Optimization design method for surrounding shielding coil of wireless charging device of electric vehicle

Abstract

The invention is suitable for the technical field of wireless electric energy transmission, and provides an optimization design method for a surrounding shielding coil of an electric vehicle wireless charging device, and the method comprises the steps: designing a surrounding shielding coil structure; on the basis of a surrounding type shielding coil structure, an agent model between coil structure parameters and optimization targets is built by utilizing an extreme learning neural network, and then the coil structure is optimized by embedding a multi-target NSGA-II algorithm. Aiming at the electromagnetic exposure safety problem of a wireless electric energy transmission system of an electric vehicle, the invention provides a surrounding active shielding coil structure, so that the leakage magnetic field intensity at the periphery of a system coupling mechanism is effectively reduced, and meanwhile, the negative influence on the transmission performance of the system is reduced; according to the proxy model constructed by the invention, accurate prediction of the optimization target value of the wireless power transmission system is realized; according to the method, the multi-target NSGA-II algorithm is combined, and synchronous improvement of multiple performance indexes of the wireless power transmission system is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless power transmission, and in particular relates to an optimization design method for a surrounding shielding coil of a wireless charging device for an electric vehicle. Background Art

[0002] With the increasing marketization of high-power wireless charging devices for electric vehicles, the safety issues of electromagnetic exposure have attracted people's attention and become the focus of research institutions at home and abroad. Improving the electromagnetic exposure environment of wireless power transmission in electric vehicles is crucial for the application and promotion of this technology. Therefore, it is particularly necessary to conduct in-depth research on the electromagnetic shielding technology of wireless power transmission systems. In order to ensure the safe use of wireless charging devices for electric vehicles, some international organizations such as the International Commission on Non-Ionizing Radiation Protection (ICNIRP) have formulated strict standards for relevant electromagnetic exposure indicators.

[0003] In the field of electromagnetic shielding technology for wireless power transmission, the traditional active shielding principle is to reversely connect a single coil in series with the main coil of wireless power transmission, and use the current inversion characteristics of the reverse-connected coil to offset the leakage magnetic field. Although this method reduces the leakage magnetic field outside the system coupling mechanism, it also weakens the magnetic field in the transmission area, which has an adverse effect on the system transmission performance. In addition, most of the existing research on the optimization design of shielding structures only focuses on the improvement of a single target performance, which is of little significance for improving the overall performance of the system. To this end, the present invention proposes an optimization design method for a shielding coil surrounding a wireless charging device for an electric vehicle. Summary of the invention

[0004] The purpose of the present invention is to provide an optimized design method for a surrounding shielding coil of a wireless charging device for an electric vehicle, aiming to solve the problems raised in the above background technology.

[0005] The purpose of the present invention is achieved through the following technical solutions: An optimization design method for a surrounding shielding coil of an electric vehicle wireless charging device comprises the following steps: Step 1: Build a surrounding shielding coil; A surrounding active shielding coil structure is built in the electromagnetic numerical calculation software. The surrounding active shielding coil structure is formed by placing four independent power supply shielding coils on the four sides of the transmitting coil of the wireless charging device. The shielding coil current is in the same frequency and opposite phase to the transmitting coil current. Step 2: Establish a proxy model of the shielding coil structure; A neural network framework based on extreme learning machine was built, with the shielding coil structural parameters as optimization variables, the wireless power transmission efficiency and the maximum leakage flux density on the peripheral observation surface of the system coupling mechanism as optimization targets, and a proxy model of optimization variables and optimization targets was established; Step 3: Multi-objective optimization design of shielding coil structure; Based on the constructed proxy model, the multi-objective NSGA-II algorithm is used to optimize the optimization variable interval to obtain the optimal solution for the shielding coil structural parameters.

