductance. The coefficient of variation of mutual inductance is expressed as k. M Indicated by k, the compensation inductance variation coefficient is Lr Indicates that the value is equal to L r / L r0 , L r Represents the primary series compensation inductance, L r0 Indicates the ideal value of the primary series compensation inductor. System tolerance is measured by the extreme value of system characteristics within the range of coupling and compensation parameter deviation. k Lr The variation range of is set to ±5%, and the mutual inductance variation ratio is obtained by simulation.

[0082] Among the compensation components, the compensation inductor generally adopts the traditional Litz wire tightly winding method. In large-scale production, the inconsistency of the wire spacing and the wire-core spacing will cause a certain deviation in the compensation inductor. During the installation process, there will also be parasitic inductance between the compensation inductor and the inverter connecting wire, causing the actual compensation inductance value to deviate from the ideal value.

[0083] The coil turn spacing, wire-core spacing, and core-aluminum plate spacing of the coupling mechanism are generally larger than the wire diameter. The slight difference in mold size has little effect on the self-inductance. The self-inductance of the coupling coil with good anti-offset characteristics also changes less with position offset. Therefore, when evaluating the system tolerance, the coupling coil self-inductance is considered a fixed value.

[0084] The worst case of system characteristics usually occurs when the parameter deviation is the largest. Therefore, in addition to simulating the alignment condition, step 7 also simulates two extreme offset conditions, namely, the conditions where (ΔX, ΔY, Δd) are (0, 0, -30) and (+75, +100, +50) respectively, to obtain the maximum and minimum values ​​of the coupled mutual inductance.

[0085] The optimization objectives of coupling mechanism efficiency, anti-deviation characteristics, etc. have different dimensions and are difficult to measure simply with a comprehensive indicator.

[0086] Therefore, in this embodiment, among the particles that meet the second constraint condition, the particles on the Pareto front are selected as the optimal solution set by comparing their target values ​​for subsequent analysis. During the update iteration, particles with the same number of populations are randomly selected from the Pareto front particles for parameter update.

[0087] In order to obtain a coupling mechanism with good performance in all characteristics and to analyze the factors affecting the performance of the coupling mechanism more accurately and comprehensively, the embodiments of the present invention set the parameters that may affect the performance of the coupling coil as independent variables.

[0088] Taking the DD type coil as an example, the meaning of the independent variable corresponding to the DD type coil and the second value range in the optimization process are shown in Table 2.

[0089] Table 2

[0090]

[0091]

[0092] The subscripts “P” and “S” in Table 2 represent the corresponding variables of the primary and secondary sides of the corresponding coupling mechanism, respectively.

[0093] During the particle generation process, non-continuous variables such as the number of coil turns and the number of parallel windings are discretized. The parameters of the randomly generated particles may be inconsistent. For example, the spacing between the generated particle coil turns may be less than zero. Therefore, before the finite element simulation, each generated particle is first tested for availability and unavailable particles are corrected.

[0094] In this embodiment, an aluminum plate is used as a shielding plate, and its length is set to the larger of the coil length and the magnetic strip length, and its width is also set to the larger of the coil width and the magnetic core laying area to ensure a better shielding effect.

[0095] The premise for the stable and safe operation of the system is to always meet the limitations of the thermal management system and insulation capacity. Therefore, the system efficiency, current density of the coupling coil, loss density of the magnetic core, voltage at the resonant element and other parameters are limited during system design. The second constraint condition is shown in Table 3.

[0096] Table 3

[0097]

[0098] Among them, "-mis" indicates the corresponding index of the offset working condition. When the magnetic flux density exceeds a certain value, the magnetic permeability of the core material will drop rapidly, resulting in magnetic failure, thus limiting the maximum magnetic induction intensity of the core.

[0099] From the perspective of electromagnetic safety, the magnetic leakage strength of the coupling mechanism should meet the corresponding international standards. Excessive leakage electromagnetic field strength will reduce the electromagnetic compatibility of the converter power module, thereby affecting the working state of the module; at the same time, high-frequency electromagnetic radiation will have an adverse effect on the safety of organisms.

