A coil optimization method and system for a rotary wireless power transfer system

Abstract

The application belongs to the technical field of wireless power transmission, and discloses a coil optimization method and system for a rotary wireless power transmission system, the optimization method comprising: establishing a finite element model under the alignment of a transmitting-side coil and a receiving-side coil; under the condition that the rotating radius of a rotating component is determined, selecting the minimum coupling coefficient of the rotary wireless power transmission system according to the power requirement of the receiving side; establishing the finite element model of the transmitting-side coil and the receiving-side coil at the boundary of the effective coupling range according to the minimum coupling coefficient; selecting a variable parameter and determining the constraint condition thereof; sampling points in the variable range of the parameter, performing finite element calculation, using the response surface method to analyze the calculation results to obtain the sensitivity of the objective function to each parameter and the response surface result; using an evolutionary algorithm in the function relationship fitted by the response surface to obtain an optimized Pareto solution set; and selecting the required Pareto solution as the final optimized scheme according to the actual situation.

Description

Technical Field

[0001] This invention belongs to the field of wireless power transmission technology, and particularly relates to a coil optimization method and system for a rotating wireless power transmission system. Background Technology

[0002] Currently, traditional power transmission often uses direct wire connections, which presents several safety hazards. For example, cables are prone to tangling and breakage. Using conductive slip rings results in poor reliability and requires frequent replacement. Wireless power transmission technology utilizes high-frequency magnetic fields for energy transfer, making it more suitable for harsh working environments and offering greater flexibility and reliability. Relatively rotating components are common in everyday and industrial products, ranging from car tires to aircraft engine airfoil blades. To power these rotating components or monitor their motion parameters, a rotary wireless power transmission system is often required.

[0003] Rotary wireless power transfer systems are a type of dynamic wireless power transfer technology. Unlike the more common dynamic wireless power transfer systems used in electric vehicles, rotary wireless power transfer systems involve not only relative movement between the transmitting and receiving coils in a specific direction, but also angular offset. Because the coils are constantly rotating, parameters such as coil dimensions require optimized design. Furthermore, to ensure the stability and efficiency of energy transfer, special design measures are necessary due to the continuous rotation of the coils.

[0004] Optimizing coil dimensions requires considering the size and shape of the rotating components. Based on the characteristics of the rotating components, a suitable coil shape, such as circular or square, can be selected to best match the movement trajectory of the rotating components. Optimizing coil parameters requires considering factors such as transmission distance, power loss, and transmission efficiency. In rotary wireless power transfer systems, relative motion and angular deviations affect energy transmission. By adjusting parameters such as the number of turns and wire diameter of the coil, transmission efficiency and power loss can be optimized.

[0005] Traditionally, the design of coil dimensions and parameters for wireless power transfer systems relies on empirical selection, which can lead to low system efficiency and difficulty in guaranteeing overall performance. A newer solution involves establishing a finite element model of the coupled coils and using commercial finite element software for optimization of multiple variable parameters. This approach is practical when there are few parameters, but the computational burden increases significantly with the number of parameters. A more advanced approach considers the relationship between the geometric parameters of the coupled coils and the circuit parameters, establishing a nonlinear programming mathematical model with multiple parameters and performance objective functions for global system optimization. This method is relatively fast but also has some problems. For example, it is difficult to establish mathematical expressions for self-inductance and mutual inductance for coils of various shapes, especially when non-axial displacement and deflection occur between the coils, making the mutual inductance expression almost impossible to establish. Rotary wireless power transfer systems are dynamic wireless power transfer systems where the transmitting and receiving coils not only move relative to each other in a certain direction but also experience angular offsets; therefore, a faster and more universal optimization method is needed.

[0006] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0007] For existing wireless power transfer technologies, it is difficult to establish mathematical expressions for the self-inductance and mutual inductance of coils of various shapes, and there is no quick and universal method for optimizing the coils of rotating wireless power transfer systems. Summary of the Invention

[0008] To address the problems existing in the prior art, the present invention provides a coil optimization method and system for a rotating wireless power transmission system.