[0006] Furthermore, in step 1, the energy transmission coil of the wireless charging device is a rectangular coil, and the spatial magnetic induction intensity analytical formula of the rectangular coil in the rectangular coordinate system is as follows: ; Where: They represent the magnetic induction intensity at the spatial coordinate point x, y, z Directional weight; represents the magnetic permeability in vacuum; I Indicates the amplitude of the alternating line current; z express z Coordinate value; Indicates the distance from the corner of the rectangular line current to the spatial coordinate point; i Indicates the corner number of the rectangular line current; Represents the half side length of the rectangular coil and x Function of coordinate values; Represents the half side length of the rectangular coil and y Function of coordinate values; In the formula Further expressed as: ; Formula 6: , , , ; Where: Represents the spatial coordinate points of the four corners of the rectangular line current to the analysis P distance; All represent functions of rectangular coil geometric parameters and space coordinate parameters; They represent the two half-side lengths of the rectangular line current in the XOY plane; x and y Respectively x , y Coordinate value; Assume that in the wireless power transmission coupling mechanism, the number of turns of the transmitting and receiving coil groups are n and m , then each single-turn coil is at a point in space P The magnetic field strength generated at B t,1 , B t,2 , B t,n as well asB r,1 , B r,2 , B r,m , the total field strength can be expressed as: ; Where: is the total field strength, Indicates that the transmitting side single-turn coil is at the spatial point P The magnetic field strength generated at Indicates that the single-turn coil on the receiving side is at the spatial point P The magnetic field strength generated at .

[0007] Furthermore, in the surrounding active shielding coil structure, it is assumed that the magnetic flux component generated by the transmitting coil of the wireless charging device is Φ 3. Φ 6. The magnetic flux component generated by the shielding coil on one side is Φ 1. Φ 2. The magnetic flux component generated by the shielding coil on the other side is Φ 4. Φ 5, then: ; ; ; ; Where: and It represents the sum of the magnetic field strengths generated by the shielding coil and the transmitting coil on one side in the transmission area and the peripheral area respectively. and It represents the sum of the magnetic field strengths generated by the shielding coil and the transmitting coil on the other side in the transmission area and the peripheral area respectively.

[0008] Furthermore, the specific process of step 2 is: using the length of the shielding coil l ,width ω And thickness d As the optimization variable, the system transmission efficiency η , Maximum leakage magnetic flux density on the observation surface 1m away from the wireless charging device on the side of the vehicle body B 1 and the maximum leakage magnetic flux density on the observation surface 1m away from the wireless charging device behind the vehicle body B 2 as the optimization target; select the leakage magnetic field observation positions on the side and rear of the vehicle body, use electromagnetic numerical simulation finite element solution, based on different variable combinations { l , ω , d}, extract the corresponding η, B 1 and B 2; build a proxy model based on the extreme learning machine neural network framework, and use the extracted sample set to train the proxy model.

[0009] Furthermore, for each input sample , the activation value of the neuron on the hidden layer of the extreme learning machine It is expressed as: ; Where: represents the activation function, Indicates l The transpose of the input weight vector of the neuron, Represents the corresponding bias term; the extreme learning machine calculates the optimal weight value from the hidden layer to the output layer, and its output is expressed as a linear combination form: ; ; Where: represents the weight matrix; Represents the weight of each node in the hidden layer; Represents the network output value; L Represents the total number of neuron nodes in the hidden layer; Indicates l The weight of each node; represents the transpose of the weight matrix; Represents the hidden layer node activation value matrix; The optimal weight matrix is ​​derived as follows , in order to minimize the overall prediction error value of the sample: ; Where: H represents the hidden layer activation value matrix, T represents the target output matrix, express H The Moore-Penrose generalized inverse matrix of .

[0010] Furthermore, the specific process of step 3 is as follows: based on the trained proxy model, the multi-objective NSGA-II algorithm is used to optimize the coil structure variable interval to obtain the Pareto optimization solution set, and the optimal solution with the maximum transmission efficiency and the minimum electromagnetic exposure index is found according to the Pareto optimization result.

[0011] Furthermore, combining the agent model and the multi-objective NSGA-II algorithm, the multi-objective optimization framework of the shielding coil structure is expressed as: ; Where: represents the overall objective function, represents the length of the shielding coil, represents the width of the shielding coil, Indicates the thickness of the shielding coil, They represent the objective functions of transmission efficiency, maximum leakage flux density on the side observation surface of the vehicle body, and maximum leakage flux density on the rear observation surface of the vehicle body, respectively. Respectively The allocation weight of Assume that the optimal design variable set is , then the optimization interval of each variable is expressed as follows: ; ; ; ; Where: They represent the optimization ranges of the length, width and thickness of the shielding coil respectively.

