A method for simultaneously identifying loads and mutual inductances of a multi-load wireless power transmission system

Abstract

The present invention discloses a method for simultaneously identifying the load and mutual inductance of a multi-load wireless power transmission system. First, the multi-load system is equivalent to a single load system with different working timings through a time-division multiplexing method, and the voltage overshoot and voltage instability caused by the time-division operation identification algorithm are prevented by dual filtering capacitors and adjusting the bus voltage; then the particle swarm algorithm is introduced, and a system steady-state circuit model is established, and a fitness function is created by sampling the difference between the output voltage and the predicted output voltage, and the parameter identification problem is converted into an algorithm optimization problem. The optimal solution search for the identification parameters is performed to replace the traditional calculation method, thereby avoiding the errors caused by the traditional equations. The method only needs to sample the input and output voltages, reduces the amount of communication data, and reduces the circuit complexity and system size. The method of the present invention realizes real-time identification of the load and mutual inductance of the multi-load wireless charging system, thereby expanding the working range of the wireless charging system.

Description

Technical Field

[0001] The present invention relates to the field of wireless charging technology, and in particular to a method for simultaneously identifying loads and mutual inductances of a multi-load wireless power transmission system. Background Art

[0002] In recent years, wireless power transfer (WPT) has been gradually applied in medium and low power electronic devices, and magnetically coupled resonant wireless power transfer (MCR-WPT) has become one of the main development directions of wireless power transmission due to its advantages of long transmission distance, high transmission power and high safety. In the development of wireless power transmission, multi-load systems have received extensive attention because of their greater industrial significance.

[0003] Compared with a single-load system, a multi-load system is more complex. Traditionally, there is a multi-frequency-based approach to simplify the multi-load system. However, since the cross-coupling between multiple receiving ends must also be considered in the case of multi-frequency, system modeling is still relatively difficult.

[0004] Whether it is a single load or multiple loads, the most likely thing to change during the use of a wireless charging system is the relative position of the resonator and the impedance of the connected device. Due to resonator offset and changes in load equivalent impedance, the system transmission performance will be reduced or even out of control. In order to ensure that the system can achieve efficient transmission performance and perform real-time correction of the system model, it is necessary to design a method that can accurately identify mutual inductance and load to improve the accuracy of the model.

[0005] Traditionally, there is a method for identifying the mutual inductance and load of MCR-WPT based on genetic algorithm. This method is based on the principle of conservation of energy and equivalent load theory, and requires sampling of high-frequency AC voltage and current. However, it is difficult for general equipment to do this, resulting in the disadvantages of difficulty in real-time identification and signal sampling.

[0006] Therefore, how to provide a method for simultaneously identifying the loads and mutual inductance of a multi-load wireless power transmission system based on time division multiplexing, realize real-time identification of the sizes of multiple parameters of the multi-load wireless charging system, and then expand the working range of the wireless charging system has become an urgent problem to be solved. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method for simultaneously identifying the load and mutual inductance of a multi-load wireless power transmission system, so as to realize real-time identification of the load and mutual inductance of the multi-load wireless charging system, thereby expanding the working range of the wireless charging system.

[0008] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0009] A method for simultaneously identifying loads and mutual inductances of a multi-load wireless power transmission system is applied to an SS-type multi-load magnetic coupling resonance system. The method comprises:

[0010] Step 10: Convert the multi-load system into a single load system with different working sequences by time division multiplexing, so as to realize the working sequence division of the multi-loads; select a suitable identification interval, and prevent the voltage overshoot and voltage instability caused by the time division operation identification algorithm by dual filter capacitors and adjusting the bus voltage;

[0011] Step 20: Obtain a mathematical mapping relationship between the load and the mutual inductance by modeling the system circuit;

[0012] Step 30: sampling the power bus voltage and current and the DC output voltage of the system in real time, calculating the calculated voltage value through the sampled data and the charging parameters of the system, and then creating an objective function based on the actual voltage value and the calculated voltage value;

[0013] Step 40: Utilize a particle swarm algorithm to optimize the objective function, thereby achieving real-time identification of the magnitude of the charging parameters.

