Photovoltaic system power control method and device based on wireless charging of electric vehicle

Abstract

The invention provides a photovoltaic system power control method and device based on wireless charging of an electric vehicle, and the method comprises the steps: 1, building a transfer function model between a phase shift angle of the electric vehicle and the total current of a power utilization system, and determining the G order of the transfer function model; 2, identifying a model G parameter based on an auxiliary variable method; and step 3, designing an internal model controller based on the identified model G parameter, and adjusting the internal model controller according to the bus voltage to realize photovoltaic system power control. According to the invention, the wireless charging dynamic model of the electric vehicle is identified based on the auxiliary variable method. An internal model controller with the gain changing along with bus voltage under the condition of direct current bus voltage fluctuation is designed, so that the electric vehicle wireless charging system can restrain household rigid load switching and power fluctuation of photovoltaic power generation, balance between supply and demand of a household photovoltaic system is guaranteed, and the photovoltaic consumption rate is improved. The problem of flexible power adjustment of an electric vehicle wireless charging system in a household photovoltaic system is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless power transmission, and in particular to a photovoltaic system power control method and device based on wireless charging of electric vehicles. Background Art

[0002] As mobile energy storage devices, electric vehicles can be interconnected with home energy management systems and deeply involved in energy scheduling and optimization. In the context of the widespread popularization of electric vehicles, it is of great significance to study the control issues of electric vehicles participating in home load scheduling and optimization as typical flexible loads on the user side. Wireless charging of electric vehicles has the advantages of charging on the go, bidirectional charging and discharging, and no reliance on cables. It can reduce the maintenance cost of the charging system and provide users with a safer and more flexible charging experience. Therefore, it is necessary to study the power regulation method for electric vehicles with wireless charging. The current regulation method has the following disadvantages:

[0003] I. Traditional mechanism-based modeling methods require knowing the physical parameters of all household appliances, which increases the difficulty of modeling;

[0004] II. In the strong electromagnetic interference noise scenario of electric vehicle wireless charging, the sampling data will be interfered by noise, and the least squares algorithm cannot be used to identify the mathematical model with unbiased parameters;

[0005] III. In the scenario of household DC bus voltage fluctuation caused by the unstable characteristics of renewable energy power generation and load power imbalance, the robustness and anti-interference ability of the controller cannot be guaranteed. Summary of the invention

[0006] In view of the technical problems existing in the prior art, the present invention provides a photovoltaic system power control method and device based on wireless charging of electric vehicles, so as to solve the problem of flexible power regulation of wireless charging systems for electric vehicles in household photovoltaic systems.

[0007] According to a first aspect of the present invention, the present invention provides a photovoltaic system power control method based on wireless charging of electric vehicles, comprising the following steps:

[0008] Establish a transfer function model between the electric vehicle phase shift angle and the total current of the power system, and determine the G order of the transfer function model;

[0009] Identify the model G parameters based on the auxiliary variable method;

[0010] The internal model controller is designed based on the identified model G parameters, and the internal model controller is adjusted according to the bus voltage to realize the power control of the photovoltaic system.

[0011] Based on the above technical solution, the present invention can also make the following improvements.

[0012] Optionally, the transfer function model between the phase shift angle of the electric vehicle and the total current of the power system is established, and the transfer function model is expressed as:

[0013]

[0014] In the formula, and Represent the small signal parts of the output signal and the input signal respectively, Represents the small signal of the total system current without noise, represents the small signal of the phase shift angle; the specific identification target is the small signal transfer function G(s,θ) of the system at the operating point, where s represents the differential operator and θ represents the parameter vector to be identified; B and A represent the transfer function polynomial of the numerator and denominator respectively; e(t) represents the sampling noise; z(t) represents the sampled output signal.

[0015] Optionally, determining the G order of the transfer function model includes:

[0016] A step signal with a small amplitude is applied to the balance point of the inverter phase shift angle α at the input end of the household photovoltaic system to obtain the household total current Id curve output by the system, and the characteristics of the output curve are observed on the host computer;

[0017] When the output curve does not oscillate, the current system is fitted based on the first-order model;

[0018] When the output curve oscillates, the current system is fitted based on a second-order or higher-order model.