[0100] In this embodiment, the leakage magnetic induction intensity S in the horizontal and vertical directions is f-h and S f-v The horizontal test point is located on a plane parallel to the coil, passing through the midpoints of the primary and secondary coils, and 1.5m away from the center of the coil. The vertical test point is located on the extension line of the center line of the primary and secondary coils under the alignment condition, and 0.5m away from the aluminum shielding plate of the secondary coil.

[0101] To speed up the simulation process, after obtaining the current of each link in step 2, the particle availability is judged according to the constraints of current density and capacitor voltage. The efficiency, leakage magnetic intensity, loss power density and other data under the alignment conditions obtained in step 4 are used to judge whether the current particle meets the constraints, and unavailable particles are discarded in time.

[0102] According to the independent variable, the second constraint, the optimization target and the second value range of the independent variable, an intelligent algorithm is used to optimize the independent variable. Among the particles that meet the second constraint, the particles on the Pareto front are screened out as the optimal solution set by comparing their target values, and the second Pareto front result is obtained.

[0103] Optionally, in order to determine the changes in system characteristics at different power levels and avoid the one-sidedness of the data distribution and characteristics of the optimization results, multi-objective optimization of systems with various power levels such as 15kW, 30kW, 50kW, 100kW, 150kW and 200kW was carried out based on the independent variable type and the second value range, the second constraint condition and the optimization target described in the above embodiment.

[0104] Figure 3 is a schematic diagram of the optimization results of the DD-type coupling mechanism of different power levels provided by an embodiment of the present invention, such as Figure 3 As shown in Figure 2, as the power level increases, the WPT system transmission efficiency η sys , Weight power density G den and area power density A den The overall trend is monotonous change.

[0105] In some embodiments, performing correlation analysis and hierarchical clustering analysis based on the second Pareto front result to classify the independent variables includes:

[0106] Performing correlation analysis based on the second Pareto front result, and determining a first variable among the independent variables;

[0107] Performing a hierarchical cluster analysis on the second Pareto front result, and determining a second variable from the independent variables;

[0108] The independent variable is classified based on the first variable and the second variable.

[0109] By performing correlation analysis on the results of the second Pareto front, the first variable can be identified among the independent variables.

[0110] Optionally, the correlation coefficient is a statistical indicator used to reflect the closeness of the correlation between variables, generally represented by the letter r, and the more commonly used one is the Pearson correlation coefficient.

[0111] The correlation coefficient is calculated according to the product difference method, which is based on the deviation of two variables x and y from their respective means. The correlation between the two variables is reflected by multiplying the two deviations, and then divided by the standard deviation to eliminate the dimension, and the Pearson correlation coefficient can be obtained. The expression is as follows:

[0112]

[0113] Among them, r represents the Pearson correlation coefficient, x i represents the i-th variable of the variable set x, represents the mean value of the variable set x, y i represents the i-th variable of the variable set y, Represents the mean value of the variable set y.

[0114] The value of the correlation coefficient r is between -1 and 1. The larger its absolute value is, the stronger the linear correlation between the two variables is, and the closer it is to 0, the weaker the linear correlation is. When r is a positive number, it indicates a positive correlation between the two variables, and a negative number indicates a negative correlation.

[0115] Correlation analysis can be used to explore the degree and trend of influence of each variable on the characteristics of the coupling mechanism. By extracting variables with strong coupling relationships with the second constraint, optimization objectives and other independent variables, it can guide the selection of independent variables during system optimization design and reduce the difficulty and workload of coupling mechanism design.

[0116] According to the second Pareto front result, the particles on the Pareto front are determined.

[0117] Optionally, the first correlation coefficient between the independent variable corresponding to the particle and the optimization target, the second correlation coefficient between the independent variable corresponding to the particle and the second constraint, and the third correlation coefficient between the independent variables corresponding to the particles can be calculated respectively. According to the size of the correlation coefficient, the correlation between the independent variable and the optimization target, the correlation between the independent variable and the second constraint, and the correlation between the independent variables can be determined.

[0118] A correlation coefficient heat map is drawn based on the first correlation coefficient, the second correlation coefficient and the third correlation coefficient. The correlation coefficient heat map is used to show the correlation strength between different variables, so that the first variable is extracted from all independent variables according to the correlation coefficient heat map. The first variable is the variable that has a greater impact on the performance of the coupling mechanism obtained through correlation analysis.

