A startup algorithm for predicting and estimating current at the receiving end of a wireless charging system

Abstract

The present invention relates to the technical field of wireless charging, and discloses a startup algorithm for predicting and estimating current at the receiving end of a wireless charging system. The algorithm adopts a Kalman filter algorithm and is applied to the wireless charging system to optimize the current rise rate under different mutual inductance conditions. The present invention can dynamically adjust the step size to ensure that the rising trend of the startup current remains consistent under different mutual inductance conditions, effectively overcoming the problems of overshoot and backflow; enhance the adaptability of the system, ensure the smooth transition of current control, and improve the stability and reliability of the wireless charging process; optimize energy management, avoid unnecessary energy waste, improve the efficiency of the entire wireless charging system, reduce energy loss caused by overshoot and backflow, help extend battery life and reduce maintenance costs; improve user experience, shorten waiting time, avoid device interference caused by current mutation, and make the wireless charging process smoother and more secure.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging, and in particular to a startup algorithm for predicting and estimating current at a receiving end of a wireless charging system. Background Art

[0002] In the field of wireless charging, magnetic field coupling technology has become a core method for wireless energy transmission due to its superior efficiency and convenience. This process primarily relies on coils as a medium to convert electromagnetic waves generated by a ground-based transmitter into alternating current (AC). This is then processed by a rectifier circuit and converted into stable and reliable DC power, ultimately providing power for various in-vehicle electronic devices.

[0003] However, the startup phase of wireless charging systems presents a challenge: fluctuations in mutual inductance due to variables such as coil position lead to unstable energy coupling, which in turn affects the power supply's ability to deliver power to the load. This phenomenon is directly related to the controllability of the current rise rate. In this context, the concept of "step size" has emerged—it defines the adjustment range of converter control parameters (such as duty cycle or phase shift angle). A larger step size allows for faster control parameter changes and a more responsive current response.

[0004] Traditional switching power supply startup strategies often use a fixed step size. When approaching the set target, they switch to a PID or 2P2Z controller for fine-tuning. However, for in-vehicle wireless charging systems, especially those using a rectifier bridge plus a Buck converter, fixed-step startup methods have limitations. Due to the uncertainty of mutual inductance, current rise performance varies significantly under the same fixed step size: with high mutual inductance, the current may rise sharply, causing overshoot; with low mutual inductance, the current increases slowly, even causing load reverse current or startup failure. Traditional switching power supply startup strategies have the following disadvantages:

[0005] 1) Lack of Adaptability: Traditional wireless charging systems use a fixed-step adjustment of control parameters during startup, failing to account for the impact of changes in mutual inductance on the current rise rate. This non-adaptive design results in varying system performance under varying mutual inductance conditions, potentially leading to overshoot or startup failure.

[0006] 2) Insufficient current control accuracy: The fixed step size method is difficult to accurately control the current rise process. Especially when the mutual inductance is large, it may cause the current to quickly exceed the target value, causing overshoot and posing a threat to system stability and safety.

[0007] 3) Potential energy waste and reduced efficiency: Overshoot and reverse current not only affect the user experience but can also lead to energy waste and reduced system efficiency. Under low mutual inductance conditions, slow current rise can lead to extended startup time, also affecting efficiency.

[0008] 4) Impaired system reliability: This reduces the overall reliability of the wireless charging system, increases the failure rate, and also affects the user experience and satisfaction.

[0009] 5) Single control strategy: The current control strategy is unable to cope with the changing mutual inductance situation, lacks flexibility and intelligence, and cannot make optimal adjustments according to actual working conditions. Summary of the Invention

[0010] The purpose of the present invention is to overcome the above shortcomings of the prior art and provide a startup algorithm for predicting and estimating current at the receiving end of a wireless charging system.

[0011] The object of the present invention is achieved by the following technical solution: A startup algorithm for predicting and estimating current at a receiving end of a wireless charging system comprises the following steps:

[0012] S1. Determine whether the receiving end has received a power-on instruction. If yes, execute step S2; if no, execute step S1 again.

