Parameter value determination method and device of wireless charging coil and wireless charging system

Abstract

The application relates to the technical field of wireless charging systems, and discloses a parameter value determination method and device of a wireless charging coil and a wireless charging system. The method comprises the following steps: determining n groups of parameter sets for the wireless charging coil; determining charging efficiency index values, offset efficiency reduction rate index values and price index values of the wireless charging system when the parameter values of the wireless charging coil are respectively the parameter values in the parameter sets, to obtain n groups of first index value sets; determining the maximum weights corresponding to the indexes; correcting the index values in the n groups of first index value sets to obtain n groups of second index value sets after correction; determining first evaluation values and second evaluation values corresponding to the parameter sets; determining comprehensive scores corresponding to the parameter sets; and determining the parameter values in the parameter set corresponding to the highest comprehensive score as the parameter values of the wireless charging coil. When the parameter values of the wireless charging coil are determined, the application can take into account multiple indexes of the system to realize balanced optimization among the multiple indexes.

Description

Technical Field

[0001] This application relates to the field of wireless charging system technology, specifically to a method, device, and wireless charging system for determining the parameter values ​​of a wireless charging coil. Background Technology

[0002] A wireless charging system consists of wireless charging coils (i.e., wireless transmitting coils and wireless receiving coils). Wireless charging coils involve various parameters, and improper parameter settings will directly affect multiple indicators such as system charging efficiency and price. Therefore, determining the parameter values ​​of the wireless charging coil to achieve a balanced optimization of multiple indicators and avoid situations where one indicator excels while others are severely degraded is a problem that needs to be solved. Summary of the Invention

[0003] In view of the above problems, this application provides a method, device, wireless charging system and storage medium for determining the parameter values ​​of a wireless charging coil, which solves the problem in the prior art that when determining the parameter values ​​of a wireless charging coil, it is impossible to take into account multiple indicators of the system, thus making it difficult for the system to achieve balanced optimization among multiple indicators.

[0004] According to one aspect of the embodiments of this application, a method for determining the parameter values ​​of a wireless charging coil is provided. The method includes: S1: determining n sets of parameter sets for the wireless charging coil, wherein each set of parameter sets includes multiple parameter values, each parameter value corresponds to a data type, different parameter sets include parameter values ​​corresponding to the same data type and at least one data type parameter value is different, the wireless charging coil is a coil in a wireless charging system, wherein n is a positive integer and n≥2; S2: when the parameter values ​​of the wireless charging coil are determined to be the parameter values ​​in each set of parameter sets, the charging efficiency index value and the offset efficiency of the wireless charging system are determined. The rate of decline and price indicators are used to obtain n initial indicator value sets that correspond one-to-one with the n sets of parameters. These n initial indicator value sets are then normalized to obtain n first indicator value sets. Each first indicator value set includes a set of normalized charging efficiency indicator values, offset efficiency decline rate indicator values, and price indicator values ​​corresponding to the parameter sets. S3: Determine a 3×3 scaling matrix A, where the first to third rows of data in scaling matrix A correspond to the charging efficiency indicator, offset efficiency decline rate indicator, and price indicator respectively; the first to third columns of data correspond to the charging efficiency indicator, offset efficiency decline rate indicator, and price indicator respectively; and the data in the u-th row and v-th column A... uvThe value represents the importance of the indicator corresponding to the u-th row relative to the indicator corresponding to the v-th column, where u and v are both positive integers, and u≤3, v≤3; S4: Based on the scaling matrix A, determine the subjective weights corresponding to the charging efficiency indicator, the offset efficiency decline rate indicator, and the price indicator; S5: Based on the n sets of first indicator values, determine the objective weights corresponding to the charging efficiency indicator, the offset efficiency decline rate indicator, and the price indicator; S6: Perform a weighted summation of the subjective and objective weights corresponding to the charging efficiency indicator, the offset efficiency decline rate indicator, and the price indicator respectively to obtain the final weights corresponding to each indicator; S7: Correct the indicator values ​​in the n sets of first indicator values ​​to obtain the corrected n sets of second indicator values; S8: Use the formula... Determine the first evaluation value corresponding to the i-th parameter set in the n-set parameter set. Where m is the number of indicators. It is the final weight corresponding to the j-th indicator among the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator. It is the j-th indicator value in the i-th group of the n-group second indicator value set; S9: through the formula Determine the second evaluation value corresponding to the i-th parameter set in the n-set parameter set. S10: Normalize each of the first evaluation value and the second evaluation value to obtain n normalized first evaluation values ​​and n normalized second evaluation values ​​corresponding to the n sets of parameters. Based on the normalized first evaluation values ​​and the normalized second evaluation values, determine the first discrimination degree of the additive weighted model and the second discrimination degree of the multiplicative weighted model for the n sets of parameters. Based on the first discrimination degree and the second discrimination degree, determine the score weight corresponding to the first evaluation value. λ S11: Through the formula Determine the comprehensive score corresponding to the i-th parameter set in the n parameter sets. S12: Determine the parameter value of the wireless charging coil as the parameter value of the parameter set corresponding to the highest comprehensive score among all comprehensive scores corresponding to the n sets of parameter sets.

[0005] In an alternative embodiment, S4 includes: determining the largest eigenvalue of the scaling matrix A. ; through formula Determine the largest eigenvalue Corresponding non-zero eigenvectors ; through formula The subjective weight is determined for the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. ,in, Non-zero eigenvectors The j-th row of data, Non-zero eigenvectors The data in row t.

[0006] In one optional embodiment, S5 includes: determining the average value of the charging efficiency index, the average value of the offset efficiency reduction rate index, and the average value of the price index based on the n sets of first index values, thereby obtaining the average value corresponding to the charging efficiency index, the average value corresponding to the offset efficiency reduction rate index, and the average value corresponding to the price index; determining the standard deviation corresponding to the charging efficiency index, the standard deviation corresponding to the offset efficiency reduction rate index, and the standard deviation corresponding to the price index based on the n sets of first index values, the average value corresponding to the charging efficiency index, the average value corresponding to the offset efficiency reduction rate index, and the average value corresponding to the price index; and using the formula... Determine the correlation coefficient between the j-th and k-th indicators among the charging efficiency index, offset efficiency degradation rate index, and price index. ,in, and These are the j-th and k-th index values ​​in the i-th group of the n-group first index value set, respectively. and These are the average values ​​of the j-th and k-th indicators among the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator; (using the formula...) Determine the degree of overall conflict between the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator, and other indicators. ; through formula Determine the amount of objective information contained in the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. ; through formula The objective weight is determined for the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. .