[0119] For example, a reference value of the correlation coefficient may be preset, such as 0.4, and the correlation coefficient between each optimization objective and the independent variable, and the correlation coefficient between each second constraint and the independent variable are calculated. The independent variables corresponding to the correlation coefficient greater than 0.4 are screened out, and then the correlation coefficient between the screened independent variables and other independent variables is calculated, and the independent variables corresponding to the correlation coefficient greater than 0.4 are screened out again. The set of independent variables screened out twice is the first variable.

[0120] By performing hierarchical cluster analysis on the results of the second Pareto front, the second variable can be identified among the independent variables.

[0121] According to the results of the second Pareto front, the particles on the Pareto front are determined, classified, and then the change characteristics of their independent variables are analyzed.

[0122] Cluster analysis is a statistical analysis technique that divides a data set into relatively homogeneous groups according to certain standards. In the different classes or clusters obtained, the objects in the same cluster have great similarities, while the attributes between different clusters vary greatly. Cluster analysis is an exploratory, unsupervised learning process that does not require pre-defined class standards. Instead, the cluster learning algorithm starts from the sample data, first obtains the distribution of the data, and then automatically classifies it.

[0123] The basic idea of ​​the hierarchical clustering algorithm is to first regard all objects as a single cluster, then calculate the distance between classes, select the two classes with the smallest distance to combine into a new class, then recalculate the distance between the new class and other classes, and merge the two classes with the smallest distance, and iterate in sequence until there is only one class that contains all the data points.

[0124] By performing hierarchical clustering analysis on the particles, the three types of particles with the largest number of particles are determined, and these three types of particles are the three representative types of particles.

[0125] The variable data corresponding to the three types of particles with the largest number of particles are standardized and a parallel coordinate graph is drawn. The parallel coordinate graph can be used to show the relationship and trend between multiple variables. Specifically, the parallel coordinate graph represents each variable as a dimension on the coordinate axis, and then connects the corresponding values ​​on each dimension with a line segment to form a line, thereby depicting the relationship and pattern between the variables.

[0126] The changes and trends between different data sets can be compared according to the parallel coordinates diagram, so as to determine the second variable among the independent variables. The second variable is the variable with the greater influence on the performance of the coupling mechanism obtained through cluster analysis.

[0127] According to the extracted first variable and second variable, the intersection of the two is taken as the third variable. The third variable is the characteristic variable of the coupling mechanism, that is, the key variable that significantly affects the performance of the coupling mechanism, that is, the most important independent variable.

[0128] Optionally, from the union of the first variable and the second variable, a variable other than the third variable is screened out as a fourth variable, and the fourth variable is an independent variable of moderate importance.

[0129] The remaining independent variables are those with lower importance.

[0130] Therefore, in the embodiment of the present invention, the 28 independent variables of the coupling mechanism are classified according to importance, and the classification results are shown in Table 4.

[0131] Table 4

[0132]

[0133] Among them, the 10 independent variables of level I will significantly affect the comprehensive characteristics of the coupling mechanism and need to be optimized.

[0134] The 11 independent variables of level II have a slightly weaker impact on the coupling mechanism. Their search range can be reduced or discretized at larger intervals according to the optimization speed and target properties to accelerate the design process.

[0135] The seven variables of level III have little influence on the comprehensive characteristics of the coupling mechanism and can be determined in advance before optimization to achieve the purpose of dimensionality reduction.

[0136] In step 102, based on the classified independent variables, the first constraint condition, the optimization target and the first value range of the classified independent variables, an intelligent algorithm is used to optimize the classified independent variables to obtain a first Pareto front result.

[0137] Optionally, after the independent variables are classified, the first value range can be determined according to the importance of the independent variables. For example, for independent variables with higher importance, they need to be optimized, and their corresponding first value range is the same as the second value range; for independent variables with moderate importance, their corresponding first value range can be slightly smaller than the second value range; for independent variables with lower importance, their values ​​can be directly determined.

[0138] In some embodiments, the first constraint includes the second constraint;

[0139] The first constraint condition also includes at least one of the following:

[0140] The ratio constraint of the self-inductance of the primary side of the coupling mechanism to the primary side series compensation inductance, the load quality factor constraint of the primary side inverter or the load quality factor constraint of the secondary side of the coupling mechanism.

