WO2020175912A1 - 무선전력전송 시스템용 형상 설계 시스템 및 방법 - Google Patents

무선전력전송 시스템용 형상 설계 시스템 및 방법 Download PDF

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Publication number
WO2020175912A1
WO2020175912A1 PCT/KR2020/002747 KR2020002747W WO2020175912A1 WO 2020175912 A1 WO2020175912 A1 WO 2020175912A1 KR 2020002747 W KR2020002747 W KR 2020002747W WO 2020175912 A1 WO2020175912 A1 WO 2020175912A1
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WIPO (PCT)
Prior art keywords
learning
shape
wireless power
power transmission
shape information
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PCT/KR2020/002747
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English (en)
French (fr)
Inventor
김윤수
최병국
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광주과학기술원
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Application filed by 광주과학기술원 filed Critical 광주과학기술원
Priority to US17/419,490 priority Critical patent/US11677277B2/en
Publication of WO2020175912A1 publication Critical patent/WO2020175912A1/ko

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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/80—Circuit arrangements or systems for wireless supply or distribution of electric power involving the exchange of data, concerning supply or distribution of electric power, between transmitting devices and receiving devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/36—Circuit design at the analogue level
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/39—Circuit design at the physical level
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/10—Circuit arrangements or systems for wireless supply or distribution of electric power using inductive coupling

Definitions

  • This invention relates to shape design technology for wireless power transmission systems, and in detail, a system and method for designing cores and coils optimized for wireless power transmission systems using machine learning.
  • a wireless power transmission device transmits power to a power receiving device using, for example, an electromagnetic induction method, and the power receiving device receives and receives power transmitted wirelessly from the wireless power transmission device.
  • the battery is charged using one electric power.
  • Wireless power transmission device delivers power stably and with high efficiency wirelessly.
  • the wireless power transmission device and the power receiving device each include a core assembly
  • the assembly consists of a core and a coil.
  • the ferrite core is used to increase the collection rate of the magnetic field and change the distribution of the magnetic field.
  • the coupling coefficient (or coupling coefficient) between the coil of the power transmission device and the coil of the power receiving device is set as high as possible.
  • Patent Document 1 Korean Patent Publication No. 10-2013-0051659 (Publication date: 2013. 05.
  • Patent Document 2 Korean Patent Publication No. 10-2016-0043678 (Publication date: 2016. 04. 2020/175912 1»(:1 ⁇ 1 ⁇ 2020/002747
  • the present invention was created to solve the problems of the conventional technology, wireless power transmission
  • It aims to provide a shape design system and method for a wireless power transmission system that can design an optimal core shape or coil shape through learning in order to maximize efficiency and power transfer.
  • the shape design system for a wireless power transmission system learns based on the shape information and compensation information input in relation to the design object, A learning module for generating information; And an analysis module providing a performance evaluation result to the learning module after evaluating the wireless power transmission performance based on the shape information from the learning module.
  • the shape information is characterized in that it consists of a matrix structure composed of a number of components used to define the shape of the design object.
  • the learning module may be implemented to change the shape of the design object by changing the value of the component in the shape information through a learning process.
  • Each of the components in the shape information initially has an initial value, and after learning by compensation has been performed, it is characterized in that it has an initial value or a set value according to the result of learning.
  • This learning module can be implemented to perform learning by receiving initial shape information at the beginning of learning, and then performing learning based on new shape information generated as a learning result and performance evaluation results from the analysis module. .
  • the analysis module can be implemented to perform an analysis on a performance impact variable when evaluating wireless power transmission performance, and to evaluate the wireless power transmission performance based on the analysis result.
  • the performance influencing variable may include at least one selected from among magnetic flux density, transmission coil inductance, reception coil inductance, mutual inductance between transceivers, coupling coefficient, transmission coil resistance, reception coil resistance, reception power, and system efficiency. .
  • the learning module may be implemented to continuously perform learning based on shape information and performance evaluation results until a predetermined learning end condition is satisfied.
  • the learning module counts the number of times the learning is performed after the learning is performed, and when the number of learning runs reaches the maximum number of learning executions, the learning end condition is satisfied.
  • the learning module compares the previous performance evaluation results with the current performance evaluation results. 2020/175912 1»(:1 ⁇ 1 ⁇ 2020/002747 If the difference in results is within the set range, it can be implemented to judge that the above learning end condition is satisfied.
  • the components within the shape information are imaged and displayed as blocks on the output module, and the size of the block is adjustable, and in 2D or 3D.
  • a shape design method for a wireless power transmission system includes the step of inputting initial shape information related to a design object; performing learning based on the initial shape information to generate learning shape information Step; After evaluating the wireless power transmission performance through the analysis of the learning shape information, generating a performance evaluation result; And generating new learning shape information by performing learning based on the performance evaluation result and the learning shape information.
  • the initial shape information has an initial value, and may be a matrix structure composed of a number of components used to define the shape of the design object.
  • the analysis of the learning shape information is characterized in that it is performed on a performance influencing variable that affects the performance of wireless power transmission.
  • the performance influencing variable may be at least one selected from among magnetic flux density, transmission coil inductance, reception coil inductance, mutual inductance between transmitting and receiving units, coupling coefficient, transmission coil resistance, reception coil resistance, reception power, and system efficiency.
  • the generating step is characterized in that it is continuously performed until a predetermined end condition is satisfied.
  • the above termination condition is characterized in that the number of times of learning execution counted according to the learning performance reaches the maximum number of learning executions.
  • the above termination condition is characterized in that the difference between the previous performance evaluation result and the current performance evaluation result is within the set range.
  • It may further include the step of imaging and displaying in 2D or 3D.
  • 2020/175912 1 (:1 ⁇ 1 ⁇ 2020/002747, which can maximize the efficiency of wireless power transmission and the amount of power transmitted by the wireless power transmission system.
  • FIG. 1 is a view showing an example configuration of a shape design system for a wireless power transmission system according to a preferred embodiment of the present invention.
  • FIG. 2 is a flow chart for explaining a shape design method for a wireless power transmission system according to a preferred embodiment of the present invention.
  • 3 to 5 are diagrams for comparing the performance of a core shape designed using a shape design technology according to a preferred embodiment of the present invention and a core shape designed by an expert.
  • 6 and 7 are diagrams showing an example of a case of designing a core using a shape design technology according to a preferred embodiment of the present invention.
  • FIGS. 8 and 9 are diagrams showing another example of a case of designing a core using a shape design technique according to a preferred embodiment of the present invention.
  • Figures and 11 are diagrams showing an example of a case of designing a coil using a shape design technique according to a preferred embodiment of the present invention.
  • Fig. 12 shows that the coil is designed as shown in Fig.
  • Fig. 13 shows that the coil is designed as shown in Fig.
  • a function or operation specified in a specific block may occur differently from the order specified in the flowchart. For example, two consecutive blocks may actually be executed substantially simultaneously and may be performed substantially simultaneously. , Depending on the function or operation involved, the blocks may be performed in reverse.
  • the target (core or coil) is composed of a plurality of blocks
  • the information first input for learning defines how many blocks the design target is composed of, and the data in the information changes during the learning process.
  • the information input for the first time for learning is referred to as'initial shape information'
  • the information input in the learning process is referred to as'learning shape information'
  • the shape information is called design 2020/175912 1» (:1 ⁇ 1 ⁇ 2020/002747) This refers to the shape of the object (core width is coil). Therefore, the shape of the design object can be visually confirmed by imaging the shape information.
  • the initial shape information When used for design, the initial shape information may be named “initial core shape information”, and when used for designing a coil shape, the initial shape information may be called “initial coil shape information”.
  • learning the shape information means changing the components of the preset number of 8) components in the shape information.
  • the present invention provides a wireless power transmission function through a learning process of changing shape information.
  • the shape information is a matrix structure composed of multiple components.
  • the component in the initial shape information is set to an initial value (for example, 0). Therefore, the initial shape information may be a matrix in which all components are set as initial values.
  • Each component of the matrix indicates whether or not it is being used for the shape design of the design object, and the initial value indicates that the corresponding component is not used for the shape design of the design object.
  • a shape design system implemented to use a 4x4 matrix for the shape design of the design object can receive initial shape information for a design object such as Galaxy 0000.
  • the 4x4 matrix means that the design object is a combination of 16 blocks, and all the components in the matrix are set to an initial value ('0').
  • the value of each component of the matrix can be changed through the learning process.
  • the value of the component whose state has changed as it is used in shape design (for example, 1) is called a'set value'.
  • the meaning of the initial value ('0') is that the block is set to'vacuum', and it is a block that is not used for the formation of the design object and does not affect the wireless power transmission performance.
  • the meaning of the set value (1') in the matrix means that the block is set as the'core', which is a block that is used to form a design object and affects the wireless power transmission performance.