[0013] S2, the controller is initialized;

[0014] S3, initializing the parameters of the Kalman filter;

[0015] S4, the Buck converter enters the on state and starts to output current to the load;

[0016] S5. Use a current sensor to measure the output current of step S4 and obtain the measured value z k ;

[0017] S6, predict the state vector of the Kalman filter at the next moment, the state vector X at the next moment k|k-1 The calculation formula is

[0018] X k|k-1 =F k X k-1|k-1 +B k u k ,

[0019] Among them, X k-1|k-1 is the state vector at the previous moment, F k is the state transfer matrix, B k is the control input matrix, u k is the step size of Buck converter, k is the time step in each frame;

[0020] S7, calculate the state vector X at the next moment of step S6 k|k-1 The corresponding error covariance matrix P at the next moment k|k-1, the error covariance matrix P k|k-1 The calculation formula is

[0021]

[0022] Among them, P k-1|k-1 is the error covariance matrix of the previous moment, Q k is the process noise covariance matrix, T is the transpose of the matrix;

[0023] S8, according to the error covariance matrix P of the next moment of step S7 k|k-1 , calculate the Kalman gain, the Kalman gain K k The calculation formula is

[0024] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ,

[0025] Where H is the observation matrix and R is the measurement noise covariance matrix;

[0026] S9, according to the measured value z of step S5 k and the state vector X at the next moment of step S6 k|k-1 , update the state vector of the Kalman filter according to the Kalman gain K in step S8 k and the error covariance matrix P at the next moment of step S7 k|k-1 , update the error covariance matrix of the Kalman filter, and obtain the updated state vector X k|k And the updated error covariance matrix P k|k , thus obtaining the Kalman filter after updating the parameters, the updated state vector X k|k The calculation formula is

[0027] X k|k =X k|k-1 +K k (z k -HX k|k-1 ),

[0028] Updated error covariance matrix P k|k The calculation formula is

[0029] P k|k =(EK k H)P k|k-1 , E is the identity matrix;

[0030] S10, after using the Kalman filter with updated parameters in step S9 to estimate, based on the target current rising rate To adjust the step size u of the Buck converter k , get the adjusted step length u k+1 , the adjusted step size u k+1 The calculation formula is

[0031]

[0032] in, is the estimated value of the current rising rate given by the Kalman filter, and α is the adjustment factor;

[0033] S11, Buck converter according to the adjusted step size u in step S10 k+1 To update the duty cycle;

[0034] S12: Determine whether the output current of the Buck converter is close to the target current value based on the duty cycle of step S11. If so, end; otherwise, return to step S5.

[0035] A better choice, the initial parameters of the Kalman filter include the initial state vector X0, the initial error covariance matrix P0, and the process noise covariance matrix Q k and the measurement noise covariance matrix R.

[0036] A better choice is that the current sensor in step S5 is a mutual inductor.

[0037] A better choice is that the current sensor in step S5 is a Hall sensor.

[0038] A better option is that the receiving end of step S1 includes a resonant cavity, a rectifier bridge and a Buck converter connected in sequence.

[0039] A better choice is that the resonant cavity is an LCC resonant cavity.

[0040] A better choice is that the resonant cavity is the SS resonant cavity.

[0041] The present invention has the following advantages and beneficial effects compared to the prior art:

[0042] The present invention uses a startup algorithm for predicting and estimating current at the receiving end of a wireless charging system, which can dynamically adjust the step size to ensure that the rising trend of the startup current remains consistent under different mutual inductance conditions, effectively overcoming the problems of overshoot and backflow; enhancing system adaptability, ensuring a smooth transition of current control, and improving the stability and reliability of the wireless charging process; optimizing energy management, avoiding unnecessary energy waste, improving the efficiency of the entire wireless charging system, reducing energy loss caused by overshoot and backflow, helping to extend battery life and reduce maintenance costs; improving user experience, shortening waiting time, avoiding equipment interference caused by current mutations, and making the wireless charging process smoother and more secure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of a startup algorithm for predicting and estimating current at a receiving end of a wireless charging system according to the present invention;

[0044] Figure 2 It is a schematic diagram of an LCC topology receiving end of a start-up algorithm for predicting and estimating current at a receiving end of a wireless charging system according to the present invention;