[0141] Optionally, the embodiment of the present invention further includes the following constraints based on the second constraint condition: a ratio constraint of the self-inductance of the primary side of the coupling mechanism to the primary side series compensation inductance, a load quality factor constraint of the primary side inverter, and a load quality factor constraint of the secondary side of the coupling mechanism.

[0142] Q in Represents the load quality factor of the primary inverter, L PR The self-inductance L of the primary side of the coupling mechanism P The compensation inductor L is connected in series with the primary side r The ratio of L P / L r , Q O Represents the load quality factor of the secondary side of the coupling mechanism.

[0143] Q in and Q O The expressions are as follows:

[0144]

[0145] Where ω represents the angular frequency, M represents the mutual inductance, and L S Represents the self-inductance of the secondary coil, R E Represents the equivalent load resistance.

[0146] right Figure 3 Further analysis of the Pareto front particle characteristics of the system with different power levels can yield Q in , L PR and Q O The distribution of Figure 4 The Q at different power levels provided by the embodiment of the present invention in , L PR , Q O Distribution diagram, such as Figure 4 As shown, Q at different power levels in , L PR and Q O The distribution of Q in Both are between 0.2 and 2.5, L PR Between 0.5 and 13, Q O It is between 1 and 10, and the distribution range of high-efficiency particles is narrower.

[0147] Optimized Q in , L PR and Q O The distribution of is basically consistent with the allowable design range. This shows that when considering the compensation inductance L r After the deviation of mutual inductance M, the quality factor Q can still be improved by reasonable optimization design. in and Q O And the primary self-inductance-mutual inductance coefficient L PR Improve system tolerance.

[0148] Q in The expression can be further deduced and converted into the relationship between frequency, compensation inductance, transmission power and input voltage, as shown below:

[0149]

[0150] Among them, P O Indicates the system output power, U P Indicates the effective value of the primary inverter output voltage.

[0151] From the above formula, we can see that when the system power and voltage gain are determined, Q in The larger the value, the larger the mutual inductance M is, and the more difficult it is to design the coupling mechanism. Therefore, Q does not appear in the optimization results. in Larger particles.

[0152] When the system input voltage, voltage gain and output power are determined, Q in , L PR and Q O The constraints can be converted into constraints on the self-inductance and mutual inductance of the coupled coils. Figure 2 In the multi-objective process shown in the figure, after step 1.1 is completed, the availability of the particle can be judged based on the self-inductance and mutual inductance of the primary coil. After the self-inductance of the secondary coil is obtained in step 1.2, the availability of the particle can be judged based on Q O It is also possible to determine the availability of particles and discard particles that are not within the allowed range in a timely manner.

[0153] Therefore, the embodiment of the present invention adds Q in , L PR and Q O The constraints can timely “purify” the particles randomly and iteratively generated by the system, and try to discard particles of little value at an early stage, which is beneficial to reducing the workload of calculation and finite element simulation.

[0154] At the same time, the primary coil current I P and inverter quality factor Q in The relationship is as follows:

[0155]

[0156] Since the primary coil current I P It is only related to the input voltage, operating frequency and compensation inductance. In the particle generation stage, if the distribution range of the primary coil current cannot be obtained, the number of primary coils in parallel n p-P , original edge diameter d P The design range is wider, which will undoubtedly make the optimization process take longer.

[0157] It can be seen from the above formula that by constraining Q in You can get I P This helps to reduce the search range of variables and thus generate as few particles of little value as possible.

[0158] exist Figure 2In the method flow shown, each particle needs to undergo 5 finite element simulations respectively, wherein step 1 requires two finite element simulations to obtain the self-inductance and mutual inductance of the primary and secondary coils. After the coil current is calculated in step 2, step 3 performs finite element simulation again to obtain data such as magnetic field distribution, core loss, and aluminum plate loss. Step 7 requires two simulations to obtain the mutual inductance at different positions.

