阅读说明：本技术 一种感应式无线电能传输系统的状态观测的方法及系统 (state observation method and system for induction type wireless power transmission system ) 是由 黄晓华 杨国勋 徐翀 寇秋林 张倩 徐锦星 龙国标 陈海洋 于 2019-06-19 设计创作，主要内容包括：本发明公开了一种感应式无线电能传输系统的状态观测的方法及系统,其中方法包括：测量感应式无线电能传输系统的原边电流幅值；基于所述原边电流幅值,利用扩展卡尔曼滤波器对所述感应式无线电能传输系统的副边信息进行预测,获取预测结果；通过扩展卡尔曼滤波器对所述预测结果进行校验,获取经过校验的所述感应式无线电能传输系统的副边信息。(The invention discloses a method and a system for observing the state of an induction type wireless power transmission system, wherein the method comprises the following steps: measuring a primary side current amplitude of the induction type wireless power transmission system; predicting secondary side information of the induction type wireless power transmission system by using an extended Kalman filter based on the primary side current amplitude to obtain a prediction result; and verifying the prediction result through an extended Kalman filter to obtain the verified secondary information of the induction type wireless power transmission system.)

1. a method of state observation of an inductive wireless power transfer system, the method comprising:

measuring a primary side current amplitude of the induction type wireless power transmission system;

Predicting secondary side information of the induction type wireless power transmission system by using an extended Kalman filter based on the primary side current amplitude to obtain a prediction result;

and verifying the prediction result through an extended Kalman filter to obtain the verified secondary information of the induction type wireless power transmission system.

2. The method of claim 1, the predicting secondary-side information of the inductive wireless power transfer system using an extended kalman filter being:

P_k+1/k＝FP_k/kF^T+Q

Wherein k/k represents filtering performed at time k based on time k, and k +1/k represents parameter prediction performed at time k +1 based on time k;Is the system state variable at the time k,Is the system state variable at time k + 1; f represents state transitionMoving a matrix, wherein F links the state at the k-1 moment with the state at the current k moment, is an n multiplied by n order square matrix, and F is a basis for predicting the state variable by an algorithm; f^Trepresents a transpose of F; u. of_kRepresents the control gain at time k; p is the covariance matrix of the error; q represents the covariance of the process excitation noise, which is the error between the state transition matrix and the actual process.

3. The method of claim 1, the verifying the prediction by the extended kalman filter being:

P_k+1/k+1＝P_k+1/k-K_k+1H_k+1P_k+1/k

Wherein the content of the first and second substances,

In the formula (I), the compound is shown in the specification,The prior state estimated value at the k moment is represented and is unreliable estimation made by the algorithm according to the previous iteration result;The posterior state estimation value of k +1 moment is represented and is the optimal estimation value of the moment to be output, and the value is the result of Kalman filtering; p_k+1/kThe prior estimation covariance at the moment k is represented, and as long as the initial covariance matrix is not 0, the sampling value of the initial covariance matrix has little influence on the filtering effect and can be quickly converged; p_k+1/k+1the posteriori estimated covariance, representing the time k +1, is one of the filtering results; k_kthe Kalman gain at the moment k is used for eliminating system estimation errors and is an intermediate result of filtering; k_k+1Representing the kalman gain at time k + 1; y is_k+1representing the measured value, is an m-order vector; h represents a measurement matrix which is an m multiplied by n order matrix and converts m dimension measurement values into n dimensions corresponding to state variables; s represents the measurement noise covariance, S is a number, which is a characteristic associated with the instrument as a known condition input filter; the filtering effect is deteriorated when the value of S is too large or too small, the smaller the value of S is, the faster the S is converged, and the proper value of S is searched by an experimental means and then the value of S is utilized to carry out real filtering; h_kA measurement matrix representing time k, H_k+1A measurement matrix representing the time k +1, H_kT₊₁Represents H_k+1transposing; y denotes the measured value at time k.

4. the method of claim 1, the secondary side information comprising: output current, output voltage, and output power.

5. A system for condition observation of an inductive wireless power transfer system, the system comprising:

The measuring unit is used for measuring the primary side current amplitude of the induction type wireless power transmission system;

The prediction unit is used for predicting the secondary side information of the induction type wireless power transmission system by using an extended Kalman filter based on the primary side current amplitude value to obtain a prediction result;

And the verification unit is used for verifying the prediction result through an extended Kalman filter to acquire verified secondary information of the induction type wireless power transmission system.

