Layout optimization method for ferrite core of secondary side in magnetic coupling structure

Abstract

The invention relates to the technical field of wireless charging, and provides a ferrite core layout optimization method of a secondary side in a magnetic coupling structure, comprising the following steps: S100: determining a key area and a non-key area of a ferrite core according to a Morris sensitivity analysis method; and S200, using an NSGA-II multi-target genetic algorithm to carry out iterative optimization on the non-key region on the premise that the key region is reserved, and obtaining a Pareto optimal solution set. According to the method, firstly, the key area of the iron salt body magnetic core is preliminarily screened out through the Morris sensitivity analysis method, and then the NSGA-II multi-target genetic algorithm is utilized to only carry out iterative optimization on the non-key area instead of carrying out iterative optimization on the whole area, so that the computing power and the time cost are greatly saved, the solving speed is greatly improved, convergence is easy, and the calculation efficiency is improved. And the risk of falling into local optimum is reduced. According to the method, under the condition that the magnetic coupling effect is ensured, the use amount of the ferrite magnetic core is reduced, so that the overall weight of the system is reduced, and the material cost is saved.

Description

Technical Field

[0001] This application relates to the field of wireless charging technology, and in particular to a method for optimizing the layout of ferrite cores on the secondary side of a magnetic coupling structure. Background Technology

[0002] Static wireless charging technology achieves efficient and convenient contactless power transfer through magnetic field coupling between transmitting and receiving coils, and has been widely used in electric vehicles, automated guided vehicles (AGVs), and other fields. With the increasing demands for charging power and efficiency from various applications, high-power charging has become a research hotspot.

[0003] In high-power wireless charging systems, the magnetic coupling mechanism is the core component that determines transmission performance. It typically consists of a coil wound with high-frequency Litz wire and a ferrite core placed above or below it. The ferrite core provides a low magnetic reluctance path for the high-frequency magnetic field, enhances the coupling between the primary coil (transmitting coil) and the secondary coil (receiving coil), and confines the magnetic flux within a predetermined path, reducing leakage into the surrounding space.

[0004] To ensure sufficient magnetic flux guidance and shielding, traditional designs typically use a large, continuous ferrite core layer, either as a single piece or tightly fitted panels. While this approach meets basic performance requirements, it also introduces significant drawbacks, especially in high-power, large-size wireless charging systems. The bulky ferrite layer not only substantially increases the overall weight and material cost of the system, causing inconvenience for installation and integration, but its hysteresis and eddy current losses generated under high-frequency alternating magnetic fields are also important components of the total system losses, directly impacting charging efficiency and heat dissipation. Summary of the Invention

[0005] To address the aforementioned problems, this application provides a method for optimizing the layout of ferrite cores on the secondary side of a magnetically coupled structure. This method first uses Morris sensitivity analysis to preliminarily screen key regions of the ferrite core. Then, it utilizes the NSGA-II multi-objective genetic algorithm to iteratively optimize only non-critical regions, rather than iteratively optimizing the entire region. This significantly saves computational power and time costs, greatly improves the solution speed, facilitates convergence, and reduces the risk of getting trapped in local optima. This method can reduce the amount of ferrite cores used while ensuring effective magnetic coupling, thereby reducing the overall weight of the system, saving material costs, reducing magnetic losses, improving charging efficiency, and alleviating the system's heat dissipation burden. The technical solution adopted in this application is as follows:

[0006] A method for optimizing the layout of ferrite cores on the secondary side of a magnetically coupled structure includes the following steps:

[0007] S100: Determine the critical and non-critical regions of the ferrite core based on the Morris sensitivity analysis method;

[0008] S200: Using the NSGA-II multi-objective genetic algorithm, while preserving the key regions, iterative optimization is performed on the non-key regions to obtain the Pareto optimal solution set.

[0009] In some implementations, the objective function in the sensitivity analysis includes two components: ferrite loss and coupling coefficient.

[0010] In some implementations, the objective function of the NSGA-II multi-objective genetic algorithm includes ferrite loss, coupling coefficient, and ferrite weight.

[0011] In some embodiments, the ferrite magnetic block is symmetrical about the x-axis and y-axis, the x-axis and y-axis are perpendicular to each other, the ferrite magnetic block is divided into four parts by the x-axis and y-axis, and any one of the four parts is selected to perform steps S100 and S200.

[0012] In some embodiments, the ferrite core is divided into upper and lower layers in the z-axis direction.