[0045] Figure 3 It is a schematic diagram of an SS topology receiving end of a start-up algorithm for predicting and estimating current at a receiving end of a wireless charging system according to the present invention;

[0046] Figure 4 This is a schematic diagram of the current variation over time (with a fixed step size) under different mutual inductance conditions of a startup algorithm for predicting and estimating current at a receiving end of a wireless charging system according to the present invention;

[0047] Figure 5 It is a schematic diagram of the current variation over time under different mutual inductance conditions of a startup algorithm for predicting and estimating current at the receiving end of a wireless charging system of the present invention (Kalman filter predicts current optimization step size is turned on). DETAILED DESCRIPTION

[0048] The purpose of the present invention is described in further detail below with reference to the accompanying drawings and specific examples. The examples cannot be described one by one here, but the implementation methods of the present invention are not limited to the following examples.

[0049] like Figure 1 As shown, in this embodiment, a startup algorithm for predicting and estimating current at the receiving end of a wireless charging system uses a Kalman filter algorithm applied to the wireless charging system to optimize the current rise rate under different mutual inductance conditions. This process is designed to dynamically adjust the step size of the Buck converter at the receiving end, specifically including the following steps:

[0050] S1, determine whether the receiving end receives the power-on instruction, if yes, execute step S2, if not, execute step S1;

[0051] like Figure 2 and 3 As shown in the figure, the receiving end is the vehicle end of the wireless charging system. The receiving end has two structures. The first structure is an LCC resonant cavity, a rectifier bridge, and a Buck converter connected in sequence; the second structure is an SS resonant cavity, a rectifier bridge, and a Buck converter connected in sequence.

[0052] S2. The controller is initialized. After receiving the power-on command at the receiving end, the controller's various communication peripherals and control parameters are initialized to prepare for the power-on action.

[0053] S3, initialize the parameters of the Kalman filter, the initial parameters of the Kalman filter include the initial state vector X0, the initial error covariance matrix P0, the process noise covariance matrix Q and the measurement noise covariance matrix R. Among them, the process noise covariance matrix Q k The measurement noise covariance matrix R needs to be adjusted according to the actual characteristics of the system and the accuracy of the measurement equipment to obtain the actual uncertainty level. That is, it is obtained by mathematical analysis of experimental data in advance or by parameter extraction of the simulation model. In the wireless charging system of this embodiment, the current rise rate is and current value I k is considered as part of the wireless charging system state. Therefore, the state vector X k The expression formula is:

[0054] Here, k is the time step in each frame.

[0055] S4, the Buck converter enters the on state and starts to output current to the load;

[0056] S5. Use a current sensor to measure the output current of step S4 and obtain the measured value z k , measured value z k Output current measurement value; the current sensor can use a transformer or a Hall sensor. ;Measurement value z k It is the direct observation data about the status of the wireless charging system obtained from sensors or other measurement devices. k It often contains noise, meaning the measurements do not always equal the true state of the wireless charging system due to sensor limitations, environmental factors, or other interference sources.

[0057] S6, predict the state vector of the Kalman filter at the next moment, the state vector X at the next moment k|k-1 The calculation formula is

[0058] X k|k-1 =F k X k-1|k-1 +B k u k ,

[0059] Among them, X k-1|k-1 is the state vector of the previous moment, X in the first cycle k-1|k-1 is the initial state vector X0, F k is the state transfer matrix, B k is the control input matrix, u k is the step size of Buck converter, k is the time step in each frame;

[0060] S7, calculate the state vector X at the next moment of step S6 k|k-1 The corresponding error covariance matrix P at the next moment k|k-1 , the error covariance matrix P k|k-1 The calculation formula is

[0061]

[0062] Among them, P k-1|k-1 is the error covariance matrix of the previous moment, Q k is the process noise covariance matrix, T is the transpose of the matrix;

[0063] S8, according to the error covariance matrix P of the next moment of step S7 k|k-1 , calculate the Kalman gain, the Kalman gain K k The calculation formula is

[0064] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ,

[0065] Where H is the observation matrix and R is the measurement noise covariance matrix;