6. The system according to claim 5, wherein the prediction unit is configured to predict the secondary information of the inductive wireless power transmission system by using an extended kalman filter, and specifically includes:

P_k+1/k＝FP_k/kF^T+Q

k/k represents filtering based on the time k at the time k, and k +1/k represents parameter prediction based on the time k at the time k + 1;Is the system state variable at the time k,Is the system state variable at time k + 1; f represents a state transition matrix, the state at the k-1 moment is associated with the current state at the k moment, the state transition matrix is an n multiplied by n order square matrix, and the F is a basis for predicting the state variable by an algorithm; f^TRepresents a transpose of F; u. of_kRepresents the control gain at time k; p is the covariance matrix of the error; q represents the covariance of the process excitation noise, which is the error between the state transition matrix and the actual process.

7. the system of claim 5, the verification unit to verify the prediction result by an extended Kalman filter as:

P_k+1/k+1＝P_k+1/k-K_k+1H_k+1P_k+1/k

Wherein the content of the first and second substances,

in the formula (I), the compound is shown in the specification,The prior state estimated value at the k moment is represented and is unreliable estimation made by the algorithm according to the previous iteration result;the posterior state estimation value of k +1 moment is represented and is the optimal estimation value of the moment to be output, and the value is the result of Kalman filtering; p_k+1/kThe prior estimation covariance at the moment k is represented, and as long as the initial covariance matrix is not 0, the sampling value of the initial covariance matrix has little influence on the filtering effect and can be quickly converged; p_k+1/k+1the posteriori estimated covariance, representing the time k +1, is one of the filtering results; k_kThe Kalman gain is expressed, can be used for eliminating system estimation errors, and is an intermediate result of filtering; k_k+1Representing the kalman gain at time k + 1; y is_k+1representing the measured value, is an m-order vector; h represents a measurement matrix which is an m multiplied by n order matrix and converts m dimension measurement values into n dimensions corresponding to state variables; s represents the measurement noise covariance, S is a number, which is a characteristic associated with the instrument as a known condition input filter; the filtering effect is deteriorated when the value of S is too large or too small, the smaller the value of S is, the faster the S is converged, and the proper value of S is searched by an experimental means and then the value of S is utilized to carry out real filtering; h_ka measurement matrix representing time k, H_k+1Represents the measurement matrix at time k +1,Represents H_k+1Transposing; y denotes the measured value at time k.

8. the system of claim 5, the secondary side information comprising: output current, output voltage, and output power.

Technical Field

the invention relates to the technical field of wireless power transmission, in particular to a method and a system for observing the state of an induction type wireless power transmission system.

background

in order to efficiently obtain the largest possible power transmission capability, the primary side system and the secondary side system of the wireless power transmission WPT system must be capable of working in coordination, so the role of the secondary side information feedback system in real-time power transmission control is increasingly important. However, due to the separation characteristic of the primary and secondary sides, the state and control information of the secondary side cannot be directly fed back to the primary side controller. To this end, researchers have investigated a variety of data communication systems to overcome the bottleneck problem of information feedback, such as: RF radio frequency communication, and power information dual modulation techniques. Due to the switching of the power switch device in the wireless power transmission WPT system and a large number of parasitic parameters in the circuit, the wireless power transmission WPT system becomes an environment with a large number of electromagnetic noises, and the normal work of a data communication system can be directly interfered. In summary, the conventional information feedback system has the problem that the electromagnetic interference can directly influence the normal work of the data communication system. With the popularization of practical application, designing an effective and convenient secondary information feedback system becomes a research direction in the field of wireless power transmission WPT systems.

therefore, there is a need for a technique to enable observation of the state of an inductive wireless power transfer system.

Disclosure of Invention

The technical scheme of the invention provides a method and a system for observing the state of an induction type wireless power transmission system, which aim to solve the problem of observing the state of the induction type wireless power transmission system.

In order to solve the above problem, the present invention provides a method for observing a state of an inductive wireless power transmission system, the method comprising:

Measuring a primary side current amplitude of the induction type wireless power transmission system;

Predicting secondary side information of the induction type wireless power transmission system by using an extended Kalman filter based on the primary side current amplitude to obtain a prediction result;

and verifying the prediction result through an extended Kalman filter to obtain the verified secondary information of the induction type wireless power transmission system.