  • Components are individual components for designing the shape of the design object. 2020/175912 1»(:1 ⁇ 1 ⁇ 2020/002747 Refers to a block, and if the components in the matrix are imaged, the size of the block can be adjusted.
  • a block can be set to be 10111 wide, 10111 long, and 1 high ( high 1 regular hexagon, but the shape and size of the block can be varied.)
  • a block when designing a design object, a block can be imaged not only in 2D but also in 3D, so 2D modeling and 3D modeling are possible.
  • a 4x4 matrix with all components set to the initial value ('0') as above is input, and through the learning process, (1, 2) components, (1, 4) components, (2, 2) Components, (2, 4) components, (3, 1) components, (3, 3) components, (4, 2) components, and (4, 3) components are used in the shape design of the design object Learning shape information of the same 4x4 matrix is generated.
  • the learning shape information of the 4x4 matrix can be changed again in the next learning process, and through this learning process, the core shape and coil shape can be designed to optimize the wireless power transmission function.
  • a two-dimensional matrix is input as shape information for a design object, but a three-dimensional matrix may be input as shape information.
  • 1 is a diagram showing an example configuration of a shape design system for a wireless power transmission system according to a preferred embodiment of the present invention.
  • the system (100, hereinafter'system') performs learning based on the input shape information, evaluates the wireless power transmission performance based on the shape information generated as a result of the execution, and reflects the performance evaluation result to reflect the shape information after learning. It is implemented to design the optimal shape for the design object through the process of learning again.
  • the system 100 can design an optimal shape for various configurations equipped in a wireless power transmission system, for example, it can be used for designing to design an optimum shape for a core and a coil.
  • the above system (100) reflects the result of the performance evaluation and returns the shape information after learning. 2020/175912 1»(:1 ⁇ 1 ⁇ 2020/002747 When learning, the shape information is changed in a way that makes the performance evaluation result better.
  • Supervised learning semi-supervised learning, unsupervised learning, and reinforcement learning can be used.
  • the system 100 learns the shape of a design object through reinforcement learning, and for example, a Q-learning algorithm can be used.
  • the system 100 When evaluating the wireless power transmission performance, the system 100 affects the wireless power transmission performance.
  • the performance influencing variables include magnetic flux density, transmission coil inductance, and reception coil.
  • the wireless power transmission performance can be changed according to changes such as magnetic flux density, transmission coil inductance, reception coil inductance, mutual inductance between transceivers, coupling coefficient, transmission coil resistance, reception coil resistance, reception power, system efficiency, etc.
  • the efficiency of wireless power transmission can be increased, and the design of the core and coil that can increase the amount of transmitted power is possible.
  • Simulation programs can be used.
  • programs such as ANSYS Maxwell, PSIM, and SNSYS Simplorer can be used.
  • a wireless power transmission system includes a wireless power transmission device that transmits wireless power.
  • It includes a power receiving device for receiving power wirelessly transmitted from the wireless power transmission device, and the wireless power transmission device and the power receiving device each include a core and a coil.
  • the system 100 includes the shape and shape of the core and coil of the wireless power transmission device.
  • the core shape (or coil shape) of the wireless power transmission device and the core shape (or coil shape) of the power receiving device may be designed identically, but may be designed differently.
  • the system 100 is matched with one receiving core to transmit wireless power.
  • an optimized transmit core In addition to being able to design an optimized transmit core, it is also possible to design an optimized transmit core to transmit wireless power by matching multiple receive cores.
  • the system 100 may be composed of a learning module 110, an analysis module 120, and an output module 130, but the configuration of the system 100 shown in FIG. for 2020/175912 1»(:1 ⁇ 1 ⁇ 2020/002747 As an example, the system 100 can be changed in various designs.
  • the system 100 is equipped with a wireless power transmission system.
  • the learning module 110 may include an engine that performs learning for core shape design in the present invention, and performs learning based on input core shape information, and after learning (or new) core You can create shape information.
  • the learning module 110 may reflect information from the analysis module 120 to perform learning on the core shape information.
  • the values of the components in the core shape information may be changed.
  • the initial value ('0') is changed to the set value (1'), and the setting The value (1') can be changed to the default value ('0').
  • Ingredients that do not change may also be present.
  • the learning module (0) receives initial core shape information at the beginning of learning, performs learning based on the inputted initial core shape information, and generates learning core shape information as a result of the learning. .
  • the generated learning core shape information is provided to the analysis module 120, and the analysis module 120 evaluates the wireless power transmission performance based on the provided learning core shape information, and then returns the performance evaluation result to the learning module (0 ).
  • the learning module 110 reflects the performance evaluation result from the analysis module 120 to perform learning on the previous learning core shape information to generate new learning core shape information, and thus newly created learning
  • the core shape information is again provided to the analysis module 120.
  • the learning module (0) reflects the performance evaluation result and generates the core shape information in a direction to make the performance evaluation result better when the previous core shape information is relearned.
  • the learning module 110 can perform learning on the core shape information using, for example, one of the reinforcement learning, 0-163111 ⁇ algorithm, Core shape information can be created according to the policy.
  • the learning module (0) performs an operation to determine whether a condition for ending the learning operation is satisfied after performing the learning operation.
  • the learning module 110 performs the learning after performing the number of times
  • Counting is performed, and the learning operation can be terminated when the number of learning executions reaches the preset maximum number of learning executions.
  • the learning module (0) is the previous performance evaluation result and the current performance evaluation
  • the analysis module 120 may include an engine for evaluating the wireless power transmission performance based on the core shape information in the present invention, and the wireless power transmission performance based on the learning core shape information provided from the learning module 110 After evaluation, the performance evaluation result is provided to the learning module (0).
  • the analysis module 120 analyzes variables that affect the performance of wireless power transmission ('performance-affecting variables'), and based on the analysis results, the wireless power transmission performance is evaluated. Can be evaluated.
  • the analysis module 120 is a magnetic flux density, transmission coil inductance, receiving coil
  • Inductance Inductance, mutual inductance between the transmitting and receiving parts, coupling coefficient, transmission coil resistance, reception coil resistance, reception power, system efficiency, etc.can be analyzed.In addition to the mentioned parameters, if parameters that can affect the performance of wireless power transmission are the subject of analysis. It can be.
  • the analysis module 120 may apply the core shape information to a preset program to perform analysis on performance influence variables and wireless power transmission performance evaluation.
  • the conditions applied during analysis can be set in various ways, for example the core
  • the constituting block can be set to be a regular hexagon with a width of 10111, a height of 10111, and a height of 11, but the shape and size of the blocks constituting the core can be changed in various ways.
  • the sending core and the receiving core can be set to have the same shape, the separation distance between the sending core and the receiving core, 10011) can be set, and ferrite or amorphous force can be set as the material of the core. In addition, various conditions can be set.
  • the output module 130 displays information from the outside according to a preset display method.
  • the output module 130 may receive and output the core shape information from the learning module 110.
  • the output module 130 may output the core shape information in the form of a one-dimensional or two-dimensional matrix, and according to an embodiment, it can be summarized in a two-dimensional or three-dimensional block arrangement.
  • the output module 130 may output an analysis process, analysis result, wireless power transmission performance evaluation result, and the like from the analysis module 120.
  • FIG. 2 is a flow chart for explaining a shape design method for a wireless power transmission system according to a preferred embodiment of the present invention.
  • step-by-step operation shown in FIG. 2 can be performed by the system 100 of FIG.
  • initial shape information is input to the learning module (0) at the beginning of the operation 200).
  • step 3200 'initial core shape information' is input when the shape is designed for the core, and'initial coil shape information' is input when the shape is designed for the coil.
  • the initial shape information consists of a matrix structure composed of multiple components, and the components in the initial shape information are set to an initial value (for example, 0).
  • the initial shape information input in step 3200 is all components
  • It can be a matrix set as an initial value.
  • the learning module 110 operates according to a preset learning algorithm, a classification algorithm, etc., and generates learning shape information by performing learning based on the initial shape information 210).
  • step 3210 'learning core shape information' is input when the shape is designed for the core, and'learning coil shape information' is generated when the shape is designed for the coil.
  • step 32 above as the shape design through learning is performed, the value of the inner component of the initial shape information is changed, and the value of the arbitrary component is changed from the initial value ('0') to the set value (1').
  • the learning shape information generated in accordance with step 3210 is provided to the analysis module 120, and may be input to the output module 130 and displayed.
  • step 8220 the analysis module 120 is the learning shape provided according to step 3210
  • the wireless power transmission performance is evaluated and the performance evaluation result is generated.
  • step 3220 the performance impact that affects the performance of wireless power transmission
  • the performance influencing variables may be magnetic flux density, transmission coil inductance, reception coil inductance, mutual inductance between transmitting and receiving units, coupling coefficient, transmission coil resistance, reception coil resistance, reception power, and system efficiency.
  • the performance evaluation result generated in accordance with the above step 3220 is provided to the learning module (0), and the performance evaluation result is (1 ⁇ 01(1) as compensation (1 ⁇ 01(1) in the 3_16 table 111 3 ⁇ 4 algorithm). It is provided as.