[0007] In one alternative approach, the first discrimination degree of the additive weighted model to the n sets of parameters and the second discrimination degree of the multiplicative weighted model to the n sets of parameters are determined based on the normalized first evaluation value and the normalized second evaluation value, and the score weight corresponding to the first evaluation value is determined based on the first discrimination degree and the second discrimination degree. λ This includes: normalizing each of the first evaluation value and the second evaluation value to obtain n normalized first evaluation values ​​and n normalized second evaluation values ​​corresponding to the n sets of parameters; and using the formula... Determine the first discriminant of the additive weighted model for the n parameter sets. ,in, This is the normalized first evaluation value corresponding to the i-th parameter set in the n-set parameter set; expressed by the formula... Determine the second discriminant of the multiplicative weighted model for the n sets of parameters. ,in, The normalized second evaluation value corresponding to the i-th parameter set in the n-set parameter set; through the formula Determine the bargaining power of the additive weighted model ; through formula Determine the bargaining power of the multiplicative weighted model Bargaining power based on the additive weighted model Bargaining power of the multiplicative weighted model Determine the score weight .

[0008] In one alternative approach, the bargaining power based on the additive weighted model... Bargaining power of the multiplicative weighted model Determine the score weight This includes: determining the objective function. ,in, ;Will When the first derivative is equal to zero The value is determined as the score weight. .

[0009] In one optional manner, S1~S12 are executed at least twice, wherein, during the first execution of S1~S12, each set of parameters includes parameter values ​​of multiple discrete parameters; during the second execution of S1~S12, each set of parameters includes parameter values ​​of multiple continuous parameters, and when S2 is executed for the first time to determine each index value, the parameter values ​​of the continuous parameters of the wireless charging coil are preset values; when S2 is executed for the second time to determine each index value, the parameter values ​​of the discrete parameters of the wireless charging coil are the parameter values ​​determined during the first execution of S1~S12.

[0010] According to another aspect of the embodiments of this application, a device for determining the parameter values ​​of a wireless charging coil is provided, including a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the method for determining the parameter values ​​of a wireless charging coil as described above.

[0011] According to another aspect of the embodiments of this application, a wireless charging system is provided, including a wireless receiving coil, wherein the parameter values ​​of the wireless receiving coil are determined by the parameter value determination method of the wireless charging coil as described above.

[0012] In an alternative embodiment, the system further includes a wireless transmitting coil, the parameter values ​​of which are determined by the method described above, and when performing S2 to determine the parameter values ​​of the wireless transmitting coil, the charging efficiency index, offset efficiency reduction rate index, and price index of the wireless charging system are determined based on the parameter values ​​of the wireless receiving coil determined by the method described above.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method for determining the parameter values ​​of a wireless charging coil as described above.

[0014] In this embodiment, through the aforementioned steps S1-S12, a complete method for determining wireless charging coil parameters, integrating subjective-objective combined weighting with addition-multiplication game theory, is constructed. By combining the Analytic Hierarchy Process (AHP) and objective weighting methods based on data dispersion and correlation (such as the CRITIC method), the final weights reflect both expert experience preferences in different application scenarios and retain the inherent statistical characteristics of the simulation data itself. Furthermore, the WASPAS method based on Nash bargaining game theory is introduced, treating the weighted sum model and the weighted product model as two players. The combination coefficients are adaptively solved based on their ability to distinguish different parameter sets, thus balancing the compensatory effect of high-efficiency indicators on weaker indicators and the penalizing effect of the weighted product model on performance weaknesses, effectively avoiding the limitations of a single evaluation model.

[0015] Compared to traditional methods of optimizing wireless charging coils with a single index or designing them based on experience, the embodiments of this application can find a comprehensive trade-off solution based on Nash negotiation improvement among multiple conflicting indicators such as charging efficiency, offset robustness, and manufacturing cost, so that the final determined wireless charging coil parameter values ​​have better overall engineering practicality and reliability.

[0016] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating the method for determining the parameter values ​​of a wireless charging coil provided in an embodiment of this application is shown. Figure 2 A schematic diagram of the structure of the wireless charging coil parameter value determination device provided in an embodiment of this application is shown; Figure 3 A schematic diagram of the wireless charging system provided in an embodiment of this application is shown; Figure 4 A schematic diagram of a wireless charging system provided in another embodiment of this application is shown. Detailed Implementation

[0018] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0019] In magnetically coupled resonant wireless charging systems, the coupling coil (i.e., the wireless charging coil) structure is the core carrier for achieving efficient energy transfer, and its design directly determines the overall performance of the wireless charging system. Structural parameters such as the coil's geometry, size, number of turns, relative position, and core layout collectively determine the mutual inductance and coupling coefficient between the wireless transmitting and receiving coils, thus affecting the system's output power, transmission efficiency, and resistance to lateral offset and air gap changes. Therefore, the rational design and optimization of the coupling coil structure is key to achieving a balance between high transmission efficiency, higher output power, and strong resistance to offset.

[0020] Current traditional wireless charging coil topology optimization methods mostly focus on single-indicator optimization, often treating charging efficiency as the sole optimization metric while neglecting equally crucial indicators in practical engineering, such as drift resistance and material cost. These performance indicators often have trade-offs; for example, unilaterally pursuing higher charging efficiency may lead to decreased drift resistance or significantly increased costs, which is impractical for production. Single-indicator optimization can only find extreme solutions for a single indicator, failing to effectively characterize the balance and substitution effects among multiple design indicators, and making it difficult to simultaneously consider and globally balance multiple metrics. Furthermore, the weighting of indicators varies in different application scenarios of wireless charging systems. Therefore, determining the parameter values ​​of wireless charging coils to achieve balanced optimization of multiple indicators and avoid situations where one indicator excels while others are severely degraded is a pressing problem that needs to be solved.