[0014] Furthermore, in step 10, the dual filter capacitor includes a freewheeling capacitor and an identification capacitor, the capacitance value of the freewheeling capacitor is much larger than the capacitance value of the identification capacitor, and the adjusting the bus voltage specifically includes the following steps:

[0015] Step 11, when one of the receiving ends is in the on stage and the other receiving ends are in the off stage, the freewheeling capacitor of the receiving end in the on stage is in a charging state, and the other receiving ends in the off stage are in a discharging state, and there is no need to adjust the inverter side voltage at this time;

[0016] Step 12: When the parameters of the opened receiving end are identified, the freewheeling capacitor is turned off and the identification capacitor is used for filtering. At the same time, the duty cycle of the inverter drive signal is adjusted to reduce the effective value of the AC voltage after inversion. The capacitance value of the identification capacitor is small, so that the output voltage quickly reaches the maximum value to facilitate the use of the identification algorithm.

[0017] Furthermore, step 20 specifically includes:

[0018] Step 21, expand the output square wave voltage after inversion by Fourier series and obtain its fundamental wave;

[0019]

[0020] where u d is the voltage after inversion, E is the input DC voltage, ω is the system working angular velocity, and t is the working time;

[0021] Step 22: Assume that the transmitter current i when the system is resonant p =I max sinωt, calculate the input impedance of the transmitter:

[0022]

[0023] Among them, I max is the amplitude of the input current;

[0024] Step 23: Analyze the single load system and calculate the input impedance of the transmitter using Kirchhoff's voltage-current law:

[0025]

[0026] Among them, L P is the transmitter inductance, C p is the capacitance of the transmitter, L s is the receiving end inductance, C s is the receiving end capacitance, R p is the internal resistance of the transmitter, R s is the internal resistance of the receiving end, R L is the load, M is the mutual inductance;

[0027] Step 24: Calculate the mapping relationship between load and mutual inductance through steps 22 and 23:

[0028]

[0029] in

[0030] Furthermore, the calculation formula and objective function for calculating the voltage value in step 30 specifically include:

[0031]

[0032] in,

[0033]

[0034] Among them, V o (K+1) mea Indicates the calculated voltage value for the (K+1)th time, C f Represents the filter capacitor, P o (K) represents the Kth output power, T represents the sampling time, V o (K) represents the actual voltage value of the Kth time, that is, the load output voltage, Represents the Kth theoretical voltage.

[0035] Furthermore, the step 40 specifically includes:

[0036] Step 41, setting an iteration threshold, and inputting the mutual inductance between the transmitter and the receiver as particles into the particle swarm algorithm;

[0037] Step 42, initializing the speed and position of each particle;

[0038] Step 43, calculating the inertia weight factor of each particle;

[0039] Step 44: updating the speed and position of each particle based on the inertia weight factor;

[0040] Step 45: Calculate the fitness of each particle based on the objective function, and determine the individual extreme value and the global extreme value of the particle based on the fitness;

[0041] Step 46: outputting a global extreme value based on the iteration threshold and fitness;

[0042] Step 47: The magnitudes of the mutual inductance and the load can be obtained simultaneously according to the mapping relationship between the load and the mutual inductance.

[0043] Furthermore, the step 45 specifically includes:

[0044] Step 451: Calculate the fitness of each particle at the nth iteration based on the objective function. (n) ;

[0045] Step 452: Determine fitness (n) Is it less than fitness? (n-1) If so, let gbest (n) =fitness (n) , and proceed to step 453; if not, proceed to step 453;

[0046] Step 453: Determine fitness (n) Is it less than zbest? (n) , if so, let zbest (n) =fitness (n) , and proceed to step 454; if not, proceed to step 454;

[0047] Step 454: After the number of iterations n is increased by 1, proceed to step 46.

