Wireless power transmission coil optimization method and related system

Abstract

The invention discloses a wireless power transmission coil optimization method and a related system, which can realize automation of coil structure parameters and multi-target global optimization by acquiring a coil geometric parameter space, constructing a comprehensive optimization function and performing iterative search in the coil geometric parameter space. The problems that in traditional design, the number of simulation iterations is large, design efficiency is low, local optimum is prone to occurring, and multiple performance indexes are difficult to balance are effectively solved, coil design efficiency and precision are remarkably improved, and dependence on artificial experience is reduced. Therefore, the number of manual intervention and simulation is remarkably reduced, the coil design efficiency is improved, and the prototype development period of the WPT system is shortened. Besides, iterative search is carried out in the parameter space, so that the whole design space can be effectively explored, a locally optimal solution trap can be jumped out, and a globally optimal or approximately globally optimal coil structure parameter combination can be obtained more possibly.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wireless power transmission, and particularly relates to a wireless power transmission coil optimization method and a related system. BACKGROUND

[0002] With the vigorous development of intelligent devices and new energy industries, wireless power transmission (WPT) technology has become a research hotspot in the global energy field due to its non-contact power supply advantage. As a core component of the WPT system, the high-frequency resonant coil is highly sensitive to core parameters such as spacing, shape, number of turns, and angle, which can significantly affect power transmission efficiency. Therefore, the design quality of the coil directly affects the transmission efficiency, space utilization, and electromagnetic compatibility performance of the entire WPT system. Therefore, during the design of the resonant coil, the coil structure parameters are usually optimized in advance, and then the coil is designed based on the finally confirmed coil structure parameters.

[0003] In the prior art, the optimization of coil structure parameters is usually performed by relying on finite element simulation combined with experience and trial-and-error. During implementation, the coil structure parameters (such as the number of turns, radius, and spacing) are initially set according to application requirements, modeling and electromagnetic field simulation are performed through finite element software, key performance indicators such as inductance, self-inductance, and mutual inductance are obtained, and then parameter adjustment and structure optimization are performed in combination with experience, repeated iteration is performed until the performance target is met, finally, the coil structure parameters obtained through simulation are used for sample making and actual measurement, and parameter fine-tuning is performed according to the experience of engineers, thereby obtaining the final coil structure parameters.

[0004] However, during the use of the prior art, at least the following problems exist: Since the high-frequency WPT system is highly sensitive to coil structure parameters (such as the number of turns, radius, gap, offset, and shielding structure), engineers often need multiple rounds of simulation and test iteration to obtain a relatively ideal coil design scheme, which not only consumes a long time and is low in efficiency, but also cannot meet the rapid prototyping development demand, and is prone to local optimization and lacks global exploration ability for the design parameter space.

[0005] In addition, during the design process, multiple targets such as magnetic field uniformity, power transmission efficiency, structural compactness, and electromagnetic shielding efficiency often conflict with each other. For example, increasing power efficiency requires expanding the coil size, while structural compactness requires minimizing the volume. The traditional optimization method usually adopts a static weighting method, which cannot dynamically adjust the target weight according to the actual application, and it is difficult to achieve flexible trade-off among multiple targets. SUMMARY

[0006] The purpose of the present application is to provide a wireless power transmission coil optimization method and related system to overcome the problem that the prior art cannot dynamically adjust the target weight according to the actual application and it is difficult to achieve flexible trade-off among multiple targets.

[0007] To achieve the above object, the technical scheme adopted by the present application is as follows: In a first aspect, a wireless power transmission coil optimization method comprises the following steps: Obtaining a coil geometric parameter space; the coil geometric parameter space corresponds to multiple coil structure parameters; Based on the coil structure parameters corresponding to the coil geometric parameter space, multiple objective functions are constructed, and a comprehensive optimization function is constructed according to the multiple objective functions; Iterative search is performed on the coil geometric parameter space to obtain a coil structure parameter optimal solution that minimizes the fitness value of the comprehensive optimization function, and a wireless power transmission coil is made based on the coil structure parameter optimal solution.

[0008] Preferably, the coil structure parameters corresponding to the coil geometric parameter space include the number of turns of the coil, the radius of the coil, the gap of the coil, the vertical distance of the coil, the lateral offset of the coil, the longitudinal offset of the coil, and the shielding distance.