[0021] To balance multiple indicators of the wireless charging system when determining the parameter values ​​of the wireless charging coil, this application proposes a method for determining the parameter values ​​of the wireless charging coil. First, multiple parameter sets are determined for the wireless charging coil, each set including multiple parameter values. The charging efficiency, offset efficiency reduction rate, and price of the wireless charging system are calculated for each set of parameter values, resulting in multiple sets of first indicator values. Next, a scaling matrix is ​​constructed to calculate the subjective weights of each indicator, and the objective weights of each indicator are determined based on the multiple sets of first indicator values. These are then weighted and fused to obtain the final weights of each indicator. Next, the indicator values ​​in the first set of indicator values ​​are corrected, and a first evaluation value and a second evaluation value are calculated for each parameter set based on the corrected set of indicator values. Finally, the score weight corresponding to the first evaluation value is determined, and based on the score weight, the first evaluation value, and the second evaluation value for each parameter set, a comprehensive score is determined for each parameter set. The parameter values ​​in the parameter set corresponding to the highest comprehensive score are then determined as the final parameter values ​​of the wireless charging coil.

[0022] Figure 1 This diagram illustrates a flowchart of a method for determining the parameter values ​​of a wireless charging coil according to an embodiment of this application. The method is executed by a device for determining the parameter values ​​of a wireless charging coil. This device can be a device including one or more processors, such as a server, computer, touchscreen phone, smartphone, tablet computer, or other device. Figure 1 As shown, the method includes the following steps S1 to S12.

[0023] S1: Determine n sets of parameters for the wireless charging coil. n is a positive integer and n > 1.

[0024] The wireless charging coil can be either a wireless transmitting coil or a wireless receiving coil.

[0025] For a wireless charging coil, its parameters include the number of coil turns, coil shape, outer radius, inner radius, additional core coverage, and transmission air gap spacing. Among these, the number of coil turns and coil shape are discrete parameters, while the outer radius, inner radius, additional core coverage, and transmission air gap spacing are continuous parameters. To better determine the parameter values ​​of the wireless charging coil, this application preferably executes steps S1-S12 multiple times to determine the parameter values ​​of both discrete and continuous parameters. For example, steps S1-S12 are executed first to determine the discrete parameter values, and then executed a second time to determine the continuous parameter values. By determining the discrete and continuous parameter values ​​in stages, the amount of data involved in each execution of steps S1-S12 is reduced, thereby reducing computational resource consumption. Furthermore, it avoids the problem of bloat in the combined determination of discrete and continuous parameters, thus improving the efficiency of parameter value determination.

[0026] In this embodiment of the application, the parameter values ​​for determining the discrete parameters of the wireless receiving coil are used as an example for explanation. Specifically, in this step, n sets of parameters can be determined by methods such as the Taguchi method or the Latin hypercube sampling method. Alternatively, the settable values ​​for the number of coil turns and the settable shape of the wireless charging coil can be determined separately. The settable values ​​for the number of coil turns and the settable shape of the coil are then combined to obtain n sets of parameters. For example, if the settable values ​​for the number of coil turns include 7, 8, and 9, and the settable shapes of the coil include circles and squares, then the six sets of parameters shown in Table 1 below can be obtained.

[0027] Table 1: Six sets of parameters for discrete parameters

[0028] Table 1 contains six sets of parameters, namely X 11 X 12 X 13 X 21 X 22 and X 23 Among them, the first set of parameters X 11 The included parameter values ​​are 7 and circle, where 7 is the parameter value for the number of coil turns, and circle is the parameter value for the coil shape. The other five parameter sets are the same as the first parameter set X. 11 Similarly, this will not be elaborated upon here.

[0029] As shown in Table 1, each parameter set includes multiple parameter values, each corresponding to a data type. Different parameter sets contain parameter values ​​with the same data type, and at least one parameter value with a different data type. Let's take the first parameter set X... 11 The second set of parameters X 12For example, both sets of parameters correspond to data types including the number of coil turns and the coil shape. Although the parameter values ​​for the coil shape data type are the same for both sets of parameters (both are "circular"), the first set of parameters X... 11 The parameter value for the number of turns of the intermediate coil is 7, and the second parameter set X 12 The parameter value for the number of turns in the intermediate coil is 8, which is different from the parameter value for the number of turns in the coil data type.

[0030] S2: When the parameter values ​​of the wireless charging coil are determined to be the parameter values ​​in each set of parameters, the charging efficiency index value, offset efficiency reduction rate index value, and price index value of the wireless charging system are obtained, resulting in n initial index value sets corresponding one-to-one with the n sets of parameters. The n initial index value sets are then normalized to obtain n first index value sets. Each first index value set includes the normalized charging efficiency index value, offset efficiency reduction rate index value, and price index value corresponding to one set of parameters.

[0031] It should be noted that if the number of parameter sets determined in step S1 is large, the parameter sets can be simplified by applying constraints or performing non-dominated sorting, and then subsequent steps can be performed on the simplified parameter sets. Non-dominated sorting involves dividing the parameter sets into multiple levels based on the mutual dominance relationships between them. The first level is the Pareto optimal solution set, where no other solution can completely surpass it in any of the metrics, thus eliminating infeasible or dominated parameter sets.

[0032] Among them, the charging efficiency index is used to characterize the energy transmission efficiency of the wireless charging system under ideal alignment; the offset efficiency reduction rate index is used to characterize the degree of decrease in charging efficiency compared to the ideal alignment state when there is a horizontal positional offset or center misalignment between the wireless receiving coil and the wireless transmitting coil of the wireless charging system. This index reflects the robustness and spatial tolerance of the system against positional offset; the price index is used to characterize the overall cost of manufacturing the wireless charging coil using this set of parameters. It is usually directly related to parameters such as the number of coil turns (determining the amount of wire used), the outer and inner radii of the coil (determining the wire length and volume), and the coil shape (determining the complexity of manufacturing process and auxiliary materials).

[0033] After determining the n sets of parameters in S1, this step first determines the initial index value set corresponding to the n sets of parameters, and then normalizes the initial index values ​​to obtain the n first index value sets. Specifically, a wireless charging system model corresponding to each set of parameters can be constructed using simulation software (such as finite element analysis software), and electromagnetic field simulations can be performed under ideal alignment conditions and conditions with specific position offsets to obtain the initial index values ​​of charging efficiency and offset efficiency reduction rate. At the same time, based on the parameter values ​​in each set of parameters and combined with a preset database of material unit prices, process costs, etc., the corresponding initial price index value can be calculated.

[0034] In this application, the selectable constraint range includes coupling coefficient K, anti-offset capability, etc., wherein the coupling coefficient K can be determined by the following formula (1).

[0035] (1) Where M is the mutual inductance between the wireless transmitting coil and the wireless receiving coil. and The self-inductance of the two coils is used to measure the coupling capability between the transmission coils. The selection of constraints and their range can be adjusted according to the actual application scenario.

