WO2023193650A1

WO2023193650A1 - Method for identifying both loads and mutual inductance of multi-load wireless power transfer system

Info

Publication number: WO2023193650A1
Application number: PCT/CN2023/085000
Authority: WO
Inventors: 黄东晓; 仇逸; 于新红; 汪凤翔
Original assignee: 泉州装备制造研究所
Priority date: 2022-04-03
Filing date: 2023-03-30
Publication date: 2023-10-12
Also published as: CN114709940A

Abstract

Disclosed in the present invention is a method for identifying both the loads and mutual inductance of a multi-load wireless power transfer system. In the method, first, by means of a time division multiplexing method, a multi-load system is equivalent to a single-load system working in different time sequences, and by means of double filter capacitors and an adjustment of a busbar voltage, voltage overshoot and voltage instability, which occur in a time division operation identification algorithm, are prevented; and then, particle swarm optimization is introduced, a fitness function is created by establishing a system steady-state circuit model and sampling the difference between an output voltage and a predicted output voltage, a parameter identification problem is transformed into an algorithm optimization problem, and optimal solution searching, instead of a traditional calculation method, is performed on a parameter to be identified, thereby avoiding an error generated by a traditional equation. By means of the method, only input and output voltages need to be sampled, thereby reducing the volume of communication data, and also reducing the complexity of a circuit and the size of a system. By means of the method of the present invention, the magnitudes of loads and mutual inductance of a multi-load wireless charging system are identified in real time, thereby expanding the working range of the wireless charging system.

Description

A method for simultaneous identification of loads and mutual inductance in multi-load wireless power transmission systems

Technical field

The present invention relates to the field of wireless charging technology, and in particular to a method for simultaneous identification of loads and mutual inductance in a multi-load wireless power transmission system.

Background technique

In recent years, wireless power transfer (WPT) has gradually been used in low- and medium-power electronic equipment, while magnetically coupled resonant wireless power transfer (MCR-WPT) has long transmission distance, high transmission power and safety. High advantages have become one of the main development directions of wireless power transmission. In the development process of wireless power transmission, multi-load systems have received widespread attention because they have more industrial significance.

Compared with single-load systems, multi-load systems are more complex. Traditionally, there is a multi-frequency-based approach to simplify multi-load systems. However, in the case of multi-frequency, it is also necessary to consider the cross-coupling between multiple receivers, so the system construction Modeling is still more difficult.

Regardless of single load or multiple loads, the most likely changes in the use of wireless charging systems are the relative position of the resonator and the impedance of the connected device. Due to resonator offset, load equivalent impedance changes, etc., 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 model accuracy.

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

Therefore, how to provide a method for simultaneous identification of the load and mutual inductance of a multi-load wireless power transmission system based on time division multiplexing to achieve real-time identification of the magnitude of multiple parameters of the multi-load wireless charging system, thereby expanding the working range of the wireless charging system. becomes an urgent problem to be solved.

Contents of the invention

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

In order to solve the above technical problems, the present invention is implemented as follows:

A method for simultaneous identification of loads and mutual inductance in a multi-load wireless power transmission system, applied to S-S type multi-load magnetic coupling resonance systems, the method includes:

Step 10. Use the time division multiplexing method to convert the multi-load system into a single-load system that operates at different timings to realize the division of working timings for multiple loads; select appropriate identification intervals and prevent time-division operation by using double filter capacitors and adjusting the bus voltage. The voltage overshoot and voltage instability caused by the identification algorithm occur;

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

Step 30: Sample the system's power bus voltage, current and DC output voltage in real time, calculate the calculated voltage value through the sampled data and the system's charging parameters, and then create an objective function based on the actual voltage value and the calculated voltage value;

Step 40: Use the particle swarm algorithm to optimize the objective function to realize real-time identification of the size of the charging parameters.