[0019] Optionally, the identification of model G parameters based on the auxiliary variable method includes:

[0020] After determining the order of G, the Box-Jenkins model is used to describe the relationship between the input and output of the system. The expression is as follows:

[0021]

[0022] Among them, θ represents the model parameter vector, n and m represent the denominator and numerator orders of the transfer function respectively; A(s,θ) and B(s,θ) represent the denominator and numerator polynomials respectively; a1,…,an represent the denominator parameters to be identified; b1,…,bm represent the denominator parameters to be identified; e(t) represents noise, which is mainly caused by the measurement process and system intrinsics.

[0023] Optionally, when the model order, i.e., the values ​​of n and m, is determined, the identification problem is converted to identifying the parameter vector θ by minimizing the output error objective function J, where the objective function J is expressed as follows:

[0024]

[0025] Among them, ε(k) represents the error between the identification model and the actual value, N represents the length of the sampling data, Represents the small signal of the total system current without noise, represents the small signal of the phase shift angle, A(s,θ) and B(s,θ) represent the denominator and numerator polynomials respectively.

[0026] Optionally, designing an internal model controller based on the identified model G parameters includes:

[0027] G m The output position of (s) is moved forward, and the internal model controller G Q (s) and G m (s) is equivalent to a unit feedback controller G c (s): means as follows:

[0028]

[0029] In the formula, G c (s) represents the feedback controller after equivalent changes; G Q (s) represents the internal model controller; G m (s) represents the reference model obtained by identification.

[0030] Optionally, designing the internal model controller based on the identified model G parameters includes:

[0031] When the reference model is first order, the internal model controller G Q (s) and G m (s) is equivalent to a unit feedback controller G c (s), at this time, G c (s) is expressed as a PI controller:

[0032]

[0033] Where s is the differential operator, λ is the filter parameter, and a and b are the first-order model parameters.

[0034] Optionally, adjusting the internal model controller according to the bus voltage to realize power control includes:

[0035] When the bus voltage V d When fluctuations occur, the gain of the identified model will change, that is, the bus voltage V d When the gain b of the reference model changes, the controller parameter coefficient 1 / (bλ) changes proportionally. Therefore, by detecting the bus voltage V d The real-time value is used to change the controller parameter coefficient 1 / (bλ) and reduce Vd Disturbance caused by fluctuations.

[0036] According to a second aspect of the present invention, a photovoltaic system power control device based on wireless charging of electric vehicles is provided, comprising: a household photovoltaic system, a data transceiver and a host computer; wherein,

[0037] The data transceiver device sends the data collected on the home photovoltaic system to the host computer for processing, or transmits the data generated by the host computer to the home photovoltaic system;

[0038] The host computer is used to generate an identification system model and an internal model controller.

[0039] Optionally, the host computer includes an identification system model generation module and a power control module, wherein;

[0040] The identification system model generation module is used to establish a transfer function model between the electric vehicle phase shift angle and the total current of the power system, determine the G order of the transfer function model, and identify the G parameters of the model based on the auxiliary variable method;

[0041] The power control module is used to design an internal model controller based on the identified model G parameters, and adjust the internal model controller according to the bus voltage to realize the power control of the photovoltaic system.

[0042] Technical effects and advantages of the present invention:

[0043] The photovoltaic system power control method and device based on wireless charging of electric vehicles provided by the present invention identifies the dynamic model of wireless charging of electric vehicles based on the auxiliary variable method. An internal model controller whose gain changes with the bus voltage under the condition of DC bus voltage fluctuation is further designed, so that the wireless charging system of electric vehicles can suppress the power fluctuation of household rigid load switching and photovoltaic power generation, ensure the balance between supply and demand of household photovoltaic systems, and improve the photovoltaic absorption rate.

[0044] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of a photovoltaic system power control method based on wireless charging of electric vehicles provided by an embodiment of the present invention;

[0046] Figure 2 is an internal model control block diagram provided by an embodiment of the present invention;

[0047] Figure 3is an internal model control equivalent transformation block diagram provided by an embodiment of the present invention;

[0048] Figure 4 is a flow chart of an identification algorithm provided by an embodiment of the present invention;

[0049] Figure 5 It is a diagram of current changes of each module when rigid load is switched under different controller parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be noted that the embodiment of the present invention mainly solves the problem of flexible power regulation of the wireless charging system of electric vehicles in a household photovoltaic system. The embodiment of the present invention proposes a photovoltaic system power control method and device based on wireless charging of electric vehicles. The method identifies the dynamic model of wireless charging of electric vehicles based on the auxiliary variable method. An internal model controller whose gain changes with the bus voltage under the condition of DC bus voltage fluctuation is further designed, so that the wireless charging system of electric vehicles can suppress the power fluctuation of household rigid load switching and photovoltaic power generation, ensure the balance between supply and demand of household photovoltaic systems, and improve the photovoltaic absorption rate.