[0013] In some implementations, the NSGA-II multi-objective genetic algorithm in step S200 includes a crossover operation and a mutation operation, wherein the crossover operation uses simulated binary crossover and the mutation operation uses polynomial mutation.

[0014] This application provides a method for optimizing the ferrite core layout on the secondary side of a magnetically coupled structure, which has the following advantages:

[0015] This method first uses Morris sensitivity analysis to preliminarily screen the critical regions of the ferrite core. Then, it utilizes the NSGA-II multi-objective genetic algorithm to iteratively optimize only the non-critical regions, rather than iteratively optimizing the entire region. This significantly saves computational power and time costs, greatly improves the solution speed, facilitates convergence, and reduces the risk of getting trapped in local optima. This method can reduce the amount of ferrite core used while ensuring magnetic coupling performance, thereby reducing the overall system weight and saving material costs, reducing magnetic losses, improving charging efficiency, and alleviating the system's heat dissipation burden. Attached Figure Description

[0016] The preferred embodiments will be described below in a clear and easy-to-understand manner, with reference to the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of a method for optimizing the layout of ferrite cores on the secondary side of a magnetic coupling structure:

[0017] Figure 1This is a flowchart of an embodiment of the ferrite core layout optimization method of this application;

[0018] Figure 2 The plane of the ferrite core before optimization is divided into a 6x6 matrix. Detailed Implementation

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the specific implementation methods of this application will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without creative effort.

[0020] To keep the drawings concise, each drawing only schematically shows the parts relevant to this application, and they do not represent the actual structure of the product. Furthermore, for ease of understanding, in some drawings, only one of the components with the same structure or function is schematically shown, or only one is labeled. In this document, "one" not only means "only one," but can also mean "more than one."

[0021] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] In this document, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0023] Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] Currently, the magnetic coupling mechanism for static wireless charging used in electric vehicles and automated guided vehicles (AGVs) includes a transmitter and a receiver. The transmitter is installed at the charging position, while the receiver is installed on the vehicle body. When the vehicle arrives at the charging position, the transmitter emits electrical energy, and the receiver receives the electrical energy, thus enabling the transmitter to charge the vehicle body. The transmitter, also known as the primary side, includes a primary coil and a primary ferrite core; the receiver, also known as the secondary side, includes a secondary coil and a secondary ferrite core. Currently, the magnetic coupling mechanism for static wireless charging has the following shortcomings:

[0025] 1. Bulky structure and high cost: Traditional magnetic coupling mechanisms rely on large-area, continuous monolithic ferrite plates for flux guidance and shielding. This makes the magnetic coupling mechanism heavy and bulky, increasing material costs and posing challenges for transportation, installation, and maintenance.

[0026] 2. Significant energy loss and limited optimization potential: A solid ferrite core generates substantial core losses during operation. However, the magnetic field distribution within the ferrite plate is actually uneven, with some regions exhibiting very low magnetic flux density. While the ferrite in these regions contributes minimally to coupling performance, the resulting losses and weight are substantial. Traditional design methods lack effective means to precisely remove this "inefficient" material.

[0027] 3. Design relies on experience and lacks systematic optimization methods: Existing designs rely heavily on the designer's experience and repeated simulations and trials, making it difficult to find the optimal balance between multiple mutually constraining objectives such as weight, loss, and coupling coefficient.

[0028] To solve the above problems, refer to Figure 1 This application provides a method for optimizing the layout of ferrite cores on the secondary side of a magnetically coupled structure, comprising the following steps:

[0029] S100: Determine the critical and non-critical regions of the ferrite core using the Morris sensitivity analysis method; S200: Utilize the NSGA-II multi-objective genetic algorithm to iteratively optimize the non-critical regions while retaining the critical regions, thereby obtaining the Pareto optimal solution set.

[0030] Understandably, this method is primarily used to optimize the layout of the ferrite core in the secondary side. To clearly illustrate the ferrite core layout optimization method of this application, a rectangular ferrite core adapted to a DD-type coil is used as an example below. Specifically: Before optimization, the rectangular ferrite core is a single-layer ferrite plate. This single-layer ferrite plate is divided into two layers along its thickness direction (z-axis direction): layer A and layer B. The plane of the ferrite plate is then divided into a 6x6 matrix (see reference). Figure 2In other words, the entire ferrite plate is divided into 6x6x2 small rectangular magnetic core blocks. Then, the Morris sensitivity analysis method is used to determine which of the 6x6x2 small rectangular magnetic core blocks belong to critical and non-critical regions. Small rectangular magnetic core blocks belonging to critical regions have a greater impact on the performance of the magnetic coupling mechanism, while those belonging to non-critical regions have a weaker impact. Small rectangular magnetic core blocks belonging to critical regions are present in every solution of the NSGA-II multi-objective genetic algorithm in step S200. This means that in step S200, the layout optimization of small rectangular magnetic core blocks located in critical regions is no longer performed; only the layout optimization of small rectangular magnetic core blocks located in non-critical regions is performed.