Further, in step 10, the double 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. The adjustment of the bus voltage specifically includes the following: step:

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 the charging state, and the other receiving ends in the off stage are in the discharging state. At this time, there is no need to adjust the inverter side. Voltage;

Step 12. When performing parameter identification on the turned-on receiving end, turn off the freewheeling capacitor and use the identification capacitor for filtering. At the same time, adjust the duty cycle of the inverter drive signal 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 reaches the maximum value quickly, making it easier to use the identification algorithm.

Further, step 20 specifically includes:

Step 21: Expand the Fourier series of the output square wave voltage after inversion and take its fundamental wave;

Among them, u _d is the voltage after inversion, E is the input DC voltage, ω is the system working angular speed, and t is the working time;

Step 22. Assume that the transmitter current i _p =I _max sin ωt when the system resonates, calculate the input impedance of the transmitter:

Among them, I _max is the amplitude of the input current;

Step 23. Analyze a single load system and calculate the input impedance of the transmitter through Kirchhoff’s voltage and current law:

Among them, L _P is the inductance size of the transmitting end, C _p is the capacitance size of the transmitting end, L _s is the inductance size of the receiving end, C _s is the capacitance size of the receiving end, R _p is the internal resistance of the transmitting end, R _s is the internal resistance of the receiving end, R _L is the load, M is the mutual inductance;

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

in,

Further, the calculation formula and objective function for calculating the voltage value in step 30 specifically include:

in,

Among them, V _o (K+1) _mea represents the calculated voltage value of the (K+1)th time, C _f represents the filter capacitor, P _o (K) represents the K-th 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.

Further, the step 40 specifically includes:

Step 41: Set the iteration threshold, and input the mutual inductance between the transmitter and the receiver as particles into the particle swarm algorithm;

Step 42: Initialize the speed and position of each particle;

Step 43: Calculate the inertia weight factor of each particle;

Step 44: Update 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 global extreme value of the particle based on the fitness;

Step 46: Output the global extreme value based on the iteration threshold and fitness;

Step 47: According to the mapping relationship between load and mutual inductance, the magnitudes of mutual inductance and load can be obtained at the same time.

Further, the step 45 specifically includes:

Step 451: Calculate the fitness _(n) of each particle for the nth iteration based on the objective function;

Step 452: Determine whether fitness _(n) is less than fitness _(n-1) . If so, set gbest _(n) = fitness _(n) and enter step 453; if not, enter step 453;

Step 453: Determine whether fitness _(n) is less than zbest _(n) . If so, set zbest _(n) = fitness _(n) and enter step 454; if not, enter step 454;

Step 454: After the number of iterations n is increased by 1, step 46 is entered.

The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

1. The multi-load system is simplified through time division multiplexing. The multi-load system is equivalent to a single load system working at different timings. There is no need to consider the cross-coupling between multiple receivers, and through double filtering The capacitance and regulating the bus voltage prevent voltage overshoot and voltage instability that occur in the time-division operation identification algorithm; then the particle swarm algorithm is introduced to establish a system steady-state circuit model and create a fitness function through the difference between the sampled output voltage and the predicted output voltage. , transform the parameter identification problem into an algorithm optimization problem, and search for optimal solutions for the parameters to be identified to replace the traditional calculation method, avoiding errors caused by traditional equations.

2. In order to solve the fluctuation of the output voltage caused by time division, a switchable double filter capacitor is used. At the same time, in order to prevent the overshoot of the output voltage during the identification stage, the duty cycle of the inverter signal is changed to adjust the inverter side. The output voltage.

3. Through system analysis, it is assumed that when 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 mutual inductance is further calculated, simplifying the original need to identify two parameters to only need to identify one parameter. That’s it, the other one can be calculated through a formula, which greatly simplifies the running time of the algorithm and improves the computing efficiency.

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 large voltage at both ends of the device in the coupling mechanism. It is safer, and the algorithm has low complexity, short calculation time, and small errors.

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; through the model predictive control algorithm (MPC algorithm) and the particle swarm algorithm (PSO) Algorithm) working together can ensure the dynamic stability and rapid response capability of the wireless charging system; that is, by combining the PSO algorithm and the MPC algorithm, the present invention can be used in both dynamic and static situations, making wireless charging more reliable; not only can Realizing the imaginary part estimation of the receiving end in the offline state can also realize the imaginary part estimation of the receiving end of the dynamic wireless charging system, which greatly improves the practicality of the present invention.