  • the performance evaluation result generated according to the step 3220 is sent to the output module 130 2020/175912 1 » (:1 ⁇ 1 ⁇ 2020/002747 Can be entered and displayed.
  • the learning module 110 reflects the wireless power transmission performance evaluation result provided in step 3220 to perform learning on the previous learning shape information to generate new learning shape information 230).
  • step 3230 according to the learning algorithm, the previous learning shape information is
  • New learning shape information is generated so that a higher performance evaluation result than the generated performance evaluation result is generated.
  • the component in the new learning shape information may have a different value from the value of the component in the previous learning shape information, and the value of the arbitrary component is at the initial value ('0').
  • the new learning shape information generated in step 3230 is again provided to the analysis module 120, and such learning and analysis processes 220 and 3230 are continuously performed until a preset termination condition is satisfied.
  • the shape design of the present invention is that the design object is composed of a combination of multiple blocks.
  • the termination condition can be determined based on the judgment as to whether or not the total number of possible combinations has been achieved.
  • the learning shape that corresponds to the learning shape information that produces the highest performance evaluation result among the performance evaluation results up to the end point is the optimal shape. Is selected, and a design object can be designed based on this.
  • the total number of possible combinations of blocks used to form the design object can be set as the maximum number of times to perform learning.
  • the maximum number of times to perform learning can be set, and the maximum number of times to perform learning is the number of times to satisfy the set probability, 90% or more, which is the probability of designing the optimal shape for the design object.
  • the termination condition for learning is set based on the number of
  • the end-of-learning condition is the result of the previous performance evaluation
  • the comparison may also be established based on a judgment result as to whether the difference between the two results is within a set range.
  • the setting range can be set through data accumulated through repeated experiments, and the probability of designing the optimal shape for the design object is set.
  • 2020/175912 1» (:1 ⁇ 1 (2020/002747 probability, more than 90%, etc.) is a value determined to be satisfied.
  • the learning module 110 determines whether the learning end condition is satisfied, and 240), if it is determined that the end condition is satisfied, 240-yes), learning
  • Module 110 ends the learning operation.
  • step 3240 As a result of the judgment in step 3240, the termination condition was not satisfied.
  • the learning module 110 If determined, 240-No), the learning module 110 provides the new learning shape information to the analysis module 120, and step 822071- is performed.
  • step 8240 the learning module (0) judges whether the number of learning executions has reached the maximum number of learning executions, and if it is reached, it is determined that the termination condition is satisfied.
  • 3 to 5 are diagrams for comparing the performance of a core shape designed using a shape design technology according to a preferred embodiment of the present invention and a core shape designed by an expert.
  • the core shape shown in FIG. 3 is a shape designed by an expert, and the coupling coefficient (3 ⁇ 4 is 0.0139) measured in a state where the transmitting core 310 and the receiving core 320 are separated by 100 11 .
  • Designed first and second forms which are the core form designed when 100 times of learning is performed.
  • the coupling coefficient ( ⁇ is 0.0150) measured with the core 410 and the receiving core 420 separated by 100 11 .
  • 6 and 7 are diagrams showing an example of a case of designing a core using a shape design technique according to a preferred embodiment of the present invention.
  • Figure 6 is a design for a transmission core (0) matching for one receiving core 620 2020/175912 1» (:1 ⁇ 1 ⁇ 2020/002747) is an example of the case, and Fig. 7 shows an example of designing a transmission core (unit 0) that matches two receiving cores (720, 730). I did it.
  • FIGS. 8 and 9 are diagrams showing another example of a case of designing a core using a shape design technique according to a preferred embodiment of the present invention.
  • the receiving cores 820 and 920 shown in Figs. 8 and 9 have the same size, whereas the size of the block 811 used in the design of the transmitting core 0 in Fig. 8 and Fig. 9 The sizes of the blocks 911 used in the design of the transmit core 910 are different.
  • Figures and 11 are views showing an example of a case of designing a coil using a shape design technique according to a preferred embodiment of the present invention.
  • FIG. 13 is a graph showing the magnetic flux density distribution measured in the region
  • FIG. 13 is a graph showing the magnetic flux density distribution measured in a predetermined spaced region while the coil is designed as shown in FIG. 11.
  • the initial value ('0') is the position corresponding to the component in the shape information.
  • the set value (1') means that the coil at the position corresponding to the component in the shape information is applied.
  • the shape (or number of winding turns) of the coil 1000 in FIG. 10 is the shape information [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] is a case where a 1x20 matrix is input, and the shape of the coil (1100) of Fig. 11 is the shape information, [1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1].
  • the magnetic flux density distribution in the show region measured while being wound around the core 1010 (shown in FIG. 12) and 8 measured while the coil 1100 is wound around the core 1110 as shown in FIG. It can be seen that the magnetic flux density distribution in the region (Fig. 13) is different.
  • Such a computer program can be implemented as a computer program that is stored in a computer-readable medium such as a USB memory, CD disk, flash memory, etc., and read and executed by a computer, Embodiments can be implemented.
  • the recording medium of the computer program may include a magnetic recording medium, an optical recording medium, a carrier wave medium, and the like.

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Abstract

본 발명은 무선전력전송 시스템용 형상 설계 기술에 관한 것으로, 본 발명의 형상 설계 시스템은 설계 대상과 관련하여 입력되는 형상 정보 및 보상 정보를 기초로 학습을 수행하여 새로운 형상 정보를 생성하는 학습 모듈; 및 상기 학습 모듈로부터의 형상 정보에 기초하여 무선전력전송 성능을 평가한 후 성능 평가 결과를 상기 학습 모듈로 제공하는 분석 모듈을 포함한다.

Description

2020/175912 1»（：1/10公020/002747 명세서
발명의명칭:무선전력전송시스템용형상설계시스템및방법 기술분야
[1] 본발명은무선전력전송시스템용형상설계기술에관한것으로,상세하게는 기계학습을이용하여무선전력전송시스템에최적화된코어및코일을 설계하는시스템및방법에관한것이다.
배경기술
[2] 최근에는단자연결방식의충전기술의여러가지문제점을해결하기위하여 , 수전장치에무선으로전력을전송하는무선전력전송기술이제시되고있다.
[3] 무선전력전송기술에 있어무선전력전송장치는예를들면,전자기유도방식을 이용하여수전장치로전력을전송하고,수전장치는무선전력전송장치로부터 무선으로전송되는전력을수신하고,수신한전력을이용하여배터리를 충전한다.
[4] 무선전력전송장치는무선으로안정적으로그리고높은효율로전력을
전송하고,수전장치는무선전력전송장치가전송하는전력을최대로수신하여 배터리를충전시킬수있도록하는기술에많은연구가이루어지고있다.
[5] 무선전력전송장치및수전장치는각각코어어셈블리를포함하고,코어
어셈블리는코어와코일로이루어진다.
[6] 자기장을이용한유도결합방식의무선전력전송기술에서페라이트코어는 자기장의집속도를높이고자기장의분포를변화시키는데에이용된다.
[7] 무선전력전송의효율또는전달전력량을높이기위해서는무선
전력전송장치의코일과수전장치의코일사이의결합계수（혹은커플링계수）가 최대한높게설정되는것이바람직하다.
[8] 일례로페라이트코어의형상을통해최적의결합계수가설정되도록하는 방법이 있는데,종래에는시스템의제한조건에따라설계자가직관으로 페라이트코어의형상을설계한후시뮬레이션을통해몇가지변수를스윕하는 방식으로페라이트코어의형상을설계하였다.
[9] 따라서,종래에는설계자의직관에기초하여페라이트코어에대한설계가 이루어져페라이트코어의최적의형상을설계하는데에많은어려움이 따랐으며，이러한방식으로설계된페라이트코어의형상이최적의결합계수를 만들어내는것인지에대한확신이어려웠다.
[1이 [선행기술문헌]
[11] [특허문헌]
[12] （특허문헌 1）대한민국공개특허공보제 10-2013-0051659호（공개일: 2013. 05.
21）
[13] （특허문헌 2）대한민국공개특허공보제 10-2016-0043678호（공개일: 2016. 04. 2020/175912 1»（：1^1{2020/002747
22)
발명의상세한설명
기술적과제
[14] 본발명은종래기술의문제점을해결하고자창안된것으로,무선전력전송
효율및전력량전달을최대화할수있도록하기위해 학습을통해최적의코어 형상혹은코일형상을설계할수있는무선전력전송시스템용형상설계시스템 및방법을제공하는것을목적으로한다.
[15] 본발명의 기술적과제는이상에서 언급한사항에제한되지 않으며 ,이하에서 설명할내용으로부터본발명이속하는기술분야의통상의지식을가진자라면 본발명이의도하는기타의과제들또한명료하게 이해할수있을것이다.