[0159] Therefore, if there are a large number of independent variables and a wide distribution range, the multi-objective optimization process of the system will require a lot of calculations and finite element simulations, making the optimization process time-consuming. Taking the optimization simulation process of a 100kW system as an example, there are a total of 28 independent variables and 5 optimization targets. It took an Intel Xeon Gold 6354 3.0GHz 16-core processor 405 hours to obtain 918 Pareto frontier particles.

[0160] In order to reduce the time consumption of calculation and finite element simulation and accelerate the multi-objective optimization design process, this section proposes a dimensionality reduction hierarchical multi-objective optimization design method that integrates constraint conditions. Figure 5 This is the second flow chart of the multi-objective optimization design method for a wireless power transmission system provided in an embodiment of the present invention.

[0161] like Figure 5 As shown, in the design preparation stage, the independent variables and their value ranges can be set according to the contents of Table 4 to reduce the dimension of the design variables and compress the search space.

[0162] During the design process, after obtaining the primary self-inductance and mutual inductance of the coupling mechanism, according to Q in and L PR The particle availability is judged by the constraint, and the load quality factor Q is calculated after obtaining the secondary side self-inductance. O The speed is achieved by identifying and removing low-value particles as early as possible.

[0163] To verify the effectiveness of the proposed design method, we use Figure 5 The multi-objective design method shown is used to optimize the design of the 100kW system. The first value range of the level I variable is the same as the value range in Table 2. The settings of the first value range of the level II variable and the level III variable are shown in Table 5.

[0164] Table 5

[0165]

[0166] Among them, Case 1 only optimizes the independent variables of level I, and Case 2 and Case 3 optimize the variables of level I and level II, but the values ​​of the level III variables are different.

[0167] Added support for Q during simulation in , LPR and Q O The remaining constraints and design objectives are the same as before.

[0168] Figure 6 It is a schematic diagram comparing the generation of Pareto front particles and the total simulation time provided by an embodiment of the present invention.

[0169] The increase of Pareto front particles with the number of iterations and the comparison of simulation time consumption are shown in Figure 2. Figure 6 As shown in the figure, Case 2 generates the fastest Pareto frontier particles, completing 80 iterations in 62 hours and generating 575 particles; Case 3 is the second, generating 508 particles in 68 hours; Case 1 has the least number of independent variables, but the speed of generating Pareto frontier particles is similar to that before optimization, and only 189 particles are generated after 116 iterations. This is because the independent variables of level II are widely distributed in the Pareto frontier results, and the way Case 1 fixes these variables greatly reduces the probability of generating particles with excellent comprehensive characteristics.

[0170] Figure 7 is a schematic diagram of multi-objective optimization simulation results when the independent variables and their ranges are different provided by an embodiment of the present invention, such as Figure 7 As shown in Figure 1, the Pareto frontier particles of Case 1 have obvious faults in efficiency, weight power density and other characteristics. It can be seen that when the number of independent variables and parameter range are set unreasonably, the number of candidate particles is greatly reduced, and it is impossible to provide enough choices for designers.

[0171] On the other hand, even if Case 1 only optimized 10 independent variables of Level I, the comprehensive characteristics of the Pareto frontier particles were not much different from those before the hierarchical dimensionality reduction optimization. For example, the maximum transmission efficiency reached 96.0% when the weight power density was 1.5 kW / kg. This shows that the Level I variables are the key variables that determine the system performance, verifying the correctness of the hierarchical sorting of the variables.

[0172] Compared with the original results and Case 2, the difference of Case 3 is mainly reflected in the weight power density characteristics, such as Figure 8 As shown, the maximum weight power density of the original result and Case 2 are 2.83kW / kg and 2.63kW / kg respectively, while that of Case 3 is only 2.05kW / kg; except near the maximum weight power density, when the weight power density is the same, the efficiency difference between Case 2 and the original result does not exceed 0.2%, while the efficiency gap between Case 3 and the original result exceeds 0.2% at 1.4kW / kg, and increases with the increase of weight power density.

[0173] The difference is caused by the number of parallel connections of the secondary coils in Case 3, n p-S and the number of secondary core strips nfe-S The settings of are all large, which shows that the reasonable values ​​of non-optimization variables are crucial to the multi-objective optimization effect of the coupling mechanism.