[0009] This invention is implemented as follows: a coil optimization method for rotating wireless power transfer systems. The core innovation of this method lies in its integration of finite element modeling, response surface methodology, and evolutionary algorithms. Through the high degree of integration and synergistic operation of these three methods, the design of both the transmitting and receiving coils is optimized while meeting the power requirements of the receiving side. This optimization method involves sampling point selection and finite element calculations within constraints and variable parameter ranges. The calculation results are then analyzed using the response surface methodology to obtain the sensitivity of the objective function to each parameter and the response surface results. Finally, an evolutionary algorithm is applied to find the optimal solution. This method can improve the performance and efficiency of rotating wireless power transfer systems while reducing the complexity of their design and optimization processes.

[0010] Furthermore, the coil optimization method for the rotating wireless power transfer system includes the following steps:

[0011] S101: Establish a finite element model of the transmitting coil and receiving coil under the condition of alignment;

[0012] S102: Given a fixed rotation radius of the rotating component, select the minimum coupling coefficient of the rotating wireless power transmission system based on the power requirements of the receiving side.

[0013] S103: Based on the minimum coupling coefficient, establish finite element models of the transmitting coil and the receiving coil at the boundary of the effective coupling range;

[0014] S104: Select the variable parameters and determine their constraints;

[0015] S105: Sample points within the variable parameter range, perform finite element calculations, and use the response surface method to analyze the calculation results to obtain the sensitivity of the objective function to each parameter and the response surface results;

[0016] S106: Apply the evolutionary algorithm to the functional relationship fitted by the response surface to obtain the optimized Pareto solution set;

[0017] S107: Select the required Pareto solution as the final optimized solution based on the actual situation.

[0018] Furthermore, in S101, the finite element model is the model used for multiphysics analysis after using computer-aided geometric modeling, meshing, and discretization.

[0019] Furthermore, in S103, the effective coupling range is selected as follows: when the coil is aligned, the rotation angle is 0°, and the rotation angle between the rotating parts corresponding to the minimum coupling coefficient on both sides is the effective coupling range of the pair of coils.

[0020] The response surface methodology is an optimization method that integrates experimental design and mathematical modeling. It takes the input variable parameters as input variables, i.e., factors, and the variables that need to be focused on as output variables, i.e., responses. It uses experimental data obtained from experiments or simulations to mathematically fit the relationship between factors and responses, and uses sensitivity analysis to determine the main influencing factors. Finally, it uses the established model to determine the optimal combination of factors through mathematical optimization, thereby obtaining the best response, i.e., the optimal solution.

[0021] Furthermore, in S106, the evolutionary algorithm is a mathematical optimization algorithm used after obtaining a fitted mathematical model using the response surface methodology. It has natural parallelism, strong robustness, and does not require additional information. Only the objective function and the corresponding fitness affect the search direction.

[0022] The Pareto solution set is the set of solutions consisting of Pareto optimal solutions. A Pareto optimal solution is a solution in the variable space that is superior to any other solution on all objective functions.

[0023] Another object of the present invention is to provide a coil optimization system for a rotating wireless power transmission system, which implements the coil optimization method for the rotating wireless power transmission system, the coil optimization system for the rotating wireless power transmission system comprising:

[0024] The initial modeling module, connected to the coupling coefficient input module, is used to establish a finite element model under the condition that the transmitting coil and the receiving coil are aligned.

[0025] The coupling coefficient input module connects the initial modeling module and the remodeling module, and is used to select the minimum coupling coefficient for different power requirements.

[0026] The model remodeling module, connected to the coupling coefficient input module and the parameter selection module, is used to establish finite element models of the transmitting coil and the receiving coil at the boundary of the effective coupling range based on the received minimum coupling coefficient.

[0027] The parameter selection module, connected to the model remodeling module and the sampling calculation module, is used to select variable parameters and determine their constraints.