[0009] Preferably, the multiple objective functions include a fusion magnetic field uniformity function, a power efficiency function, a structure compactness function, a shielding effectiveness function, and a constraint penalty term function; the fusion magnetic field uniformity function is: In the formula, indicates the fusion magnetic field uniformity, indicates the lateral offset of the coil, indicates the longitudinal offset of the coil, indicates the vertical distance of the coil, indicates the radius of the coil; The power efficiency function is: In the formula, indicates the power efficiency, indicates the number of turns of the coil, indicates the gap of the coil; The structure compactness function is: In the formula, indicates the structure compactness function; The shielding effectiveness function is: In the formula, ​​​​This represents the shielding effectiveness function. Indicates the shielding distance; The constraint penalty term function is: ; In the formula, Indicates the amount of constraint penalty. This represents the function that takes the maximum value.

[0010] Preferably, the comprehensive optimization function is obtained by dynamically weighting multiple objective functions; correspondingly, the comprehensive optimization function is: ; In the formula, , , , and These represent the preset weights that match the fusion magnetic field uniformity function, power efficiency function, structural compactness function, shielding effectiveness function, and constraint penalty term function, respectively.

[0011] Preferably, the iterative search of the coil geometric parameter space to obtain the optimal solution of coil structure parameters that minimizes the fitness value of the comprehensive optimization function includes: An initial population is randomly generated within the coil geometric parameter space, and the initial population is defined as the current population. The fitness value of each individual in the current population is calculated based on the comprehensive optimization function, and the individual with the smallest fitness value in the current population is selected as the current optimal individual. Based on the current best individual, the current population is updated to obtain a new population; Then, the fitness value of each individual in the updated population is calculated according to the comprehensive optimization function until the maximum number of iterations is reached or the preset convergence condition is met. The best individual obtained is then taken as the optimal solution of coil structure parameters that minimizes the fitness value of the comprehensive optimization function.

[0012] Preferably, after obtaining the optimal solution for the coil structure parameters, the method further includes: Construct an inductance prediction model and train the inductance prediction model to obtain a trained inductance prediction model; The optimal solution of the coil structure parameters is used as the input to the trained inductance prediction model to obtain the inductance prediction result.

[0013] Preferably, the inductance prediction results include the self-inductance of the transmitting coil under test, the self-inductance of the receiving coil, and the mutual magnetic flux.

[0014] Secondly, a wireless power transfer coil optimization system includes: The parameter space acquisition module is used to acquire the coil geometric parameter space; the coil structure parameters corresponding to the coil geometric parameter space can be set in multiple ways. The function construction module is communicatively connected to the parameter space acquisition module, and is used to construct multiple objective functions based on the coil structure parameters corresponding to the coil geometric parameter space, and to construct a comprehensive optimization function based on the multiple objective functions; The parameter optimization module, which is communicatively connected to the function construction module, is used to iteratively search the geometric parameter space of the coil to obtain the optimal solution of the coil structure parameters that minimizes the fitness value of the comprehensive optimization function, and to fabricate a wireless power transmission coil based on the optimal solution of the coil structure parameters.

[0015] Thirdly, an electronic device includes: Memory, used to store computer program instructions; and, A processor is configured to execute the computer program instructions to perform the operation of a wireless power transfer coil optimization method as described in any of the preceding claims.

[0016] Fourthly, a computer program product includes a computer program or instructions that, when executed by a computer, implement a wireless power transfer coil optimization method as described in any of the preceding claims.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects: This invention discloses a method for optimizing wireless power transfer coils. By acquiring the coil's geometric parameter space, constructing a comprehensive optimization function, and performing iterative searches within that space, it achieves automated and multi-objective global optimization of coil structural parameters. This effectively solves problems in traditional design such as numerous simulation iterations, low design efficiency, susceptibility to local optima, and difficulty in balancing multiple performance indicators. It significantly improves coil design efficiency and accuracy, reducing reliance on human experience. This significantly reduces manual intervention and simulation times, improves coil design efficiency, and shortens the prototype development cycle of WPT systems. Furthermore, by iteratively searching within the coil's geometric parameter space, the entire design space can be effectively explored, escaping local optimum traps and increasing the likelihood of obtaining globally optimal or near-globally optimal combinations of coil structural parameters.