6. Compared with the traditional method of using the BOOST-BUCK circuit to control the output voltage of the system, the present invention has good adaptability whether it is dynamic or static, 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.

The above description is only an overview of the technical solutions of the present invention. In order to understand the technical solutions of the present invention more clearly, The technical means can be implemented according to the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are listed below.

Description of the drawings

The present invention will be further described below with reference to the accompanying drawings and embodiments.

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

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

Figure 3 is a schematic diagram of the circuit topology after the rectifier bridge according to the embodiment of the present invention;

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

Figure 5 is a simplified structural schematic diagram of dual power supplies according to an embodiment of the present invention;

Figure 6 is a time division multiplexing receiving end control timing diagram according to an embodiment of the present invention.

Detailed ways

Embodiments of the present application provide a method for simultaneous identification of loads and mutual inductances of a multi-load wireless power transmission system to realize real-time identification of loads and mutual inductances of a multi-load wireless charging system, thereby expanding the working range of the wireless charging system.

The general idea of the technical solution in the embodiment of this application is as follows:

By dividing the working timing of multiple loads, the multi-load system is equivalent to a single-load system with different working timings; then by analyzing the system model, the mathematical mapping relationship between the load and mutual inductance is calculated; and then by analyzing the power bus voltage and current and DC output voltage are sampled in real time, and the calculated voltage value is calculated through the sampled data and the obtained charging parameters; then an objective function is created based on the actual voltage value and the calculated voltage value, and the parameter identification problem is transformed into an algorithm optimization problem. The parameters to be identified are The optimal solution search is carried out to replace the traditional calculation method to avoid errors caused by traditional equations; 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, reducing the circuit complexity and system volume, and expanding the working range of the wireless charging system.

Before introducing the specific embodiments, a multi-load wireless charging system of SS type resonant circuit based on the method of the embodiment of the present application is introduced. As shown in Figure 2, it includes a DC power supply, a resonant mechanism, a controller, an inverter, Rectifier filter module and load; the regulated DC power supply passes through the full-bridge inverter (S ₁ ~ S ₄ ) is converted into high-frequency alternating current, and then the energy is transmitted to multiple receiving ends through the magnetic field through the resonator composed of L _P and C _p . The resonator composed of L _s and C _s receives the energy, and finally Rectify and filter, and pass it to the load; among them, the switching tubes S ₁ ~ _Si respectively control the opening and closing status of each receiving end. The controller uses TI's DSP28379D, the inverter is composed of GaN-MOSFET, and the resonator is composed of equal and symmetrical circular coils wound with Litz wire: it consists of a controller and a switch driver, and a full-bridge inverter is used at the transmitter for conversion DC power supply to generate high-frequency AC voltage; an uncontrollable bridge rectifier is used at the receiving end to simplify the control difficulty; the resonant circuit is composed of inductors and capacitors connected in series, and the resonant frequency

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

(1) Filter capacitor structure design

The present invention proposes a circuit topology of a switchable capacitor, as shown in Figure 3. When _Si = 1 is turned on, the receiving end receives energy, and the capacitors C _f1 and C _f2 function as filtering and energy storage, and C _f2 << C _f1 . This circuit can control the size of the equivalent filter capacitor by controlling the switching state of the switch S _ci to stabilize the system output voltage. The equivalent filter capacitor value is C _max =C _f2 +C _f1 when parameter identification is not performed. The capacity value during parameter identification is C _min =C _f1 . Since larger filter capacitors cannot fully store energy in a short working time, small filter capacitors can achieve rapid stability in the same period of time. At the same time, in order to meet the parameter identification requirements, the equivalent filter capacitor needs to be between C _max and C _min When switching, this process will produce a large voltage jump, as shown in Figure 4.