[0052] Another purpose of the embodiment of the present invention is to solve how to establish a system model without measuring the parameters of household appliances and electric vehicle components. A dynamic model between the electric vehicle phase shift angle and the total current of the power system is established based on a data-driven modeling method, without the need to derive the mathematical relationship between the various physical quantities, thereby simplifying the modeling process;

[0053] Another purpose of the embodiment of the present invention is to solve the controller design method under the condition of bus voltage fluctuation, study the change law of the identified model parameters with respect to the bus voltage, and design a controller that follows the bus voltage change based on the internal model principle to adjust the wireless charging power of electric vehicles.

[0054] It can be understood that, based on the defects in the background technology, the embodiment of the present invention proposes a photovoltaic system power control method based on wireless charging of electric vehicles, including the following steps:

[0055] Step 1, establishing a transfer function model between the phase shift angle of the electric vehicle and the total current of the power system, and determining the G order of the transfer function model;

[0056] It should be noted that, first, the small signal transfer function model G from the inverter phase shift angle (system input u) to the household total current (system output y) is identified. The transfer function model G is expressed as follows:

[0057]

[0058] In the formula, and Represent the small signal parts of the output signal and the input signal respectively, Represents the small signal of the total system current without noise, represents the small signal of the phase shift angle; the specific identification target is the small signal transfer function G(s,θ) of the system at the operating point, where s represents the differential operator and θ represents the parameter vector to be identified; B and A represent the transfer function polynomial of the numerator and denominator respectively; e(t) represents the sampling noise; z(t) represents the sampled output signal.

[0059] Determining the G order of the transfer function model includes:

[0060] First, a step signal with a small amplitude is applied to the balance point of the system input end (inverter phase shift angle α) to obtain the system output (household total current Id) curve, and the characteristics of the output curve are observed on the host computer. When the output curve does not oscillate, the current system is fitted based on the first-order model; when the output curve oscillates, the current system is fitted based on the second-order or higher-order model.

[0061] Step 2, identifying the model G parameters based on the auxiliary variable method;

[0062] According to step 1 above, after determining the order of G, the Box-Jenkins model is used to describe the relationship between the input and output of the system. The expression is as follows:

[0063]

[0064] Among them, θ represents the model parameter vector, n and m represent the denominator and numerator orders of the transfer function respectively; A(s,θ) and B(s,θ) represent the denominator and numerator polynomials respectively; a1,…,an represent the denominator parameters to be identified; b1,…,bm represent the denominator parameters to be identified; e(t) represents noise, which is mainly caused by the measurement process and system intrinsics.

[0065] When the model order, that is, the values ​​of n and m, are determined, the identification problem is transformed into identifying the parameter vector θ by minimizing the output error objective function, which is expressed as follows:

[0066]

[0067] Among them, ε(k) represents the error between the identification model and the actual value, N represents the length of the sampling data, Represents the small signal of the total system current without noise, represents the small signal of the phase shift angle, A(s,θ) and B(s,θ) represent the denominator and numerator polynomials respectively.

[0068] The embodiment of the present invention identifies the model parameters based on the auxiliary variable method. When the experimental data contains colored noise, the method can still obtain an unbiased estimate and rewrite the error into the following form:

[0069]

[0070] The subscript f represents the experimental data processed by the filter f(s,θ)=1 / A(s,θ).

[0071] After the above processing, the model can be written in pseudo linear regression form:

[0072] y f (t k )=φ T (t k )θ+e(t k ) (6)

[0073] in

[0074]

[0075] Although the model in the above formula is linear with respect to θ, since the regression vector ФT(tk) is related to noise, in order to obtain an unbiased estimate of the parameter vector, it is necessary to identify it based on the auxiliary variable method:

[0076] θ(t k )=θ(t k -1)+g(t k )[y f (t k )-φ T (t k )θ(t k -1)] (7)

[0077]

[0078] P(t k )=P(t k -1)-g(t k )φ T (t k )P(t k -1) (9)

[0079] Where P is the covariance matrix of the identification error; is the specified auxiliary variable, which is uncorrelated with the noise and is expressed as follows:

[0080]

[0081] in, is the output after filtering by the auxiliary model;

[0082]

[0083] in, and are the denominator and numerator polynomials of the transfer function after system iterative update.