Preferably, the predicting the secondary side information of the inductive wireless power transmission system by using the extended kalman filter is as follows:

P_k+1/k＝FP_k/kF^T+Q

wherein k/k represents filtering performed at time k based on time k, and k +1/k represents parameter prediction performed at time k +1 based on time k;Is the system state variable at the time k,is the system state variable at time k + 1; f represents a state transition matrix, the state at the k-1 moment is associated with the current state at the k moment, the state transition matrix is an n multiplied by n order square matrix, and the F is a basis for predicting the state variable by an algorithm; f^TRepresents a transpose of F; u. of_krepresents the control gain at time k; p is the covariance matrix of the error; q represents the covariance of the process excitation noise, which is the shapeerror between the state transition matrix and the actual process.

preferably, the checking the prediction result through the extended kalman filter is performed by:

P_k+1/k+1＝P_k+1/k-K_k+1H_k+1P_k+1/k

Wherein the content of the first and second substances,

in the formula (I), the compound is shown in the specification,The prior state estimated value at the k moment is represented and is unreliable estimation made by the algorithm according to the previous iteration result;The posterior state estimation value of k +1 moment is represented and is the optimal estimation value of the moment to be output, and the value is the result of Kalman filtering; p_k+1/kthe prior estimation covariance at the moment k is represented, and as long as the initial covariance matrix is not 0, the sampling value of the initial covariance matrix has little influence on the filtering effect and can be quickly converged; p_k+1/k+1The posteriori estimated covariance, representing the time k +1, is one of the filtering results; k_kThe Kalman gain is expressed, can be used for eliminating system estimation errors, and is an intermediate result of filtering; k_k+1representing the kalman gain at time k + 1; y is_k+1representing the measured value, is an m-order vector; h represents a measurement matrix which is an m multiplied by n order matrix and converts m dimension measurement values into n dimensions corresponding to state variables; s represents the measurement noise covariance, S is a number, which is a characteristic associated with the instrument as a known condition input filter; value of SIf the S value is too small, the filtering effect is poor, the smaller the S value is, the faster the S value is converged, and the proper S value is found through an experimental means and then the S value is utilized to carry out real filtering; h_ka measurement matrix representing time k, H_k+1Represents the measurement matrix at time k +1,represents H_k+1transposing; y denotes the measured value at time k.

preferably, the secondary side information includes: output current, output voltage, and output power.

According to another aspect of the present invention, there is provided a system for state observation of an inductive wireless power transfer system, the system comprising:

The measuring unit is used for measuring the primary side current amplitude of the induction type wireless power transmission system;

The prediction unit is used for predicting the secondary side information of the induction type wireless power transmission system by using an extended Kalman filter based on the primary side current amplitude value to obtain a prediction result;

And the verification unit is used for verifying the prediction result through an extended Kalman filter to acquire verified secondary information of the induction type wireless power transmission system.

the technical scheme of the invention provides a method and a system for observing the state of an induction type wireless power transmission system, wherein the method comprises the following steps: measuring a primary side current amplitude of the induction type wireless power transmission system; predicting secondary side information of the induction type wireless power transmission system by using an extended Kalman filter based on the primary side current amplitude to obtain a prediction result; and verifying the prediction result through an extended Kalman filter to obtain the verified secondary information of the induction type wireless power transmission system. The technical scheme of the invention solves the problem that the accurate online acquisition of the secondary side data becomes relatively difficult due to the non-contact form of the primary and secondary side subsystems. In order to obtain relatively accurate secondary data to perform feedback control on power, a state observer based on an Extended Kalman Filter (EKF) theory is applied to a Wireless Power Transmission (WPT) system. The extended Kalman filter EKF model contains statistical information of system errors and measurement errors, so that the state observer based on the extended Kalman filter EKF can observe the state of the system and filter out the influence of electromagnetic interference and measurement noise.

Drawings

A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:

Fig. 1 is a flowchart of a method for state observation of an inductive wireless power transfer system according to a preferred embodiment of the present invention;

Fig. 2 is a system configuration diagram for state observation of an inductive wireless power transfer system according to a preferred embodiment of the present invention.

Detailed Description

the exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.

Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.

Due to the non-contact form of the primary and secondary side subsystems, it becomes relatively difficult to accurately obtain the data of the secondary side on line. In order to obtain relatively accurate secondary data to perform feedback control on power, a state observer based on an Extended Kalman Filter (EKF) theory is applied to a Wireless Power Transmission (WPT) system. The extended Kalman filter EKF model contains statistical information of system errors and measurement errors, so that the state observer based on the extended Kalman filter EKF can observe the state of the system and filter out the influence of electromagnetic interference and measurement noise. In this application, the secondary side information includes: output current, output voltage, and output power.

