Design method of asymmetric wireless energy transfer coupling mechanism containing uncertain parameters

Abstract

The invention provides a design method of an asymmetric wireless energy transmission coupling mechanism containing uncertain parameters. The design method comprises the following steps: arranging a primary side energy transmitting mechanism and a secondary side energy receiving mechanism which are relatively parallel to each other; the primary side energy transmitting mechanism and the secondary side energy receiving mechanism are both of a double-layer structure and comprise coil layers and magnetic core layers, and the coil layers are circular coils which are wound by litz wires and are equal in inner diameter and unequal in outer diameter; the magnetic core layer on the receiving side is formed by combining eight same strip-shaped magnetic cores and one annular magnetic core; the magnetic core layer on the transmitting side is a circular magnetic core. According to the coupling mechanism, on the basis that the size of the magnetic core is reduced, a high coupling coefficient can be kept, and better horizontal anti-offset performance is achieved.

Description

Technical Field

[0001] The present invention relates to the field of asymmetric wireless energy transmission system coupling mechanism design, and in particular to a design method of an asymmetric wireless energy transmission coupling mechanism containing uncertain parameters. Background Art

[0002] In today's wireless energy transfer field, the coupling mechanism, as the carrier of the energy transmission system, plays a crucial role. To maximize the energy transmission efficiency of the transmitting and receiving systems, the coupling performance between the coupling mechanisms needs to be carefully considered. Therefore, a design scheme combining asymmetric coil and core arrangements has been introduced. However, the increasing complexity of the system makes performance evaluation and parameter optimization increasingly difficult through finite element analysis (FEA).

[0003] In recent years, sparse polynomial chaos expansion (SPCE) has shown significant advantages in reducing computational costs. However, the design and analysis of asymmetric wireless energy transmission coupling mechanisms still rely primarily on traditional finite element analysis, a method rarely used. Therefore, the SPCE method is considered for the design and analysis of coupling mechanisms. Summary of the Invention

[0004] The purpose of the present invention is to provide a design method for an asymmetric wireless energy transmission coupling mechanism with uncertain parameters, so as to solve the problems of long calculation time and low efficiency of traditional finite element analysis method.

[0005] The technical solution adopted by the present invention is as follows: an asymmetric wireless energy transmission coupling mechanism, including a primary side energy transmitting mechanism and a secondary side energy receiving mechanism, wherein:

[0006] The primary energy transmitting mechanism includes a transmitting coil 101 and a transmitting side magnetic core 102 arranged on the upper side thereof; the secondary energy receiving mechanism includes a receiving coil 201 and a first receiving side magnetic core 202 and a second receiving side magnetic core 203 arranged on the upper side thereof;

[0007] The transmitting side magnetic core 102 is a disc-shaped magnetic core, the receiving side magnetic core 202 is composed of 8 bar-shaped magnetic cores of the same size, and the receiving side magnetic core 203 is a circular magnetic core. The transmitting side magnetic core 102 is aligned with the center of the transmitting coil 101; the receiving side magnetic core 202 is arranged above the receiving coil 201 and is radially evenly distributed; the receiving side magnetic core 203 is arranged above the receiving side magnetic core 202 and is aligned with the center.

[0008] Furthermore, the outer radius of the transmitting coil 101 is R r0 , the turn spacing is d r0 , the number of turns is n r0, the current flowing into the transmitting coil 101 is I r0 ; The radius of the transmitting side core 102 is R f0 And satisfy R f0 =R r0 +10mm, thickness d tf0 The outer radius of the receiving coil 202 is R r1 , the turn spacing is d r1 , the number of turns is n r1 The length of each strip core in the first receiving side magnetic core 202 is dx, the width is dy, the thickness is dz, and the distance to the center of the receiving coil 201 is d c The ring width of the second receiving side magnetic core 203 is w rf1 , the outer radius is R rf1 And satisfy R rf1 =R r1 , thickness d tf1 And satisfy d tf1 =d tf0 The center distance between the receiving coil 201 and the transmitting coil 101 is the transmission distance H of the coupling mechanism r0 ;

[0009] Among them, the length dx, width dy, and thickness dz of the strip core are all uncertain parameters.

[0010] Furthermore, the receiving coil 201 and the transmitting coil 101 are both wound with Litz wire of 0.1mm*200 strands; the transmitting side magnetic core 102 and the first receiving side magnetic core 202 and the second receiving side magnetic core 203 are all made of PC40 manganese-zinc ferrite.