[0028] The sampling calculation module, connected to the parameter selection module and the result comparison module, is used to sample points within the variable parameter range, perform finite element calculations, and use the response surface method to analyze the calculation results to obtain the sensitivity of the objective function to each parameter and the response surface results.

[0029] The results comparison module, connected to the sampling calculation module and the results output module, is used to introduce an evolutionary algorithm into the functional relationship fitted by the response surface to obtain an optimized Pareto solution set, and select the required Pareto solution according to the actual situation.

[0030] The results output module, connected to the results comparison module, is used to output the Pareto solution as the final optimized solution.

[0031] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0032] First, this invention solves a problem in the optimization of coils in existing wireless power transfer systems: if a direct mathematical model is used, it is difficult to establish mathematical expressions for the self-inductance and mutual inductance of coils of various shapes; if the finite element method is used entirely, the calculation time is too long. This invention provides a fast and universal method for optimizing coils in rotating wireless power transfer systems.

[0033] Second, compared with the traditional method of selecting size and parameters based on experience, the coil optimization scheme obtained by the coil optimization method of this invention has better wireless power transmission performance.

[0034] Compared to the most widely used method of pure finite element method (FEM) calculation and optimization, this invention addresses the issue that, with a large number of variable parameters, FEM calculations require numerous coil schemes, which are significantly more time-consuming than algorithmic function optimization. Our coil optimization method, however, only requires FEM calculations on a small number of coil schemes. The results are then fitted using response surface methodology to determine the relationship between variables and response values, followed by optimization using an evolutionary algorithm. This method demonstrates a computational efficiency far exceeding that of the pure FEM method.

[0035] Compared to methods that establish and optimize nonlinear programming mathematical models of wireless power transmission systems, this invention has a wider range of applications and is better suited for rotating wireless power transmission systems. For methods that directly establish mathematical models for optimization, it is difficult to establish expressions for mutual inductance when non-axial displacement and deflection occur between coils. Furthermore, the system's mathematical model differs depending on the coil shape, making it impossible to establish a mathematical model and thus hindering algorithmic optimization. Therefore, coil optimization methods using mathematical models are only applicable to a very limited number of cases. The method proposed in this invention first uses response surface methodology to regress and fit the functional relationship between parameters and results over a global range using actual experimental data points, and then optimizes using an evolutionary algorithm, thus making it more universal.

[0036] Third, the expected benefits and commercial value of the technical solution of this invention after transformation are as follows: This invention improves the optimization speed of coils in wireless power transmission systems, especially in rotating wireless power transmission systems or when the coil shape is relatively complex. Because an evolutionary algorithm is used, multiple Pareto solutions can be obtained, and the desired solution can be selected as needed. This improves the efficiency of coupled coil optimization design in wireless power transmission systems and saves time and manpower costs.

[0037] Fourth, the significant technological advancements brought about by this coil optimization method for rotary wireless power transfer systems include:

[0038] 1. Improve energy transmission efficiency: By optimizing the design and configuration of the coil, the energy transmission efficiency of the system can be improved, allowing more electrical energy to be wirelessly transmitted to the receiving side.

[0039] 2. Improve system stability: The optimized coil design can more stably meet the power requirements of the receiving side when the rotation radius of the rotating component is fixed, thereby improving the system stability.

[0040] 3. Reduce the complexity of the design and optimization process: By using finite element models, response surface methodology and evolutionary algorithms, the optimal coil design can be found quickly and accurately, greatly reducing the complexity of the design and optimization process.

[0041] 4. Expanded application scope: The optimized coil design allows this wireless power transmission system to be used in a wider range of applications, such as aircraft, drones, electric vehicles, and industrial equipment.

[0042] 5. Reduced energy consumption and operating costs: The optimized coil design can improve energy transmission efficiency, thereby reducing energy consumption and operating costs, which is conducive to promoting the commercial application of this technology.