[0018] This invention constructs multiple objective functions and forms a comprehensive optimization function to uniformly model several conflicting design objectives, such as magnetic field uniformity, power transmission efficiency, structural compactness, and electromagnetic shielding effectiveness. It also dynamically weighs these objectives during the search process, improving the overall performance of the WPT system across multiple performance dimensions. This avoids the limitations of traditional methods that prioritize one objective at the expense of others. Compared to traditional static weighted optimization, this invention can flexibly adjust optimization weights and strategies according to application requirements, adapting to different usage scenarios and target needs. This enhances the intelligence and adaptability of coil design, demonstrating good engineering practicality and promotional value. Attached Figure Description

[0019] Figure 1 This is a flowchart of the wireless power transfer coil optimization method in Example 1.

[0020] Figure 2 This is a schematic diagram of the coil structure shown in Example 1.

[0021] Figure 3 This is a schematic diagram of the inductance prediction model in Example 1.

[0022] Figure 4 This is a block diagram of the wireless power transfer coil optimization system in Example 2.

[0023] Figure 5 This is a block diagram of the electronic device in Example 3. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1: This embodiment discloses a wireless power transmission coil optimization method, which can be executed by, but is not limited to, a computer device or virtual machine with certain computing resources, such as a personal computer, smartphone, personal digital assistant or wearable electronic device, or by a virtual machine.

[0027] like Figure 1 As shown, a method for optimizing a wireless power transfer coil includes the following steps: S1. Obtain the coil geometric parameter space; the coil structure parameters corresponding to the coil geometric parameter space can be set in various ways. Specifically, the coil geometric parameter space includes multiple combinations of values ​​for each coil structure parameter, which is also referred to as the coil geometric parameter set; In step S1, such as Figure 2 As shown, the coil structural parameters corresponding to the coil geometric parameter space include the number of coil turns. Coil radius Coil gap Vertical distance of coil (Also known as the distance between the upper and lower coils, or the z-offset), the lateral offset of the coil. (Also known as x-offset), coil longitudinal offset (Also known as y-offset) and shielding distance . Figure 2 (a) is a schematic diagram of the planar model of the coil, which shows the coil radius. Coil gap and shielding distance Relationship, Figure 2 (b) is a schematic diagram of the three-dimensional model of the coil, which shows the vertical distance of the coil. lateral offset of the coil Longitudinal offset of the coil The relationship.

[0028] Specifically, as an example, in this embodiment, the number of coil turns in the coil geometric parameter space... The possible combinations of values ​​are: {20, 21, ..., 40}, and the coil radius is... The possible values ​​are: 30-60mm, coil gap s The possible values ​​are: 1-3mm, vertical distance of the coil. The possible values ​​are: 40-80mm, coil lateral offset. The possible values ​​are: 3-5mm, coil longitudinal offset. The possible values ​​are: 3-5mm, shielding distance. dThe possible values ​​are: 2-10mm.

[0029] S2. Based on the coil structure parameters corresponding to the coil geometric parameter space, construct multiple objective functions, and construct a comprehensive optimization function based on the multiple objective functions.

[0030] In step S2, the multiple objective functions include a fusion magnetic field uniformity function, a power efficiency function, a structural compactness function, a shielding effectiveness function, and a constraint penalty term function; wherein, the fusion magnetic field uniformity function is: ; In the formula, Indicates the uniformity of the fused magnetic field. This indicates the lateral offset of the coil. This indicates the longitudinal offset of the coil. This indicates the vertical distance of the coil. Indicates the radius of the coil; The power efficiency function is: ; In the formula, Indicates power efficiency. Indicates the number of turns of the coil. This indicates the gap between the coils; The structural compactness function is: ; In the formula, Represents the structural compactness function; The shielding effectiveness function is: ; In the formula, This represents the shielding effectiveness function. Indicates the shielding distance; The constraint penalty term function is: ; In the formula, Indicates the amount of constraint penalty. This represents the function that takes the maximum value.