[0011] A design method for an asymmetric wireless energy transmission coupling mechanism containing uncertain parameters, used for optimizing the design of the asymmetric wireless energy transmission coupling mechanism, comprises the following steps:

[0012] S1, obtain the outer radius R of the transmitting coil 101 r0 , number of turns n r0 , turn spacing d r0 , Transmission distance H of coupling mechanism r0 , the current I passing through the transmitting coil 101 r0 , determine the outer radius R of the corresponding receiving coil 201 r1 ;

[0013] S2: Select the length dx, width dy, and thickness dz of the strip core in the first receiving-side magnetic core 202 as random input variables, the coupling coefficient between the primary energy transmitting mechanism and the secondary energy receiving mechanism as the output parameter, and use the Latin hypercube sampling method to obtain a sample set;

[0014] S3, using the sparse chaotic polynomial expansion method, the orthogonal basis function is determined to be Legendre polynomials, and the truncation order is selected according to the adaptive truncation order method to generate the corresponding SPCE proxy model. After the k-fold error meets the accuracy, the optimal SPCE proxy model is generated for the design of asymmetric wireless energy transmission coupling mechanism with uncertain parameters;

[0015] S4, combining the optimal SPCE agent model and the sequential quadratic programming algorithm to calculate the optimal design parameters of the first receiving-side magnetic core under given constraints.

[0016] Further, S1, obtain the outer radius R of the transmitting coil 101 r0 , number of turns n r0 , turn spacing d r0 , Transmission distance H of coupling mechanism r0 , the current I passing through the transmitting coil 101 r0 , determine the outer radius R of the corresponding receiving coil 201 r1 , the specific method is:

[0017]

[0018] Among them, K1 represents the complete elliptic integral of the first kind, E1 represents the complete elliptic integral of the second kind, and H r0 represents the transmission distance of the coupling mechanism, ellipke represents the complete elliptic integral operation function, L represents the distance from the i-th turn of the transmitting coil 101 from the inside to the outside to the center of the transmitting coil 101, and d r0 represents the turn spacing of the transmitting coil 101, I r0 represents the magnitude of the current flowing into the transmitting coil 101, μ0 is the vacuum magnetic permeability, φ i represents the magnetic flux generated by the i-th turn of the transmitting coil 101 in the receiving coil 201, ψ represents the total flux linkage passing through the receiving coil 201;

[0019] The outer radius of the receiving coil 201 is R r1 Plane space, given the outer radius R of the transmitting coil 101 r0 , the turn spacing d of the transmitting coil 101 r0 , the transmission distance H of the coupling mechanism r0 , the i-th turn of the transmitting coil 101 from the inside to the outside satisfies the following elliptic function integral expression:

[0020]

[0021] Among them, K1 represents the complete elliptic integral of the first kind, and E1 represents the complete elliptic integral of the second kind;

[0022] Each turn of the transmitting coil 101 vertically crosses the receiving coil 201 with an outer radius of R r1The magnetic flux in the plane space φ i And the total magnetic flux ψ is:

[0023]

[0024] Calculate the total flux linkage to the receiving coil with an outer radius of R r1 The derivative of , and set it to 0, get the outer radius R of the receiving coil when the corresponding extreme point is 0 r1 as follows:

[0025]

[0026] Furthermore, when the difficulty of sample acquisition is low and the sample capacity is large, the Monte Carlo simulation method is selected to replace the Latin hypercube sampling method to obtain the sample set.

[0027] Furthermore, in S3, the orthogonal basis function is determined to be Legendre polynomials according to the distribution type of the uncertainty parameters. The truncation order p is selected according to the adaptive truncation order method, and the SPCE proxy model corresponding to the truncation order p is generated. After the k-fold error meets the accuracy, the optimal SPCE proxy model is generated. The specific method is:

[0028] S31, considering that the distribution characteristic of the sample set is uniform distribution, the corresponding orthogonal basis function is selected as Legendre polynomial;

[0029] S32, initializing the truncation order p according to the total number of input parameters and output parameters;

[0030] S33, using the Bayesian compressed sensing algorithm, solve the SPCE chaotic polynomial coefficients corresponding to the truncation order p;

[0031] 1) When the total number of sample sets is N, the coupling coefficient Y between the primary energy transmitting mechanism and the secondary energy receiving mechanism is expanded by the following formula:

[0032]