[0043] The application of this coil optimization method can significantly improve the performance and efficiency of rotary wireless power transfer systems, expand their application scope, reduce energy consumption and operating costs, and bring about significant technological progress to the development of wireless power transfer technology. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a rotary wireless power transmission system provided in an embodiment of the present invention.

[0045] Figure 2 This is a schematic flowchart of a coil optimization method for a rotating wireless power transmission system provided in an embodiment of the present invention.

[0046] Figure 3 This is a structural diagram of a coil optimization system for a rotating wireless power transmission system provided in an embodiment of the present invention.

[0047] Figure 4 These are finite element model diagrams provided in this embodiment of the invention, showing the alignment of the transmitting coil and the receiving coil before optimization and the situation at the effective coupling range boundary. a is the magnetic field line result under the alignment condition, and b is the magnetic field line result under the effective coupling boundary condition.

[0048] Figure 5 These are finite element model diagrams provided in this embodiment of the invention, showing the optimized transmitting coil and receiving coil alignment and the case within the effective coupling range boundary; a is the magnetic field line result under the alignment condition, and b is the magnetic field line result under the case within the effective coupling boundary.

[0049] Figure 6 This is the sensitivity result of the response value (coupling coefficient) obtained by using the response surface methodology in the embodiments of the present invention to each parameter.

[0050] Figure 7 The results are response surface results obtained using the response surface methodology provided in this embodiment of the invention; a is the response surface affected by the coupling coefficient when the inner and outer radii of the transmitting coil are aligned, and b is the response surface affected by the coupling coefficient when the inner and outer radii of the transmitting coil are rotated by 15°.

[0051] Figure 8 This is the Pareto solution set result obtained by using an evolutionary algorithm, as provided in the embodiments of the present invention.

[0052] Figure 9 This is a performance comparison of the rotary wireless power transfer system coil before and after optimization, provided in the embodiments of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] The core innovation of this coil optimization method for a rotating wireless power transfer system lies in its integration of finite element modeling, response surface methodology, and evolutionary algorithms. Through the high degree of integration and synergistic operation of these three methods, the design of both the transmitting and receiving coils is optimized while meeting the power requirements of the receiving side. This optimization method involves sampling point selection and finite element calculations within constraints and variable parameter ranges. The calculation results are then analyzed using the response surface methodology to obtain the sensitivity of the objective function to each parameter and the response surface results. Finally, an evolutionary algorithm is applied to find the optimal solution. This method can improve the performance and efficiency of rotating wireless power transfer systems while reducing the complexity of their design and optimization processes.

[0055] In rotary wireless power transfer systems, the positional relationship between the transmitting and receiving coils is often misaligned and angularly offset. Furthermore, when the coil coupling coefficient is too small, power transfer is difficult. Based on the minimum coupling coefficient, there exists an effective coupling range boundary. For rotary wireless power transfer systems, the most important indicators are the power transfer capability and robustness within the effective coupling range.

[0056] In this embodiment, refer to Figure 1 This is a schematic diagram of the rotating wireless power transfer system in this embodiment. The rotating wireless power transfer system mainly consists of a DC power supply, a high-frequency inverter section, a control section, a compensation and coupling section, a rectifier module section, and rotating components. For the purposes of this invention, the main focus is on the compensation and coupling section; therefore, details of other parts will not be elaborated.

[0057] Specifically, the coupling coil used in this embodiment is circular. Since the receiving coil is located on a rotating component and is constantly moving, there are certain limitations on its size and weight. Therefore, the coupling coils are all coreless planar helical coils.

[0058] Specifically, in this embodiment, the compensation circuit topology is an LCC / S type topology. The LCC / S type topology is a structure where the transmitting side uses a series inductor-parallel capacitor-series capacitor for compensation, and the receiving side uses a series capacitor for compensation. When the coupling coefficient decreases, the voltage gain of the wireless power transmission system decreases, and the current gain increases. The larger the coupling coefficient, the greater the power that can be transmitted. The transmission efficiency is essentially constant within a certain coupling range, but when the coupling coefficient is very small, the operating frequency shifts relative to the resonant frequency, which reduces the transmission efficiency.