과제해결수단
[16] 상기와같은과제를해결하기 위한본발명의 일실시 예에 따른무선전력전송 시스템용형상설계시스템은,설계대상과관련하여 입력되는형상정보및 보상정보를기초로학습을수행하여새로운형상정보를생성하는학습모듈; 및상기 학습모듈로부터의 형상정보에기초하여무선전력전송성능을평가한 후성능평가결과를상기 학습모듈로제공하는분석모듈을포함한다.
[17] 상기 형상정보는상기 설계대상의 형상을규정하는데에 이용되는다수의 성분으로구성되는행렬구조로이루어지는것을특징으로한다.
[18] 상기 학습모듈은학습과정으로통해상기 형상정보내성분의값을변경하여 상기 설계대상의 형상을변경하도록구현될수있다.
[19] 상기 형상정보내성분은초기에는모두초기값을갖고,보상에의한학습이 이루어진이후에는학습수행결과에 따라초기값혹은설정값을갖는것을 특징으로한다.
[2이 상기 학습모듈은학습초기에초기 형상정보를입력받아학습을수행하고, 이후에는학습결과로서 생성된새로운형상정보와상기분석모듈로부터의 성능평가결과에 기초하여 학습을수행하도록구현될수있다.
[21] 상기분석모듈은무선전력전송성능평가시에,성능영향변수에 대한분석을 수행하고,분석 결과를토대로무선전력전송성능을평가하도록구현될수있다.
[22] 상기성능영향변수는자속밀도,송신코일인덕턴스,수신코일인덕턴스, 송수신부간상호인덕턴스,결합계수,송신코일저항,수신코일저항,수신전력 및시스템효율중에서선택되는적어도하나이상을포함할수있다.
[23] 상기 학습모듈은기 설정된학습종료조건이 만족되기까지 형상정보와성능 평가결과를바탕으로지속적으로학습을수행하도록구현될수있다.
[24] 상기 학습모듈은학습수행후학습수행횟수를카운팅하고,학습수행횟수가 학습수행최대횟수에도달하면상기 학습종료조건이 만족된것으로
판단하도록구현될수있다.
[25] 상기 학습모듈은이전성능평가결과와현재성능평가결과를비교하고,두 2020/175912 1»（：1^1{2020/002747 결과의차이가설정 범위내이면상기 학습종료조건이 만족된것으로 판단하도록구현될수있다.
[26] 상기 형상정보내성분은출력모듈상에서블록으로이미지화되어표시되고, 상기블록의크기는조절가능하며 , 2차원상에서혹은 3차원상에서
이미지화되어표시되는것을특징으로한다.
[27]
[28] 본발명의 일실시 예에따른무선전력전송시스템용형상설계방법은,설계 대상과관련한초기 형상정보가입력되는단계 ;상기초기 형상정보를기초로 학습을수행하여 학습형상정보를생성하는단계 ;상기 학습형상정보에 대한 분석을통해무선전력전송성능을평가한후성능평가결과를생성하는단계; 및상기성능평가결과및상기 학습형상정보를기초로학습을수행하여 새로운학습형상정보를생성하는단계를포함한다.
[29] 상기초기 형상정보는초기값을가지며 ,상기 설계대상의 형상을규정하는 데에 이용되는다수의성분으로구성되는행렬구조일수있다.
[3이 상기초기 형상정보내성분이 학습수행이 이루어짐에따라변경되어상기 학습형상정보가생성되는것을특징으로한다.
[31] 상기 학습형상정보에 대한분석은무선전력전송의성능에 영향을미치는 성능영향변수에 대해 이루어지는것을특징으로한다.
[32] 상기성능영향변수는,자속밀도,송신코일인덕턴스,수신코일인덕턴스, 송수신부간상호인덕턴스,결합계수,송신코일저항,수신코일저항,수신전력 및시스템효율중에서선택되는적어도하나이상일수있다.
[33] 상기성능평가결과를생성하는단계 및상기새로운학습형상정보를
생성하는단계는,기설정된종료조건이 만족되기까지지속적으로이루어지는 것을특징으로한다.
[34] 상기종료조건은학습수행에따라카운팅되는학습수행횟수가학습수행 최대횟수에도달한경우인것을특징으로한다.
[35] 상기종료조건은이전성능평가결과와현재성능평가결과의차이가설정 범위 내인경우인것을특징으로한다.
[36] 상기방법은,상기 형상정보내성분을기설정된크기의블록으로
이미지화하여 2차원혹은 3차원상에서표시하는단계를더포함할수있다. 발명의효과
[37] 본발명의 일실시 예에따른무선전력전송시스템용형상설계기술을
이용하면,학습을통해설계되는코어의 형상혹은코일의 형상에기초하여 무선전력전송성능을평가할수있다.
[38] 그리고,무선전력전송성능평가결과를학습에 이용하여 더좋은성능을
기대할수있는코어의 형상혹은코일의 형상을설계할수있으며,이러한 학습과성능평가에 기초하여최적의코어 형상혹은코일형상을설계할 2020/175912 1»（：1^1{2020/002747 있으며,이에 따라무선전력전송시스템의무선전력전송의 효율및전달 전력량을최대화할수있다.
도면의간단한설명
[39] 이하에 첨부되는도면들은본실시 예에 관한이해를돕기위한것으로,상세한 설명과함께실시 예들을제공한다.다만,본실시예의기술적특징이특정 도면에 한정되는것은아니며,각도면에서 개시하는특징들은서로조합되어 새로운실시 예로구성될수있다.
[4이 도 1은본발명의 바람직한실시 예에따른무선전력전송시스템용형상설계 시스템의 일례의구성을도시한도면이다.
[41] 도 2는본발명의 바람직한실시 예에따른무선전력전송시스템용형상설계 방법을설명하기위한순서도이다.
[42] 도 3내지 5는본발명의 바람직한실시 예에따른형상설계기술을이용하여 설계된코어 형상과전문가에의해설계된코어 형상에 대한성능을비교하기 위한도면들이다.
[43] 도 6및 7은본발명의 바람직한실시 예에따른형상설계기술을이용하여 코어를설계하는경우의 일례를도시한도면들이다.
[44] 도 8및 9는본발명의 바람직한실시 예에따른형상설계기술을이용하여 코어를설계하는경우의다른예를도시한도면들이다.
[45] 도 및 11은본발명의 바람직한실시 예에따른형상설계기술을이용하여 코일을설계하는경우의 일례를도시한도면이다.
[46] 도 12는코일이도 10에도시된바와같이 설계된상태에서소정 이격된
영역에서측정된자속밀도분포를도시한그래프이다.
[47] 도 13은코일이도 11에도시된바와같이 설계된상태에서소정 이격된
영역에서측정된자속밀도분포를도시한그래프이다.
발명의실시를위한형태
[48] 본문에 개시되어 있는본발명의실시 예들에 대해서,특정한구조적내지 기능적 설명들은단지본발명의실시 예를설명하기위한목적으로예시된 것으로,본발명의실시 예들은다양한형태로실시될수있으며본문에설명된 실시 예들에 한정되는것으로해석되어서는안된다.
[49] 본발명은다양한변경을가할수있고여러 가지 형태를가질수있는바,특정 실시 예들을도면에 예시하고본문에상세하게설명하고자한다.그러나,이는 본발명을특정한개시 형태에 대해한정하려는것이아니며,본발명의사상및 기술범위에포함되는모든변경,균등물내지 대체물을포함하는것으로 이해되어야한다.
[5이 제 1,제 2등의용어는다양한구성요소들을설명하는데사용될수있지만,상기 구성요소들은상기용어들에 의해한정되어서는안된다.상기용어들은하나의 구성요소를다른구성요소로부터구별하는목적으로만사용된다.예를들어,본 2020/175912 1»（：1^1{2020/002747 발명의 권리범위로부터 이탈되지 않은채제 1구성요소는제 2구성요소로 명명될수있고,유사하게제 2구성요소도제 1구성요소로명명될수있다.
[51] 어떤구성요소가다른구성요소에“연결되어”있다거나“접속되어”있다고 언급된때에는,그다른구성요소에직접적으로연결되어 있거나또는접속되어 있을수도있지만,중간에다른구성요소가존재할수도있다고이해되어야할 것이다.반면에,어떤구성요소가다른구성요소에“직접 연결되어”있다거나
“직접접속되어”있다고언급된때에는,중간에다른구성요소가존재하지 않는 것으로이해되어야할것이다.구성요소들간의 관계를설명하는다른표현들, 즉“〜사이에”와“바로〜사이에”또는“〜에 이웃하는”과“〜에직접 이웃하는” 등도마찬가지로해석되어야한다.