[0033] Among them, Φ i (X) is the orthogonal basis function of the i-th expansion, are the polynomial coefficients, c i represents the coefficient of the constant term in the polynomial, c i represents the coefficient of the first-order term and so on. are the corresponding sample sets of the length dx, width dy, and thickness dz of the strip core, respectively. d is the input dimension, that is, the sum of the types of the length dx, width dy, and thickness dz of the strip core;

[0034] Considering the truncation order to be p, it can be rewritten as:

[0035]

[0036] Rewritten as:

[0037]

[0038] Among them, Ψ is the orthogonal basis function matrix, y is the chaotic polynomial coefficient matrix, and ε is the noise vector;

[0039] 2) Bayesian compressed sensing algorithm calculates SPCE polynomial coefficients:

[0040] For y, we introduce the prior distribution p(y|γ), where γ is a hyperparameter that controls sparsity, and assume that y i The mean is 0 and the variance is γ i The normal distribution of γ i Obeys the exponential distribution with parameter λ, λ obeys the Gamma distribution with parameter v, σ 2 is the noise variance, then the coupling coefficient Y between the primary energy transmitting mechanism and the secondary energy receiving mechanism satisfies the following relationship:

[0041]

[0042] Based on the Bayesian compressed sensing algorithm, the problem of solving the chaotic polynomial coefficients of the SPCE model is rewritten as follows:

[0043]

[0044] Calculate y when the above formula is the optimal solution;

[0045] 3) Generation of SPCE proxy model:

[0046] The SPCE model is generated by the truncation order p, chaotic polynomial coefficient matrix y, noise vector ε, and orthogonal basis function Φ(X) in 1) and 2) and the corresponding k-fold error is calculated;

[0047] 4) Generation of the optimal SPCE proxy model:

[0048] Repeat steps 1), 2), and 3) and select the SPCE model corresponding to the k-fold error that meets the accuracy as the optimal SPCE proxy model.

[0049] Compared with the existing technology, the present invention has the following significant advantages: 1) the Latin hypercube sampling method is used for sample collection to solve the problem that the traditional Monte Carlo method takes a long time in finite element simulation; 2) the constructed SPCE proxy model has a higher response speed and reliable accuracy than finite element simulation analysis; 3) the selected BCS polynomial coefficient sparse solution SPCE method further reduces the demand for the number of samples compared with the PCE method, so that the SPCE proxy model has higher accuracy while reducing the generation cost of the SPCE proxy model; 4) the selection of the SPCE proxy model analysis method reduces the analysis cost of the sample compared with finite element simulation analysis, and can more directly obtain the statistical characteristics of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the design process of an asymmetric wireless energy transmission coupling mechanism with uncertain parameters;

[0051] Figure 2 Schematic diagram of the optimized structure of the coupling mechanism;

[0052] Figure 3 is the probability density function of the SPCE proxy model;

[0053] Figure 4 Comparison of five sets of random input results between the SPCE proxy model and the finite element simulation;

[0054] Figure 5 is the cumulative distribution function of the SPCE proxy model;

[0055] Figure 6 is the global Sobol total sensitivity index based on SPCE;

[0056] Figure 7 is the global Sobol first-order sensitivity index based on SPCE;

[0057] Figure 8 is the global Sobol second-order sensitivity index based on SPCE;

[0058] Figure 9 are the moments of the SPCE surrogate model. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0060] An asymmetric wireless energy transmission coupling mechanism includes a primary side energy transmitting mechanism and a secondary side energy receiving mechanism, wherein:

[0061] The primary energy transmitting mechanism includes a transmitting coil 101 and a transmitting side magnetic core 102 arranged on the upper side thereof; the secondary energy receiving mechanism includes a receiving coil 201 and a first receiving side magnetic core 202 and a second receiving side magnetic core 203 arranged on the upper side thereof;

[0062] The transmitting side magnetic core 102 is a disc-shaped magnetic core, the receiving side magnetic core 202 is composed of 8 bar-shaped magnetic cores of the same size, and the receiving side magnetic core 203 is a circular magnetic core. The transmitting side magnetic core 102 is aligned with the center of the transmitting coil 101; the receiving side magnetic core 202 is arranged above the receiving coil 201 and is arranged in a centrally symmetrical manner; the receiving side magnetic core 203 is arranged above the receiving side magnetic core 202 and is centrally aligned.