[0012] Compared with the prior art, the present invention has the following beneficial effects: In response to the electromagnetic exposure safety issues of the wireless power transmission system of electric vehicles, the present invention proposes a surrounding active shielding coil structure, which effectively reduces the leakage magnetic field strength outside the system coupling mechanism and reduces the negative impact on the transmission performance. On this basis, the present invention constructs a shielding coil structure optimization agent model based on an extreme learning machine, which realizes the accurate prediction of the optimization target value of the wireless power transmission system. Then, the shielding coil structure is optimized and designed in combination with the multi-objective NSGA-II algorithm, realizing the simultaneous improvement of multiple performance indicators of the wireless power transmission system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure is a flow chart of the method of the present invention.

[0014] Figure 2 It is a rectangular line current model.

[0015] Figure 3 This is a schematic diagram of the layout of the surround active shielding coil.

[0016] Figure 4 Schematic diagram of cross-sectional magnetic flux line distribution.

[0017] Figure 5 This is the area of ​​concern for leakage magnetic field around the vehicle body.

[0018] Figure 6 A multi-objective optimization design process for shielding coil structures.

[0019] Figure 7 A simulation model for wireless power transfer for electric vehicles.

[0020] Figure 8 is the leakage flux density distribution of observation surface 1; (a) is the leakage flux density of observation surface 1 without shielding, and (b) is the leakage flux density of observation surface 1 with active shielding.

[0021] Fig. 9 is the leakage flux density distribution of the observation surface 2; (a) is the leakage flux density of the observation surface 2 without shielding, and (b) is the leakage flux density of the observation surface 2 with active shielding.

[0022] Fig.10 The results are Pareto optimized.

[0023] Fig.11 Comparison of transmission efficiency probability distribution before and after multi-objective optimization in actual scenarios.

[0024] Fig.12 Comparison of probability distribution of maximum leakage flux density of observation surface 1 before and after multi-objective optimization in actual scenario.

[0025] Fig.13 Comparison of probability distribution of maximum leakage flux density on observation surface 2 before and after multi-objective optimization in actual scenario. DETAILED DESCRIPTION

[0026] In order to have a clearer understanding of the technical features, purposes and beneficial effects of the present invention, the technical solution of the present invention is now described in detail below, but it should not be construed as limiting the applicable scope of the present invention.

[0027] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0028] An embodiment of the present invention provides an optimization design method for a surrounding shielding coil of an electric vehicle wireless charging device, and its flow chart is as follows: Figure 1 As shown, the method comprises the following steps: Step 1: Build a surrounding shielding coil; A surrounding active shielding coil structure is built in electromagnetic numerical simulation software. Its basic structure consists of four shielding coils placed on the four sides of the transmitting coil of the wireless charging device. The shielding coil is an independent power supply, and the shielding current is in the same frequency and opposite phase as the transmitting coil current. Step 2: Establish a proxy model of the shielding coil structure; Build a neural network framework based on extreme learning machine to shield the length of the coil l ,width ω And thickness d As an optimization variable, transmission efficiency η, Maximum leakage magnetic flux density on the observation surface 1m away from the wireless charging device on the side of the vehicle body B 1 and the maximum leakage magnetic flux density on the observation surface 1m away from the wireless charging device behind the vehicle body B 2 as the optimization target; select the leakage magnetic field observation positions on the side and rear of the vehicle body, use electromagnetic numerical simulation finite element solution, based on different variable combinations { l , ω , d}, extract the corresponding η , B 1 and B 2; an extreme learning machine neural network is used to build a proxy model, and the proxy model is trained using the extracted sample set.

[0029] Step 3: Multi-objective optimization design of shielding coil structure; Based on the trained proxy model, the embedded multi-objective NSGA-II algorithm searches for the optimization variable interval, obtains the Pareto optimization solution set, and finds the optimal solution with the maximum transmission efficiency and the minimum electromagnetic exposure index.