[0066] S9, according to the measured value z of step S5 k and the state vector X at the next moment of step S6 k|k-1 Update the state vector of the Kalman filter according to the Kalman gain K in step S8 k and the error covariance matrix P at the next moment of step S7 k|k-1 , update the error covariance matrix of the Kalman filter, and obtain the updated state vector X k|k And the updated error covariance matrix P k|k, thus obtaining the Kalman filter after updating the parameters, the updated state vector X k|k The calculation formula is

[0067] X k|k =X k|k-1 +K k (z k -HX k|k-1 ),

[0068] Updated error covariance matrix P k|k The calculation formula is

[0069] P k|k =(EK k H)P k|k-1 ,

[0070] Among them, z k is the measured value from step S5; E is the identity matrix, which is a square matrix. The elements on the diagonal from the upper left corner to the lower right corner (also called the main diagonal) are all 1. All other elements are 0.

[0071] S10, after using the Kalman filter with updated parameters in step S9 to estimate, based on the target current rising rate To adjust the step size u of the Buck converter k , get the adjusted step length u k+1 To ensure the consistency of the current rising rate under different mutual inductance conditions, the adjusted step size u k+1 The calculation formula is

[0072]

[0073] in, is the estimated value of the current rising rate given by the Kalman filter, and α is the adjustment factor used to control the adjustment speed of the change step size;

[0074] S11, Buck converter according to the adjusted step size u in step S10 k+1 To update the duty cycle;

[0075] S12: Based on the duty cycle from step S11, the Buck converter determines whether its output current is close to the target current value. If so, the startup algorithm ends and the normal power control process begins. Otherwise, the process returns to step S5 and enters the next frame loop. For example, if the target current is set to 10A and the range is ±2A, then when the output current reaches 8A, it is determined to be "close to the target current value" and the startup algorithm ends.

[0076] The following is a code for a startup algorithm for predicting and estimating current at a receiving end of a wireless charging system in this embodiment. The code is used to demonstrate the implementation effect. The code is specifically as follows:

[0077] close all;clear;clc;

[0078] %System parameters

[0079] time=(1:100).';% Discrete time series, unit is ms

[0080] initial_dI=0.5;% initial current rise rate

[0081] target_I=10;% target current value

[0082] %Current rise rate change rate corresponding to different mutual inductance

[0083] u1=0.01;% low mutual inductance

[0084] u2=0.02;% medium mutual inductance

[0085] u3=0.03;%high mutual inductance

[0086] % Measurement noise

[0087] noise=0.5*randn(length(time),1);

[0088] % Kalman filter parameters

[0089] Q = [1e-4 0; 0 1e-4]; % covariance matrix of process noise R = 1; % covariance of measurement noise

[0090] % True Current and Rise Rate

[0091] dI1=initial_dI+(time-1)*u1;

[0092] dI2=initial_dI+(time-1)*u2;

[0093] dI3=initial_dI+(time-1)*u3;

[0094] I1=cumsum([0;dI1(1:end-1)]); % Accumulate to get the real current value I2=cumsum([0;dI2(1:end-1)]);

[0095] I3=cumsum([0;dI3(1:end-1)]);

[0096] I1(I1>target_I)=target_I; % remains unchanged after reaching the target current I2(I2>target_I)=target_I;

[0097] I3(I3>target_I)=target_I;

[0098] % measured value

[0099] I_meas1=I1+noise;

[0100] I_meas2=I2+noise;

[0101] I_meas3=I3+noise;

[0102] %initialization

[0103] X1 = [0; initial_dI]; % Initial state, [current, rate of rise] X2 = [0; initial_dI];

[0104] X3 = [0; initial_dI];

[0105] P = [0.1 0; 0 0.1]; % Initial value of the prior error covariance matrix

[0106] % Known linear transformation matrix

[0107] F=[1 1;0 1];%State transfer matrix

[0108] B1=[1 / 2;1];%Control matrix, may be different for different mutual inductances, here it is simplified B2=B1;

[0109] B3=B1;

[0110] H=[1 0];%Observation matrix

[0111] %Record of status at each moment

[0112] XLog1 = [];

[0113] XLog2 = [];

[0114] XLog3 = [];