[0063] The outer radius of the transmitting coil 101 is R r0 , the turn spacing is d r0 , the number of turns is n r0 , the current flowing into the transmitting coil 101 is I r0 ; The radius of the transmitting side core 102 is R f0 And satisfy R f0 =R r0 +10mm, thickness d tf0 ; The outer radius of the receiving coil 202 is R r1 , the turn spacing is d r1 , the number of turns is n r1 The length of each strip core in the first receiving side magnetic core 202 is dx, the width is dy, the thickness is dz, and the distance to the center of the receiving coil 201 is d c The ring width of the second receiving side magnetic core 203 is w rf1 , the outer radius is R rf1 And satisfy R rf1 =R r1 , thickness d tf1 And satisfy d tf1 =d tf0 The center distance between the receiving coil 201 and the transmitting coil 101 is the transmission distance H of the coupling mechanism r0 ;

[0064] Among them, the length dx, width dy, and thickness dz of the strip core are all uncertain parameters;

[0065] The receiving coil 201 and the transmitting coil 101 are both wound with 0.1mm*200 strands of Litz wire; the transmitting side magnetic core 102 and the first receiving side magnetic core 202 and the second receiving side magnetic core 203 are all made of PC40 manganese-zinc ferrite.

[0066] The preferred solution may be: the ring width w of the second receiving side magnetic core 203 is rf1=50mm, the distance d from each strip core in the first receiving side magnetic core 202 to the center of the receiving coil 201 c =125mm, the thickness d of the transmitting side magnetic core 102 tf0 =4 mm, the thickness d of the second receiving-side magnetic core 203 tf1 =d tf0 =4mm.

[0067] A design method for an asymmetric wireless energy transmission coupling mechanism with uncertain parameters includes the following steps:

[0068] S1, obtain the outer radius R of the transmitting coil 101 r0 , number of turns n r0 , turn spacing d r0 , Transmission distance H of coupling mechanism r0 , the current I passing through the transmitting coil 101 r0 , determine the outer radius R of the corresponding receiving coil 201 r1 , the formula is:

[0069]

[0070] Among them, K1 represents the complete elliptic integral of the first kind, E1 represents the complete elliptic integral of the second kind, and H r0 represents the transmission distance of the coupling mechanism, ellipke represents the complete elliptic integral operation function, and L represents the distance from the i-th turn of the transmitting coil 101 from the inside to the outside to the center of the transmitting coil 101 (1≤i≤n r0 ,i∈N),d r0 represents the turn spacing of the transmitting coil 101, I r0 represents the magnitude of the current flowing into the transmitting coil 101, μ0 is the vacuum magnetic permeability, φ i represents the magnetic flux generated by the i-th turn of the transmitting coil 101 in the receiving coil 201, and ψ represents the total magnetic flux passing through the receiving coil 201.

[0071] The outer radius of the receiving coil 201 is R r1 Plane space, given the outer radius R of the transmitting coil 101 r0 , the turn spacing d of the transmitting coil 101 r0 , the transmission distance H of the coupling mechanism r0 The i-th turn of the transmitting coil 101 from the inside to the outside satisfies the following elliptic function integral expression:

[0072]

[0073] Among them, K1 represents the complete elliptic integral of the first kind and E1 represents the complete elliptic integral of the second kind.

[0074] Each turn of the transmitting coil 101 vertically crosses the receiving coil 201 with an outer radius of R r1 The magnetic flux in the plane space φ i And the total magnetic flux ψ is:

[0075]

[0076] Calculate the total flux linkage to the receiving coil with an outer radius of R r1 The derivative of , and set it to 0, get the outer radius R of the receiving coil when the corresponding extreme point is 0 r1 as follows:

[0077]

[0078] S2, select the length dx, width dy, and thickness dz of the strip magnetic core in the first receiving-side magnetic core 202 as random input variables, and the coupling coefficient between the primary-side energy transmitting mechanism and the secondary-side energy receiving mechanism as the output parameter, and adopt the Latin hypercube sampling method to obtain a sample set; the sampling method should select an efficient and accurate method depending on the capacity of the sample set. In this example, Latin hypercube sampling is selected. When the difficulty of sample acquisition is low and the sample capacity is large, Monte Carlo simulation sampling can be selected.