[0059] Specifically, in this embodiment, the rotation radius of the rotating component is twice the diameter of the receiving coil.

[0060] This application proposes a coil optimization method for a rotary wireless power transfer system, referring to... Figure 2 This is a flowchart illustrating the optimization method. The method includes:

[0061] S101: Establish a finite element model of the transmitting coil and the receiving coil under the condition of alignment.

[0062] The finite element model is a model used for multiphysics analysis after computer-aided geometric modeling, mesh generation, and discretization. Specifically, in this embodiment, the distance between the transmitting coil and the receiving coil is 1 / 3 of the diameter of the receiving coil.

[0063] S102: Given a fixed rotation radius of the rotating component, select the minimum coupling coefficient of the rotating wireless power transmission system based on the power requirements of the receiving side.

[0064] Specifically, in this embodiment, the minimum coupling coefficient is selected as 0.1. For LCC / S type topology, the larger the coupling coefficient, the greater the power that can be transmitted. Therefore, different minimum coupling coefficients can be selected for different power requirements.

[0065] S103: Based on the minimum coupling coefficient, establish the finite element models of the transmitting coil and the receiving coil at the boundary of the effective coupling range.

[0066] Reference Figure 1 The effective coupling range boundary is the rotation angle of the rotating component corresponding to the minimum coupling coefficient. When the coils are aligned, the rotation angle is 0°, and the effective coupling range of the pair of coils is the distance between the rotation angles of the rotating components corresponding to the minimum coupling coefficients on both sides.

[0067] S104: Select the variable parameters and determine their constraints.

[0068] Specifically, the variable parameters selected in this embodiment are the inner radius of the transmitting coil, the outer radius of the transmitting coil, the inner radius of the receiving coil, and the coil thickness. The outer radius of the receiving coil is limited to a fixed value, and the maximum outer radius of the transmitting coil is no more than twice the outer radius of the receiving coil. Since the helical coil has a certain number of turns, the difference between the inner and outer radii of the coil is constrained.

[0069] S105: Sample points within the variable parameter range and perform finite element analysis. Analyze the calculation results using the response surface methodology to obtain the sensitivity of the objective function to each parameter and the response surface results.

[0070] Specifically, the sampling point selection method used in this embodiment is Latin hypercube sampling. Latin hypercube sampling is a type of stratified random sampling. Its advantage is that it can achieve the same result as many random samplings with fewer sampling times.

[0071] The response surface methodology is an optimization method that integrates experimental design and mathematical modeling. It uses variable input parameters as input variables (factors) and the variables requiring focus as output variables (responses). Experimental data obtained through experiments or simulations are used to mathematically fit the relationship between factors and responses, while sensitivity analysis identifies the main influencing factors. Finally, the established model is used to determine the optimal combination of factors through mathematical optimization, thereby obtaining the best response, i.e., the optimal solution.

[0072] S106: Apply the evolutionary algorithm to the functional relationship fitted by the response surface to obtain the optimized Pareto solution set.

[0073] Specifically, the optimization objective in this embodiment is to maximize the coupling coefficient when the coil on the rotating component rotates by 15°, and minimize the difference between the coupling coefficient at this point and the coupling coefficient when the coupling coil is aligned.

[0074] The evolutionary algorithm described is a mathematical optimization algorithm used after obtaining a fitted mathematical model using response surface methodology. It differs from traditional search and optimization algorithms in that it possesses inherent parallelism and strong robustness, and requires no additional information; only the objective function and the corresponding fitness influence the search direction. In multi-objective optimization problems like those in this embodiment, the algorithm provides a set of Pareto optimal solutions, which can serve as multiple alternatives. A Pareto optimal solution is one in which no other solution in the variable space outperforms it on all objective functions. The Pareto solution set is the set of these Pareto optimal solutions.

[0075] S107: Select the required Pareto solution as the final optimized solution based on the actual situation.