[0048] The technical solution provided in the embodiments of the present invention has at least the following technical effects or advantages:

[0049] 1. The multi-load system is simplified by time-division multiplexing, and the multi-load system is equivalent to a single-load system working in different timings. There is no need to consider the cross-coupling between multiple receiving ends. The voltage overshoot and voltage instability caused by the time-division operation identification algorithm are prevented by dual filter capacitors and adjusting the bus voltage. Subsequently, the particle swarm algorithm is introduced to establish a steady-state circuit model of the system, and create a fitness function by the difference between the sampled output voltage and the predicted output voltage. The parameter identification problem is converted into an algorithm optimization problem, and the optimal solution search for the identified parameters replaces the traditional calculation method, avoiding the errors caused by the traditional equations.

[0050] 2. In order to solve the fluctuation of output voltage caused by time division, a switchable dual filter capacitor is adopted. At the same time, in order to prevent the overshoot of output voltage in the identification stage, the output voltage on the inverter side is adjusted by changing the duty cycle of the inverter signal.

[0051] 3. Through system analysis, assuming that the system is in resonance, the input impedance of the transmitter is calculated through different methods, and the mathematical mapping relationship between the load and the mutual inductance is further calculated, which simplifies the original need to identify two parameters to only need to identify one parameter. The other can be calculated through the formula, which greatly simplifies the running time of the algorithm and improves the computing efficiency.

[0052] 4. By sampling the power bus voltage and DC output voltage of the wireless charging system in real time, there is no need to directly measure the high-frequency and high voltage at both ends of the device in the coupling mechanism, which is safer, has low algorithm complexity, short calculation time and small error.

[0053] 5. When the mutual inductance is disturbed and deviates from the set value, the size between the mutual inductance and the load can be effectively identified and its value in the prediction model can be corrected; the dynamic stability and rapid response capability of the wireless charging system can be ensured by the joint work of the model predictive control algorithm (MPC algorithm) and the particle swarm algorithm (PSO algorithm); that is, by combining the PSO algorithm with the MPC algorithm, the present invention can be used in both dynamic and static conditions, making wireless charging more reliable; not only can the imaginary part estimation of the receiving end in the offline state be realized, but also the imaginary part estimation of the receiving end of the dynamic wireless charging system can be realized, which greatly improves the practicality of the present invention.

[0054] 6. Compared with the traditional method of controlling the output voltage of the system by using a BOOST-BUCK circuit, the present invention has good adaptability whether in dynamic or static conditions, and can self-tune the algorithm model according to changes in the environment. It can run well in practical applications, and the system of the present invention is easier to implement in hardware.

[0055] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The present invention will be further described below in conjunction with embodiments with reference to the accompanying drawings.

[0057] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0058] Figure 2 is a circuit diagram of a wireless charging system according to an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the circuit topology after the rectifier bridge according to an embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of the output voltages of two receiving ends in time division multiplexing according to an embodiment of the present invention;

[0061] Figure 5 This is a simplified schematic diagram of the dual power supply structure of an embodiment of the present invention;

[0062] Figure 6 This is a timing diagram of time division multiplexing receiving end control according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The embodiment of the present application provides a method for simultaneously identifying the load and mutual inductance of a multi-load wireless power transmission system, thereby achieving real-time identification of the load and mutual inductance of the multi-load wireless charging system, thereby expanding the working range of the wireless charging system.

[0064] The technical solution in the embodiment of the present application has the following general idea:

[0065] By dividing the working sequence of multiple loads, the multi-load system is equivalent to a single load system with different working sequence; then by analyzing the system model, the mathematical mapping relationship between load and mutual inductance is calculated; then by real-time sampling of the power bus voltage and current and the DC output voltage, the calculated voltage value is calculated through the sampled data and the obtained charging parameters; then based on the actual voltage value and the calculated voltage value, the objective function is created, the parameter identification problem is converted into an algorithm optimization problem, and the optimal solution search for the identified parameters is performed to replace the traditional calculation method, avoiding the errors caused by the traditional equation; finally, the particle swarm algorithm is used to identify the size of the charging parameters in real time. This method only needs to sample the input and output voltages, reducing the amount of communication data, while reducing the complexity of the circuit and the size of the system, and expanding the working range of the wireless charging system.