[0030] The specific process is as follows: Step 1: Build a surrounding shielding coil; 1.1 Spatial magnetic field distribution characteristics of wireless charging coil; The magnetic energy coil group is the main medium for transmitting electric energy in the wireless charging system. It mainly relies on the spatial alternating magnetic field to achieve wireless power transmission. Therefore, the magnetic energy coil group largely determines the magnetic field distribution in the transmission channel and the leakage magnetic field distribution in the surrounding environment. In the application of magnetic coupling wireless power transmission, the coil group is usually composed of multiple independently insulated twisted wires wound together. According to the magnetic field distribution pattern, it can be mainly divided into two types: monopolar coils and bipolar coils. The structure of monopolar coils is relatively simple, such as circular coils and rectangular coils, while the structure of bipolar coils is relatively complex, such as DD coils and BBP coils.

[0031] In this study, a rectangular coil is used as the research object. It is assumed that the two half-side lengths of the rectangular line current in the XOY plane are a , b , I represents the amplitude of the line current, then any point in the open space P ( x , y , z )exist x and y The expression of magnetic vector potential in the direction is: ; ; Where: and Respectively indicate points P ( x , y , z )exist x and y Magnetic vector potential in direction; represents the magnetic permeability in vacuum; Represents the four corners of the rectangular line current to P The distance of the points; x and y Respectively P Pointed x , y Coordinate value; Figure 2 shown.

[0032] In general, the relationship between the magnetic vector potential and spatial magnetic induction intensity of a rectangular line current in the air domain can be expressed as follows:

[0033] Where: They represent the magnetic induction intensity at the spatial coordinate point Directional weight; z express z Coordinate value; By combining equations 1-3, we can get the analytical formula of the magnetic induction intensity of the rectangular coil in the rectangular coordinate system as follows: ; Where: Represents the spatial coordinate points of the rectangular line current corners to the analysis P The distance i Indicates the corner number of the rectangular line current; Represents the half side length of the rectangular coil and P point x Function of coordinate values; Represents the half side length of the rectangular coil and P point y Function of coordinate values; In formula 4, It can be further expressed as: ; Formula 6: , , , ; Where: Both represent the geometric parameters of the rectangular coil and P Function of the point space coordinate parameters.

[0034] Assume that in the wireless power transmission coupling mechanism, the number of turns of the transmitting and receiving coil groups are n and m , then each single-turn coil P The magnetic field strength generated at the point can be expressed as B t,1 , B t,2 , B t,n as well as B r,1 , B r,2 , B r,m , the magnetic flux density at a certain spatial coordinate point generated by a single-turn coil under alternating current is the basis for calculating the total field strength at that point, which can be expressed as follows: ; Where: is the total field strength, Indicates that the transmitting side single-turn coil is at the spatial point P The magnetic field strength generated at Indicates that the single-turn coil on the receiving side is at the spatial point P The magnetic field strength generated at .

[0035] 1.2 Surround active shielding coil; In the application process of wireless power transmission in electric vehicles, a large amount of leakage magnetic field will be generated around the coupling mechanism. In order to effectively protect it from electromagnetic safety, the present invention places a shielding coil on each of the four sides of the transmitting coil of the wireless charging device, such as Figure 3 As shown. The four shielding coils are all actively fed, and the shielding coil current is opposite to the wireless power transmission transmitting coil current. Through this coil layout, a specific target magnetic flux line direction is generated. Taking the side view section of the system coupling mechanism as an example, the overall magnetic flux line distribution after adding the shielding coil is as follows Figure 4 shown.

[0036] Assume that the magnetic flux component generated by the wireless power transmission transmitting coil is Φ 3. Φ 6. The magnetic flux component generated by the shielding coil on one side is Φ 1. Φ 2. The magnetic flux component generated by the shielding coil on the other side is Φ 4. Φ 5, it can be seen that in the transmission area between the transmitter and receiver of the wireless charging device, the magnetic field strength shows a superposition effect, which can supplement the transmission performance. In the outer protection area, the magnetic field strength shows a cancellation effect, which can effectively weaken the leakage magnetic field, as shown in equations 8 to 11:

[0037] Where: and It represents the sum of the magnetic field strengths generated by the shielding coil and the transmitting coil on one side in the transmission area and the peripheral area respectively. and It represents the sum of the magnetic field strengths generated by the shielding coil and the transmitting coil on the other side in the transmission area and the peripheral area respectively.