[0079] S3. Using the sparse chaotic polynomial expansion method (SPCE analysis method), the orthogonal basis functions are determined to be Legendre polynomials. The truncation order is selected using the adaptive truncation order method. After the k-fold error meets the accuracy requirements, the SPCE proxy model is generated. Compared with the traditional PCE or generalized PCE method, the SPCE analysis method generates a sparse polynomial coefficient matrix, requires a smaller sample size, and has a faster proxy model response speed. The specific method is as follows:

[0080] S31, considering that the distribution characteristic of the sample set is uniform distribution, the corresponding orthogonal basis function is selected as Legendre polynomial;

[0081] S32, initializing the truncation order p according to the total number of input parameters and output parameters;

[0082] S33, using the Bayesian compressed sensing algorithm, solve the SPCE chaotic polynomial coefficients corresponding to the truncation order p;

[0083] 1) When the truncation order is p and the total number of sample sets is N, the coupling coefficient Y between the primary energy transmitting mechanism and the secondary energy receiving mechanism is expanded by the following formula:

[0084]

[0085] Among them, Φ i (X) is the orthogonal basis function of the i-th expansion, are the polynomial coefficients, are the corresponding sample sets of the length dx, width dy, and thickness dz of the strip core, respectively. d is the input dimension, that is, the sum of the types of the length dx, width dy, and thickness dz of the strip core;

[0086] Rewritten as:

[0087] Y=Ψy+ε (5)

[0088] Where y is the chaotic polynomial coefficient matrix, ε is the noise vector;

[0089] 2) Bayesian compressed sensing algorithm calculates SPCE polynomial coefficients:

[0090] For y, we introduce the prior distribution p(y|γ), where γ is a hyperparameter that controls sparsity, and assume that y i The mean is 0 and the variance is γ i The normal distribution of γ i Obeys the exponential distribution with parameter λ, λ obeys the Gamma distribution with parameter v, σ 2 is the noise variance, then the coupling coefficient Y between the primary energy transmitting mechanism and the secondary energy receiving mechanism satisfies the following relationship:

[0091]

[0092] Based on the Bayesian compressed sensing algorithm, the problem of solving the chaotic polynomial coefficients of the SPCE model is rewritten as follows:

[0093]

[0094] Calculate y when the above formula is the optimal solution;

[0095] 3) Generation of SPCE proxy model:

[0096] The SPCE model is generated by the truncation order p, chaotic polynomial coefficient matrix y, noise vector ε, and orthogonal basis function Φ(X) in 1) and 2) and the corresponding k-fold error is calculated;

[0097] 4) Generation of the optimal SPCE proxy model:

[0098] Repeat steps 1), 2), and 3) and select the SPCE model corresponding to the k-fold error that meets the accuracy as the optimal SPCE proxy model.

[0099] S4, statistical analysis is performed on the generated optimal SPCE proxy model, and the accuracy and computational efficiency of the proxy model are verified by random input examples selected by the Monte Carlo method compared with the finite element simulation model. The specific method is:

[0100] S41, using the generated optimal SPCE model to analyze the distribution of the coupling coefficient between the primary energy transmitting mechanism and the secondary energy receiving mechanism due to the length dx, width dy, and thickness dz of the strip core, including: probability density function (PDF), cumulative distribution function (CDF), and moments of each order;

[0101] S42, using the generated optimal SPCE model and the global Sobol sensitivity analysis method to analyze the total sensitivity, first-order sensitivity, and second-order sensitivity of the length dx, width dy, and thickness dz of the strip core to the coupling coefficient between the primary energy transmitting mechanism and the secondary energy receiving mechanism;

[0102] S43, using the Monte Carlo method to select random input samples that meet the distribution and input them into the generated optimal SPCE model and finite element simulation model respectively, and comparing the output results of the optimal SPCE model and the finite element simulation model in terms of accuracy and computational efficiency to verify the performance of the optimal SPCE model.

[0103] S5, combining the optimal SPCE agent model and the sequential quadratic programming algorithm to calculate the optimal design parameters of the first receiving-side magnetic core under given constraints.

[0104] Example

[0105] In order to verify the effectiveness of the scheme of the present invention, the following experimental design was carried out.

[0106] 1) Select input parameters

[0107] The outer radius R of the transmitting coil 101 r0 is 150mm, the turn spacing d r0 is 4.4mm, number of turns n r0 The current I flowing into the transmitting coil 101 is 10 turns. r0 is 2.5A, the radius R of the transmitting side magnetic core 102 f0 160mm, thickness d tf0 The turn spacing d of the receiving coil 202 is 4 mm. r1 is 0.1mm, number of turns n r1 For 10 turns.