[0036] In this embodiment, no additional constraints are set for coupling coefficient and offset efficiency retention rate. The parameter set is determined only under basic constraints such as structural size and electrical parameters. Optimization is performed using efficiency, anti-offset capability, and price as optimization indicators.

[0037] When calculating efficiency, since the mutual resistance between the two coils is extremely low and negligible according to actual simulation, the initial index value of the charging efficiency of the wireless charging system can be calculated by the following formula (2). .

[0038] (2) in, The output power of the wireless charging system, This refers to the input power of the wireless charging system.

[0039] The initial index value of the offset efficiency reduction rate can be determined by the following formula (3). .

[0040] (3) in, This indicates the efficiency before the offset. This indicates the efficiency after offset, which can be seen... The smaller the value, the smaller the effect of offset on efficiency, and the better the offset resistance of this type of wireless charging system.

[0041] In calculating the price, fluctuations occur due to variations in the coil cross-sectional area resulting from different numbers of coil turns and differences in the size and thickness of the magnetic core. In this embodiment, the coil material is copper, and the magnetic core material is PC95. For simplicity, the price of copper is taken as 100 yuan / kg, the price of PC95 is also taken as 100 yuan / kg, and the density of copper is taken as 9 g / cm³. 3 The density of PC95 is taken as 5 g / cm³. 3 Taking this as an example, the initial price index value of the circular coil can be calculated using the following formulas (4) to (5). Initial price index value of square coil .

[0042] (4) (5) in, The outer radius of the coil, Let be the inner radius of the coil. For a square coil, the radius is approximately half the side length. The number of coil turns. denoted as the core thickness, which is 5 mm in this embodiment, and z represents the additional coverage of the core.

[0043] The above calculations yield n sets of initial index values ​​corresponding to n sets of parameter sets. Here, m represents the number of indicators, and each set of initial index values ​​includes three initial index values ​​corresponding to one set of parameter sets.

[0044] Next, normalize all initial indicator value sets corresponding to all parameter sets. Assuming there are m indicators and n sets of parameters, normalize the n sets of parameters on the m indicators to obtain n sets of first indicator value sets that correspond one-to-one with the n sets of parameters. The larger the normalized indicator value, the better the effect.

[0045] Since different indicators have different properties, the normalization methods used are slightly different. In this embodiment, the charging efficiency indicator is a positive indicator. Therefore, the following formula (6) Max-Min linear normalization formula is used to normalize the initial value of the charging efficiency indicator to obtain the normalized indicator value. .

[0046] (6) in, and These are the maximum and minimum values ​​among all initial index values ​​to be normalized under a given index.

[0047] Since the price indicator and the rate of decline in offset efficiency are negative indicators, the initial indicator values ​​are normalized using the following formula (7).

[0048] (7) By normalizing the initial index value matrix R, n sets of first index values ​​corresponding to the n parameter sets are obtained. Table 2 shows the six sets of first index values ​​corresponding to the six parameter sets provided in Table 1. .

[0049] Table 2: Sets of six first index values ​​corresponding to discrete parameters

[0050] It should be noted that, since this application adopts the order of determining discrete parameters first and then continuous parameters, the continuous parameters involved in calculating the charging efficiency index, offset efficiency reduction rate index, and price index of the wireless charging system at the current stage are all taken as preset values.

[0051] S3: Determine the 3×3 scaling matrix A.

[0052] For wireless charging systems, different application scenarios prioritize different metrics. For example, mobile phones and other electronic products prioritize resistance to offset and coil size, electric vehicle charging prioritizes efficiency, and mass-produced products prioritize cost. Therefore, this embodiment of the application, considering the characteristics of wireless charging optimization tasks, employs the Analytic Hierarchy Process (AHP) to determine the subjective weights of each metric. Subjective preferences or expert opinions are used as determining factors to assign subjective weights to each metric. The core logic of the AHP method is to convert experience or preferences into weighted results through pairwise comparisons. This method is particularly suitable for situations where weight preferences differ across wireless charging systems in different scenarios. In this embodiment, a five-level scaling method is used to form a scaling matrix A before calculating the weights of each metric and performing a consistency check. The meanings of each scale in the five-level scaling method are shown in Table 3 below.

[0053] Table 3: Meaning of each scale in the five-scale method

[0054] In this embodiment of the application, the data in the first to third rows of the scaling matrix A correspond to the charging efficiency index, the offset efficiency degradation rate index, and the price index, respectively. The data in the first to third columns correspond to the charging efficiency index, the offset efficiency degradation rate index, and the price index, respectively. The data in the u-th row and v-th column A... uv This indicates the importance of the indicator corresponding to row u relative to the indicator corresponding to column v, where u and v are both positive integers, and u≤3, v≤3.

[0055] For example, the scaling matrix A could be: The row and column order is efficiency index, offset efficiency decline rate index, and price index. Elements in scaling matrix A... This represents the importance of the i-th indicator relative to the j-th indicator.

[0056] It should be noted that after determining the scaling matrix A, a consistency check needs to be performed on the scaling matrix A to verify whether the scaling matrix A is logically consistent. Specifically, the consistency index CI and the consistency ratio CR can be determined first through the following formulas (8) to (9).

[0057] (8) (9) in, Let denoted as the largest eigenvalue of scaling matrix A, m be the number of indicators, RI be the random consistency index, CI be the consistency index, and CR be the consistency index. A smaller CR value indicates better consistency of scaling matrix A; a value less than 0.1 passes the consistency test. When the scaling matrix order is 3, the random consistency index RI is set to 0.58, resulting in CI = 0.0046 and CR = 0.0079334, thus passing the consistency test.

[0058] S4: Based on scaling matrix A, determine the subjective weights corresponding to the charging efficiency index, the offset efficiency decline rate index, and the price index.

[0059] In this step, for the j-th indicator among the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator, the subjective weight corresponding to the indicator can be determined through the following steps a1 to a3. Specifically, by setting j to 1, 2, and 3 respectively, the subjective weights corresponding to the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator can be determined sequentially through the following steps a1 to a3.

[0060] Step a1: Determine the largest eigenvalue of the scaling matrix A .

[0061] In this process, after determining all the eigenvalues ​​of the scaling matrix A, the largest eigenvalue can be determined from it. .

[0062] Step a2: Using the formula Determine the largest eigenvalue of a 3×1 matrix. Corresponding non-zero eigenvectors .