[0066] Before introducing the specific embodiments, a multi-load wireless charging system of an SS type resonant circuit based on which the method of the embodiment of the present application is based is first introduced. Figure 2 As shown, it includes a DC power supply, a resonant mechanism, a controller, an inverter, a rectifier filter module and a load; the regulated DC power supply is converted into high-frequency AC power through a full-bridge inverter (S1-S4), and then L P , C p The resonator composed of L transmits energy to multiple receiving ends through the magnetic field. s and C s The resonator receives energy, and finally performs rectification and filtering, and transmits it to the load; among which the switch tubes S1~S i The controller uses TI's DSP28379D, the inverter is composed of GaN-MOSFET, and the resonator is composed of equal symmetrical circular coils wound with Litz wire: that is, it is composed of a controller and a switch driver. The transmitter uses a full-bridge inverter to convert the DC power supply to generate a high-frequency AC voltage; the receiver uses an uncontrollable bridge rectifier to simplify the control difficulty; the resonant circuit is composed of a series inductor and capacitor, and the resonant frequency

[0067] Time Division Multiplexing (TDM) refers to using time as a parameter. On the time axis, the signals of each channel do not overlap each other, so that different signals are transmitted at different times. The present invention adopts a time division multiplexing mechanism. When performing parameter identification, only one receiving end and the transmitting end perform normal energy transmission. At the same time, in order to ensure that the load can have a stable charging voltage and the load voltage is in a stable state during identification, the filter capacitor needs to be reasonably designed.

[0068] (1) Filter capacitor structure design

[0069] The present invention proposes a circuit topology of a switchable capacitor, such as Figure 3 As shown, S i =1 When it is turned on, the receiving end receives energy, and the capacitor C f1 With C f2 It plays the role of filtering and energy storage and C f2 < <C f1 The circuit can be controlled by switching tube S ci The switch state is used to control the size of the equivalent filter capacitor to achieve a stable output voltage of the system. When parameter identification is not performed, the equivalent filter capacitor value is C max =C f2 +C f1 , when performing parameter identification, the capacitance value is C min =C f1Since a larger filter capacitor cannot fully store energy in a shorter working time, and a smaller filter capacitor can achieve rapid stability in the same time, in order to meet the parameter identification requirements, the equivalent filter capacitor needs to be C max With C min Switching will produce a large voltage jump, such as Figure 4 shown.

[0070] (2) Input power design

[0071] In order to solve the voltage jump problem, the present invention proposes a dual power supply mode for the transmitter input, including a normal working power supply voltage E H And identify the power supply voltage E L That is, by changing the duty cycle of the inverter signal at the transmitter end, the equivalent input power supply voltage is adjusted. max Switch to C min By adjusting the equivalent input power supply voltage, the voltage jump phenomenon caused by switching can be eliminated. The input power supply simplified topology is as follows Figure 5 As shown, the switch tube S v =1 when conducting, E = E H ; S v =0 when closed, E = E L .

[0072] (3) System control timing design

[0073] In order to realize the parameter identification of multiple loads, the system adopts a time division multiplexing mechanism to realize the energy transmission between the transmitter and each receiving end respectively. Taking the dual receiving end as an example, the control timing of each switch tube is as follows: Figure 6 shown.

[0074] The control sequence of receiving end 1 is described as follows:

[0075] i. If it is in normal operation, the receiving end transmits energy at a certain frequency. At this time, S v =1 is on, the equivalent power supply voltage E = E H ; S c1 =1, conduction, the equivalent filter capacitor size is C f1 +C f2 .

[0076] ii. If the parameter is being identified, S1 = 1, and the receiving end is in the energy receiving state, that is, S v =1 off, equivalent power supply voltage E = E L ; S c1 = 0 is off, the equivalent filter capacitor size is C f2 .