[0108] The length dx of each strip magnetic core in the first receiving-side magnetic core 202 satisfies the distribution U(128,192), the length dy satisfies the distribution U(32,48), and the length dz satisfies the distribution U(1.8,4.2), all in mm.

[0109] 2) Calculate the optimal outer radius of the receiving coil 201

[0110] The optimal outer radius R of the receiving coil 201 can be calculated as follows: r0 It is 214mm.

[0111] 3) Obtain finite element simulation sample set by Latin hypercube sampling method

[0112] Table 1 Finite element simulation sample set

[0113]

[0114]

[0115]

[0116]

[0117] 4) Obtaining the optimal SPCE proxy model by the BCS method

[0118] The adaptive truncation order p is 4, and the corresponding k-fold error is 8.8251845e-04.

[0119] 5) Statistical analysis and accuracy comparison

[0120] Statistical analysis showed that the optimal SPCE proxy model had a mean of 0.3222, a standard deviation of 0.0040, a skewness of 0.0012, and an excess kurtosis of -0.9168. The total sensitivity of the strip core's length dx to the coupling coefficient between the primary and secondary energy transmitters was 0.834417, with a first-order sensitivity of 0.832960. The total sensitivity of the width dy to the coupling coefficient between the primary and secondary energy transmitters was 0.102731, with a first-order sensitivity of 0.0647974. The total sensitivity of the thickness dz to the coupling coefficient between the primary and secondary energy transmitters was 0.101439, with a first-order sensitivity of 0.063654. The second-order sensitivities of dx and dy, dx and dz, and dz and dy were 0.000803, 0.000654, and 0.000489, respectively. Therefore, the length of the strip core plays a dominant role in the system coupling coefficient and should be used as the main design parameter. The contributions of the length, width and thickness of the strip core to the system coupling coefficient are basically independent.

[0121] Accuracy analysis revealed that for five randomly selected input samples of strip core lengths dx, widths dy, and thicknesses dz, the optimal SPCE proxy model outputted corresponding system coupling coefficients of 0.323197, 0.330360, 0.322484, 0.321028, and 0.319787; finite element simulation outputs were 0.330511, 0.323163, 0.322356, 0.321086, and 0.319843. The error range was 0.02%-3.8%, demonstrating extremely high accuracy. Furthermore, because the SPCE method requires a much smaller sample size than traditional Monte Carlo methods, it can obtain statistical metrics much faster. Furthermore, the BCS-SPCE method generates proxy models more quickly than the PCE method due to the sparse solution of the chaotic polynomial coefficients.

[0122] 6) Optimization design of the first receiving side magnetic core parameters

[0123] Table 2 Iterative calculation table of sequential quadratic programming algorithm

[0124] Iter Func-count Fval Feasibility StepLength NormofStep First-order optimality 0 4 -1.092913E+00 0.000E+00 1.000E+00 0.000E+00 5.327E-01 1 8 -1.477365E+00 0.000E+00 1.000E+00 5.328E-01 9.763E-01 2 12 -2.142958E+00 0.000E+00 1.000E+00 4.677E-01 1.192E+00 3 16 -2.143355E+00 0.000E+00 1.000E+00 1.981E-02 1.907E-02 4 20 -2.145347E+00 0.000E+00 1.000E+00 9.912E-02 1.909E-02 5 24 -2.155446E+00 0.000E+00 1.000E+00 4.973E-01 1.918E-02 6 28 -2.209328E+00 4.899E-05 1.000E+00 2.522E+00 2.044E-02 7 32 -2.572827E+00 2.854E-04 1.000E+00 1.366E+01 7.474E-02 8 36 -2.714685E+00 8.393E-05 1.000E+00 4.250E+00 3.812E-02 9 40 -2.714614E+00 0.000E+00 1.000E+00 1.214E-03 1.124E-03 10 44 -2.714614E+00 8.158E-11 1.000E+00 1.180E-06 2.180E-07

[0125] The dx value range is limited to 130 to 200 mm, the dy value range is 30 to 50 mm, and the dz value range is 1-5 mm. The constraint dx / dy≤3.0 is satisfied, and the energy efficiency index M / V=M / (dx*dy*dz) is required to be maximized, where M is the system mutual inductance.