[0084] Step 3: Design an internal model controller based on the identified model G parameters, and adjust the internal model controller according to the bus voltage to realize photovoltaic system power control.

[0085] According to the model G identified in step 2, the internal model controller is further designed. The control strategy of the internal model controller is as follows: Figure 2 As shown, in order to make Y completely track R, construct G Q (s)=[G m (s)]-1. However, in practice, it is impossible to construct a system where the numerator order is greater than the denominator order, so it is necessary to m (s)]-1, so the internal model controller G Q The expression of (s) is:

[0086]

[0087] Among them, F(s) is a low-pass filter, r is usually set to 1; λ is the filter parameter, which is the only adjustable parameter of the internal model controller. Its size affects the rapidity and robustness of the controller; the smaller the parameter, the faster the response of the closed-loop system, and vice versa.

[0088] G m The output position of (s) is moved forward, such as Figure 3 As shown, the internal model controller G Q (s) and G m (s) is equivalent to a unit feedback controller G c (s):

[0089]

[0090] In the formula, G c (s) represents the feedback controller after equivalent changes; G Q (s) represents the internal model controller; G m (s) represents the reference model obtained by identification.

[0091] In particular, when the reference model is first order, that is, Gm When (s) = b / (s+a) (where a and b are first-order model parameters), G c (s) According to equations (12)-(14), it can be expressed as a PI controller:

[0092]

[0093] Where s is the differential operator, λ is the filter parameter, and a and b are the first-order model parameters.

[0094] When the bus voltage V d When fluctuations occur, the gain of the identified model will change, but the zeros and poles will remain unchanged. According to equation (15), the parameters of the controller are linearly related to the reference model. When the gain b of the reference model changes, the parameter coefficient 1 / (bλ) of the controller will also change proportionally. Therefore, by detecting the bus voltage V d The real-time value is used to change the controller parameter coefficient 1 / (bλ). Specifically, when the bus voltage V d When it increases, the system gain b of the identified model will increase proportionally, and the controller parameter coefficient 1 / (bλ) will decrease proportionally, thereby suppressing V d Disturbance caused by fluctuations.

[0095] Next, a typical household energy photovoltaic system including a string-string compensation wireless charging system (the main system parameters are shown in Table I) is taken as an example for further explanation. Specifically, the following steps are included:

[0096] Step 1, determine the G order of the transfer function model; identify the small signal transfer function from the inverter phase shift angle (system input u) to the household total current (system output y).

[0097] Table 1 Experimental equipment parameters

[0098]

[0099] First, determine the current operating point and operating frequency fs of the household photovoltaic system. Set to fs=100kHz, when the DC input voltage is 400V, the static output current of the system A step sequence is applied to the system input (i.e., the inverter phase shift angle), that is, Set to The data is transmitted to the STM32 chip through the RS485 module. The STM32 module transmits the signal to the FPGA chip on the circuit board to adjust the phase shift angle u(k) of the inverter. The collected current signal y(k) is transmitted to the STM32 chip using ST-LINK. Then the data is converted and transmitted to the host computer. The voltage waveform is generated using Matlab software. The waveform shows that when the system input step changes, the system output does not show obvious oscillation. Therefore, the system can be fitted based on the first-order system G.

[0100] Step 2, identify the model G parameters:

[0101] According to the above step 1, after determining that the model order is first order, at the inverter phase shift angle static operating point The M sequence is superimposed on the basis of the system input (inverter phase shift angle) and output (total input current of household load) at equal intervals with a sampling time Ts of 1 ms to obtain 1000 sets of sampling data. Upload 1000 sets of sampled data to the host computer, press Figure 4 The process shown in the figure is used to identify the model parameters. First, the model order is set to {n = 1, m = 1}, the initial parameter vector θ0 = [0, 0]T, and the maximum number of iterations Lmax = 1000. Then, based on equations (9), (11-13) and (15), the auxiliary variables and parameter vectors are calculated. When the maximum number of iterations is reached, the algorithm is terminated to obtain the model parameters θ = [1857, -2.3e4]T, so that the model transfer function G is:

[0102]

[0103] Step 3, design an internal model controller and adjust the internal model controller according to the bus voltage.