[0063] Step a3: Using the formula Determine the subjective weight of the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. .

[0064] in, Non-zero eigenvectors The j-th row of data, Non-zero eigenvectors The data in row t.

[0065] Taking the data in Tables 1 and 2 above as examples, in this embodiment of the application, the subjective weight corresponding to the determined charging efficiency index is 0.5396, the subjective weight corresponding to the offset efficiency reduction rate index is 0.2970, and the subjective weight corresponding to the price index is 0.1634.

[0066] S5: Based on the n sets of first indicator values, determine the objective weights corresponding to the charging efficiency indicator, the offset efficiency decline rate indicator, and the price indicator.

[0067] In this step, for the j-th indicator among the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator, the objective weight corresponding to the indicator can be determined through steps b1 to b6 below. Specifically, by setting j to 1, 2, and 3 respectively, the objective weights corresponding to the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator can be determined sequentially through steps b1 to b6 below.

[0068] Step b1: Based on the n sets of first index values, determine the average value of the charging efficiency index, the average value of the offset efficiency reduction rate index, and the average value of the price index, respectively, to obtain the average value of the charging efficiency index, the average value of the offset efficiency reduction rate index, and the average value of the price index.

[0069] For example, if the current determination is the average value corresponding to the charging efficiency index (i.e., j=1), then the average value corresponding to the charging efficiency index is obtained by summing the n charging efficiency index values ​​in the n sets of the first index values ​​and then dividing by n. Similarly, the average value corresponding to the offset efficiency reduction rate index and the average value corresponding to the price index can be calculated separately.

[0070] Step b2: Based on the n sets of first index values, the average value corresponding to the charging efficiency index, the average value corresponding to the offset efficiency reduction rate index, and the average value corresponding to the price index, determine the standard deviation corresponding to the charging efficiency index, the standard deviation corresponding to the offset efficiency reduction rate index, and the standard deviation corresponding to the price index.

[0071] Specifically, the standard deviation corresponding to the j-th indicator can be determined by the following formula (10). .

[0072] (10) in, It is the j-th indicator value in the i-th group of the first indicator value set of n groups. It is the average value corresponding to the j-th indicator. The larger the standard deviation, the stronger the discrimination of the indicator on different groups of parameter sets.

[0073] Step b3: Using the formula Determine the correlation coefficient between the j-th and k-th indicators among the charging efficiency index, offset efficiency degradation rate index, and price index. .

[0074] in, and Let j and k be the j-th and k-th index values ​​respectively in the i-th group of the first index value set of n groups. and These are the average values ​​of the j-th and k-th indicators among the charging efficiency index, offset efficiency reduction rate index, and price index. A higher correlation coefficient indicates greater overlap between the two indicators, and vice versa.

[0075] Step b4: Using the formula Determine the degree of overall conflict between the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator, and other indicators. .

[0076] Step b5: Using the formula Determine the amount of objective information contained in the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. .

[0077] Step b6: Using the formula Determine the objective weight of the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. If a certain indicator is indistinguishable, it is assigned an objective weight of 0.

[0078] Taking the data in Tables 1 and 2 above as examples, in this embodiment of the application, the objective weight corresponding to the determined charging efficiency index is 0.405, the objective weight corresponding to the offset efficiency reduction rate index is 0.367, and the objective weight corresponding to the price index is 0.228.

[0079] S6: The subjective and objective weights of the charging efficiency index, the offset efficiency reduction rate index, and the price index are weighted and summed to obtain the final weight of each index.

[0080] In this step, the final weight corresponding to the j-th indicator is determined by the following formula (11). .

[0081] (11) in, It is the subjective weight corresponding to the j-th indicator determined in step a3 above. The weight can be set as needed. In this embodiment, The weight is 0.5, and the corresponding final weights for the charging efficiency index, the offset efficiency reduction rate index, and the price index are 0.472, 0.332, and 0.1959, respectively.

[0082] S7: Correct the index values ​​in the first set of n index values ​​to obtain the corrected set of the second set of n index values.

[0083] As shown in Table 2 above, since the first set of n index values ​​includes index values ​​with a value of zero, the first set of index values ​​needs to be corrected so that the corrected index values ​​can be used to determine the comprehensive score corresponding to each set of parameters. Specifically, the corrected index values ​​can be obtained by correcting each index value in the first set of index values ​​using the following formula (12).

[0084] (12) in, Let j be the j-th index value in the i-th group of the first index value set of n groups. To The corrected index value is the j-th index value in the i-th set of the second index values ​​in the n-group set. It is a preset small positive number, such as 1*10. -6 Furthermore, the revised indicator value must satisfy: .

[0085] WASPAS (Weighted Aggregated Sum Product Assessment) is a multi-criteria decision-making method proposed by several scholars. It combines the advantages of the Weighted Sum Model (WSM) and the Weighted Product Model (WPM), using an adjustment parameter λ to combine the scores of the two models, thereby obtaining a more robust ranking of alternatives.

[0086] In this embodiment, after determining the final weights of each indicator, the WASPAS method is used to sort each parameter set through the following steps. WASPAS combines a weighted addition model with a weighted multiplication model, treating the weighted sum model and the weighted product model as two decision-makers with different evaluation tendencies. The bargaining power is determined based on their ability to distinguish between the n parameter sets, and the combination coefficient of WASPAS is obtained through Nash bargaining game. The weighted sum model emphasizes mutual compensation between indicators, while the weighted product model is more sensitive to weaker indicators. For wireless charging coils, when the parameter values ​​are from one of the n parameter sets, the coil may have high transmission efficiency in the aligned state, but its performance degrades significantly with lateral offset, angular offset, or air gap changes. In this case, simply using the weighted sum model may cause high efficiency to overcompensate for poor anti-offset performance, while the weighted product model can more significantly penalize this weakness. Therefore, combining the two in a game has certain engineering significance for wireless charging. The following section will introduce the specific calculation steps of the WASPAS method based on Nash bargaining game, including the calculation of the evaluation values ​​of the weighted addition model and the weighted multiplication model, the quantification of the distinguishing ability (bargaining ability) of each model, the solution of the Nash combination coefficients, and the specific process of determining the comprehensive evaluation value corresponding to each set of parameters and the ranking of the parameter sets.

[0087] S8: Through formula Determine the first evaluation value corresponding to the i-th parameter set in the n-set parameter set. .