Fig. 1 is a flowchart illustrating a method for observing a state of an inductive wireless power transmission system according to a preferred embodiment of the present invention.

Preferably, in step 101: and measuring the primary current amplitude of the induction type wireless power transmission system.

since the current of the primary coil of the system is easily measured, the measurement input of the observer model is defined as the current i of the primary coil₁Amplitude, denoted as | i₁|，|i₁| can be expressed as:

the 8 th order dynamic model of the WPT system can be expressed in the following structural form:

Wherein x is_e＝＜·＞₁And u represents the dc bus voltage input of the H-bridge resonant converter.

Although the formula (2) is linear, since the formula (3) is a non-linear expression, the state observer of the system cannot be established directly by using the kalman filtering theory of the linear problem, but the extended kalman filtering theory of the non-linear problem should be selected to establish the state observer of the system. In addition, the extended kalman filter theory is implemented based on a discrete system, and therefore, equations (2) and (3) must be discretized into a gaussian-markov equation form:

Wherein the content of the first and second substances,

T_sIs the system sampling period. Equations (6) and (7) are relatively complex to compute, and F and G can be approximated by the following method:

When the accuracy requirement is high, several terms can be taken as approximations in the expansion.

in addition, w (k) and v (k) are respectively systematic error and measurement noise, which are random white noise variables, and can be expressed by mean value and covariance in statistics, and the mean value expression is:

E{w(k)}＝E{v(k)}＝0 (10)

The covariance matrix of the systematic error is defined as:

E{w(k)w(k)^T}＝Q (11)

The covariance matrix of the measured noise can be defined as:

E{v(k)v(k)^T}＝S (12)

where Q is an 8 × 8 constant matrix and S is a 1 × 1 constant matrix. The initial value vector xe (0) of the state variables can be represented by its mean and covariance matrix:

H (k) is the formula (13) with respect to the vector x_ethe gradient is a time-varying gradient.

since equation (12) models the linearization of the WPT system, the optimization observer is an approximate estimate of the system whose accuracy will be verified in simulation and experimental results.

The premise for realizing the state observer based on the EKF theory is as follows: primary winding current i₁Must be measured before each recursive computation of the algorithm begins. Then, based on | i₁The EKF algorithm can estimate the vector I₂Andtherefore, the system controller can control the working frequency or the modulation mode of the primary side resonant converter according to the estimated secondary side state information, and high-performance power flow control is realized. The application relates to establishment and implementation of a system state observer.

Preferably, at step 102: and predicting the secondary side information of the induction type wireless power transmission system by using an extended Kalman filter based on the primary side current amplitude to obtain a prediction result. Preferably, the secondary side information of the inductive wireless power transmission system is predicted by using an extended kalman filter as follows:

P_k+1/k＝FP_k/kF^T+Q

k/k denotes filtering performed at time k, and k +1/k denotes parameter prediction performed at time k + 1.Is the system state variable at the time k,is the system state variable at time k + 1. F represents a state transition matrix, which relates the state at the moment k-1 to the state at the current moment k, and is an n x n-order square matrix which is the basis for the prediction of the state variables by the algorithm. u. of_kindicating the control gain at time k. P is the covariance matrix of the error. Q represents the covariance of the process excitation noise, which is the error between the state transition matrix and the actual process.

The EKF algorithm can be divided into two components, prediction and correction, and in conjunction with the dynamic system models (4) and (5) discussed above, the EKF algorithm can be expressed as follows:

step1 prediction

P_k+1/k＝FP_k/kF^T+Q (16)

Wherein k/k represents filtering performed at time k based on time k, and k +1/k represents parameter prediction performed at time k +1 based on time k;Is the system state variable at the time k,Is the system state variable at time k + 1; f represents a state transition matrix, the state at the k-1 moment is associated with the current state at the k moment, the state is an n multiplied by n order square matrix, and the F is a basis for predicting the state variable by an algorithm; f^Trepresents a transpose of F; u. of_kRepresents the control gain at time k; p is the covariance matrix of the error; q represents the covariance of the process excitation noise, which is the error between the state transition matrix and the actual process.