[0126] When dx = 130 mm, dy = 43.3 mm, and dz = 1 mm, the optimal solution is obtained. At this time, the mutual inductance value M = 15271.80 nH and the volume of the first receiving side core V = 45064 mm 3 , energy efficiency index M / V=0.338875nH / mm 3 Compared with the initial parameters dx = 150mm, dy = 40mm, dz = 3mm, the corresponding mutual inductance value M = 16210.00nH, the first receiving side magnetic core volume V = 144000mm 3 , energy efficiency index M / V=0.112575nH / mm 3 , the mutual inductance value M is reduced by 5.78%, the volume V is reduced by 219.5%, and the energy efficiency index M / V is increased by 201%.

[0127] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. An asymmetric wireless energy transmission coupling mechanism, characterized in that: It includes a primary side energy transmitting mechanism and a secondary side energy receiving mechanism, wherein: The primary energy transmitting mechanism includes a transmitting coil and a transmitting side magnetic core arranged on the upper side thereof; the secondary energy receiving mechanism includes a receiving coil and a first receiving side magnetic core and a second receiving side magnetic core arranged on the upper side thereof; The transmitting side magnetic core is a disc-shaped magnetic core, the receiving side magnetic core is composed of 8 bar-shaped magnetic cores of the same size, the receiving side magnetic core is a circular ring-shaped magnetic core, and the transmitting side magnetic core is aligned with the center of the transmitting coil; the receiving side magnetic core is arranged above the receiving coil and is radially evenly distributed; the receiving side magnetic core is arranged above the receiving side magnetic core and the centers are aligned.

2. The asymmetric wireless energy transmission coupling mechanism according to claim 1, characterized in that: The outer radius of the transmitting coil is R r0 , the turn spacing is d r0 , the number of turns is n r0 , the current flowing into the transmitting coil is I r0 ;The radius of the transmitting side core is R f0 And satisfy R f0 =R r0 +10mm, thickness d tf0 ; The outer radius of the receiving coil is R r1 , the turn spacing is d r1 , the number of turns is n r1 The length of each strip core in the first receiving side magnetic core is dx, the width is dy, the thickness is dz, and the distance to the center of the receiving coil is d c ; The ring width of the second receiving side magnetic core is w rf1 , the outer radius is R rf1 And satisfy R rf1 =R r1 , thickness d tf1 And satisfy d tf1 =d tf0 The center distance between the receiving coil and the transmitting coil is the transmission distance H of the coupling mechanism. r0 ; Among them, the length dx, width dy, and thickness dz of the strip core are all uncertain parameters.

3. The design method of an asymmetric wireless energy transmission coupling mechanism with uncertain parameters according to claim 1, characterized in that: Both the receiving coil and the transmitting coil are wound with Litz wire with parameters of 0.1mm*200 strands; the transmitting side magnetic core, the first receiving side magnetic core, and the second receiving side magnetic core are all made of PC40 manganese-zinc ferrite.

4. A design method for an asymmetric wireless energy transmission coupling mechanism with uncertain parameters, characterized in that: The optimized design of the asymmetric wireless energy transmission coupling mechanism according to any one of claims 1 to 3 comprises the following steps: S1, get the outer radius R of the transmitting coil r0 , number of turns n r0 , turn spacing d r0 , Transmission distance H of coupling mechanism r0 , the current I passing through the transmitting coil r0 , determine the outer radius R of the corresponding receiving coil r1 ; S2: Select the length dx, width dy, and thickness dz of the strip core in the first receiving-side magnetic core as random input variables, the coupling coefficient between the primary energy transmitting mechanism and the secondary energy receiving mechanism as the output parameter, and use the Latin hypercube sampling method to obtain a sample set; S3, using the sparse chaotic polynomial expansion method, the orthogonal basis function is determined to be Legendre polynomials, and the truncation order is selected according to the adaptive truncation order method to generate the corresponding SPCE proxy model. After the k-fold error meets the accuracy, the optimal SPCE proxy model is generated for the design of asymmetric wireless energy transmission coupling mechanism with uncertain parameters; S4, combining the optimal SPCE agent model and the sequential quadratic programming algorithm to calculate the optimal design parameters of the first receiving-side magnetic core under given constraints.