[0031] The comprehensive optimization function is obtained by dynamically weighting multiple objective functions; correspondingly, the comprehensive optimization function is: ; In the formula, , , , and These represent the preset weights for matching the fusion magnetic field uniformity function, power efficiency function, structural compactness function, shielding effectiveness function, and constraint penalty term function, respectively; the fitness value is calculated through a comprehensive optimization function.

[0032] Specifically, in this embodiment, through normalization and linear superposition, multiple objectives (such as power transmission efficiency, magnetic field uniformity, structural compactness, and electromagnetic shielding effect) are transformed into a single-objective comprehensive optimization function. This allows various indicators to be considered in a coordinated manner during the optimization process, thereby achieving a balance among performance parameters. The overall performance is optimal when the fitness value R obtained by the comprehensive optimization function approaches 0. Compared to traditional single-objective or static weighted methods, the comprehensive optimization function in this embodiment can flexibly adjust the relative importance of each objective according to different application requirements, improving the flexibility, adaptability, and effectiveness of overall system performance optimization, and avoiding bias towards a single indicator at the expense of other key performance aspects.

[0033] S3. Perform an iterative search on the space of the coil geometric parameters to obtain the optimal solution of the coil structure parameters that minimizes the fitness value of the comprehensive optimization function, and fabricate a wireless power transmission coil based on the optimal solution of the coil structure parameters.

[0034] In step S3, the optimal solution for the coil structure parameters is the combination of coil structure parameters that minimizes the fitness value of the comprehensive optimization function; correspondingly, an iterative search is performed on the coil geometric parameter space to obtain the optimal solution for the coil structure parameters that minimizes the fitness value of the comprehensive optimization function, including: S301. Randomly generate an initial population in the coil geometric parameter space, and define the initial population as the current population; specifically, in this embodiment, the initial population is generated by uniform random distribution, and each individual in the initial population is a 7-dimensional vector, corresponding to 7 kinds of coil structure parameters, and these individuals represent candidate combinations of coil geometric parameters; S302. Calculate the fitness value of each individual in the current population according to the comprehensive optimization function, and select the individual with the smallest fitness value in the current population as the current optimal individual; S303. Based on the current best individual, update the current population to obtain a new population; Specifically, in this embodiment, the current population is updated, that is, the position of each individual in the current population is updated with reference to the current best individual, forming new candidate solutions, and the new candidate solutions corresponding to each individual form a new population.

[0035] In this embodiment, the position update of each individual in the current population is achieved through exploration or development strategies. In this embodiment, the weight of exploration and development strategies is dynamically adjusted by the k parameter. Early iterations primarily use exploration strategies, while later iterations primarily use development strategies to achieve a balance between the two. A dynamic Levy flight strategy enhances the global search capability, and Beluga Whale Optimization (BWO) is used as a supplementary mechanism, triggered when the algorithm gets stuck in a local optimum, further improving its ability to escape local optima.

[0036] The exploration strategy is used to globally search for new regions in the geometric parameter space of the coil to escape local optima. Specifically, the process of updating the individual position through the exploration strategy is as follows: a reference individual is randomly selected; different update dimensions are selected according to the number of parameter dimensions (less updates for low-dimensional, more updates for high-dimensional) to update the reference individual; random numbers r1 (ranging from 0 to 1, used to control the update amplitude) and r2 (ranging from 0 to 1, used to control the update direction) are used to control the search direction and amplitude to achieve the individual position update; boundary checks are performed, and the updated individuals that pass the boundary checks are taken as new candidate solutions.

[0037] The development strategy is used to perform a fine-grained search near the current high-quality solution to approximate the optimal solution. Specifically, the process of updating the individual position through the development strategy is as follows: Calculate the new position based on the current best individual and the reference individual; use random numbers r3 (ranging from 0 to 1, used to control the influence weight of the optimal solution) and r4 (ranging from 0 to 1, used to control the influence weight of the current individual) to control the influence weight; introduce the Levy Flight mechanism (a random walk-based optimization algorithm that simulates the random step size in the Levy distribution) to generate the Levy Flight step size and perform a jump-style search; update the individual position based on the current best solution and the reference individual; perform boundary checks, and use the updated individuals that pass the boundary checks as new candidate solutions.