[52] 본출원에서사용한용어는단지특정한실시 예를설명하기위해사용된
것으로,본발명을한정하려는의도가아니다.단수의표현은문맥상명백하게 다르게뜻하지 않는한,복수의표현을포함한다.본출원에서,“포함하다”또는 “가지다”등의용어는개시된특징,숫자,단계,동작,구성요소,부분품또는 이들을조합한것이존재함을지정하려는것이지 ,하나또는그이상의다른 특징들이나숫자,단계,동작,구성요소,부분품또는이들을조합한것들의존재 또는부가가능성을미리 배제하지 않는것으로이해되어야한다.
[53] 다르게정의되지 않는한,기술적이거나과학적인용어를포함해서 여기서 사용되는모든용어들은본발명이속하는기술분야에서통상의지식을가진 자에 의해 일반적으로이해되는것과동일한의미를가지고있다.일반적으로 사용되는사전에정의되어 있는것과같은용어들은관련기술의문맥상가지는 의미와일치하는의미를가지는것으로해석되어야하며,본출원에서 명백하게 정의하지 않는한,이상적이거나과도하게형식적인의미로해석되지 않는다.
[54] 한편,어떤실시 예가달리구현가능한경우에특정블록내에 명기된기능 또는동작이순서도에 명기된순서와다르게 일어날수도있다.예를들어, 연속하는두블록이실제로는실질적으로동시에수행될수도있고,관련된기능 또는동작에따라서는상기블록들이 거꾸로수행될수도있다.
[55]
[56] 이하,본발명의 바람직한실시 예에따른무선전력전송시스템용형상설계 시스템및이를이용한무선전력전송시스템용형상설계방법에 대하여 첨부된 도면을참조하여상세하게설명한다.
[57]
[58] 본발명에서 제안되는무선전력전송시스템용형상설계기술은설계
대상（코어혹은코일）이복수의블록으로조합되어구성되는것을전제로하며, 학습을위해최초로입력되는정보는설계대상이 몇개의블록의조합으로 이루어지는지를정의하며,정보내 데이터는학습과정에서 변하게된다.
[59] 이하에서는학습을위해최초로입력되는정보를’초기 형상정보’라하고,학습 과정에서 입력되는정보를’학습형상정보’라하며 ,형상정보는곧,설계 2020/175912 1»（：1^1{2020/002747 대상（코어폭은코일）의 형상을의미한다.따라서,형상정보를이미지화하는 것으로부터 설계대상의 형상을시각적으로확인할수있다.
[6이 본발명의무선전력전송시스템용형상설계기술이코어의 형상에 대한
설계에 이용되는경우에는초기 형상정보가’초기코어 형상정보’라명명되고, 코일의 형상에 대한설계에 이용되는경우에는초기 형상정보가’초기코일형상 정보’라명명될수있다.
[61] 본발명에 있어 형상정보를학습한다는것은형상정보내성분들중기설정된 개수 8개）의성분을변화시킨다는것을의미한다.
[62] 본발명은형상정보를변경하는학습과정을통해무선전력전송기능을
최적화시킬수있는설계대상의 형상을설계하는기술을제공한다.
[63] 실시 예에 있어,형상정보는다수의성분으로구성되는행렬구조로
이루어지고,초기 형상정보내성분은초기값（예를들어, 0）으로설정되어 있다. 따라서,초기 형상정보는모든성분이초기값으로설정된행렬일수있다.
[64] 그리고,행렬의각성분은설계 대상의 형상설계에 이용되고있는지의 여부를 나타내고,초기값은해당성분이 설계대상의 형상설계에 이용되고있지 않음을 나타낸다.
0000
[65] 예를들어,설계 대상의 형상설계를위해 4x4행렬을이용하도록구현된형상 설계시스템은하 0000기와같은설계 대상에 대한초기 형상정보를입력 받을수 있다.
[66] [초기 형상 0000정보의 일례]
[67] 0000
[68] 상기 4x4행렬은설계대상이 16개의블록의조합으로이루어짐을의미하고, 행렬내성분은모두초기값（’ 0’）으로설정되어 있다.
[69] 한편,행렬의각성분의값은학습과정을통해변할수있으며 ,이하에서는 형상설계에 이용됨에 따라상태 변경된성분의 값（예를들어, 1）을’설정값’이라 한다.
[7이 행렬내에서초기값（’ 0’）의의미는블록을’진공’으로설정한다는것으로,설계 대상의 형성에 이용되지 않아무선전력전송성능에 영향을미치지못하는 블록을말한다.
[71] 그리고,행렬내에서 설정값（1’）의의미는블록을’코어’로설정한다는것으로, 설계 대상의 형성에 이용되어무선전력전송성능에 영향을미치는블록을 말한다.이와같이,행렬내각성분은설계 대상의 형상을설계하기위한개개의 2020/175912 1»（：1^1{2020/002747 블록을의미하고,행렬내성분이 이미지화되는경우블록의크기는조절이 가능하다.
예를들어,블록은가로 10111,세로 10111,높이 1(고1의 정육각형인것으로설정될 수있으나,블록의 형상및크기는다양하게변경될수있다.
[73] 또한,설계대상에 대한설계시,블록은 2차원상에서 이미지화될수있을뿐만 아니라 3차원상에서도이미지화될수있기 때문에, 2차원모델링 및 3차원 모델링이 가능하다.
예를들어,초기 형상정보로서상기와같이모든성분이초기값 (’ 0’)으로 설정된 4x4행렬이 입력되고,학습과정을통해 (1, 2)성분, (1, 4)성분, (2, 2) 성분, (2, 4)성분, (3, 1)성분, (3, 3)성분, (4, 2)성분및 (4, 3)성분이설계 대상의 형상설계에 이용된경우하기와같은 4x4행렬의 학습형상정보가생성된다.
[75] ［학습형상정보의 일례］
[76] 0011 0011 즉,초기 형0111상정보로서 입력된행렬내성분중설계대상의 형상의 일부분으로 이용되는성분은설정값 (1’)을가지게된다.
0001
상기의 4x4행렬의 학습형상정보는다음학습과정에서다시 변경될수 있으며，이러한학습과정을통해무선전력전송기능을최적화시킬수있는코어 형상및코일형상을설계할수있다.
스 ] ]
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7281]1
7 7 1 한편,상기에서는설계 대상에 대한형상정보로서 2차원행렬이 입력되는것을 예로들었으나, 3차원행렬이 형상정보로서 입력될수도있다. 도 1은본발명의 바람직한실시 예에따른무선전력전송시스템용형상설계 시스템의 일례의구성을도시한도면이다.
82] 본발명의 바람직한실시 예에따른무선전력전송시스템용형상설계
시스템 (100,이하’시스템’)은입력되는형상정보를기초로학습을수행하고, 수행결과로서 생성되는형상정보에 기초하여무선전력전송성능을평가하고, 성능평가결과를반영하여 학습후의 형상정보를다시 학습하는과정을통해 설계 대상에 대한최적의 형상을설계할수있도록구현된다.
상기시스템 (100)은무선전력전송시스템에구비되는다양한구성들에 대한 최적의 형상을설계할수있는데,예를들어코어 및코일에 대한최적의 형상을 설계할설계하는데에 이용될수있다.
84] 상기시스템 (100)은성능평가결과를반영하여 학습후의 형상정보를다시 2020/175912 1»（：1^1{2020/002747 학습하는경우,성능평가결과가더좋도록하는방향으로형상정보를 변경하게된다.
[85] 상기시스템 (100)은기계학습 (machine learning)을이용하여설계대상의
형상에대한학습을수행하도록구현될수있으며,예를들어,지도
학습 (supervised learning,감독학습),준지도학습 (semi-supervised learning), 비지도학습 (unsupervised learning,자율학습),강화학습 (reinforcement learning) 등을이용할수있다.
[86] 본실시 예에있어서는시스템 (100)은강화학습을통해설계대상의형상에 대한학습을수행하며 ,예를들어 Q-learning알고리즘을이용할수있다.
[87] 무선전력전송성능평가시,시스템 (100)은무선전력전송의성능에영향을
미치는변수 (’성능영향변수’)들에대한분석을수행하며,분석결과를토대로 무선전력전송성능을평가할수있다.그리고,무선전력전송성능에대한평가는 다음학습과정에서보상 (reward)으로서이용된다.
[88] 예를들어,성능영향변수로는,자속밀도,송신코일인덕턴스,수신코일
인덕턴스,송수신부간상호인덕턴스,결합계수,송신코일저항,수신코일저항, 수신전력,시스템효율등이 있을수있다.
[89] 즉,무선전력전송성능은자속밀도,송신코일인덕턴스,수신코일인덕턴스, 송수신부간상호인덕턴스,결합계수,송신코일저항,수신코일저항,수신전력, 시스템효율등변화에따라변경될수있다.
[9이 이와같이무선전력전송에영향을주는성능영향변수에대한종합적인
분석을코어및코일의설계에반영함으로써,무선전력전송의효율을증대시킬 수있고,전달전력량을높일수있는코어및코일의설계가가능하다.