5. The design method of an asymmetric wireless energy transmission coupling mechanism with uncertain parameters according to claim 4, characterized in that: S1, get the outer radius R of the transmitting coil r0 , number of turns n r0 , turn spacing d r0 , Transmission distance H of coupling mechanism r0 , the current I passing through the transmitting coil r0 , determine the outer radius R of the corresponding receiving coil r1 , the specific method is: Among them, K1 represents the complete elliptic integral of the first kind, E1 represents the complete elliptic integral of the second kind, and H r0 represents the transmission distance of the coupling mechanism, ellipke represents the complete elliptic integral operation function, L represents the distance from the inside to the outside of the transmitting coil to the center of the transmitting coil, d r0 Indicates the turn spacing of the transmitting coil, I r0 represents the current flowing into the transmitting coil, μ0 is the vacuum permeability, φ i represents the magnetic flux generated by the i-th turn of the transmitting coil in the receiving coil, ψ represents the total magnetic flux passing through the receiving coil; For the receiving coil with an outer radius of R r1 Plane space, given the outer radius R of the transmitting coil r0 , the turn spacing d of the transmitting coil r0 , the transmission distance H of the coupling mechanism r0 , the i-th turn of the transmitting coil from the inside to the outside satisfies the following elliptic function integral expression: Among them, K1 represents the complete elliptic integral of the first kind, and E1 represents the complete elliptic integral of the second kind; Each turn of the transmitting coil vertically passes through the outer radius of the receiving coil R r1 The magnetic flux in the plane space φ i And the total magnetic flux ψ is: Calculate the total flux linkage to the receiving coil with an outer radius of R r1 The derivative of , and set it to 0, get the outer radius R of the receiving coil when the corresponding extreme point is 0 r1 as follows:

6. The method for designing an asymmetric wireless energy transmission coupling mechanism with uncertain parameters according to claim 4, characterized in that: When the difficulty of sample acquisition is low and the sample capacity is large, the Monte Carlo simulation method is selected to replace the Latin hypercube sampling method to obtain the sample set.

7. The method for designing an asymmetric wireless energy transmission coupling mechanism with uncertain parameters according to claim 4, characterized in that: S3, the orthogonal basis function is determined to be Legendre polynomials according to the distribution type of the uncertainty parameter. The truncation order p is selected according to the adaptive truncation order method, and the SPCE proxy model corresponding to the truncation order p is generated. After the k-fold error meets the accuracy, the optimal SPCE proxy model is generated. The specific method is: S31, considering that the distribution characteristic of the sample set is uniform distribution, the corresponding orthogonal basis function is selected as Legendre polynomial; S32, initializing the truncation order p according to the total number of input parameters and output parameters; S33, using the Bayesian compressed sensing algorithm, solve the SPCE chaotic polynomial coefficients corresponding to the truncation order p; 1) When the total number of sample sets is N, the coupling coefficient Y between the primary energy transmitting mechanism and the secondary energy receiving mechanism is expanded by the following formula: Among them, Φ i (X) is the orthogonal basis function of the i-th expansion, are the polynomial coefficients, c i represents the coefficient of the constant term in the polynomial, c i represents the coefficient of the first-order term and so on. are the corresponding sample sets of the length dx, width dy, and thickness dz of the strip core, respectively. d is the input dimension, that is, the sum of the types of the length dx, width dy, and thickness dz of the strip core; Considering the truncation order to be p, it can be rewritten as: Rewritten as: Among them, Ψ is the orthogonal basis function matrix, y is the chaotic polynomial coefficient matrix, and ε is the noise vector; 2) Bayesian compressed sensing algorithm calculates SPCE polynomial coefficients: For y, we introduce the prior distribution p(y|γ), where γ is a hyperparameter that controls sparsity, and assume that y i The mean is 0 and the variance is γ i The normal distribution of γ i Obeys the exponential distribution with parameter λ, λ obeys the Gamma distribution with parameter v, σ 2 is the noise variance, then the coupling coefficient Y between the primary energy transmitting mechanism and the secondary energy receiving mechanism satisfies the following relationship: Based on the Bayesian compressed sensing algorithm, the problem of solving the chaotic polynomial coefficients of the SPCE model is rewritten as follows: minl=-logp(Y / y)-logp(y / γ)-logp(γ / λ)-logp(λ) (7) Calculate y when the above formula is the optimal solution; 3) Generation of SPCE proxy model: The SPCE model is generated by the truncation order p, chaotic polynomial coefficient matrix y, noise vector ε, and orthogonal basis function Φ(X) in 1) and 2) and the corresponding k-fold error is calculated; 4) Generation of the optimal SPCE proxy model: Repeat steps 1), 2), and 3) and select the SPCE model corresponding to the k-fold error that meets the accuracy as the optimal SPCE proxy model.

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