[0031] It is understood that the method of this application can be applied to ferrite cores with an original structure of arbitrary shape, such as circular ferrite cores other than rectangular ferrite cores.

[0032] The Morris Screening Method is a highly efficient global sensitivity analysis method suitable for quickly screening important variables when there are many input variables and limited computational resources. It assesses the impact of individual input variables on the model output by progressively changing their values, making it particularly suitable for the initial screening stage.

[0033] In this embodiment, the Morris sensitivity analysis method uses 6x6x2 input variables, meaning the number of input variables corresponds to the number of small rectangular magnetic core blocks into which the entire ferrite board is divided. Each input variable has two states: the presence of small rectangular magnetic core blocks and the absence of small rectangular magnetic core blocks. These states can be represented by 0 and 1, respectively; for example, the value is 1 if small rectangular magnetic core blocks are present and 0 if they are absent. In this embodiment, the Morris sensitivity analysis method uses two objective functions (model outputs): ferrite loss and coupling coefficient. This means two types of sensitivity analysis are required: one using ferrite loss as the objective function and the other using the coupling coefficient. By employing two objective functions to screen critical and non-critical areas of the ferrite core, the probability of identifying critical (or non-critical) areas is increased. This avoids the situation where only one objective function is used, and all areas of the ferrite core are insensitive (or sensitive) to this single objective function, leading to the failure to identify critical (or non-critical) areas. It is understood that in other embodiments, the objective function in the Morris sensitivity analysis method may be only one, such as ferrite loss, coupling coefficient, etc.

[0034] Understandably, in step S100, a simulation system is needed to obtain the specific values ​​of the objective function corresponding to each ferrite block layout scheme. The simulation system can be viewed as a black box model, and the specific simulation system can be ANSYS Maxwell, etc.

[0035] Understandably, in other embodiments, the ferrite core may not be layered along its thickness direction (z-axis) or may be divided into three, four, or more layers. Dividing the entire ferrite core into two layers (top and bottom) helps reduce eddy current and hysteresis losses compared to not dividing it. This is because when the ferrite core is divided into small rectangular core blocks, the smaller size and thinner thickness of each block reduce the likelihood of circulating currents within it. Furthermore, dividing the entire ferrite core into two layers makes it easier to install and secure the ferrite cores compared to dividing it into three, four, or more layers.

[0036] It is worth noting that DD-type coils exhibit significant spatial symmetry due to their geometric arrangement and excitation method, including x-axis symmetry and y-axis symmetry, where the x-axis, y-axis, and z-axis (mentioned earlier) are mutually perpendicular. This symmetry exists not only in the coil's geometric arrangement but also in its excitation method and the distribution of the generated electromagnetic field. If the rectangular ferrite core compatible with the DD-type coil also satisfies the condition of a symmetrical structure, or if the entire ferrite plate (ferrite core) before optimization is symmetrical about the x-axis and y-axis, the entire ferrite plate is divided into four parts (or divided into four quadrants) by the x-axis and y-axis. In this case, only the operations in steps S100 and S200 need to be performed on any one of the four parts, referring to... Figure 2 For example, the layout optimization can be performed only on the ferrite core in the third quadrant, and then the optimized layout of the entire ferrite core can be obtained by utilizing the symmetry characteristic. In this way, it is not necessary to optimize the layout of the entire ferrite core before optimization, thus reducing the computational complexity of the optimization process.

[0037] The layout optimization of the ferrite core in step S200 is based on step S100. Specifically, the critical regions obtained from S100 are not included in the optimization in step S200, as the critical regions obtained from S100 exist in each layout scheme. In step S200, only the non-critical regions obtained from S100 are optimized.

[0038] In step S200, the objective function of the NSGA-II multi-objective genetic algorithm can have various combinations, but regardless of the combination, it must include ferrite weight (mass). Specific combination examples: Combination 1: Ferrite loss, coupling coefficient, and ferrite weight; Combination 2: Ferrite weight and coupling coefficient; Combination 3: Ferrite weight and ferrite loss; Combination 4: Ferrite weight and transmission efficiency. It is understandable that, within a certain range, the more objective functions selected, the better the overall performance of the optimized ferrite core layout scheme can be ensured.