[0104] Based on the model parameters identified in step 2, the internal model controller is calculated according to equation (15):

[0105]

[0106] In equation (15), λ is set to 4e-3 to ensure the robustness of the system. The control period in the experiment is set to 1ms. The discrete controller dG is obtained by using the c2d command in Matlab for equation (17). c (s):

[0107]

[0108] Corresponding to the PI controller, its proportional coefficient P = -0.0109, and the integral coefficient I = -0.0202.

[0109] Figure 5The figure shows the current changes of each module when the rigid load is switched under different controller parameters, where the blue line represents the total current on the demand side; the green line represents the rigid load current; and the purple line represents the wireless charging module current. Figure 5 a is to change V d , the internal model controller parameters remain unchanged; Figure 5 b is to change V d , the internal model controller parameters change proportionally. As can be seen from the figure, when V d When changes occur, the corresponding model parameters and controller parameters will change linearly, so it is necessary to detect the current V d Real-time value, dynamically adjust the controller parameters to reduce V d Fluctuation interference. Therefore, the DC bus voltage was adjusted from 400V to 300V, and a rigid load power change experiment was carried out, assuming that the total photovoltaic power generation power was 2.7kW at this time. First, using the controller parameters used at the original 400V (i.e., P = -0.0109, integral coefficient I = -0.0202), the resistance of the rigid load was switched from 50Ω to 100Ω. When the inverter DC input voltage of 300V remained constant, the rigid load circuit dropped from 6A to 3A, and the total household load power dropped from 2.7kW to 1.8kW. After detecting the total power drop, the wireless charging system began to adjust the inverter phase shift angle to increase the wireless charging power from 0.9kW to 1.8kW. After about 40ms, the total power returned to the rated value of 2.7kW. In order to reduce V d To prevent the interference of the change on the control effect, the controller parameters are changed proportionally with the voltage according to Table 2 (i.e. P = -0.0150, integral coefficient I = -0.0152), and the above experiment is repeated. The current of each module is as follows: Figure 5 As shown in (b), it can be seen that the total power returns to the rated value after about 20ms, and the system regulation time is significantly reduced. d When the power fluctuation occurs, the flexible power control method for a photovoltaic system based on wireless charging of an electric vehicle described in an embodiment of the present invention can adjust the PI parameters according to the magnitude of the fluctuation to ensure the control effect.

[0110] Table 2 Different V d Model and controller parameters

[0111]

[0112] The first embodiment of the present invention is aimed at the scenario of an independent household power system, and a dynamic model between the phase shift angle of an electric vehicle and the total current of the power system is established based on a data-driven modeling method, without the need to derive the mathematical relationship between various physical quantities, thereby simplifying the modeling process;

[0113] Secondly, for the scenario where the bus voltage fluctuates due to the influence of the power grid or load, the change law of the identified model parameters with respect to the bus voltage is studied, and a controller that follows the bus voltage changes based on the internal model principle is designed to adjust the wireless charging power of electric vehicles.

[0114] According to the second aspect of the present invention, an embodiment of the present invention further provides a photovoltaic system power control device based on wireless charging of electric vehicles, comprising: a home photovoltaic system, a data transceiver and a host computer; wherein,

[0115] The data transceiver device can send the data collected on the system to the host computer for processing, and can also transmit the data generated by the host computer to the system;

[0116] The host computer can generate the identification system model and internal model controller.

[0117] Furthermore, the host computer includes an identification system model generation module and a power control module, wherein;

[0118] The identification system model generation module is used to establish a transfer function model between the electric vehicle phase shift angle and the total current of the power system, determine the G order of the transfer function model, and identify the G parameters of the model based on the auxiliary variable method;

[0119] The power control module is used to design an internal model controller based on the identified model G parameters, and adjust the internal model controller according to the bus voltage to realize the power control of the photovoltaic system.

[0120] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0121] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic system power control method based on wireless charging of electric vehicles, characterized in that: The following steps are involved: Establish a transfer function model between the electric vehicle phase shift angle and the total current of the power system, and determine the G order of the transfer function model; Identify the model G parameters based on the auxiliary variable method; The internal model controller is designed based on the identified model G parameters, and the internal model controller is adjusted according to the bus voltage to realize the power control of the photovoltaic system.