[0077] The control timing description of receiving end 2 is similar.

[0078] Please refer to Figures 1 to 6 As shown, a preferred embodiment of a method for simultaneously identifying loads and mutual inductances of a multi-load wireless power transmission system of the present invention is applied to an SS type multi-load magnetic coupling resonance system, and the method comprises the following steps:

[0079] Step 10: Convert the multi-load system into a single load system with different working sequences by time division multiplexing, so as to realize the working sequence division of the multi-loads; select a suitable identification interval, and prevent the voltage overshoot and voltage instability caused by the time division operation identification algorithm by dual filter capacitors and adjusting the bus voltage;

[0080] Step 20: Obtain a mathematical mapping relationship between the load and the mutual inductance by modeling the system circuit;

[0081] Step 30: sampling the power bus voltage and current and the DC output voltage of the system in real time, calculating the calculated voltage value through the sampled data and the charging parameters of the system, and then creating an objective function based on the actual voltage value and the calculated voltage value;

[0082] Step 40: Utilize a particle swarm algorithm to optimize the objective function, thereby achieving real-time identification of the magnitude of the charging parameters.

[0083] A specific implementation method of the embodiment of the present invention is as follows:

[0084] In step 10, the dual filter capacitor includes a freewheeling capacitor and an identification capacitor, the capacitance value of the freewheeling capacitor is much larger than the capacitance value of the identification capacitor, and the bus voltage adjustment specifically includes the following steps:

[0085] Step 11, when one of the receiving ends is in the on stage and the other receiving ends are in the off stage, the freewheeling capacitor of the receiving end in the on stage is in a charging state, and the other receiving ends in the off stage are in a discharging state, and there is no need to adjust the inverter side voltage at this time;

[0086] Step 12: When the parameters of the opened receiving end are identified, the freewheeling capacitor is turned off and the identification capacitor is used for filtering. At the same time, the duty cycle of the inverter drive signal is adjusted to reduce the effective value of the AC voltage after inversion. The capacitance value of the identification capacitor is small, so that the output voltage quickly reaches the maximum value to facilitate the use of the identification algorithm.

[0087] The step 20 specifically includes:

[0088] Step 21, expand the output square wave voltage after inversion by Fourier series and obtain its fundamental wave;

[0089]

[0090] where u d is the voltage after inversion, E is the input DC voltage, ω is the system working angular velocity, and t is the working time;

[0091] Step 22: Assume that the transmitter current i when the system is resonant p =I max sinωt, calculate the input impedance of the transmitter:

[0092]

[0093] Among them, I max is the amplitude of the input current;

[0094] Step 23: Analyze the single load system and calculate the input impedance of the transmitter using Kirchhoff's voltage-current law:

[0095]

[0096] Among them, L P is the transmitter inductance, C p is the capacitance of the transmitter, L s is the receiving end inductance, C s is the receiving end capacitance, R p is the internal resistance of the transmitter, R s is the internal resistance of the receiving end, R L is the load, M is the mutual inductance;

[0097] Step 24: Calculate the mapping relationship between load and mutual inductance through steps 22 and 23:

[0098]

[0099] in,

[0100] In step 30, the calculation formula and the objective function for calculating the voltage value specifically include:

[0101]

[0102] in,

[0103]

[0104] Among them, V o (K+1) mea Indicates the calculated voltage value for the (K+1)th time, C f Represents the filter capacitor, P o (K) represents the Kth output power, T represents the sampling time, V o(K) represents the actual voltage value of the Kth time, that is, the load output voltage, V o * (K) represents the Kth theoretical voltage.