[0088] In this step, i is set to positive integers in the range [1, n], and the first evaluation value corresponding to each set of parameters is determined using a weighted sum model. Here, m is the number of indicators. It is the final weight corresponding to the j-th indicator among the charging efficiency indicator, offset efficiency reduction rate indicator, and price indicator determined in step S6 above. It is the j-th index value in the i-th set of the n sets of second index values ​​determined in step S7 above.

[0089] S9: Through formula Determine the second evaluation value corresponding to the i-th parameter set in the n-set parameter set. .

[0090] In this step, i is set to each positive integer in [1, n], and the second evaluation value corresponding to each set of parameters is determined by the weighted product model.

[0091] S10: Normalize each first and second evaluation value to obtain n normalized first evaluation values ​​and n normalized second evaluation values ​​corresponding to the n sets of parameters. Based on the normalized first and second evaluation values, determine the first discrimination degree of the additive weighted model and the second discrimination degree of the multiplicative weighted model for the n sets of parameters. Based on the first and second discrimination degrees, determine the score weight corresponding to the first evaluation value. λ .

[0092] In this step, specifically, the first evaluation value can be normalized using the following formula (13).

[0093] (13) in, It is the i-th first evaluation value among n first evaluation values ​​before normalization. Yes The first evaluation value after normalization is obtained by setting i to positive integers in [1, n] and normalizing each first evaluation value using the above formula (13).

[0094] The second evaluation value can be normalized using the following formula (14).

[0095] (14) in, It is the i-th second evaluation value among n unnormalized second evaluation values. Yes The second evaluation value is obtained after normalization. By setting i to each positive integer in [1, n] and normalizing each second evaluation value using the above formula (14), n normalized second evaluation values ​​can be obtained.

[0096] Specifically, in this step, the first discrimination index and the second discrimination index can be determined through steps c1 to c5, and the score weight corresponding to the first evaluation value can be determined based on the first discrimination index and the second discrimination index. .

[0097] Step c1: Using the formula Determine the first discriminant of the additive weighted model for the n parameter sets. .

[0098] in, It is the normalized first evaluation value corresponding to the i-th parameter set in the n-set parameter set. The larger the value, the more significant the differences in the scores of the parameter sets obtained by the additive weighted model, and the stronger the ability to identify parameters such as coil shape.

[0099] Step c2: Using the formula Determine the second discriminant of the multiplicative weighted model for n parameter sets. .

[0100] in, It is the normalized second evaluation value corresponding to the i-th parameter set in the n-set parameter set. The larger the value, the stronger the multiplicative weighted model's ability to identify coil topologies (i.e., n sets of parameters) with significant performance shortcomings.

[0101] Step c3: Using the formula Determine the bargaining power of the additive weighted model .

[0102] Step c4: Using the formula Determine the bargaining power of the multiplicative weighted model .

[0103] Step c5: Bargaining power based on the additive weighted model Bargaining power of the multiplicative weighted model Determine the score weights .like Then the score weight Take 0.5, and there is no need to execute steps c3 to c5.

[0104] In step c5, the following steps c51 to c52 can be used.

[0105] Step c51: Determine the objective function ,in, .

[0106] In this embodiment, the additive weighted model and the multiplicative weighted model are regarded as two game players, and the adjustment parameter λ is used as the decision variable that both parties need to coordinate, thus establishing a weighted Nash bargaining objective function. .

[0107] Step c52: When the first derivative is equal to zero The value is determined as the score weight. .

[0108] in, By taking the first derivative with respect to λ and setting it to zero, the WASPAS adjustment parameter under Nash bargaining conditions is obtained as follows: . The value range is [0,1].

[0109] S11: Through formula Determine the comprehensive score corresponding to the i-th parameter set in the n parameter sets. .

[0110] Among them, due to The specific values ​​are obtained by solving the Nash bargaining game model based on the weighted sum model and the weighted product model to determine the distinguishing power (i.e., bargaining power) of the n parameter sets. Therefore, in this step, the specific values ​​are obtained by solving the Nash bargaining game model based on the weighted sum model and the weighted product model. and By performing a linear combination, we can obtain the comprehensive score of the i-th parameter set that takes into account both mutual compensation among indicators and penalty for weaknesses. By setting i to each positive integer in [1, n] and performing this step, we can obtain n comprehensive scores corresponding to n sets of parameters.

[0111] S12: Determine the parameter values ​​of the wireless charging coil from the parameter set corresponding to the highest comprehensive score among all comprehensive scores of the n parameter sets.

[0112] After determining the comprehensive score corresponding to each of the n parameter sets, the highest comprehensive score is selected by comparing the magnitudes of all comprehensive scores. Since a higher comprehensive score indicates that the parameter set performs better in comprehensive evaluation dimensions such as charging efficiency, anti-offset capability, and manufacturing cost, the parameter set corresponding to the highest comprehensive score is determined as the target parameter set. The specific parameter values ​​included in the target parameter set, such as the number of coil turns and the coil shape, are ultimately determined as the actual manufacturing parameter values ​​of the wireless charging coil.

[0113] In this embodiment, through the aforementioned steps S1-S12, a complete method for determining wireless charging coil parameters, integrating subjective-objective combined weighting with addition-multiplication game theory, is constructed. By combining the Analytic Hierarchy Process (AHP) and objective weighting methods based on data dispersion and correlation (such as the CRITIC method), the final weights reflect both expert experience preferences in different application scenarios and retain the inherent statistical characteristics of the simulation data itself. Furthermore, the WASPAS method based on Nash bargaining game theory is introduced, treating the weighted sum model and the weighted product model as two players. The combination coefficients are adaptively solved based on their ability to distinguish different parameter sets, thus balancing the compensatory effect of high-efficiency indicators on weaker indicators and the penalizing effect of the weighted product model on performance weaknesses, effectively avoiding the limitations of a single evaluation model.

[0114] Compared to traditional methods of optimizing wireless charging coils with a single index or designing them based on experience, the embodiments of this application can obtain a balanced evaluation scheme based on Nash bargaining improvement among multiple conflicting indicators such as charging efficiency, anti-offset robustness, and manufacturing cost, so that the final determined wireless charging coil parameter values ​​have better comprehensive engineering practicality and reliability.

[0115] Building upon the previously defined discrete parameters of the wireless charging coil, this section will further describe how to determine the continuous parameters of the wireless charging coil. As previously mentioned, the continuous parameters include the coil outer radius, coil inner radius, additional core coverage, and transmission air gap spacing. Similar to the previous embodiments, to save resources, this application uses the Taguchi method to sample the continuous parameters, and the design levels of each continuous parameter are shown in Table 4 below.