[0076] Specifically, in this embodiment, the method for filtering the obtained Pareto optimal solution is to normalize the results of the two optimization objectives mentioned above and then sum them with equal weights to obtain the final optimized solution.

[0077] like Figure 3 As shown, the coil optimization system for a rotating wireless power transfer system includes:

[0078] The initial modeling module, connected to the coupling coefficient input module, is used to establish a finite element model under the condition that the transmitting coil and the receiving coil are aligned.

[0079] The coupling coefficient input module connects to the initial modeling module and the remodeling module, and is used to select the minimum coupling coefficient for different power requirements;

[0080] The model remodeling module, connected to the coupling coefficient input module and the parameter selection module, is used to establish finite element models of the transmitting coil and the receiving coil at the boundary of the effective coupling range based on the received minimum coupling coefficient.

[0081] The parameter selection module, connected to the model remodeling module and the sampling calculation module, is used to select variable parameters and determine their constraints.

[0082] The sampling calculation module, connected to the parameter selection module and the result comparison module, is used to sample points within the variable parameter range, perform finite element calculations, and use the response surface method to analyze the calculation results to obtain the sensitivity of the objective function to each parameter and the response surface results.

[0083] The results comparison module, connected to the sampling calculation module and the results output module, is used to introduce an evolutionary algorithm into the functional relationship fitted by the response surface to obtain an optimized Pareto solution set, and select the required Pareto solution according to the actual situation.

[0084] The results output module, connected to the results comparison module, is used to output the Pareto solution as the final optimized solution.

[0085] In this embodiment, refer to Figure 4 The image shows the finite element analysis results of the coupled coil before optimization. Figure 4 Figure a shows the magnetic field lines under aligned conditions. Figure 4 Figure b shows the magnetic field lines under the condition of effective coupling boundary. Specifically, when the minimum coupling coefficient is taken as 0.1, the effective coupling range of the coupling coil before optimization is ±13° of the receiving coil rotation.

[0086] The optimized pre-coupling coil size was selected empirically, following this method: the outer diameter of the receiving coil can be determined when the rotation radius of the receiving-side rotating component is known. As indicated by the article "Magnetic Characterization of Unsymmetrical Coil Pairs Using Archimedean Spirals for Wider Misalignment Tolerance in IPT Systems" published in IEEE Transactions on Transportation Electrification, to improve the anti-misalignment capability of wireless power transmission systems, the outer radii of the transmitting and receiving coils should be equal, and the inner radius of the receiving coil should be larger than that of the transmitting coil. The inner radius of the transmitting coil, referencing the article "Geometric approach for coupling enhancement of magnetically coupled coils" published in IEEE Transactions on Biomedical Engineering, is taken as 40% of the outer radius. In this embodiment, the inner radius of the receiving coil is selected as 2 / 3 of the outer radius, slightly larger than the outer radius of the transmitting coil, consistent with the requirements of the reference.

[0087] In this embodiment, refer to Figure 5 The figure shows the finite element analysis results of the optimized coupled coil. Figure 5 Figure a shows the magnetic field lines under aligned conditions. Figure 5 Figure b shows the magnetic field lines under the condition of effective coupling boundary. Specifically, when the minimum coupling coefficient is taken as 0.1, the effective coupling range of the optimized coupling coil is ±19° of the receiving coil rotation. After optimization, the outer radius of the receiving coil remains unchanged, while the inner and outer radii of the transmitting coil are increased.

[0088] In this embodiment, refer to Figure 6 This is a graph showing the sensitivity of the response values ​​obtained using the response surface methodology to various parameters. Here, Coil_s_i is the inner radius of the receiving coil, Coil_p_o is the outer radius of the transmitting coil, Coil_p_i is the inner radius of the transmitting coil, and thickness is the coil thickness. Coupling factor_0° and Coupling factor_15° are the coupling coefficients at alignment and 15° rotation, respectively. A positive sensitivity number indicates a positive correlation, and a negative number indicates a negative correlation; the larger the absolute value, the higher the correlation. Figure 6 It can be seen that the inner and outer radii of the transmitting coil are most correlated with the coupling coefficient under alignment and rotation conditions, that is, the response is most sensitive to the inner and outer radii of the transmitting coil.