(2)Input power supply design

In order to solve the voltage jump problem, the present invention proposes a dual power supply mode for the transmitter input, including the normal operating power supply voltage E _H and the identification power supply voltage _EL . That is, the equivalent input power supply voltage is adjusted by changing the duty cycle of the inverter signal at the transmitter. . When the equivalent filter capacitor switches from C _max to C _min , the equivalent input power supply voltage is adjusted to eliminate the voltage jump phenomenon generated during switching. The simplified topology of the input power supply is shown in Figure 5. When the switch S _v =1 is on, E = E _H ; when S _v =0 is off, E = E _L .

(3) System control timing design

In order to realize parameter identification of multiple loads, the system uses a time division multiplexing mechanism to realize energy transmission at the transmitter and each receiver respectively. Taking dual receivers as an example, the control timing of each switch tube is shown in Figure 6.

The control timing for receiver 1 is described as follows:

i. If it is in normal operation, the receiving end transmits energy at a certain frequency. At this time, S _v = 1 is turned on, the equivalent power supply voltage E = E _H ; S _c1 = 1, turned on, and the equivalent filter capacitance size is C _f1 +C _f2 .

ii. If during parameter identification, S ₁ =1 is turned on, and the receiving end is in the energy receiving state. That is, at this time, S _v =1 is turned off, the equivalent power supply voltage E = E _L ; S _c1 =0 is turned off, and the equivalent filter capacitor is turned off. The size is C _f2 .

The control timing description of receiver 2 is the same.

Referring to Figures 1 to 6, a preferred embodiment of a method for simultaneous identification of loads and mutual inductances in a multi-load wireless power transmission system according to the present invention is applied to an S-S type multi-load magnetic coupling resonance system. The method includes the following steps :

A specific implementation manner of the embodiment of the present invention:

In step 10, the double 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. The adjustment of the bus voltage specifically includes the following steps:

The step 20 specifically includes:

Among them, I _max is the amplitude of the input current;

in,

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

in,

The step 40 specifically includes:

Step 42: Initialize the speed and position of each particle;

Step 43: Calculate the inertia weight factor of each particle;

Specifically, the step 45 includes:

Step 452: Determine whether fitness _(n) is less than fitness _(n-1) . If so, let gbest _(n) = fitness _(n) , And go to step 453; if not, go to step 453;

The technical solutions provided in the embodiments of the present invention at least have the following technical effects or advantages: the multi-load system is simplified through the time division multiplexing method, and the multi-load system is equivalent to a single-load system working in different timings, without considering multiple receiving terminals. cross-coupling between each other, and through double filter capacitors and adjusting the bus voltage to prevent voltage overshoot and voltage instability in the time-division operation identification algorithm; then the particle swarm algorithm was introduced, by establishing a system steady-state circuit model, and by sampling the output voltage and The difference in predicted output voltage creates a fitness function, converts the parameter identification problem into an algorithm optimization problem, and searches for optimal solutions for the parameters to be identified to replace the traditional calculation method, avoiding errors caused by traditional equations.

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

Through system analysis, it is assumed that when the system is in resonance, the input impedance of the transmitter is calculated in different ways, and the mathematical mapping relationship between the load and mutual inductance is further calculated, simplifying the original need to identify two parameters into only one parameter. , the other can be calculated through a formula, which greatly simplifies the running time of the algorithm and improves the computing efficiency.

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 large voltage at both ends of the device in the coupling mechanism, which is safer, and the algorithm has low complexity, short calculation time, and small errors.

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; through the model predictive control algorithm (MPC algorithm) and the particle swarm algorithm (PSO algorithm) Working together, the dynamic stability and rapid response capability of the wireless charging system can be ensured; that is, by combining the PSO algorithm and the MPC algorithm, the present invention can be used in both dynamic and static situations, making wireless charging more reliable; not only can offline The imaginary part estimation of the receiving end under the state can also realize the imaginary part estimation of the receiving end of the dynamic wireless charging system. design, greatly improving the practicality of the present invention.