[0116] Table 4: Multiple parameter sets for continuous parameters

[0117] Based on Table 4 above, the orthogonal array shown in Table 5 below can be determined.

[0118] Table 5: Orthogonal Array

[0119] Table 5 is similar in form to Table 1. After determining Table 5, an operation similar to generating Table 2 from Table 1 is performed to obtain Table 6, which is similar to Table 2.

[0120] Table 6: Sets of Multiple First Index Values ​​Corresponding to Continuous Parameters

[0121] Table 6 is similar in form to Table 2. After determining Table 6, operations similar to those performed on Table 2 are executed to finally determine the parameter values ​​for the continuous parameters. It should be noted that when performing step S2 on Table 5 to determine Table 6, the discrete parameters of the wireless charging coil are taken as the values ​​determined in the aforementioned steps S1~S12. It should also be noted that group 17 in Table 6 represents the basic values, i.e., the basic parameters obtained after optimizing the upper-level variables.

[0122] Based on Tables 1-6 above, the final determined shape of the wireless charging coil is circular with 9 turns. The optimized parameters for this continuous type correspond to the parameter set of Scheme 11: an outer radius of 160mm, an inner radius of 50mm, 0% additional core coverage, and a transmission spacing of 135mm. Compared to the original scheme, the parameter values ​​determined in this way significantly improve the coil's anti-misalignment capability while slightly sacrificing efficiency.

[0123] In this application, preferably, by decoupling the parameters into a two-layer structure of discrete upper-layer parameters and continuous lower-layer parameters, the computational resource consumption of a single optimization can be reduced, avoiding the problem of combinatorial explosion that easily occurs when directly optimizing mixed parameters, thus improving the optimization speed. This application uses the Taguchi method or Latin hypercube sampling combined with non-dominated sorting to generate and screen n sets of parameters, ensuring the representativeness and uniformity of multiple parameter sets, while effectively eliminating infeasible parameter sets and dominated inferior solutions, improving the overall quality of the parameter sets entering decision optimization. This application combines the AHP method and the WASPAS method. The AHP method mathematically represents engineering experience and design preferences as index weights that pass the consistency test, while the WASPAS method simultaneously integrates two different evaluation logics: weighted sum and weighted product. By introducing adjustment parameters to flexibly balance the influence ratio of the two, the final determined parameter set ensures excellent objective performance while highly conforming to the designer's subjective intentions; suitable for solving the problem of different optimization directions in different working scenarios of wireless charging technology.

[0124] Figure 2 The diagram shows a schematic of the structure of a wireless charging coil parameter value determination device provided in an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the wireless charging coil parameter value determination device.

[0125] like Figure 2 As shown, the wireless charging coil parameter value determination device 100 may include a processor 102 and a memory 104.

[0126] The memory 104 is used to store the computer program 106. The memory 104 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device. The computer program 106 may include computer-executable instructions.

[0127] The processor 102 is used to execute the computer program 106 to implement the above-described method embodiment for determining the parameter values ​​of the wireless charging coil.

[0128] Processor 102 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The wireless charging coil parameter value determination device 100 includes one or more processors, which may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0129] Figure 3 A schematic diagram of a wireless charging system provided in an embodiment of this application is shown. Figure 3 As shown, the wireless charging system 10 includes a wireless receiving coil 200. The parameter values ​​of the wireless receiving coil 200 are determined using the aforementioned method embodiment for determining the parameter values ​​of a wireless charging coil.

[0130] Wireless charging systems typically include a wireless transmitting coil and a wireless receiving coil. If the parameter values ​​of the wireless transmitting coil and the wireless receiving coil can be determined separately using the aforementioned method for determining the parameter values ​​of the wireless charging coil, bidirectional collaborative optimization of the wireless charging system can be achieved. This avoids performance mismatches caused by single-end optimization, thereby further improving the overall transmission efficiency and enhancing the robustness against offset changes at the global level. Furthermore, it achieves balanced optimization of the overall performance of the wireless charging system while reasonably controlling the overall manufacturing cost. Based on this, Figure 4 A schematic diagram of a wireless charging system according to another embodiment of this application is shown. Figure 4 As shown, the wireless charging system 10 includes a wireless receiving coil 200 and a wireless transmitting coil 300. The parameter values ​​of the wireless transmitting coil 300 are determined using the aforementioned method for determining the parameter values ​​of the wireless charging coil. It should be noted that when performing step S2 to determine the parameter values ​​of the wireless transmitting coil 300, the charging efficiency index, offset efficiency reduction rate index, and price index of the wireless charging system are calculated based on the parameter values ​​of the wireless receiving coil 200 determined by the aforementioned method. Through this method, parameter matching and collaborative design between the wireless transmitting coil 300 and the wireless receiving coil 200 are achieved, ensuring that the various indicators determined when optimizing the wireless transmitting coil parameters accurately reflect the actual overall performance of the system. This guarantees the optimal combination of power transmission efficiency (i.e., charging efficiency), anti-offset capability, and manufacturing cost for the entire wireless charging system 10.

[0131] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for determining the parameter values ​​of a wireless charging coil.

[0132] This application provides a computer program that can be executed by a processor to implement the above-described method for determining the parameter values ​​of a wireless charging coil.

[0133] This application provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for determining the parameter values ​​of a wireless charging coil.

[0134] In the several embodiments provided in this application, any function, if implemented as a software functional module / unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of this application can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or other electronic device) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0135] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0136] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In claims enumerating several means, several units or modules of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