[0174] Compared with other simulation cases, Case 2 has the highest consistency with the original results. The generation rate of Pareto frontier particles increased from 2.3 / generation to 7.2 / generation, verifying the effectiveness of hierarchical dimensionality reduction and rapid positioning of optimal particles. The total simulation calculation time of each generation of particles increased from 1.0 generation / h in the original result to 1.3 generations / h, verifying the effectiveness of real-time judgment of particle availability for accelerating the simulation process. The number of Pareto frontier particles generated per unit time in the proposed design process increased from 2.24 / h to 9.27 / h, a rate increase of 3.1 times.

[0175] In step 103, based on the first Pareto front result, multi-objective optimization is performed on the wireless power transmission system.

[0176] Therefore, according to the final result of the first Pareto frontier, a parameter combination with specific characteristics is found in the high-dimensional space to perform multi-objective optimization of the coupling mechanism, thereby realizing multi-objective optimization of the wireless power transmission system.

[0177] The multi-objective optimization design method for a wireless power transmission system provided in an embodiment of the present invention classifies the independent variables corresponding to the coupling mechanism, and uses an intelligent algorithm to optimize the classified independent variables to obtain the first Pareto frontier result, thereby performing multi-objective optimization on the wireless power transmission system, reducing the design dimension and search space, and can quickly locate the vicinity of the optimal particle, and reduce the number of finite element simulations of unavailable particles, thereby accelerating the design process and reducing the amount of calculation and time consumption.

[0178] The multi-objective optimization design device for a wireless power transmission system provided by the present invention is described below. The multi-objective optimization design device for a wireless power transmission system described below and the multi-objective optimization design method for a wireless power transmission system described above can be referenced to each other.

[0179] Figure 8 is a schematic diagram of the structure of a multi-objective optimization design device for a wireless power transmission system provided by an embodiment of the present invention, such as Figure 8 As shown, the multi-objective optimization design device for a wireless power transmission system provided by an embodiment of the present invention includes:

[0180] A classification module 810, for classifying the independent variables corresponding to the coupling mechanism;

[0181] A first optimization module 820 is used to optimize the classified independent variables using an intelligent algorithm based on the classified independent variables, the first constraint condition, the optimization target and the first value range of the classified independent variables to obtain a first Pareto frontier result;

[0182] The second optimization module 830 is used to perform multi-objective optimization on the wireless power transmission system based on the first Pareto front result.

[0183] It should be noted here that the multi-objective optimization design device for a wireless power transmission system provided in an embodiment of the present invention can implement all the method steps implemented in the above-mentioned multi-objective optimization design method embodiment for a wireless power transmission system, and can achieve the same technical effects. The parts and beneficial effects of this embodiment that are the same as those of the method embodiment will not be described in detail here.

[0184] Optionally, the classification module 810 is used to:

[0185] Based on the independent variable, the second constraint condition, the optimization target and the second value range of the independent variable, an intelligent algorithm is used to optimize the independent variable to obtain a second Pareto frontier result;

[0186] Correlation analysis and hierarchical clustering analysis are performed based on the second Pareto front result to classify the independent variables.

[0187] Optionally, the first constraint condition includes the second constraint condition;

[0188] The first constraint condition also includes at least one of the following:

[0189] The ratio constraint of the self-inductance of the primary side of the coupling mechanism to the primary side series compensation inductance, the load quality factor constraint of the primary side inverter or the load quality factor constraint of the secondary side of the coupling mechanism.

[0190] Optionally, the second constraint condition includes at least one of the following:

[0191] Transmission efficiency constraint, coil current density constraint, core maximum magnetic induction intensity constraint, core loss density constraint, resonant capacitor voltage constraint, horizontal leakage magnetic intensity constraint or vertical leakage magnetic intensity constraint.

[0192] Optionally, the classification module 810 is further configured to:

[0193] Performing correlation analysis based on the second Pareto front result, and determining a first variable among the independent variables;

[0194] Performing a hierarchical cluster analysis on the second Pareto front result, and determining a second variable from the independent variables;

[0195] The independent variable is classified based on the first variable and the second variable.