[0038] Step 2: Establish a proxy model of the shielding coil structure; In actual situations, there is a strong nonlinear mapping relationship between the structural parameters of the wireless power transmission system and the electrical performance parameters of the system. To address the above problems, the present invention uses an extreme learning machine neural network to construct an agent model, which achieves accurate prediction of the optimization target value of the wireless power transmission system, mainly including the wireless power transmission efficiency and the leakage magnetic field strength around the system coupling mechanism, wherein the leakage magnetic field observation plane is selected at the rear of the vehicle body (such as Figure 5 ②) and the side of the vehicle body (such as Figure 5 In the middle, this is the area where people are more likely to move in the actual scenario. Figure 5 shown.

[0039] Different from the traditional back-propagation neural network, the hidden layer weights of the extreme learning machine do not need to be adjusted during the training process. Therefore, it has the advantages of high solution efficiency and strong generalization performance, and is very suitable for fitting complex nonlinear function mapping relationships. For each input sample , the activation value of the neuron in the hidden layer of the extreme learning machine It can be expressed as: ; Where: represents the activation function, Indicates l The transpose of the input weight vector of the neuron, Represents the corresponding bias term; these parameter values ​​are randomly initialized and remain unchanged during the network training and learning process. The primary task of the extreme learning machine is to calculate the optimal weight value from the hidden layer to the output layer, and its output can be expressed as a linear combination:

[0040] Where: represents the weight matrix; Represents the weight of each node in the hidden layer; Represents the network output value; L Represents the total number of neuron nodes in the hidden layer; Indicates l The weight of each node; represents the transpose of the weight matrix; Represents the hidden layer node activation value matrix.

[0041] In order to minimize the overall prediction error of the sample, the optimal weight matrix can be derived according to the following formula: :

[0042] Where: H represents the hidden layer activation value matrix, T represents the target output matrix, express H The Moore-Penrose generalized inverse matrix of .

[0043] In the wireless power transmission shielding coil structure proxy model, the length of the shielding coil l ,width ω And thickness d is set as input variable; output variable is [ η , B 1, B 2], where η It represents the transmission efficiency. B 1 represents the maximum leakage magnetic flux density on the observation surface at a distance of 1m from the wireless charging device on the side of the vehicle body. B 2 represents the maximum leakage magnetic flux density on the observation surface 1m away from the wireless charging device behind the vehicle body, η, B 1. B 2 are the actual optimization targets. This study uses the built-in parametric scanning module of the electromagnetic numerical simulation software COMSOL to find different variable combinations { l , ω , d}Corresponding η , B 1 and B 2 sample set is used as the training sample of the proxy model. According to the row and column dimensions of the optimization variables and the target, the number of nodes in the input layer and the output layer are set to 3 in the extreme learning machine network layer.

[0044] Step 3: Multi-objective optimization design of shielding coil structure; The present invention intends to adopt a non-dominated sorting genetic algorithm (NSGA-II) to further optimize the design of the surrounding shielding coil structure. The NSGA-II algorithm overcomes the defects of the early NSGA algorithm, such as high computational cost and slow execution speed. It mainly improves in the following three aspects: 1) The NSGA-II algorithm uses a fast non-explicit sorting method to reduce the complexity of the original NSGA algorithm from Ɵ ( mN 3 ) is reduced to Ɵ ( mN 2 ), significantly reducing the computational time of the algorithm, where Ɵ Indicates complexity, m represents the number of objective functions, N Indicates the number of populations.

[0045] 2) The elite strategy is used to merge the parent individuals and the child individuals and perform non-dominated sorting, which expands the search space. When generating the next generation of population, individuals with higher priority are selected according to the sorting, which effectively increases the probability of retaining excellent individuals.

[0046] 3) The NSGA-II algorithm uses the crowding strategy to replace the fitness sharing strategy of the specified sharing radius in the original NSGA algorithm, which improves the diversity of individuals in the population. This method is beneficial to the selection, crossover and compilation behavior of individuals in the entire interval.