[0115] % Start loop

[0116] for i=1:length(time)

[0117] X1_=F*X1+B1*u1;

[0118] X2_=F*X2+B2*u2;

[0119] X3_=F*X3+B3*u3;

[0120] P_=F*P*F'+Q;

[0121] K1=P_*H' / (H*P_*H'+R);

[0122] K2=K1;

[0123] K3=K1;

[0124] X1=X1_+K1*(I_meas1(i)-H*X1_);

[0125] X2=X2_+K2*(I_meas2(i)-H*X2_);

[0126] X3=X3_+K3*(I_meas3(i)-H*X3_);

[0127] P=(eye(2)-K1*H)*P_;

[0128] XLog1=[XLog1,X1];

[0129] XLog2=[XLog2,X2];

[0130] XLog3=[XLog3,X3];

[0131] end

[0132] %Draw results

[0133] figure(1);

[0134] plot(time,I1,'b',time,I2,'g',time,I3,'r');hold on;

[0135] plot(time,XLog1(1,:),'b.',time,XLog2(1,:),'g.',time,XLog3(1,:),'r.'); title('Current changes over time under different mutual inductance conditions (fixed turn-on step)');

[0136] xlabel('Time(s)'); ylabel('Current(A)');

[0137] legend('Low mutual inductance ideal current', 'Medium mutual inductance ideal current', 'High mutual inductance ideal current',...

[0138] 'low mutual inductance estimated current', 'medium mutual inductance estimated current', 'high mutual inductance estimated current');

[0139] %System parameters

[0140] time=(1:100).';%Discrete time series, unit is sinitial_dI=0.5;%Initial current rise rate

[0141] target_I=10;% target current value

[0142] %Current rise rate change rate corresponding to different mutual inductance

[0143] u1=0.005;% low mutual inductance

[0144] u2=0.05;% medium mutual inductance

[0145] u3=0.5;%high mutual inductance

[0146] % Measurement noise

[0147] noise=0.5*randn(length(time),1);

[0148] % Kalman filter parameters, adjusted based on mutual inductance

[0149] Q1 = [1e-3 0; 0 1e-3]; % High process noise with low mutual inductance R1 = 1e-2; % Low measurement noise with low mutual inductance

[0150] Q2 = [1e-4 0; 0 1e-4]; % Process noise of medium mutual inductance R2 = 1e-2; % Measurement noise of medium mutual inductance

[0151] Q3 = [1e-2 0; 0 1e-2]; % low process noise with high mutual inductance R3 = 1e-2; % high measurement noise with high mutual inductance

[0152] % True Current and Rise Rate

[0153] dI1=initial_dI+(time-1)*u1;

[0154] dI2=initial_dI+(time-1)*u2;

[0155] dI3=initial_dI+(time-1)*u3;

[0156] I1=cumsum([0;dI1(1:end-1)]); % Accumulate to get the real current value I2=cumsum([0;dI2(1:end-1)]);

[0157] I3=cumsum([0;dI3(1:end-1)]);

[0158] I1(I1>target_I)=target_I; % remains unchanged after reaching the target current I2(I2>target_I)=target_I;

[0159] I3(I3>target_I)=target_I;

[0160] % measured value

[0161] I_meas1=I1+noise;

[0162] I_meas2=I2+noise;

[0163] I_meas3=I3+noise;

[0164] %initialization

[0165] X1 = [0; initial_dI]; % Initial state, [current, rate of rise]

[0166] X2 = [0; initial_dI];

[0167] X3 = [0; initial_dI];

[0168] P1 = [0.1 0; 0 0.1]; % Initial value of the prior error covariance matrix P2 = P1;

[0169] P3=P1;

[0170] % Known linear transformation matrix

[0171] F=[1 1;0 1];%State transfer matrix

[0172] B1=[1 / 2;1];%Control matrix, may be different for different mutual inductances, here it is simplified B2=B1;

[0173] B3=B1;

[0174] H=[1 0];%Observation matrix

[0175] %Record of status at each moment

[0176] XLog1 = [];

[0177] XLog2 = [];

[0178] XLog3 = [];

[0179] % Start loop

[0180] for i = 1:length(time)