[0039] Understandably, in step S200, a simulation system is needed to obtain the specific values ​​of multiple objective functions corresponding to each ferrite block layout scheme. The simulation system can be viewed as a black box model, and the specific simulation system can be ANSYS Maxwell, etc.

[0040] The steps for optimizing non-critical regions using the NSGA-II multi-objective genetic algorithm are as follows:

[0041] a) Population initialization

[0042] The first step of NSGA-II is to initialize the population. This step involves randomly generating a certain number of individuals in the solution space, that is, randomly generating a certain number of different ferrite core layout schemes, to form an initial population. In the initial population, each individual represents a potential solution. Subsequently, for each individual (a specific ferrite core layout scheme), the values ​​of all objective functions are calculated (using a simulation system, such as Ansys Maxwell, to simulate the magnetic coupling mechanism and obtain the objective function values). In this application, the objective functions are preferably ferrite loss, coupling coefficient, and ferrite weight.

[0043] b) Non-dominated sorting

[0044] After evaluating the objective function values ​​of all individuals in the population, the algorithm performs a non-dominated sort. "Non-dominated" means that a solution dominates another solution if it is no worse than another solution on all objectives and is better than another solution on at least one objective. Based on this relationship, NSGA-II divides the entire population into several Pareto levels (Fronts). The first front (Front 1) contains all solutions not dominated by any other individual, the second front contains solutions dominated only by the first front, and so on. The non-dominated sort is the core mechanism by which NSGA-II maintains the approximation of the Pareto optimal solution set.

[0045] c) Crowding Calculation

[0046] To maintain the diversity of the solution set distribution in the objective space, NSGA-II introduces the concept of "crowding distance" within each front. This value measures the distance between a solution and its neighboring solutions in each objective dimension, thus reflecting its sparsity in the solution set. The algorithm uses a normalization method to calculate the total crowding distance of the solution across all objective functions. Solutions located on the boundaries are assigned a distance of positive infinity and are preferentially retained. The introduction of crowding not only helps in selecting representative solutions but also avoids the phenomenon of overly dense solution sets in certain regions.

[0047] d) Select Operation

[0048] During the selection phase, NSGA-II employs a binary tournament selection strategy, choosing individuals from the current population as parents. Specifically, two individuals are randomly selected, prioritizing the one with the higher non-dominant rank; if the ranks are the same, the individual with the greater crowding distance is chosen. In this way, the algorithm achieves both Pareto approximation and diversity considerations. This step provides well-fitted individuals for subsequent crossover and mutation operations, facilitating the rapid search for high-quality solutions.

[0049] e) Crossover and Mutation

[0050] After the selection operation is completed, NSGA-II performs crossover and mutation operations on the parent individuals to generate the offspring population. The crossover operation uses simulated binary crossover, which can preserve the superior genes of the parents in continuous variable problems; the mutation operation uses polynomial mutation, which introduces perturbation to enhance population diversity.

[0051] f) Merging of Elites and the Next Generation Choice

[0052] To improve stability and convergence speed, NSGA-II employs an elitist strategy. In each generation, the current parent generation Pt and the offspring generation Qt are merged into a joint population Rt of size 2N. Then, a non-dominated sort is performed on Rt again, generating multiple Pareto fronts. The next generation population Pt+1 selects solutions sequentially from these fronts: starting with the first front (Front 1), filling continues until a certain Pareto level cannot be fully filled. At this point, sparse individuals within that Pareto level are selected to fill the gap based on crowding distance. This process ensures the preservation of excellent solutions and a uniform distribution of the solution set.

[0053] g) Termination condition determination

[0054] The algorithm checks whether the termination condition is met at the end of each generation. The termination condition is reaching the maximum number of iterations or the Pareto front is stable and no longer changes.

[0055] It is understood that the method of this application can yield several relatively better solutions, or several relatively better different ferrite core layout schemes, and one of them can be selected as the optimized ferrite core layout scheme.

[0056] In this application, the key regions of the ferrite core were initially screened out using the Morris sensitivity analysis method. Then, the NSGA-II multi-objective genetic algorithm was used to iteratively optimize only the non-key regions instead of iteratively optimizing the entire region. This greatly saved computing power and time costs, significantly improved the solution speed, facilitated convergence, and reduced the risk of getting trapped in local optima.