[91] 상기시스템 (100)은무선전력전송의성능을평가하는경우,다양한종류의
시뮬레이션프로그램을이용할수있으며,예를들어 ANSYS Maxwell, PSIM, SNSYS Simplorer등의프로그램을이용할수있다.
[92] 무선전력전송시스템은무선전력을전송하는무선전력전송장치와
무선전력전송장치로부터무선으로전송되는전력을수신하는수전장치를 포함하고,무선전력전송장치와수전장치는각각코어및코일을포함한다.
[93] 상기시스템 (100)은무선전력전송장치의코어및코일에대한형상과
수전장치의코어및코일에대한형상을모두설계할수있으며,
무선전력전송장치의코어형상 (혹은코일형상)과수전장치의코어형상 (혹은 코일형상)은동일하게설계될수있으나,상이하게설계될수도있다.
[94] 상기시스템 (100)은하나의수신코어와매칭되어무선전력을송신하도록
최적화된송신코어를설계할수있을뿐만아니라,다수의수신코어와 매칭되어무선전력을송신하도록최적화된송신코어를설계할수도있다.
[95]
[96] 도 1을참조하면,상기시스템 (100)은학습모듈 (110),분석모듈 (120)및출력 모듈 (130)로구성될수있으나,도 1에도시된시스템 (100)의구성은설명을위한 2020/175912 1»（：1^1{2020/002747 일례로서시스템 (100)은다양하게설계변경가능하다.
[97] 앞서설명한바와같이,시스템 (100)은무선전력전송시스템에구비되는
다양한구성들에대한최적의형상을설계할수있는데,설명의편의를위해, 이하에서는코어에대한최적의형상을설계하는것을가정하여설명한다.
[98] 상기학습모듈 (110)은본발명에서의코어형상설계를위한학습을수행하는 엔진을포함할수있으며,입력되는코어형상정보를기초로학습을수행하고, 수행결과로서학습후 (혹은새로운)코어형상정보를생성할수있다.
[99] 또한,상기학습모듈 (110)은분석모듈 (120)로부터의정보를반영하여코어 형상정보에대한학습을수행할수있다.
[100] 상기학습모듈 ( 0)의학습이수행됨에따라,코어형상정보내성분들의값이 변경될수있으며,예를들어,초기값 (’ 0’)이설정값 (1’)으로변경되고, 설정값 (1’)이초기값 (’ 0’)으로변경될수있다.
[101] 물론,학습모듈 ( 0)의학습이수행되더라도,코어형상정보내성분중
변경되지않는성분도존재할수있다.
[102] 구체적으로,상기학습모듈 ( 0)은학습초기에는,초기코어형상정보를입력 받고,입력받은초기코어형상정보를기초로학습을수행하고,학습수행 결과로서학습코어형상정보를생성한다.
[103] 생성된학습코어형상정보는분석모듈 (120)로제공되고,분석모듈 (120)은 제공받은학습코어형상정보에기초하여무선전력전송성능을평가한후성능 평가결과를학습모듈 ( 0)로제공한다.
[104] 이에따라,상기학습모듈 (110)은분석모듈 (120)로부터의성능평가결과를 반영하여이전의학습코어형상정보에대한학습을수행하여새로운학습코어 형상정보를생성하며，이렇게새로생성된학습코어형상정보는다시분석 모듈 (120)로제공된다.
[105] 상기학습모듈 ( 0)은성능평가결과를반영하여이전의코어형상정보를 다시학습하는경우,성능평가결과가더좋도록하는방향으로코어형상 정보를생성한다.
[106] 상기학습모듈 (110)은예를들어,강화학습중하나인 0-163111^알고리즘을 이용하여코어형상정보에대한학습을수행할수있으며,
Figure imgf000011_0001
policy에 따라코어형상정보를생성할수있다.
[107] 또한,상기학습모듈 ( 0)은학습동작을수행한후,학습동작을종료하기 조건이만족되었는지를판단하기위한동작을수행한다.
[108] 예를들어,상기학습모듈 (110)은학습을수행한후학습수행횟수를
카운팅하며,학습수행횟수가기설정된학습수행최대횟수에도달하면학습 동작을종료할수있다.
[109] 다른예로,상기학습모듈 ( 0)은이전성능평가결과와현재성능평가
결과를비교하여두결과의차이가설정범위내이면학습동작을종료할수 있다. 2020/175912 1»（：1^1{2020/002747
[110] 상기분석모듈 (120)은본발명에서의코어형상정보에기초한무선전력전송 성능평가를위한엔진을포함할수있으며,학습모듈 (110)로부터제공되는학습 코어형상정보에기초하여무선전력전송성능을평가한후성능평가결과를 학습모듈 ( 0)로제공한다.
[111] 무선전력전송성능평가시,분석모듈 (120)은무선전력전송의성능에영향을 미치는변수 (’성능영향변수’)들에대한분석을수행하며,분석결과를토대로 무선전력전송성능을평가할수있다.
[112] 예를들어,상기분석모듈 (120)은자속밀도,송신코일인덕턴스,수신코일
인덕턴스,송수신부간상호인덕턴스,결합계수,송신코일저항,수신코일저항, 수신전력,시스템효율등을분석할수있으며,언급된파라미터이외에도, 무선전력전송의성능에영향을미칠수있는파라미터이면분석의대상이될수 있다.
[113] 상기분석모듈 (120)은무선전력전송의성능을평가하는경우,다양한종류의 시뮬레이션프로그램을이용할수있으며,예를들어 ANSYS Maxwell,
Figure imgf000012_0001
1패10 등의프로그램을이용할수있다.
[114] 상기분석모듈 (120)은코어형상정보를기설정된프로그램에적용하여성능 영향변수에대한분석및무선전력전송성능평가를수행할수있다.
[115] 분석시적용되는조건은다양하게설정될수있는데,예를들어코어를
구성하는블록은가로 10111,세로 10111,높이 1 11의정육각형인것으로설정될수 있으나,코어를구성하는블록의형상및크기는다양하게변경될수있다.
[116] 또한,송신용코어와수신용코어가동일한형상인것으로설정될수있고, 송신용코어와수신용코어사이의이격거리 , 10011)가설정될수있으며, 코어의재질로페라이트혹은아몰포스가설정될수있고,이외에도다양한 조건이설정될수있다.
[117] 상기출력모듈 (130)은외부로부터의정보를기설정된표시방법에따라
출력하도록구현된다.
[118] 본실시 예에있어서,출력모듈 (130)은학습모듈 (110)로부터코어형상정보를 제공받아출력할수있다.
[119] 예를들어,상기출력모듈 (130)은코어형상정보를 1차원혹은 2차원의행렬 형태로출력할수있으며,실시예에따라서는 , 2차원혹은 3차원의블록배치로 줄력할수있다.
[120] 본실시 예에있어서,출력모듈 (130)은분석모듈 (120)로부터의분석과정, 분석결과,무선전력전송성능평가결과등을출력할수있다.
[121]
[122] 이상에서는본발명의바람직한실시예에따른무선전력전송시스템용형상 설계시스템의구성및구성별기능에대해살펴보았다.이하에서는본발명의 바람직한실시예에따른무선전력전송시스템용형상설계시스템을이용한 설계대상의형상을설계하는방법에대해서설명한다. 2020/175912 1»（：1^1{2020/002747
[123]
[124] 도 2는본발명의 바람직한실시 예에따른무선전력전송시스템용형상설계 방법을설명하기위한순서도이다.
[125] 도 2에도시된단계별동작은도 1의시스템 (100)에의해수행될수있는
것으로서,동작초기에초기 형상정보가학습모듈 ( 0)로입력된다 200).
[126] 상기단계 3200에서 ,코어에 대한형상설계시에는’초기코어 형상정보’가 입력되고,코일에 대한형상설계시에는’초기코일형상정보’가입력된다.
[127] 상기단계 3200에 있어서초기 형상정보는다수의성분으로구성되는행렬 구조로이루어지고,초기 형상정보내성분은초기값 (예를들어, 0)으로 설정되어 있다.
[128] 따라서 ,상기단계 3200에서 입력되는초기 형상정보는모든성분이
초기값으로설정된행렬일수있다.
[129] 상기단계 3200에서 학습모듈 (110)로입력되는초기 형상정보는출력
모듈 (130)로입력되어표시될수있다.
[13이 상기단계 8200이후,학습모듈 (110)은기 설정된학습알고리즘 ,分노따 明 알고리즘등)에따라동작하여,초기 형상정보를기초로학습을수행하여 학습 형상정보를생성한다 210).
[131] 상기단계 3210에서 ,코어에 대한형상설계시에는’학습코어 형상정보’가 입력되고,코일에 대한형상설계시에는’학습코일형상정보’가생성된다.
[132] 상기단계 32 에서 ,학습을통한형상설계가수행됨에따라초기 형상정보 내성분의값이 변경되며,임의의성분의 값이초기값 (’ 0’)에서설정값 (1’)으로 변경될수있다.