[0181] X1_ = F*X1 + B1*u1;

[0182] X2_ = F*X2 + B2*u2;

[0183] X3_ = F*X3 + B3*u3;

[0184] P1_ = F*P1*F' + Q1;

[0185] P2_ = F*P2*F' + Q2;

[0186] P3_ = F*P3*F' + Q3;

[0187] K1 = P1_*H' / (H*P1_*H' + R1);

[0188] K2 = P2_*H' / (H*P2_*H' + R2);

[0189] K3 = P3_*H' / (H*P3_*H' + R3);

[0190] X1 = X1_ + K1*(I_meas1(i) - H*X1_);

[0191] X2 = X2_ + K2*(I_meas2(i) - H*X2_);

[0192] X3 = X3_ + K3*(I_meas3(i) - H*X3_);

[0193] P1 = (eye(2) - K1*H)*P1_;

[0194] P2 = (eye(2) - K2*H)*P2_;

[0195] P3 = (eye(2) - K3*H)*P3_;

[0196] XLog1 = [XLog1,X1];

[0197] XLog2 = [XLog2,X2];

[0198] XLog3 = [XLog3,X3];

[0199] end

[0200] % Plot the results

[0201] figure(2);

[0202] plot(time,I1,'b',time,I2,'g',time,I3,'r');hold on;

[0203] plot(time,XLog1(1,:),'b.',time,XLog2(1,:),'g.',time,XLog3(1,:),'r.');

[0204] title('Current changes over time under different mutual inductance conditions (Karman prediction current optimization start step)');

[0205] xlabel('Time(s)'); ylabel('Current(A)');

[0206] legend('low mutual inductance ideal current', 'medium mutual inductance ideal current', 'high mutual inductance ideal current', ... 'low mutual inductance estimated current', 'medium mutual inductance estimated current', 'high mutual inductance estimated current');

[0207] like Figure 4 As shown in the figure, if the step size of the buck converter is fixed during the startup, the output current will rise at different rates due to different mutual inductances after startup, which will lead to a more serious overshoot phenomenon.

[0208] like Figure 5 As shown, a startup algorithm for predicting and estimating current at the receiving end of a wireless charging system is used to predict the current and current rate and adjust the startup step size, so that the startup current can be controlled to finally converge to near the target output current.

[0209] The startup algorithm for predicting and estimating current at the receiving end of a wireless charging system according to this embodiment cleverly solves key problems in the startup phase of the wireless charging system by introducing Kalman filtering technology, which is specifically reflected in the following aspects:

[0210] 1) Dynamically adjust the step size: The traditional fixed step size strategy cannot adapt to the difference in current rise rate caused by changes in mutual inductance. The algorithm in this embodiment monitors the output current in real time and uses the predictive ability of the Kalman filter to dynamically adjust the step size to ensure that the rising trend of the starting current remains consistent under different mutual inductance conditions, effectively overcoming the overshoot and backflow problems.

[0211] 2) Enhanced system adaptability: The introduction of the Kalman filter enhances the adaptability of the wireless charging system to environmental changes. Even when the mutual inductance value fluctuates greatly, it can ensure a smooth transition of current control, thereby improving the stability and reliability of the wireless charging process.

[0212] 3) Optimized Energy Management: By precisely controlling the current rise process, unnecessary energy waste is avoided, improving the efficiency of the entire wireless charging system. This also reduces energy loss due to overshoot and reverse current, helping to extend battery life and reduce maintenance costs.

[0213] 4) Improved user experience: The algorithm in this embodiment improves the performance of the startup phase, shortens the waiting time, and avoids device interference caused by sudden current changes, thereby significantly improving the user experience and making the wireless charging process smoother and safer.

[0214] This embodiment introduces a mechanism that automatically senses rising output current and adjusts the step size in real time, potentially achieving more precise current control and improving the system's adaptability and stability under varying mutual inductance conditions, thereby enhancing wireless charging efficiency, safety, and user experience. This adaptive control strategy not only addresses the limitations of existing technologies but also opens up new directions for the development of wireless charging technology.