[0137] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the parameter values ​​of a wireless charging coil, characterized in that, The method includes: S1: Determine n sets of parameters for the wireless charging coil, wherein each set of parameters includes multiple parameter values, each parameter value corresponds to a data type, different parameter sets include parameter values ​​corresponding to the same data type and at least one data type parameter value is different, the wireless charging coil is a coil in the wireless charging system, n is a positive integer and n≥2; S2: When the parameter values ​​of the wireless charging coil are determined to be the parameter values ​​in each of the parameter sets, the charging efficiency index value, offset efficiency reduction rate index value, and price index value of the wireless charging system are obtained, resulting in n initial index value sets corresponding one-to-one with the n parameter sets. The n initial index value sets are then normalized to obtain n first index value sets, wherein each first index value set includes a set of normalized charging efficiency index values, offset efficiency reduction rate index values, and price index values ​​corresponding to the parameter sets. S3: Determine a 3×3 scaling matrix A, wherein the data in the first to third rows of the scaling matrix A correspond to the charging efficiency index, the offset efficiency reduction rate index, and the price index, respectively; the data in the first to third columns correspond to the charging efficiency index, the offset efficiency reduction rate index, and the price index, respectively; and the data in the u-th row and v-th column is A. uv This indicates the importance of the indicator corresponding to the u-th row relative to the indicator corresponding to the v-th column, where u and v are both positive integers, and u≤3, v≤3; S4: Based on the scaling matrix A, determine the subjective weights corresponding to the charging efficiency index, the offset efficiency reduction rate index, and the price index. S5: Based on the n sets of first indicator values, determine the objective weights corresponding to the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator. S6: The subjective and objective weights corresponding to the charging efficiency index, the offset efficiency reduction rate index, and the price index are weighted and summed to obtain the final weight of each index. S7: Correct the index values ​​in the n sets of first index values ​​to obtain the corrected n sets of second index values; S8: Through formula Determine the first evaluation value corresponding to the i-th parameter set in the n-set parameter set. Where m is the number of indicators. It is the final weight corresponding to the j-th indicator among the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator. It is the j-th index value in the i-th group of the n-group second index value set; S9: Through formula Determine the second evaluation value corresponding to the i-th parameter set in the n-set parameter set. ; S10: Normalize each of the first evaluation value and the second evaluation value to obtain n normalized first evaluation values ​​and n normalized second evaluation values ​​corresponding to the n sets of parameters. Based on the normalized first evaluation values ​​and the normalized second evaluation values, determine the first discrimination degree of the additive weighted model and the second discrimination degree of the multiplicative weighted model for the n sets of parameters. Based on the first discrimination degree and the second discrimination degree, determine the score weight corresponding to the first evaluation value. λ ; S11: Through formula Determine the comprehensive score corresponding to the i-th parameter set in the n parameter sets. ; S12: Determine the parameter value of the wireless charging coil as the parameter value of the parameter set corresponding to the highest comprehensive score among all comprehensive scores corresponding to the n sets of parameter sets.

2. The method according to claim 1, characterized in that, S4 includes: Determine the largest eigenvalue of the scaling matrix A. ; Through formula Determine the largest eigenvalue Corresponding non-zero eigenvectors ; Through formula The subjective weight is determined for the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. ,in, Non-zero eigenvectors The j-th row of data, Non-zero eigenvectors The data in row t.

3. The method according to claim 1, characterized in that, S5 includes: Based on the n sets of first index values, the average value of the charging efficiency index, the average value of the offset efficiency reduction rate index, and the average value of the price index are determined respectively, so as to obtain the average value of the charging efficiency index, the average value of the offset efficiency reduction rate index, and the average value of the price index. Based on the n sets of first index values, the average value corresponding to the charging efficiency index, the average value corresponding to the offset efficiency reduction rate index, and the average value corresponding to the price index, determine the standard deviation corresponding to the charging efficiency index, the standard deviation corresponding to the offset efficiency reduction rate index, and the standard deviation corresponding to the price index. Through formula Determine the correlation coefficient between the j-th and k-th indicators among the charging efficiency index, offset efficiency degradation rate index, and price index. ,in, and These are the j-th and k-th index values ​​in the i-th group of the n-group first index value set, respectively. and These are the average values ​​corresponding to the j-th and k-th indicators among the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator, respectively. Through formula Determine the degree of overall conflict between the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator, and other indicators. ; Through formula Determine the amount of objective information contained in the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. ,in, Let be the standard deviation of the j-th indicator among the charging efficiency indicator, the offset efficiency reduction rate indicator, and the price indicator; Through formula The objective weight is determined for the j-th indicator among the charging efficiency indicator, offset efficiency degradation rate indicator, and price indicator. .

4. The method according to claim 1, characterized in that, The process involves determining the first discrimination metric of the additive weighted model for the n sets of parameters and the second discrimination metric of the multiplicative weighted model for the n sets of parameters based on the normalized first evaluation value and the normalized second evaluation value, and then determining the score weight corresponding to the first evaluation value based on the first discrimination metric and the second discrimination metric. λ ,include: Through formula Determine the first discriminant of the additive weighted model for the n parameter sets. ,in, The normalized first evaluation value corresponding to the i-th parameter set in the n-set parameter set; Through formula Determine the second discriminant of the multiplicative weighted model for the n sets of parameters. ,in, The normalized second evaluation value corresponding to the i-th parameter set in the n-set parameter set; Through formula Determine the bargaining power of the additive weighted model ; Through formula Determine the bargaining power of the multiplicative weighted model ; Bargaining power based on the additive weighted model Bargaining power of the multiplicative weighted model Determine the score weight .

5. The method according to claim 4, characterized in that, The bargaining power based on the additive weighted model Bargaining power of the multiplicative weighted model Determine the score weight ,include: Determine the objective function ,in, ; Will When the first derivative is equal to zero The value is determined as the score weight. .

6. The method according to claim 1, characterized in that, S1~S12 is executed at least twice. During the first execution of S1~S12, each set of parameters includes the parameter values ​​of multiple discrete parameters. During the second execution of S1~S12, each set of parameters includes the parameter values ​​of multiple continuous parameters. When S2 is executed for the first time to determine the values ​​of each indicator, the parameter values ​​of the continuous parameters of the wireless charging coil are preset values. When S2 is executed for the second time to determine the values ​​of each indicator, the parameter values ​​of the discrete parameters of the wireless charging coil are the parameter values ​​determined during the first execution of S1~S12.

7. A device for determining the parameter values ​​of a wireless charging coil, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method for determining the parameter values ​​of the wireless charging coil according to any one of claims 1 to 6.

8. A wireless charging system, comprising a wireless receiving coil, characterized in that, The parameter values ​​of the wireless receiving coil are determined by the parameter value determination method of the wireless charging coil according to any one of claims 1 to 6.

9. The system according to claim 8, characterized in that, The system further includes a wireless transmitting coil, the parameter values ​​of which are determined by the method of claim 1, and when performing step S2, the charging efficiency index, offset efficiency reduction rate index, and price index of the wireless charging system are determined based on the parameter values ​​of the wireless receiving coil determined by the method of claim 1.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for determining the parameter values ​​of the wireless charging coil as described in any one of claims 1 to 6.

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