[0196] Fig. 9 An example of a physical structure diagram of an electronic device is shown in FIG. Fig. 9 As shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930 and a communication bus 940, wherein the processor 910, the communication interface 920 and the memory 930 communicate with each other through the communication bus 940. The processor 910 may call the logic instructions in the memory 930 to execute the multi-objective optimization design method of the wireless power transmission system, the method comprising: classifying the independent variables corresponding to the coupling mechanism; based on the classified independent variables, the first constraint condition, the optimization target and the first value range of the classified independent variables, using an intelligent algorithm to optimize the classified independent variables to obtain a first Pareto frontier result; based on the first Pareto frontier result, performing multi-objective optimization on the wireless power transmission system.

[0197] In addition, the logic instructions in the above-mentioned memory 930 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0198] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the multi-objective optimization design method of the wireless power transmission system provided by the above-mentioned methods, and the method includes: classifying the independent variables corresponding to the coupling mechanism; based on the classified independent variables, the first constraint condition, the optimization objective and the first value range of the classified independent variables, using an intelligent algorithm to optimize the classified independent variables to obtain a first Pareto front result; based on the first Pareto front result, performing multi-objective optimization on the wireless power transmission system.

[0199] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the multi-objective optimization design method for a wireless power transmission system provided by the above-mentioned methods, the method comprising: classifying the independent variables corresponding to the coupling mechanism; based on the classified independent variables, the first constraint condition, the optimization objective and the first value range of the classified independent variables, using an intelligent algorithm to optimize the classified independent variables to obtain a first Pareto front result; based on the first Pareto front result, performing multi-objective optimization on the wireless power transmission system.

[0200] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0201] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-objective optimization design method for a wireless power transmission system, characterized in that: include: Based on the independent variable, the second constraint condition, the optimization target and the second value range of the independent variable, the intelligent algorithm is used to optimize the independent variable to obtain a second Pareto frontier result; Based on the second Pareto front result, correlation analysis and hierarchical cluster analysis are performed to classify the independent variables; Based on the classified independent variable, the first constraint condition, the optimization target and the first value range of the classified independent variable, an intelligent algorithm is used to optimize the classified independent variable to obtain a first Pareto frontier result; Based on the first Pareto front result, a multi-objective optimization is performed on the wireless power transmission system.

2. The multi-objective optimization design method for wireless power transmission system according to claim 1, characterized in that: The first constraint condition includes the second constraint condition; The first constraint condition also includes at least one of the following: The ratio constraint of the self-inductance of the primary side of the coupling mechanism to the primary side series compensation inductance, the load quality factor constraint of the primary side inverter or the load quality factor constraint of the secondary side of the coupling mechanism.

3. The multi-objective optimization design method for wireless power transmission system according to claim 1, characterized in that: The second constraint condition includes at least one of the following: Transmission efficiency constraint, coil current density constraint, core maximum magnetic induction intensity constraint, core loss density constraint, resonant capacitor voltage constraint, horizontal leakage magnetic intensity constraint or vertical leakage magnetic intensity constraint.

4. The multi-objective optimization design method for wireless power transmission system according to claim 1, characterized in that: The performing correlation analysis and hierarchical clustering analysis based on the second Pareto front result to classify the independent variables includes: Performing correlation analysis based on the second Pareto front result, and determining a first variable among the independent variables; Performing a hierarchical cluster analysis on the second Pareto front result, and determining a second variable from the independent variables; The independent variable is classified based on the first variable and the second variable.

5. A multi-objective optimization design device for a wireless power transmission system, characterized in that: include: A classification module, configured to optimize the independent variable using an intelligent algorithm based on the independent variable, the second constraint, the optimization target and the second value range of the independent variable to obtain a second Pareto frontier result; Based on the second Pareto front result, correlation analysis and hierarchical cluster analysis are performed to classify the independent variables; A first optimization module is used to optimize the classified independent variables using an intelligent algorithm based on the classified independent variables, the first constraint condition, the optimization target and the first value range of the classified independent variables to obtain a first Pareto frontier result; The second optimization module is used to perform multi-objective optimization on the wireless power transmission system based on the first Pareto front result.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the multi-objective optimization design method for a wireless power transmission system as claimed in any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-objective optimization design method for a wireless power transmission system as claimed in any one of claims 1 to 4 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-objective optimization design method for a wireless power transmission system as claimed in any one of claims 1 to 4 is implemented.

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