[0105] The step 40 specifically includes:

[0106] Step 41, setting an iteration threshold, and inputting the mutual inductance between the transmitter and the receiver as particles into the particle swarm algorithm;

[0107] Step 42, initializing the speed and position of each particle;

[0108] Step 43, calculating the inertia weight factor of each particle;

[0109] Step 44: updating the speed and position of each particle based on the inertia weight factor;

[0110] Step 45: Calculate the fitness of each particle based on the objective function, and determine the individual extreme value and the global extreme value of the particle based on the fitness;

[0111] Step 46: outputting a global extreme value based on the iteration threshold and fitness;

[0112] Step 47: The magnitudes of the mutual inductance and the load can be obtained simultaneously according to the mapping relationship between the load and the mutual inductance.

[0113] Specifically, the step 45 includes:

[0114] Step 451: Calculate the fitness of each particle at the nth iteration based on the objective function. (n) ;

[0115] Step 452: Determine fitness (n) Is it less than fitness? (n-1) If so, let gbest (n) =fitness (n) , and proceed to step 453; if not, proceed to step 453;

[0116] Step 453: Determine fitness (n) Is it less than zbest? (n) , if so, let zbest (n) =fitness (n) , and proceed to step 454; if not, proceed to step 454;

[0117] Step 454: After the number of iterations n is increased by 1, proceed to step 46.

[0118] The technical solution provided in the embodiment of the present invention has at least the following technical effects or advantages: the multi-load system is simplified by the time division multiplexing method, and the multi-load system is equivalent to a single load system working with different timings, without considering the cross-coupling between multiple receiving ends, and the voltage overshoot and voltage instability caused by the time division operation identification algorithm are prevented by dual filtering capacitors and adjusting the bus voltage; then the particle swarm algorithm is introduced, and a system steady-state circuit model is established, and a fitness function is created by the difference between the sampled output voltage and the predicted output voltage, so that the parameter identification problem is converted into an algorithm optimization problem, and the optimal solution search for the parameters to be identified is replaced by the traditional calculation method, thereby avoiding the errors caused by the traditional equations.

[0119] In order to solve the output voltage fluctuation caused by time division, a switchable dual filter capacitor is used. At the same time, in order to prevent the output voltage overshoot during the identification stage, the output voltage on the inverter side is adjusted by changing the duty cycle of the inverter signal.

[0120] Through system analysis, assuming that the system is in resonance, the input impedance of the transmitter is calculated through different methods, and the mathematical mapping relationship between the load and the mutual inductance is further calculated, which simplifies the original need to identify two parameters to only need to identify one parameter. The other can be calculated through the formula, which greatly simplifies the running time of the algorithm and improves the computing efficiency.

[0121] By sampling the power bus voltage and DC output voltage of the wireless charging system in real time, there is no need to directly measure the high-frequency and large voltage across the device in the coupling mechanism. This is safer, has low algorithm complexity, short calculation time and small error.

[0122] When the mutual inductance is disturbed and deviates from the set value, the size between the mutual inductance and the load can be effectively identified and its value in the prediction model can be corrected; the dynamic stability and rapid response capability of the wireless charging system can be ensured by the joint work of the model predictive control algorithm (MPC algorithm) and the particle swarm algorithm (PSO algorithm); that is, by combining the PSO algorithm with the MPC algorithm, the present invention can be used in both dynamic and static conditions, making wireless charging more reliable; not only can the imaginary part estimation of the receiving end in the offline state be realized, but also the imaginary part estimation of the receiving end of the dynamic wireless charging system can be realized, which greatly improves the practicality of the present invention.

[0123] Compared with the traditional method of controlling the output voltage of the system by using a BOOST-BUCK circuit, the present invention has good adaptability whether in a dynamic or static state, and can self-tune the algorithm model according to changes in the environment, can run well in practical applications, and the system of the present invention is easier to implement in hardware.