[0047] Combining the extreme learning machine agent model and the multi-objective NSGA-II algorithm, the multi-objective optimization framework of the shielding coil structure can be expressed as:

[0048] Where: represents the overall objective function, represents the length of the shielding coil, represents the width of the shielding coil, Indicates the thickness of the shielding coil, They represent the objective functions of transmission efficiency, maximum leakage flux density on the side observation surface of the vehicle body, and maximum leakage flux density on the rear observation surface of the vehicle body, respectively. Respectively The allocation weight of .

[0049] Assume that the optimal design variable set is , then the optimization interval of each variable can be expressed as follows:

[0050]

[0051] Where: They represent the optimization ranges of the length, width and thickness of the shielding coil respectively.

[0052] The present invention uses the coil structure variables and optimization target values ​​extracted from the electromagnetic numerical simulation software as the training sample set to train the extreme learning machine neural network. When the training error reaches the required accuracy, the training process of the proxy model is completed. The proxy model can accurately predict the optimization target values ​​corresponding to different variable groups. Then, the test samples are extracted from the coil structure variable group. On the basis of extreme learning machine modeling, the multi-objective NSGA-II algorithm is combined to optimize the optimization interval of the shielding coil structure variables, and the Pareto optimization solution set is obtained to find the shielding coil structure parameter group with the maximum transmission efficiency and the minimum electromagnetic exposure. The overall process is as follows: Figure 6 shown.

[0053] Embodiment 1: The present invention uses electromagnetic numerical simulation software COMSOL to build a simulation model of the electric vehicle wireless power transmission system. Figure 7 As shown, the body size parameters are as follows: 4.5 meters long, 2 meters wide, and 1.5 meters high, which are basically consistent with the volume dimensions of common family cars on the market. In the simulation model, the body material is set to aluminum metal, the tire material is set to rubber, and the window material is set to tempered glass. In this study, the wireless power transmission system is based on the principle of double coil coupling resonance on the transmitting side and the receiving side to achieve energy transmission. The operating frequency is set to 85khz. In the simulation model, the coil material is set to copper. Referring to the vertical height of the chassis of most family cars from the ground, the vertical transmission gap between the transmitting end and the receiving end coil group in the coupling mechanism is set to 0.2m.

[0054] In order to test the electromagnetic safety protection performance of the surround active shielding coil, this study built a shielding coil model in COMSOL software, and set up leakage magnetic field observation planes 1m away from the side and rear of the wireless charging device, which were respectively named observation plane 1 and observation plane 2, to observe the distribution of leakage magnetic field around the vehicle body. At the same time, the simulation results without the surround active shielding coil were compared. The results are shown in Figure 2. Figure 8 and Fig. 9 As shown. It can be seen that without the protection of the shielding coil, the magnetic field leakage around the coupling mechanism is serious, and the leakage flux density on the observation plane around the vehicle body is large. After taking shielding protection measures, the electromagnetic exposure around the coupling mechanism has been greatly reduced. Among them, see Figure 8 In (a) and (b), the maximum leakage flux density on observation plane 1 decreases from 80uT to 10uT, a decrease of about 88%; see Fig. 9 In (a) and (b), the maximum leakage flux density on observation plane 2 is reduced from 90uT to 12uT, a decrease of about 87%. The above results verify that the surrounding active shielding coil proposed in the present invention has an excellent electromagnetic shielding effect and can effectively suppress the leakage of the surrounding magnetic field.

[0055] In the shielding coil optimization design part, l , ω , d The optimization intervals are [330mm, 350mm], [90mm, 100mm], [3mm, 8mm]. This study collected 5000 sample points in the specified interval to train the extreme learning machine agent model. Based on the trained model, combined with the multi-objective NSGA-II algorithm, the Pareto optimal solution distribution of the system transmission efficiency, the maximum leakage flux density on observation plane 1 and observation plane 2 is finally obtained. Fig.10 As shown. The optimal solution for the design of the surrounding active shielding coil structure can be obtained, as Fig.10 It can be seen that the optimal solution has higher transmission efficiency and lower leakage magnetic field intensity, which meets the actual needs of electric vehicle users.