[0089] In this embodiment, refer to Figure 7 , is the response surface methodology diagram. Figure 7 'a' represents the effect of the coupling coefficient on the response surface when the inner and outer radii of the transmitting coil are aligned. Figure 7 b. The inner and outer radii of the transmitting coil affect the coupling coefficient and response surface when rotated by 15°. Coil_p_o and Coil_s_i are input variables, i.e., factors, and Coupling factor_0° and Coupling factor_15° are output variables, i.e., responses. The relationship between factors and responses is mathematically fitted using experimental data obtained from simulation, and the fitted mathematical model can be obtained from the response surface.

[0090] In this embodiment, refer to Figure 8 The result represents the Pareto solution set obtained after optimization using an evolutionary algorithm. Specifically, in this embodiment, the difference in coupling coefficients was limited, and some Pareto optimal solutions were initially obtained through screening. Then, the optimization objective results were normalized and summed with equal weights to obtain the final optimized scheme. The optimization objective is to maximize the coupling coefficient when the receiving coil rotates by 15° to transmit higher power, while ensuring better robustness of the coupling coil from alignment to when the receiving coil rotates to 15°.

[0091] In this embodiment, reference Figure 9 The figure shows a performance comparison before and after coil optimization. As can be seen from the figure, the robustness of the optimized receiver power and coupling coefficient is significantly better than before optimization, and the effective coupling range increases from ±13° to ±19°, an increase of 46.2%. Using 0.1 as the minimum coupling coefficient, the wireless power transmission efficiency within the effective coupling range remains above 90%.

[0092] The coil optimization method for a rotating wireless power transmission system provided in the application embodiment of the present invention is applied to a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor performs the steps of the coil optimization method for a rotating wireless power transmission system.

[0093] The coil optimization method for a rotating wireless power transmission system provided in the application embodiment of the present invention is applied to an information data processing terminal, which is used to implement the coil optimization system for the rotating wireless power transmission system.

[0094] Based on the principles described in this invention, it can be used for coil optimization in rotating wireless power transfer systems. Specifically, for coil optimization in this embodiment, directly using mathematical models for calculation requires a deep understanding of mathematics and electromagnetic field theory. When using the pure finite element method, the number of finite element model sets required for calculation increases exponentially with the number of parameters. Even with a small number of sampling points for a single parameter, the number of finite element model sets required for calculation can easily reach thousands when using multiple parameters. In this embodiment, only a few dozen finite element model calculations are performed for both coil alignment and coil rotation. The advantages of this invention are even more pronounced when the coil shape is complex.

[0095] Example: Rotating Machinery Fault Diagnosis System

[0096] The optimized rotary wireless power transfer system in the rotating machinery fault diagnosis system can provide a more efficient and stable energy supply. The specific implementation scheme is as follows:

[0097] The transmitting coil is mounted on the stationary side, and the receiving coil is mounted on the rotating spindle.

[0098] The initial coil size and minimum coupling coefficient are determined based on the power required by the sensor on the rotating spindle.

[0099] The coil design was optimized using the finite element model and response surface methodology to obtain the optimal coil size and shape.

[0100] An evolutionary algorithm is used to optimize the coil parameters to obtain the optimal coil parameters.

[0101] The coil is manufactured and installed based on the optimized coil design and parameters.

[0102] This embodiment improves the energy transmission efficiency and stability of the rotating wireless power transmission system by optimizing the coil design and parameters, making it more convenient and effective to power the rotating machinery fault diagnosis system.