[0038] S304. Then, calculate the fitness values ​​of each individual in the updated population based on the comprehensive optimization function, i.e., return to step S302, until the set maximum number of iterations is reached or the preset convergence condition is met, and terminate. The obtained optimal individual is taken as the optimal solution of the coil structure parameters that minimizes the fitness value of the comprehensive optimization function. In this embodiment, the convergence condition is as follows: the change in fitness value of the optimal individual for several consecutive generations is lower than a preset threshold, indicating that the population is close to a stable state; or, the overall fitness variance of the population tends to decrease, indicating that the change in fitness value of the optimal individual tends to stabilize and the search space has been fully explored.

[0039] It should be noted that this embodiment initializes the population and introduces search strategies such as exploration and development in the seven-dimensional coil geometric parameter space. It evaluates and screens candidate solutions by combining fitness values ​​and dynamically adjusts the search direction and range in multiple iterations to eventually approach the optimal individual and obtain the optimal solution of coil structure parameters with the best overall performance. Thus, it realizes the global search and optimal solution approximation of coil structure parameters in a multi-objective optimization scenario.

[0040] Verification has shown that, based on this embodiment, the global search success rate can be increased by 32.6%, the local optimal avoidance rate can be increased by 53.8%, the overall computational efficiency can be optimized by 21.4%, and the coil structure parameter optimization performance can be enhanced.

[0041] Furthermore, in this embodiment, after obtaining the optimal solution for the coil structure parameters, the method further includes: S4. Construct an inductance prediction model and train the inductance prediction model to obtain a trained inductance prediction model; Specifically, in this embodiment, the inductance prediction model adopts a deep neural network (DNN) model, such as... Figure 3 As shown, the inductance prediction model adopts a three-layer fully connected architecture, including an input layer (7 nodes), two hidden layers (256 nodes in the first layer and 128 nodes in the second layer), and an output layer (3 nodes). During implementation, the inductance prediction model is trained and validated using preset training and testing data to ensure the accuracy of the trained model. The training data is generated based on parametric simulation using COMSOL Multiphysics (a multiphysics simulation software) and divided into training and validation sets in a 7:3 ratio.

[0042] S5. Use the optimal solution of the coil structure parameters as the input of the trained inductance prediction model to obtain the inductance prediction result.

[0043] Specifically, the inductance prediction results include the self-inductance Lp of the transmitting coil under test, the self-inductance Ls of the receiving coil, and the mutual magnetic flux M12. Based on this, the coupling relationship and transmission characteristics of key electromagnetic parameters in a wireless power transmission system can be comprehensively reflected, providing an accurate basis for calculating the coupling coefficient, resonance condition, and energy transmission efficiency, thereby effectively guiding coil structure design and optimization. Compared to considering only some inductance parameters, this prediction result is more complete and has greater engineering practicality, contributing to the design of efficient and stable wireless power transmission systems.

[0044] It should be understood that, to ensure the accuracy of the model calculation and output results, the optimal solution of the coil structure parameters is first converted into a floating-point type NumPy array (a multi-dimensional, homogeneous container used to store elements of the same type), and then standardized. The standardized features are then converted into PyTorch (an open-source Python machine learning library) tensors, which are then used as input to the trained inductance prediction model to obtain the inductance prediction result. Furthermore, after obtaining the inductance prediction result, it can be converted back into a NumPy array for subsequent data processing, visualization analysis, and result storage.

[0045] It should be noted that in this embodiment, by pre-constructing and training an inductance prediction model, and inputting the optimal solution of the coil structure parameters into the trained inductance prediction model, the inductance prediction result of the wireless power transmission coil designed based on the optimal solution of the coil structure parameters can be quickly obtained. Experiments show that the average relative error rate of inductance prediction based on this embodiment is less than 1.3%, the self-inductance deviation rate is 0.2%, the mutual inductance deviation rate is 1.3%, and the mean square error (MSE) is 0.0987, thus improving the inductance prediction accuracy. Simultaneously, this embodiment shortens the inductance prediction time to 55.9 seconds, the algorithm iteration convergence time to 7.8 seconds, and the design cycle is compressed from several hours to minutes, resulting in higher inductance prediction efficiency.