Compared with the traditional method of using the BOOST-BUCK circuit to control the output voltage of the system, the present invention has good adaptability whether it is dynamic or static, and can self-adjust the algorithm model according to changes in the environment, and can be easily It runs well in practical applications, and the system of the present invention is easier to implement in hardware.

Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments we have described are only illustrative and are not used to limit the scope of the present invention. Those skilled in the art Equivalent modifications and changes made by skilled persons in accordance with the spirit of the present invention shall be covered by the scope of protection of the claims of the present invention.

Claims

A method for simultaneous identification of loads and mutual inductance in a multi-load wireless power transmission system, which is characterized in that it is applied to an S-S type multi-load magnetic coupling resonance system. The method includes:

Step 10. Use the time division multiplexing method to convert the multi-load system into a single-load system that operates at different timings, so as to divide the working timings of multiple loads; select the appropriate identification interval, use double filter capacitors and adjust the bus voltage to prevent time division Voltage overshoot and voltage instability occur when running the identification algorithm;

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

Step 30: Sample the system's power bus voltage, current and DC output voltage in real time, calculate the calculated voltage value through the sampled data and the system's charging parameters, and then create an objective function based on the actual voltage value and the calculated voltage value;

Step 40: Use the particle swarm algorithm to optimize the objective function to realize real-time identification of the size of the charging parameters.
The method according to claim 1, characterized in that: in the step 10, the double filter capacitor includes a freewheeling capacitor and an identification capacitor, and the capacitance value of the freewheeling capacitor is much larger than the capacitance of the identification capacitor. Numerical value, the adjustment of the bus voltage 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 the charging state, and the other receiving ends in the off stage are in the discharging state. At this time, there is no need to adjust the inverter side. Voltage;

Step 12. When performing parameter identification on the turned-on receiving end, turn off the freewheeling capacitor and use the identification capacitor for filtering. At the same time, adjust the duty cycle of the inverter drive signal 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 reaches the maximum value quickly, making it easier to use the identification algorithm.
The method according to claim 1, characterized in that step 20 specifically includes:

Step 21: Expand the Fourier series of the output square wave voltage after inversion and take its fundamental wave;

Among them, u d is the voltage after inversion, E is the input DC voltage, ω is the system working angular speed, and t is the working time;

Step 22. Assume that the transmitter current i p =I max sinωt when the system resonates, calculate the input impedance of the transmitter:

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

Step 23. Analyze a single load system and calculate the input impedance of the transmitter through Kirchhoff’s voltage and current law:

Among them, L P is the inductance size of the transmitting end, C p is the capacitance size of the transmitting end, L s is the inductance size of the receiving end, C s is the capacitance size of the receiving end, R p is the internal resistance of the transmitting end, R s is the internal resistance of the receiving end, R L is the load, M is the mutual inductance;

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

in,
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,

Among them, V o (K+1) mea represents the calculated voltage value of the (K+1)th time, C f represents the filter capacitor, P o (K) represents the K-th 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, and V o * (K) represents the theoretical voltage of the Kth time.
The method according to claim 1, characterized in that step 40 specifically includes:

Step 41: Set the iteration threshold, and input the mutual inductance between the transmitter and the receiver as particles into the particle swarm algorithm;

Step 42: Initialize the speed and position of each particle;

Step 43: Calculate the inertia weight factor of each particle;

Step 44: Update 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 global extreme value of the particle based on the fitness;

Step 46: Output the global extreme value based on the iteration threshold and fitness;

Step 47: According to the mapping relationship between load and mutual inductance, the magnitudes of mutual inductance and load can be obtained at the same time.
The method according to claim 5, characterized in that said step 45 specifically includes:

Step 451: Calculate the fitness (n) of each particle for the nth iteration based on the objective function;

Step 452: Determine whether fitness (n) is less than fitness (n-1) . If so, set gbest (n) = fitness (n) and enter step 453; if not, enter step 453;

Step 453: Determine whether fitness (n) is less than zbest (n) . If so, set zbest (n) = fitness (n) and enter step 454; if not, enter step 454;

Step 454: After the number of iterations n is increased by 1, step 46 is entered.