[0215] In summary, the method in this embodiment not only solves the key technical difficulties in the startup phase of the wireless charging system, but also achieves smarter and more efficient startup current control, laying a solid foundation for the widespread application and popularization of wireless charging technology.

[0216] The above specific implementation manner is a preferred embodiment of the present invention and does not limit the present invention. Any other changes or other equivalent replacement methods that do not deviate from the technical solution of the present invention are included in the protection scope of the present invention.

Claims

1. A startup algorithm for predicting and estimating current at the receiving end of a wireless charging system, characterized by: The following steps are involved: S1. Determine whether the receiving end has received a power-on instruction. If yes, execute step S2; if no, execute step S1 again. S2, the controller is initialized; S3, initializing the parameters of the Kalman filter; S4, the Buck converter enters the on state and starts to output current to the load; S5. Use a current sensor to measure the output current of step S4 and obtain the measured value z k ; S6, predict the state vector of the Kalman filter at the next moment, the state vector X at the next moment k|k-1 The calculation formula is X k|k-1 =F k X k-1|k-1 +B k u k , Among them, X k-1|k-1 is the state vector at the previous moment, F k is the state transfer matrix, B k is the control input matrix, u k is the step size of Buck converter, k is the time step in each frame; S7, calculate the state vector X at the next moment of step S6 k|k-1 The corresponding error covariance matrix P at the next moment k|k-1 , the error covariance matrix P k|k-1 The calculation formula is Among them, P k-1|k-1 is the error covariance matrix of the previous moment, Q k is the process noise covariance matrix, T is the transpose of the matrix; S8, according to the error covariance matrix P of the next moment of step S7 k|k-1 , calculate the Kalman gain, the Kalman gain K k The calculation formula is K k =P k|k-1 H T (HP k|k-1 H T +R) -1 , Where H is the observation matrix and R is the measurement noise covariance matrix; S9, according to the measured value z of step S5 k and the state vector X at the next moment of step S6 k|k-1 , update the state vector of the Kalman filter according to the Kalman gain K in step S8 k and the error covariance matrix P at the next moment of step S7 k|k-1 , update the error covariance matrix of the Kalman filter, and obtain the updated state vector X k|k And the updated error covariance matrix P k|k , thus obtaining the Kalman filter after updating the parameters, the updated state vector X k|k The calculation formula is X k|k =X k|k-1 +K k (z k -HX k|k-1 ), Updated error covariance matrix P k|k The calculation formula is P k|k =(E-K k H)P k|k-1 , Where E is the identity matrix; S10, after using the Kalman filter with updated parameters in step S9 to estimate, based on the target current rising rate To adjust the step size u of the Buck converter k , get the adjusted step length u k+1 , the adjusted step size u k+1 The calculation formula is in, is the estimated value of the current rising rate given by the Kalman filter, and α is the adjustment factor; S11, Buck converter according to the adjusted step size u in step S10 k+1 To update the duty cycle; S12: Determine whether the output current of the Buck converter is close to the target current value based on the duty cycle of step S11. If so, end; otherwise, return to step S5.

2. The startup algorithm for predicting and estimating current at the receiving end of a wireless charging system according to claim 1, characterized in that: The initial parameters of the Kalman filter include the initial state vector X0, the initial error covariance matrix P0, and the process noise covariance matrix Q k and the measurement noise covariance matrix R.

3. The startup algorithm for predicting and estimating current at the receiving end of a wireless charging system according to claim 1, characterized in that: The current sensor in step S5 is a mutual inductor.

4. The startup algorithm for predicting and estimating current at the receiving end of a wireless charging system according to claim 1, characterized in that: The current sensor in step S5 is a Hall sensor.

5. The startup algorithm for predicting and estimating current at the receiving end of a wireless charging system according to claim 1, characterized in that: The receiving end of step S1 includes a resonant cavity, a rectifier bridge and a Buck converter connected in sequence.

6. The startup algorithm for predicting and estimating current at the receiving end of a wireless charging system according to claim 5, characterized in that: The resonant cavity is an LCC resonant cavity.

7. The startup algorithm for predicting and estimating current at the receiving end of a wireless charging system according to claim 5, characterized in that: The resonant cavity is an SS resonant cavity.

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