[133] 상기단계 3210에따라생성된학습형상정보는분석모듈 (120)로제공되며, 출력모듈 (130)로입력되어표시될수있다.
[134] 상기단계 8220이후,분석모듈 (120)은단계 3210에따라제공된학습형상
정된시뮬레이션프로그램 , Maxwell,모
Figure imgf000013_0001
용하여분석하여 ,무선전력전송성능을평가한후성능평가 결과를생성한다.
[135] 상기단계 3220에서 ,무선전력전송의성능에 영향을미치는성능영향
변수들에 대한분석이 이루어지고,분석결과를토대로무선전력전송성능이 평가될수있다.
[136] 이때,성능영향변수는,자속밀도,송신코일인덕턴스,수신코일인덕턴스, 송수신부간상호인덕턴스,결합계수,송신코일저항,수신코일저항,수신전력, 시스템효율등일수있다.
[137] 상기단계 3220에따라생성된성능평가결과는학습모듈 ( 0)로제공되고, 성능평가결과는 (3_16표111 ¾알고리즘에 있어서보상 (1 \¥01(1)으로써 학습 모듈 (110)로제공되는것이다.
[138] 또한,상기단계 3220에따라생성된성능평가결과는출력모듈 (130)로 2020/175912 1»（：1^1{2020/002747 입력되어표시될수있다.
[139] 상기단계 8220이후,학습모듈 (110)은단계 3220에따라제공된무선전력전송 성능평가결과를반영하여 이전의 학습형상정보에 대한학습을수행하여 새로운학습형상정보를생성한다 230).
[14이 상기단계 3230에 있어서,학습알고리즘에따라,이전의 학습형상정보에
기초하여 생성된성능평가결과보다높은성능평가결과가생성되도록하기 위한새로운학습형상정보가생성된다.
[141] 이에따라,새로운학습형상정보내성분은이전의 학습형상정보내성분의 값과다른값을가질수있으며,임의의성분의 값이초기값 (’ 0’)에서
설정값 (1’)으로,혹은설정값 (1，)에서초기값 (’ 0’)으로변경될수있다.물론, 새로운학습형상정보내성분중변경되지 않는성분도존재할수있다.
[142] 상기단계 3230에서 생성된새로운학습형상정보는다시분석모듈 (120)로 제공되며,이와같은학습및분석과정 220, 3230)은기 설정된종료조건이 만족되기까지지속적으로이루어진다.
[143] 본발명의 형상설계는설계대상이복수블록의조합으로구성되는것을
전제로하며,이에따라블록의 개수에 따라총조합가능개수가결정될수있기 때문에,종료조건은학습이총조합가능개수만큼이루어졌는지에 대한판단을 기준으로결정될수있다.
[144] 예를들어,학습이총조합가능개수만큼이루어진경우학습과정이종료되고, 종료된시점까지의성능평가결과중가장높은성능평가결과를생성하는 학습형상정보에상응하는학습형상이최적의 형상으로선택되고,이를 기반으로설계대상이 설계될수있다.
[145] 즉,설계대상의 형성에 이용되는블록의총조합가능개수가학습수행최대 횟수로설정될수있다.
[146] 한편,설계대상을이루는블록의총개수와분석의 대상이 되는성능영향
변수의 개수가많으면많을수록,학습및분석과정에소요되는시간은증가할 수밖에 없다.
[147] 이에,학습과정에소요되는시간을줄이기위하여,반복적인실험을통해
누적되는데이터를통해 학습수행최대횟수가설정될수있으며，이 때의 학습 수행최대횟수는설계대상에 대한최적의 형상을설계할수있는확률이기 설정된확률 , 90%이상등)을만족할수있도록하는횟수이다.
[148] 다만,이상에서는학습의종료조건이 학습수행횟수에기반하여 설정되는
것을예로들었으나,이에 한정되는것은아니다.
[149] 예를들어,학습종료조건은이전성능평가결과와현재성능평가결과의
비교하여두결과의차이가설정 범위내인지에 대한판단결과를기반으로 설정될수도있다.
[150] 여기서설정 범위는반복적인실험을통해누적되는데이터를통해설정될수 있는것으로,설계대상에 대한최적의 형상을설계할수있는확률이기 설정된 2020/175912 1»（：1^1{2020/002747 확률 , 90%이상등)을만족할수있도록결정된값이다.
[151] 이에,상기단계 8230이후,학습모듈 (110)은학습종료조건이만족되었는지를 판단하고 240),종료조건이만족된것으로판단되면 240 -예),학습
모듈 (110)은학습동작을종료한다.
[152] 상기단계 3240에서의판단결과,종료조건이만족되지않은것으로
판단되면 240 -아니오),학습모듈 (110)은새로운학습형상정보를분석 모듈 (120)로제공하여 ,단계 822071-수행된다.
[153] 상기단계 8240에서,학습모듈 ( 0)은학습수행횟수가학습수행최대횟수에 도달하였는지를판단하여도달하였으면종료조건이만족된것으로
판단하거나,이전성능평가결과와현재성능평가결과를비교하여두결과의 차이가설정범위내인지를판단하여설정범위내이면종료조건이만족된 것으로판단할수있다.
[154]
[155] 도 3내지 5는본발명의바람직한실시예에따른형상설계기술을이용하여 설계된코어형상과전문가에의해설계된코어형상에대한성능을비교하기 위한도면들이다.
[156] 본실시예에서,도 3내지도 5에도시된코어에대한결합계수를측정하여 성능을비교하기위하여,코어형상이외의다른조건들은모두동일하게 적용되었다.
[157] 도 3에도시된코어형상은전문가에의해설계된형태로서,송신코어 (310)와 수신코어 (320)를 10011이격시킨상태에서측정된결합계수 (¾는 0.0139이다.
[158] 도 4및도 5에도시된코어형상은본발명의형상설계기술을이용하여
설계된제 1형태및제 2형태로서, 100번학습을수행한경우에설계된코어 형태이다.
[159] 도 4에서확인할수있는바와같이,도 3에서의조건과동일하게송신
코어 (410)와수신코어 (420)를 10011이격시킨상태에서측정된결합계수 (幻는 0.0150이다.
[16이 그리고,도 5에서확인할수있는바와같이,도 3에서의조건과동일하게송신 코어 (510)와수신코어 (520)를 10011이격시킨상태에서측정된결합계수 (幻는 0.0152이다.
[161] 이상에서의실험결과에서확인할수있는바와같이,본발명의형상설계 기술을이용하여설계된코어의형상에대한결합계수가전문가에의해설계된 코어의형상에대한결합계수보다더높기때문에,무선전력전송성능을 향상시킬수있다.
[162]
[163] 도 6및 7은본발명의바람직한실시예에따른형상설계기술을이용하여 코어를설계하는경우의일례를도시한도면들이다.
[164] 도 6은하나의수신코어 (620)에대해매칭하는송신코어 ( 0)를설계하는 2020/175912 1»（：1^1{2020/002747 경우의 일례를도시한것이고,도 7은두개의수신코어 (720, 730)에 대해 매칭하는송신코어 (기 0)를설계하는경우의 일례를도시한것이다.
[165] 이와같이,본발명의 형상설계기술을이용하면,하나의수신코어와매칭하는 최적의송신코어에 대한설계뿐만아니라,다수의수신코어와매칭하는최적의 송신코어에 대한설계도가능하다.
[166]
[167] 도 8및 9는본발명의 바람직한실시 예에따른형상설계기술을이용하여 코어를설계하는경우의다른예를도시한도면들이다.
[168] 도 8및 9에도시된수신코어 (820, 920)는동일한크기를갖는것에 비해,도 8에 있어송신코어 ( 0)의설계에 이용된블록 (811)의크기와도 9에 있어송신 코어 (910)의설계에 이용된블록 (911)의크기는서로다르다.
[169] 이와같이,본발명의 형상설계기술을이용하는경우,설계 대상의 형상을 이루는블록의크기에 대한조절이 가능하다.
[17이 도 및 11은본발명의 바람직한실시 예에따른형상설계기술을이용하여 코일을설계하는경우의 일례를도시한도면이다.
[171] 도 12는코일이도 10에도시된바와같이 설계된상태에서소정 이격된
영역에서측정된자속밀도분포를도시한그래프이고,도 13은코일이도 11에 도시된바와같이 설계된상태에서소정 이격된영역에서측정된자속밀도 분포를도시한그래프이다.
[172] 도 및 11에 있어서초기값 (’ 0’)은형상정보내성분과대응하는위치의
코일이 적용되지 않음을의미하고,설정값 (1’)은형상정보내성분과대응하는 위치의코일이 적용됨을의미한다.
[173] 도 10및 11에 있어서코일 (1000, 1100)을제외한다른조건들은모두동일하게 적용되었으며,코어 (1010, 1110)에감긴코일 (1000, 1100)의 턴수만다르게 적용되었다.