Preferably, in step 103: and verifying the prediction result through the extended Kalman filter to obtain the verified secondary information of the induction type wireless power transmission system. Preferably, the prediction result is verified by the extended kalman filter as follows:

P_k+1/k+1＝P_k+1/k-K_k+1H_k+1P_k+1/k

wherein the content of the first and second substances,

in the formula (I), the compound is shown in the specification,the prior state estimated value at the k moment is represented and is unreliable estimation made by the algorithm according to the previous iteration result;The posterior state estimation value of k +1 moment is represented and is the optimal estimation value of the moment to be output, and the value is the result of Kalman filtering; p_k+1/kThe prior estimation covariance at the moment k is represented, and as long as the initial covariance matrix is not 0, the sampling value of the initial covariance matrix has little influence on the filtering effect and can be quickly converged; p_k+1/k+1The posteriori estimated covariance, representing the time k +1, is one of the filtering results; k_kThe Kalman gain is expressed, can be used for eliminating system estimation errors, and is an intermediate result of filtering; k_k+1Representing the kalman gain at time k + 1; y is_k+1representing the measured value, is an m-order vector; h represents a measurement matrix which is an m multiplied by n order matrix and converts m dimension measurement values into n dimensions corresponding to state variables; s denotes the measurement noise covariance, S is a number, which is a property associated with the instrument as a known conditionan input filter; the filtering effect is deteriorated when the value of S is too large or too small, the smaller the value of S is, the faster the S is converged, and the proper value of S is searched by an experimental means and then the value of S is utilized to carry out real filtering; h_kA measurement matrix representing time k, H_k+1Represents the measurement matrix at time k +1,Represents H_k+1transposing; y denotes the measured value at time k.

Step2 correction

P_k+1/k+1＝P_k+1/k-K_k+1H_k+1P_k+1/k (19)

Wherein the content of the first and second substances,

p, Q, S are covariance matrices of error, system noise and measurement noise, respectively. K_k+1For kalman gain, it can be used to eliminate systematic estimation errors. Superscript "^" represents the estimate. k/k denotes filtering performed at time k, and k +1/k denotes parameter prediction performed at time k + 1.

E_k+1For state quantity vector x in GSSA model_eThe transformation matrix with the actual system state vector can be expressed as:

Wherein, t_k+1Denotes the (k + 1) th sampling moment, and omega is the operating angular frequency of the WPT system.

State quantities in GSSA models are not the actual system operationLine state quantity, so the output of EKF algorithm must pass through E_k+1and the actual state observed value of the system can be obtained after processing. The treatment method comprises the following steps:

Wherein the content of the first and second substances,

then, the observation vector Z_k+1The amplitude and phase of (a) can be expressed as follows:

wherein M is_k＝[|i₁(k)||i₂(k)|Θ_kis Z_kthe phase component of (a). Wherein, | i₁(k) I and I₂(k) i is the current in the primary and secondary winding coils respectively,andThe terminal voltages of the primary side compensation capacitor and the secondary side compensation capacitor are respectively.

From the above algorithm analysis, the EKF theory-based state observer provided in this chapter can observe the state information of the secondary side of the WPT system, so that it is not necessary to add an information measurement sensor network and a secondary side feedback information communication subsystem on the secondary side without special requirements.

The extended Kalman filter observer provided by the embodiment of the application has high convergence speed and high precision, and basically can meet the real-time observation requirement on the secondary state. According to the WPT system, the operation state information of the secondary side of the WPT system, such as output current, output voltage, output power and the like, can be dynamically estimated simply by measuring the amplitude of the current of the primary side winding, so that a state measurement sensor network of the secondary side and an information feedback module from the secondary side to the primary side can be omitted, the complexity and the cost of the system are reduced, and the reliability of the system is improved.

fig. 2 is a system configuration diagram for state observation of an inductive wireless power transfer system according to a preferred embodiment of the present invention. As shown in fig. 2, a system for state observation of an inductive wireless power transfer system includes:

The measuring unit 201 is used for measuring a primary side current amplitude of the inductive wireless power transmission system;

The prediction unit 202 is configured to predict secondary information of the inductive wireless power transmission system by using an extended kalman filter based on the primary current amplitude, and obtain a prediction result, and specifically includes: output current, output voltage, and output power.

And the checking unit 203 is configured to check the prediction result through the extended kalman filter, and obtain the secondary information of the checked inductive wireless power transmission system.

The system 200 for observing the state of the inductive wireless power transmission system in the preferred embodiment of the present invention corresponds to the method 100 for observing the state of the inductive wireless power transmission system in the preferred embodiment of the present invention, and will not be described herein again.

The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.

Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.