[0103] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0104] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A coil optimization method for a rotating wireless power transfer system, characterized in that, The coil optimization method for a rotating wireless power transfer system includes the following steps: S101: Establish a finite element model of the transmitting coil and receiving coil under the condition of alignment; S102: Given a fixed rotation radius of the rotating component, select the minimum coupling coefficient of the rotating wireless power transmission system based on the power requirements of the receiving side. S103: Based on the minimum coupling coefficient, establish finite element models of the transmitting coil and the receiving coil at the boundary of the effective coupling range; S104: Select the variable parameters and determine their constraints; S105: Sample points within the variable parameter range, perform finite element calculations, and use the response surface method to analyze the calculation results to obtain the sensitivity of the objective function to each parameter and the response surface results; S106: Apply the evolutionary algorithm to the functional relationship fitted by the response surface to obtain the optimized Pareto solution set; S107: Select the required Pareto solution as the final optimized solution based on the actual situation; In S103, the effective coupling range is selected as follows: when the coil is aligned, the rotation angle is 0°, and the effective coupling range between the rotation angles of the rotating parts corresponding to the minimum coupling coefficients on both sides is the effective coupling range between the transmitting coil and the receiving coil. In S104, the variable parameters include the inner radius of the transmitting coil, the outer radius of the transmitting coil, the inner radius of the receiving coil, and the coil thickness; In S105, the sampling point selection method includes the Latin hypercube sampling method; The response surface methodology is an optimization method that integrates experimental design and mathematical modeling. It takes the input variable parameters as input variables, i.e., factors, and the coupling coefficient as output variables, i.e., the response. It uses experimental data obtained from experiments or simulations to mathematically fit the relationship between factors and response. At the same time, it uses sensitivity analysis to determine the main influencing factors. Finally, it uses the established model to determine the optimal combination of factors through mathematical optimization, thereby obtaining the best response, i.e., the optimal solution.

2. The coil optimization method for a rotating wireless power transfer system as described in claim 1, characterized in that, In S101, the finite element model is the model used for multiphysics analysis after computer-aided geometric modeling, meshing, and discretization.

3. The coil optimization method for a rotating wireless power transfer system as described in claim 2, characterized in that, In S106, the evolutionary algorithm is a mathematical optimization algorithm used after obtaining a fitted mathematical model using the response surface methodology. It does not require additional information; only the objective function and the corresponding fitness affect the search direction. The Pareto solution set is the set of solutions consisting of Pareto optimal solutions. A Pareto optimal solution is a solution in the variable space that is superior to any other solution on all objective functions.

4. A coil optimization system for a rotating wireless power transmission system, implementing the coil optimization method for a rotating wireless power transmission system as described in any one of claims 1-3, characterized in that, The system includes: The initial modeling module, connected to the coupling coefficient input module, is used to establish a finite element model under the condition that the transmitting coil and the receiving coil are aligned. The coupling coefficient input module, connected to the initial modeling module and the remodeling module, is used to select the minimum coupling coefficient for different power requirements; The model remodeling module, connected to the coupling coefficient input module and the parameter selection module, is used to establish finite element models of the transmitting coil and the receiving coil at the boundary of the effective coupling range based on the received minimum coupling coefficient. The parameter selection module, connected to the model remodeling module and the sampling calculation module, is used to select variable parameters and determine their constraints.

5. The coil optimization system for a rotating wireless power transfer system as described in claim 4, characterized in that, The system further includes: The sampling calculation module, connected to the parameter selection module and the result comparison module, is used to sample points within the variable parameter range, perform finite element calculations, and use the response surface method to analyze the calculation results to obtain the sensitivity of the objective function to each parameter and the response surface results. The results comparison module, connected to the sampling calculation module and the results output module, is used to introduce an evolutionary algorithm into the functional relationship fitted by the response surface to obtain an optimized Pareto solution set, and select the required Pareto solution according to the actual situation.

6. The coil optimization system for a rotating wireless power transfer system as described in claim 4 or 5, characterized in that, The system further includes: The results output module, connected to the results comparison module, is used to output the Pareto solution as the final optimized solution.

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