[0124] Although the specific implementation modes of the present invention are described above, those skilled in the art should understand that the specific implementation modes described are only illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for simultaneously identifying loads and mutual inductances of a multi-load wireless power transmission system, characterized in that: Applied to an SS type multi-load magnetic coupling resonance system, the method comprises: Step 10: Convert the multi-load system into a single load system with different working sequences by time division multiplexing to realize the working sequence division of the multi-loads; select a suitable identification interval, adjust the bus voltage by dual filter capacitors, and adjust the output voltage of the inverter side by changing the duty cycle of the inverter signal to prevent voltage overshoot and voltage instability caused by the time division operation identification algorithm; the dual filter capacitor is a switchable capacitor, Step 20: Obtain a mathematical mapping relationship between the load and the mutual inductance by modeling the system circuit; Step 30: sampling the power bus voltage and current and the DC output voltage of the system in real time, calculating the calculated voltage value through the sampled data and the charging parameters of the system, and then creating an objective function based on the actual voltage value and the calculated voltage value; Step 40: Utilize a particle swarm algorithm to optimize the objective function, thereby achieving real-time identification of the magnitude of the charging parameters.

2. The method according to claim 1, characterized in that: In step 10, the dual filter capacitor includes a freewheeling capacitor and an identification capacitor, the capacitance value of the freewheeling capacitor is much larger than the capacitance value of the identification capacitor, and the bus voltage adjustment specifically includes the following steps: Step 11, when one of the receiving ends is in the on stage and the other receiving ends are in the off stage, the freewheeling capacitor of the receiving end in the on stage is in a charging state, and the other receiving ends in the off stage are in a discharging state, and there is no need to adjust the inverter side voltage at this time; Step 12: When the parameters of the opened receiving end are identified, the freewheeling capacitor is turned off and the identification capacitor is used for filtering. At the same time, the duty cycle of the inverter drive signal is adjusted to reduce the effective value of the AC voltage after inversion. The capacitance value of the identification capacitor is small, so that the output voltage quickly reaches the maximum value to facilitate the use of the identification algorithm.

3. The method according to claim 1, characterized in that: Step 20 specifically includes: Step 21, expand the output square wave voltage after inversion by Fourier series and obtain its fundamental wave; in is the voltage after inversion, E is the input DC voltage, is the system working angular velocity, t is the working time; Step 22: Assume the transmitter current when the system is resonant , calculate the input impedance of the transmitter: in, is the amplitude of the input current; Step 23: Analyze the single load system and calculate the input impedance of the transmitter using Kirchhoff's voltage-current law: in, L P is the transmitter inductance, C p is the capacitance of the transmitter, L s is the inductance of the receiving end, C s is the capacitance of the receiving end, R p is the internal resistance of the transmitter, R s is the internal resistance of the receiving end, R L For load, M For mutual induction; Step 24: Calculate the mapping relationship between load and mutual inductance through steps 22 and 23: in, , .

4. The method according to claim 3, characterized in that: The calculation formula and objective function for calculating the voltage value in step 30 specifically include: in, ; in, Indicates The calculated voltage value is Represents the filter capacitor, Indicates The output power of represents the sampling time, Indicates The actual voltage value, that is, the load output voltage, Indicates K times the theoretical voltage.

5. The method according to claim 1, characterized in that The step 40 specifically includes: Step 41, setting an iteration threshold, and inputting the mutual inductance between the transmitter and the receiver as particles into the particle swarm algorithm; Step 42, initializing the speed and position of each particle; Step 43, calculating the inertia weight factor of each particle; Step 44: updating the speed and position of each particle based on the inertia weight factor; Step 45: Calculate the fitness of each particle based on the objective function, and determine the individual extreme value and the global extreme value of the particle based on the fitness; Step 46: outputting a global extreme value based on the iteration threshold and fitness; Step 47: According to the mapping relationship between the load and the mutual inductance, the magnitude of the mutual inductance and the load can be obtained simultaneously.

6. The method according to claim 5, characterized in that: The step 45 specifically includes: Step 451: Calculate the number of particles based on the objective function. The fitness of the iteration ; Step 452: Determination Is it less than , if so, let , and proceed to step 453; if not, proceed to step 453; Step 453: Determination Is it less than , if so, let , and proceed to step 454; if not, proceed to step 454; Step 454: Number of iterations After adding 1, go to step 46.

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