[0046] This embodiment can achieve multi-objective collaborative optimization of high-frequency resonant coil structural parameters, effectively solving the problems of numerous simulation iterations, low design efficiency, easy getting trapped in local optima, and difficulty in balancing multiple performance indicators in traditional design. It significantly improves coil design efficiency and accuracy and reduces reliance on human experience. Specifically, in this embodiment, by acquiring the coil geometric parameter space, constructing a comprehensive optimization function, and performing iterative searches within the coil geometric parameter space, the automation and multi-objective global optimization of coil structural parameters can be achieved. This significantly reduces manual intervention and simulation times, improves coil design efficiency, and shortens the prototype development cycle of the WPT system. Furthermore, by iteratively searching within the coil geometric parameter space, the entire design space can be effectively explored, escaping local optimum traps and increasing the likelihood of obtaining globally optimal or near-globally optimal combinations of coil structural parameters. In addition, this embodiment constructs multiple objective functions and forms a comprehensive optimization function to uniformly model multiple conflicting design objectives such as magnetic field uniformity, power transmission efficiency, structural compactness, and electromagnetic shielding effectiveness. Dynamic trade-offs are performed during the search process, improving the overall performance of the system across multiple performance dimensions. This avoids the limitations of traditional methods that prioritize one objective at the expense of other performance aspects. Compared to the traditional static weighted optimization method, this embodiment can flexibly adjust optimization weights and strategies according to application requirements, adapting to different usage scenarios and target needs. This enhances the intelligence and adaptability of coil design, demonstrating good engineering practicality and promotional value.

[0047] Example 2: This embodiment discloses a wireless power transfer coil optimization system for implementing the wireless power transfer coil optimization method in Embodiment 1; such as Figure 4 As shown, the wireless power transfer coil optimization system includes: The parameter space acquisition module is used to acquire the coil geometric parameter space; the coil structure parameters corresponding to the coil geometric parameter space can be set in multiple ways. The function construction module is communicatively connected to the parameter space acquisition module, and is used to construct multiple objective functions based on the coil structure parameters corresponding to the coil geometric parameter space, and to construct a comprehensive optimization function based on the multiple objective functions; The parameter optimization module, which is communicatively connected to the function construction module, is used to iteratively search the geometric parameter space of the coil to obtain the optimal solution of the coil structure parameters that minimizes the fitness value of the comprehensive optimization function, and to fabricate a wireless power transmission coil based on the optimal solution of the coil structure parameters.

[0048] It should be noted that the working process, working details and technical effects of the wireless power transmission coil optimization system provided in this embodiment 2 can be found in embodiment 1, and will not be repeated here.

[0049] Example 3: Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smartphone, tablet computer, laptop computer, or desktop computer, etc. The electronic device may be referred to as a user terminal, portable terminal, desktop terminal, etc. Figure 5 As shown, the electronic device includes: Memory, used to store computer program instructions; and, A processor is configured to execute the computer program instructions to perform the operation of a wireless power transfer coil optimization method as described in any of Embodiment 1.

[0050] Specifically, processor 301 may include one or more processing cores, such as a quad-core processor or an octa-core processor. Processor 301 may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). Processor 301 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 301 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen.

[0051] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 302 are used to store at least one instruction, which is executed by the processor 301 to implement the wireless power transfer coil optimization method provided in Embodiment 1 of this application.

[0052] In some embodiments, the terminal may also optionally include a communication interface 303 and at least one peripheral device. The processor 301, memory 302, and communication interface 303 can be connected via a bus or signal line. Each peripheral device can be connected to the communication interface 303 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 304, a display screen 305, and a power supply 306.

[0053] The communication interface 303 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0054] The radio frequency (RF) circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 304 communicates with communication networks and other communication devices via electromagnetic signals.

[0055] Display screen 305 is used to display the UI (User Interface). The UI may include any combination of graphics, text, icons, and video.

[0056] Power supply 306 is used to supply power to various components in electronic devices.