[174] 도 10의코일 (1000)의 형상 (혹은감긴턴수)은형상정보로서 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]의성분을갖는 1x20행렬이 입력된경우이고,도 11의코일 (1100)의 형상은형상정보로서 [1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1]의성분을갖는 1x20행렬이 입력된경우이다.
[175] 도 12및도 13을참조하면,도 10에도시된바와같이코일 (1000)이
코어 (1010)에감겨진상태에서측정된쇼영역에서의자속밀도분포 (도 12의 쇼’)와,도 11에도시된바와같이코일 (1100)이코어 (1110)에감겨진상태에서 측정된 8영역에서의자속밀도분포 (도 13의 )는다른것을확인할수있다.
[176] 즉,본발명의 형상설계기술을이용하면,원하는자속밀도분포를갖도록하는 코일의 설계가가능하다.
[177]
[178] 이상에서설명한본발명의실시 예를구성하는모든구성요소들이하나로 결합하거나결합하여동작하는것으로기재되어 있다고해서,본발명이반드시 2020/175912 1»（：1^1{2020/002747 이러한실시예에한정되는것은아니다.즉,본발명의목적범위안에서라면,그 모든구성요소들이하나이상으로선택적으로결합하여동작할수도있다.또한, 그모든구성요소들이각각하나의독립적인하드웨어로구현될수있지만,각 구성요소들의그일부또는전부가선택적으로조합되어하나또는복수개의 하드웨어에서조합된일부기능혹은모든기능을수행하는프로그램모듈을 갖는컴퓨터프로그램으로서구현될수도있다.또한,이와같은컴퓨터 프로그램은 USB메모리, CD디스크,플래쉬메모리등과같은컴퓨터가읽을수 있는기록매체 (Computer Readable Media)에저장되어컴퓨터에의하여읽혀지고 실행됨으로써,본발명의실시 예를구현할수있다.컴퓨터프로그램의 기록매체로서는자기기록매체 ,광기록매체，캐리어웨이브매체등이포함될 수있다.
[179]
[18이 이상에서와같이,본발명에따른무선전력전송시스템용형상설계시스템및 방법을실시예에따라설명하였지만,본발명의범위는특정실시 예에 한정되는것은아니며,본발명과관련하여통상의지식을가진자에게자명한 범위내에서여러가지의대안,수정및변경하여실시할수있다.
[181] 따라서,본발명에기재된실시 예및첨부된도면들은본발명의기술사상을 한정하기위한것이아니라설명하기위한것이고,이러한실시예및첨부된 도면에의하여본발명의기술사상의범위가한정되는것은아니다.본발명의 보호범위는청구범위에의하여해석되어야하며,그와동등한범위내에 있는 모든기술사상은본발명의권리범위에포함되는것으로해석되어야할 것이다.
[182] [부호의설명]
[183] 100 :형상설계시스템
[184] 110 :학습모듈
[185] 120 :분석모듈
[186] 130 :출력모듈
[187] 310, 410, 510, 610, 710, 810, 910 :송신코어
[188] 320, 420, 520, 620, 720, 730, 820, 920 :수신코어

Claims

2020/175912 1»（：1/10公020/002747 청구범위
[청구항 1] 설계대상과관련하여입력되는형상정보및보상정보를기초로학습을 수행하여새로운형상정보를생성하는학습모듈;및
상기학습모듈로부터의형상정보에기초하여무선전력전송성능을 평가한후성능평가결과를상기학습모듈로제공하는분석모듈을 포함하는
무선전력전송시스템용형상설계시스템.
[청구항 2] 제 1항에 있어서,
상기형상정보는상기설계대상의형상을규정하는데에이용되는 다수의성분으로구성되는행렬구조로이루어지는것을특징으로하는 무선전력전송시스템용형상설계시스템.
[청구항 3] 제 1항에 있어서,
상기학습모듈은학습과정으로통해상기형상정보내성분의값을 변경하여상기설계대상의형상을변경하도록구현된
무선전력전송시스템용형상설계시스템.
[청구항 4] 제 1항에 있어서,
상기형상정보내성분은초기에는모두초기값을갖고,보상에의한 학습이이루어진이후에는학습수행결과에따라초기값혹은설정값을 갖는것을특징으로하는
무선전력전송시스템용형상설계시스템.
[청구항 5] 제 1항에 있어서,
상기학습모듈은학습초기에초기형상정보를입력받아학습을 수행하고,이후에는학습결과로서생성된새로운형상정보와상기분석 모듈로부터의성능평가결과에기초하여학습을수행하도록구현된 무선전력전송시스템용형상설계시스템.
[청구항 6] 제 1항에 있어서,
상기분석모듈은무선전력전송성능평가시에 ,성능영향변수에대한 분석을수행하고,분석결과를토대로무선전력전송성능을평가하도록 구현된
무선전력전송시스템용형상설계시스템.
[청구항 7] 제 6항에 있어서,
상기성능영향변수는자속밀도,송신코일인덕턴스,수신코일 인덕턴스,송수신부간상호인덕턴스,결합계수,송신코일저항,수신 코일저항,수신전력및시스템효율중에서선택되는적어도하나이상을 포함하는
무선전력전송시스템용형상설계시스템.
[청구항 8] 제 1항에 있어서, 2020/175912 1»（：1^1{2020/002747 상기학습모듈은기설정된학습종료조건이만족되기까지형상정보와 성능평가결과를바탕으로지속적으로학습을수행하도록구현된 무선전력전송시스템용형상설계시스템.
[청구항 9] 제 8항에있어서,
상기학습모듈은학습수행후학습수행횟수를카운팅하고,학습수행 횟수가학습수행최대횟수에도달하면상기학습종료조건이만족된 것으로판단하도록구현된
무선전력전송시스템용형상설계시스템.
[청구항 10] 제 8항에있어서 ,
상기학습모듈은이전성능평가결과와현재성능평가결과를 비교하고,두결과의차이가설정범위내이면상기학습종료조건이 만족된것으로판단하도록구현된
무선전력전송시스템용형상설계시스템.
[청구항 11] 제 1항에있어서,
상기형상정보내성분은출력모듈상에서블록으로이미지화되어 표시되고,상기블록의크기는조절가능하며 , 2차원상에서혹은 3차원 상에서이미지화되어표시되는것을특징으로하는
무선전력전송시스템용형상설계시스템.
[청구항 12] 설계대상과관련한초기형상정보가입력되는단계;
상기초기형상정보를기초로학습을수행하여학습형상정보를 생성하는단계 ;
상기학습형상정보에대한분석을통해무선전력전송성능을평가한후 성능평가결과를생성하는단계 ;및
상기성능평가결과및상기학습형상정보를기초로학습을수행하여 새로운학습형상정보를생성하는단계를포함하는
무선전력전송시스템용형상설계방법.
[청구항 13] 제 12항에있어서 ,
상기초기형상정보는초기값을가지며,상기설계대상의형상을 규정하는데에이용되는다수의성분으로구성되는행렬구조인 무선전력전송시스템용형상설계방법.
[청구항 14] 제 12항에있어서 ,
상기초기형상정보내성분이학습수행이이루어짐에따라변경되어 상기학습형상정보가생성되는것을특징으로하는
무선전력전송시스템용형상설계방법.
[청구항 15] 제 12항에있어서 ,
상기학습형상정보에대한분석은무선전력전송의성능에영향을 미치는성능영향변수에대해이루어지는것을특징으로하는 무선전력전송시스템용형상설계방법. 2020/175912 1»（：1^1{2020/002747
[청구항 16] 제 15항에있어서 ,
상기성능영향변수는,자속밀도,송신코일인덕턴스,수신코일 인덕턴스,송수신부간상호인덕턴스,결합계수,송신코일저항,수신 코일저항,수신전력및시스템효율중에서선택되는적어도하나이상인 무선전력전송시스템용형상설계방법.
[청구항 17] 제 12항에있어서 ,
상기성능평가결과를생성하는단계및상기새로운학습형상정보를 생성하는단계는,기설정된종료조건이만족되기까지지속적으로 이루어지는것을특징으로하는
무선전력전송시스템용형상설계방법.
[청구항 18] 제 17항에있어서 ,
상기종료조건은학습수행에따라카운팅되는학습수행횟수가학습 수행최대횟수에도달한경우인것을특징으로하는
무선전력전송시스템용형상설계방법.
[청구항 19] 제 17항에있어서 ,
상기종료조건은이전성능평가결과와현재성능평가결과의차이가 설정범위내인경우인것을특징으로하는
무선전력전송시스템용형상설계방법.
[청구항 20] 제 12항에있어서 ,
상기방법은,상기형상정보내성분을기설정된크기의블록으로 이미지화하여 2차원혹은 3차원상에서표시하는단계를더포함하는 무선전력전송시스템용형상설계방법.
PCT/KR2020/002747 2019-02-26 2020-02-26 무선전력전송 시스템용 형상 설계 시스템 및 방법 WO2020175912A1 (ko)

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