[0057] It is worth noting that, in addition to the advantages mentioned above, the following beneficial effects can also be obtained by optimizing the layout of the ferrite core using the method described in this application:

[0058] 1. Reduced weight and material costs: By optimizing the layout of the ferrite board, a similar magnetic field coupling effect is achieved using fewer ferrites. The reduction in ferrite material directly reduces the weight of the receiver module, decreasing the vehicle load and improving range and power performance. Simultaneously, the procurement cost of ferrites decreases, significantly improving the economics of the entire wireless charging system.

[0059] 2. Reduced losses, improved efficiency and thermal performance: By optimizing the layout of ferrite in non-critical areas, this application effectively reduces hysteresis and eddy current losses in the ferrite core, significantly reducing the total loss of the wireless charging system under full load and improving transmission efficiency. The reduction in ferrite volume also decreases the total heat source, reduces hotspot areas, lowers the risk of temperature rise, and improves the system's heat dissipation and thermal stability.

[0060] 3. Maintaining high coupling and magnetic shielding safety: Although the method of this application reduces the ferrite core in some areas, rigorous sensitivity analysis ensures the integrity of the magnetic path at critical locations, and the magnetic coupling coefficient is almost unaffected, still achieving efficient energy transfer. Furthermore, the remaining ferrite core layout effectively constrains the main magnetic field distribution, significantly reducing the magnetic field strength in leakage areas without sacrificing magnetic shielding performance. This meets relevant electromagnetic radiation safety standards and has no adverse effects on the surrounding environment and equipment.

[0061] This application addresses the optimization problem of ferrite cores in wireless charging for electric vehicles. Through sensitivity analysis and multi-objective optimization algorithms, it effectively filters and adjusts the ferrite layout, significantly reducing material usage and system weight without sacrificing coupling performance, thereby reducing the size of the on-board module and enhancing integration.

[0062] This application is applicable to the wireless charging needs of industrial equipment such as AGVs, forklifts, and robots. Especially in miniaturized, high-frequency charging scenarios, it improves system stability and efficiency by reducing weight, minimizing losses, and improving heat dissipation, making it easy to deploy in smart logistics and automated factories.

[0063] This application proposes an optimization method to address the problems of heavy weight, high cost, and poor thermal performance of magnetic cores in wireless charging, adapting to the lightweight and energy-saving trends in the new energy vehicle industry. The proposed zonal optimization method is universal and can be integrated into the design of charging systems for electric vehicles or industrial equipment, improving cost-effectiveness and engineering practicality. It has future application value in vehicle-mounted wireless charging modules, charging foundation systems, and industrial power supply platforms, contributing to the standardization, lightweighting, and large-scale deployment of wireless charging technology.

[0064] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of this application. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for optimizing the layout of ferrite cores on the secondary side of a magnetically coupled structure, characterized in that, Includes the following steps: S100: Determine the critical and non-critical regions of the ferrite core based on the Morris sensitivity analysis method; S200: Using the NSGA-II multi-objective genetic algorithm, while preserving the key regions, iterative optimization is performed on the non-key regions to obtain the Pareto optimal solution set.

2. The method for optimizing the ferrite core layout of the secondary side in a magnetically coupled structure according to claim 1, characterized in that, The objective functions in the sensitivity analysis include two aspects: ferrite loss and coupling coefficient.

3. The method for optimizing the ferrite core layout of the secondary side in a magnetically coupled structure according to claim 1, characterized in that, The objective functions of the NSGA-II multi-objective genetic algorithm include ferrite loss, coupling coefficient, and ferrite weight.

4. The method for optimizing the ferrite core layout of the secondary side in a magnetically coupled structure according to claim 1, characterized in that, The ferrite magnetic block is symmetrical about the x-axis and y-axis, and the x-axis and y-axis are perpendicular to each other. The ferrite magnetic block is divided into four parts by the x-axis and y-axis. Any one of the four parts is selected to perform steps S100 and S200.

5. The method for optimizing the ferrite core layout of the secondary side in a magnetically coupled structure according to claim 1, characterized in that, The ferrite core is divided into upper and lower layers in the z-axis direction.

6. The method for optimizing the ferrite core layout of the secondary side in a magnetically coupled structure according to claim 1, characterized in that, In step S200, the NSGA-II multi-objective genetic algorithm includes crossover and mutation operations. The crossover operation uses simulated binary crossover, and the mutation operation uses polynomial mutation.

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