[0056] Considering that there are some potential uncertainties in the wireless charging scenario of electric vehicles, such as improper operation of the driver causing spatial misalignment of the wireless power transmission coil group, which will have a significant impact on the performance indicators of the charging system, this study also carried out uncertainty quantification analysis to further verify the effectiveness of the proposed shielding solution in solving practical problems. Combined with the actual scenario, the present invention adopts Latin hypercube sampling in this embodiment to collect samples of related uncertain variables, specifically including: the horizontal offset of the main transceiver coil of the coupling mechanism Δx , Δy , vertical spacing of the main transceiver coils h , coil group deflection angle α , specifically listed in Table 1, where U represents uniform distribution.

[0057]

[0058] According to the random input variable distribution type and distribution interval set in Table 1, the probability density distribution comparison of each performance target value of the wireless power transmission system before and after multi-objective optimization is calculated, as shown in the following figure. Figure 11-13 The average transmission efficiency before and after optimization is 0.78 and 0.93 respectively, which is effectively improved by 19.2% ( Fig.11 ); The average values ​​of the maximum leakage flux density on observation plane 1 before and after optimization were 9.35μT and 6.07μT, respectively, which was effectively reduced by 35.1% ( Fig.12 ); The average values ​​of the maximum leakage flux density on observation plane 2 before and after optimization were 11.37μT and 7.66μT, respectively, which effectively reduced by 32.6% ( Fig.13). The above results show that when the wireless power transmission process is interfered by external uncertain factors, the transmission efficiency of the system after multi-objective optimization is more likely to be at a high level, and the risk of electromagnetic exposure is also reduced. Therefore, the optimization design method of the surrounding shielding coil of the wireless charging device of an electric vehicle proposed in the present invention can ensure excellent system transmission performance and electromagnetic safety protection efficiency, and provide a scientific and effective theoretical basis and solution for engineering designers.

[0059] The above are only preferred embodiments of the present invention. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention. These should also be regarded as the protection scope of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. An optimization design method for a surrounding shielding coil of an electric vehicle wireless charging device, characterized in that: The following steps are involved: Step 1: Build a surrounding shielding coil; A surrounding active shielding coil structure is built in the electromagnetic numerical calculation software. The surrounding active shielding coil structure is formed by placing four shielding coils on the four sides of the transmitting coil of the wireless charging device. The shielding coil current is in the same frequency and opposite phase to the transmitting coil current. Step 2: Establish a proxy model of the shielding coil structure; A neural network framework based on extreme learning machine was built, with the shielding coil structural parameters as optimization variables, the wireless power transmission efficiency and the maximum leakage flux density on the peripheral observation surface of the system coupling mechanism as optimization targets, and a proxy model of optimization variables and optimization targets was established; Step 3: Multi-objective optimization design of shielding coil structure; Based on the constructed proxy model, the multi-objective NSGA-II algorithm is used to optimize the optimization variable interval to obtain the optimal solution for the shielding coil structural parameters.

2. The optimization design method of the surrounding shielding coil of the wireless charging device of an electric vehicle according to claim 1 is characterized in that: In step 1, the energy transmission coil of the wireless charging device is a rectangular coil, and the spatial magnetic induction intensity analytical formula of the rectangular coil in the rectangular coordinate system is as follows: ； Where: They represent the magnetic induction intensity at the spatial coordinate point x, y, z Directional weight; represents the magnetic permeability in vacuum; I Indicates the amplitude of the alternating line current; z express z Coordinate value; Indicates the distance from the corner of the rectangular line current to the spatial coordinate point; i Indicates the corner number of the rectangular line current; Represents the half side length of the rectangular coil and x Function of coordinate values; Represents the half side length of the rectangular coil and y Function of coordinate values; In the formula Further expressed as: ； Formula 6: , , , ; Where: Represents the spatial coordinate points of the four corners of the rectangular line current to the analysis P distance; All represent functions of rectangular coil geometric parameters and space coordinate parameters; They represent the two half-side lengths of the rectangular line current in the XOY plane; x and y Respectively x , y Coordinate value; Assume that in the wireless power transmission coupling mechanism, the number of turns of the transmitting and receiving coil groups are n and m , then each single-turn coil is at a point in space P The magnetic field strength generated at B t,1 , B t,2 , B t,n as well as B r,1 , B r,2 , B r,m , the total field strength can be expressed as: ； Where: is the total field strength, Indicates that the transmitting side single-turn coil is at the spatial point P The magnetic field strength generated at Indicates that the single-turn coil on the receiving side is at the spatial point P The magnetic field strength generated at .