[0057] Example 4: Based on any one of Embodiments 1 to 3, this embodiment discloses a computer program product, including a computer program or instructions, which, when executed by a computer, implements a wireless power transfer coil optimization method as described in any one of Embodiments 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0058] Obviously, those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps as a single integrated circuit module. Thus, the present invention is not limited to any particular hardware and software combination.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wireless power transfer coil optimization method, characterized by, The method comprises the following steps: obtaining a coil geometry parameter space; the coil geometry parameter space corresponds to a plurality of coil structure parameters; based on the coil structure parameters corresponding to the coil geometry parameter space, constructing a plurality of objective functions, and constructing a comprehensive optimization function according to the plurality of objective functions; iterative searching the coil geometry parameter space to obtain a coil structure parameter optimal solution that minimizes the fitness value of the comprehensive optimization function, and manufacturing a wireless power transmission coil based on the coil structure parameter optimal solution.

2. The method of claim 1, wherein, The coil structure parameters corresponding to the coil geometry parameter space include coil turns, coil radius, coil gap, coil vertical distance, coil lateral offset, coil longitudinal offset, and shielding distance.

3. The method of claim 2, wherein, The plurality of objective functions include a fusion magnetic field uniformity function, a power efficiency function, a structure compactness function, a shielding effectiveness function, and a constraint penalty term function; wherein the fusion magnetic field uniformity function is: ； wherein represents the fusion magnetic field homogeneity, represents the lateral coil offset, represents the longitudinal coil offset, represents the vertical coil distance, represents the coil radius; The power efficiency function is: ； wherein represents the power efficiency, represents the number of turns of the coil, represents the coil gap; The structure compactness function is: ； In the formula, denotes a compactness function; The shielding effectiveness function is: ； wherein represents a shielding effectiveness function, represents the shielding distance; The constraint penalty term function is: ； In the formula, denotes the constraint penalty quantity, denotes the maximum function.

4. The method of claim 3, wherein, The comprehensive optimization function is obtained by dynamically weighting the plurality of objective functions; the comprehensive optimization function is: ； wherein, , , , and respectively represent preset weights matching the fusion magnetic field uniformity function, the power efficiency function, the structural compactness function, the shielding effectiveness function, and the constraint penalty term function.

5. The method of claim 1, wherein, The iterative searching of the coil geometry parameter space to obtain the coil structure parameter optimal solution that minimizes the fitness value of the comprehensive optimization function comprises: randomly generating an initial population in the coil geometry parameter space, and defining the initial population as a current population; calculating the fitness value of each individual in the current population according to the comprehensive optimization function, and selecting the individual with the minimum fitness value in the current population as a current optimal individual; updating the current population based on the current optimal individual to obtain a new population; calculating the fitness value of each individual in the updated new population according to the comprehensive optimization function until a preset maximum number of iterations is reached or a preset convergence condition is met, and terminating, and obtaining the optimal individual as the coil structure parameter optimal solution that minimizes the fitness value of the comprehensive optimization function.

6. The method of claim 1, wherein, After obtaining the coil structure parameter optimal solution, the method further comprises: constructing an inductance prediction model and training the inductance prediction model to obtain a trained inductance prediction model; inputting the coil structure parameter optimal solution into the trained inductance prediction model to obtain an inductance prediction result.

7. The method of claim 6, wherein, The inductance prediction result includes the self-inductance of a to-be-tested transmitting coil, the self-inductance of a receiving coil, and mutual magnetic flux.

8. A wireless power transfer coil optimization system, characterized by, It comprises: a parameter space obtaining module for obtaining a coil geometry parameter space; the coil geometry parameter space corresponds to a plurality of coil structure parameters; a function constructing module in communication connection with the parameter space obtaining module, for constructing a plurality of objective functions based on the coil structure parameters corresponding to the coil geometry parameter space, and constructing a comprehensive optimization function according to the plurality of objective functions; a parameter optimization module in communication connection with the function constructing module, for iterative searching the coil geometry parameter space to obtain a coil structure parameter optimal solution that minimizes the fitness value of the comprehensive optimization function, and manufacturing a wireless power transmission coil based on the coil structure parameter optimal solution.

9. An electronic device, comprising: It comprises: a memory for storing computer program instructions; and a processor for executing the computer program instructions to perform the operations of the wireless power transfer coil optimization method of any one of claims 1 to 7.

10. A computer program product, characterised in that, comprising computer program or instructions which, when executed by a computer, implement the wireless power transfer coil optimization method of any one of claims 1 to 7.

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