2. The photovoltaic system power control method based on wireless charging of electric vehicles according to claim 1 is characterized in that: In the transfer function model between the phase shift angle of the electric vehicle and the total current of the power system, the transfer function model expression is: In the formula, and Represent the small signal parts of the output signal and the input signal respectively, Represents the small signal of the total system current without noise, represents the small signal of the phase shift angle; the specific identification target is the small signal transfer function G(s,θ) of the system at the operating point, where s represents the differential operator and θ represents the parameter vector to be identified; B and A represent the transfer function polynomial of the numerator and denominator respectively; e(t) represents the sampling noise; z(t) represents the sampled output signal.

3. The photovoltaic system power control method based on wireless charging of electric vehicles according to claim 1 is characterized in that: Determining the G order of the transfer function model includes: A step signal with a small amplitude is applied to the balance point of the inverter phase shift angle α at the input end of the household photovoltaic system to obtain the household total current Id curve output by the system, and the characteristics of the output curve are observed on the host computer; When the output curve does not oscillate, the current system is fitted based on the first-order model; When the output curve oscillates, the current system is fitted based on a second-order or higher-order model.

4. The photovoltaic system power control method based on wireless charging of electric vehicles according to claim 1 is characterized in that: The identification of model G parameters based on the auxiliary variable method includes: After determining the order of the transfer function model G, the Box-Jenkins model is used to describe the relationship between the input and output of the system. The expression is as follows: Among them, θ represents the model parameter vector, n and m represent the denominator and numerator orders of the transfer function respectively; A(s,θ) and B(s,θ) represent the denominator and numerator polynomials respectively; a1,…,an represent the denominator parameters to be identified; b1,…,bm represent the denominator parameters to be identified; e(t) represents noise, which is mainly caused by the measurement process and system intrinsics.

5. The photovoltaic system power control method based on wireless charging of electric vehicles according to claim 4 is characterized in that: When the model order, i.e., the values ​​of n and m, are determined, the identification problem is transformed into identifying the parameter vector θ by minimizing the output error objective function J. The objective function J is expressed as follows: Among them, ε(k) represents the error between the identification model and the actual value, N represents the length of the sampling data, Represents the small signal of the total system current without noise, represents the small signal of the phase shift angle, A(s,θ) and B(s,θ) represent the denominator and numerator polynomials respectively.

6. The photovoltaic system power control method based on wireless charging of electric vehicles according to claim 1 is characterized in that: The design of the internal model controller based on the identified model G parameters includes: G m The output position of (s) is moved forward, and the internal model controller G Q (s) and G m (s) is equivalent to a unit feedback controller G c (s): means as follows: In the formula, G c (s) represents the feedback controller after equivalent changes; G Q (s) represents the internal model controller; G m (s) represents the reference model obtained by identification.

7. The photovoltaic system power control method based on wireless charging of electric vehicles according to claim 6 is characterized in that: When the reference model is first order, the internal model controller G Q (s) and G m (s) is equivalent to a unit feedback controller G c (s), at this time, G c (s) is expressed as a PI controller: Where s is the differential operator, λ is the filter parameter, and a and b are the first-order model parameters.

8. The photovoltaic system power control method based on wireless charging of electric vehicles according to claim 7 is characterized in that: The method of adjusting the internal model controller according to the bus voltage to realize power control comprises: When the bus voltage V d When fluctuations occur, the gain of the identified model will change, that is, the bus voltage V d When the gain b of the reference model changes, the controller parameter coefficient 1 / (bλ) changes proportionally; therefore, by detecting the bus voltage V d The real-time value is used to change the controller parameter coefficient 1 / (bλ) to reduce the bus voltage V d Disturbance caused by fluctuations.

9. A photovoltaic system power control device based on wireless charging of electric vehicles, characterized in that: include: Home photovoltaic system, data transceiver and host computer; among them, The data transceiver device sends the data collected from the home photovoltaic system to the host computer for processing, and transmits the data generated by the host computer to the home photovoltaic system; The host computer is used to generate an identification system model and an internal model controller.

10. The photovoltaic system power control device based on wireless charging of electric vehicles according to claim 9, characterized in that: The host computer includes an identification system model generation module and a power control module, wherein; The identification system model generation module is used to establish a transfer function model between the electric vehicle phase shift angle and the total current of the power system, determine the G order of the transfer function model, and identify the G parameters of the model based on the auxiliary variable method; The power control module is used to design an internal model controller based on the identified model G parameters, and adjust the internal model controller according to the bus voltage to realize the power control of the photovoltaic system.

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