3. The optimization design method of the surrounding shielding coil of the wireless charging device of an electric vehicle according to claim 1 is characterized in that: In the surrounding active shielding coil structure, it is assumed that the magnetic flux component generated by the transmitting coil of the wireless charging device is Φ 3. Φ 6. The magnetic flux component generated by the shielding coil on one side is Φ 1. Φ 2. The magnetic flux component generated by the shielding coil on the other side is Φ 4. Φ 5, then: ； ； ； ； Where: and It represents the sum of the magnetic field strengths generated by the shielding coil and the transmitting coil on one side in the transmission area and the peripheral area respectively. and It represents the sum of the magnetic field strengths generated by the shielding coil and the transmitting coil on the other side in the transmission area and the peripheral area respectively.

4. The optimization design method of the surrounding shielding coil of the wireless charging device of an electric vehicle according to claim 1 is characterized in that: The specific process of step 2 is: l ,width ω And thickness d As the optimization variable, the system transmission efficiency η , Maximum leakage magnetic flux density on the observation surface 1m away from the wireless charging device on the side of the vehicle body B 1 and the maximum leakage magnetic flux density on the observation surface 1m away from the wireless charging device behind the vehicle body B 2 as the optimization target; The leakage magnetic field observation positions are selected on the side and rear of the vehicle body, and the electromagnetic numerical simulation finite element solution is used. Based on different variable combinations { l , ω , d }, extract the corresponding η , B 1 and B 2; build a proxy model based on the extreme learning machine neural network framework, and use the extracted sample set to train the proxy model.

5. The optimization design method of the surrounding shielding coil of the wireless charging device of an electric vehicle according to claim 1 is characterized in that: For each input sample , the activation value of the neuron on the hidden layer of the extreme learning machine It is expressed as: ； Where: represents the activation function, Indicates l The transpose of the input weight vector of the neuron, Represents the corresponding bias term; the extreme learning machine calculates the optimal weight value from the hidden layer to the output layer, and its output is expressed as a linear combination form: ； ； Where: represents the weight matrix; Represents the weight of each node in the hidden layer; Represents the network output value; L Represents the total number of neuron nodes in the hidden layer; Indicates l The weight of each node; represents the transpose of the weight matrix; Represents the hidden layer node activation value matrix; The optimal weight matrix is ​​derived as follows , in order to minimize the overall prediction error value of the sample: ； Where: H represents the hidden layer activation value matrix, T represents the target output matrix, express H The Moore-Penrose generalized inverse matrix of .

6. The optimization design method of the surrounding shielding coil of the wireless charging device of an electric vehicle according to claim 1 is characterized in that: The specific process of step 3 is as follows: based on the trained proxy model, the multi-objective NSGA-II algorithm is used to optimize the coil structure variable interval to obtain the Pareto optimization solution set, and the optimal solution with the maximum transmission efficiency and the minimum electromagnetic exposure index is found according to the Pareto optimization result.

7. The optimization design method of the surrounding shielding coil of the wireless charging device of an electric vehicle according to claim 1 is characterized in that: Combining the agent model and the multi-objective NSGA-II algorithm, the multi-objective optimization framework of the shielding coil structure is expressed as: ； Where: represents the overall objective function, represents the length of the shielding coil, represents the width of the shielding coil, Indicates the thickness of the shielding coil, They represent the objective functions of transmission efficiency, maximum leakage flux density on the side observation surface of the vehicle body, and maximum leakage flux density on the rear observation surface of the vehicle body, respectively. Respectively The allocation weight of Assume that the optimal design variable set is , then the optimization interval of each variable is expressed as follows: ； ； ； ； Where: They represent the optimization ranges of the length, width and thickness of the shielding coil respectively.

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