A method for optimal power allocation in a multiple-input multiple-output wireless power transfer system

Abstract

The application discloses a kind of optimal power distribution methods of multiple-input multiple-output wireless power transmission system, first constructs n input m output wireless power transmission system equivalent circuit model, and determines optimization target;Next, objective function and constraint condition are established;Finally, the optimal power distribution of multiple-input multiple-output wireless power transmission system is realized by using multi-objective genetic algorithm.The application can distribute power according to the power demand of different equipment, the power distribution is accurate, the transmission efficiency is high, while iterative operation is adopted, real-time is high, easy to microcontroller or computer implementation, with good engineering application value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless power transmission, and particularly relates to a method for optimal power distribution of a multiple-input multiple-output wireless power transmission system. BACKGROUND

[0002] Wireless power transmission technology is a technology for transmitting power to a load through an electromagnetic field rather than a cable. With the increasingly wide and in-depth application of wireless power transmission technology, single-transmission single-reception wireless charging technology has been difficult to meet the needs of some complex applications. Single-input single-output wireless power transmission systems adopt a one-to-one mode for power transmission and cannot simultaneously provide energy for multiple loads. Compared with single-input single-output wireless power transmission systems, multiple-input multiple-output wireless power transmission systems have multiple transmission ends and receiving ends, and through control of multiple coils of the transmission end, energy can be provided to multiple devices. In a multiple-input multiple-output wireless power transmission system, there are multiple loads (power-consuming devices), which are of different types, have different working states, and require different powers when working. How to allocate the transmission power of the system according to the power requirements of different loads is the key to realizing the practicality of a multiple-input multiple-output wireless power transmission system.

[0003] For a multiple-input multiple-output wireless power transmission system, through literature induction, the existing power distribution methods mainly include:

[0004] (1) A DC / DC converter is added to each receiving end to provide the required power to the load by controlling the converter.

[0005] (2) A time-sharing control method is adopted, a super capacitor is used as an intermediate energy storage element, and the required power is allocated to each load in a time-sharing manner; or different resonant frequencies are set at the receiving end to achieve power distribution to the load through time-sharing control.

[0006] (4) A power converter with different resonant frequencies is used to form a power transmission channel, and the power distribution to each receiver is achieved by adjusting the duty cycle.

[0007] In the above methods, method (1) is used for power distribution, and the increase of the DC / DC circuit increases the power loss of the system and also increases the hardware cost of the system. The time-sharing control in method (2) is simple and effective in principle, but is only applicable to occasions with low real-time requirements for power supply, and the application range is limited. The circuit structure of method (3) is complex, and the control is difficult, which is difficult to be engineered and practical. SUMMARY

[0008] In order to overcome the prior art, the present application provides a kind of optimal power distribution method of multiple-input multiple-output wireless power transmission system, first constructs n input m output wireless power transmission system equivalent circuit model, and determines optimization target;Next, objective function and constraint condition are established;Finally, the optimal power distribution of multiple-input multiple-output wireless power transmission system is realized by using multi-objective genetic algorithm.The present application can distribute power according to the power requirement of different equipment, and the power distribution is accurate, with high transmission efficiency, while iterative operation is adopted, real-time is high, easy to realize by microcontroller or computer, with good engineering application value.

[0009] The technical solution adopted by the present application to solve its technical problems comprises the following steps:

[0010] Step 1: constructing n input m output wireless power transmission system equivalent circuit model, and determining optimization target;

[0011] Step 1-1: in the i-th transmitting end loop of the n input m output wireless power transmission system equivalent circuit model, i=1,2,...,n, v Ti represents the excitation voltage phase of the i-th transmitting end, Z Ti represents the impedance in the i-th transmitting end loop, L Ti ,C Ti respectively represent the self-inductance and capacitance in the i-th transmitting end loop;In the j-th receiving end loop, j=1,2,...,m, Z Rj represents the impedance in the j-th receiving end loop, which contains the load, L Rj ,C Rj respectively represent the self-inductance and capacitance in the j-th receiving end loop;M i,j represents the mutual inductance between the i-th transmitting coil and the j-th receiving coil;

[0012] Step 1-2: the total error of power distribution is represented as the sum of the absolute error of each receiving end power, as the optimization target in power distribution:

[0013]

[0014] Wherein, is the actual power of the receiving end, is the target power of the receiving end;

[0015] The self-impedance of all transmitting ends of the system is equal, denoted as r, then the transmitting loop loss P T is represented as:

[0016]

[0017] From equation (2), when the current amplitude of each transmitting end |i TiThe transmission loop loss P can be achieved when the sum of squares of F is minimum T is minimum;

[0018] Step 2: Establish the objective function and constraint conditions;

[0019] Take the excitation voltage vectors [v T1 ,v T2 …,v Tn ] of n transmission ends as decision variables, each excitation voltage vector includes voltage amplitude and phase value, that is The objective function is as follows: F1 represents the total error of power distribution, and F2 represents the loss of the transmission loop:

[0020]

[0021] The constraint conditions are as follows:

[0022]

[0023] That is, the amplitude of the excitation voltage is constrained, and the maximum is not more than the maximum output voltage V max , and the minimum is 0V;

[0024] Step 3: Optimal power distribution implementation;

[0025] The multi-objective genetic algorithm is used to realize the optimal power distribution of the multi-input multi-output wireless power transmission system. The specific implementation steps are as follows:

[0026] Step 3-1: Determine the relevant parameters;

[0027] For m receiving ends, according to the different power requirements of the receiving load, the target power value of each receiving end is determined Set the required parameters of the algorithm: the maximum number of iterations G max , the population size q, the mutation probability cp, the crossover recombination probability mp, and the target function improvement termination threshold T;

[0028] Step 3-2: Initialize n excitation voltages, randomly generate a set of excitation voltage sequences [v T1 ,v T2 …,v Tn ]×q, randomly assign initial voltage values and initial phase values to the excitation voltages as the parent population, and set the iteration number identifier G=1;

[0029] Step 3-3: For the excitation voltage sequences in the parent population, perform mutation operation with probability cp; perform crossover recombination operation with probability mp, generate new excitation voltage sequences as the child population;

[0030] Step 3-4: the parent population and the offspring population are combined to form a new population, the target functions F1 and F2 corresponding to the wireless power transmission system working at a given excitation voltage are calculated according to formula (3), the combined population is quickly non-dominated sorted and crowdedness sorted according to the values of F1 and F2, and the current optimal q excitation voltage sequences are selected as the new parent population;

[0031] Step 3-5: whether the algorithm iteration termination condition is met is judged: 1. the iteration number G reaches the upper limit G max ; 2. the improvement value of the target function in continuous set times of iterations is less than the set threshold T; any one of the above two conditions is met, and it is considered that the termination condition is reached, step 3-6 is executed, otherwise, step 3-3 is jumped to, and the iteration number identifier is updated, so that G is added by 1;

[0032] Step 3-6: q solutions are obtained from the above step operation, the solution making the target function F1 minimum is selected as the optimal solution, and the n excitation voltage sources output corresponding excitation voltage amplitude and phase values according to the optimal solution, so that optimal power distribution is realized.

[0033] The beneficial effects of the present application are as follows:

[0034] The present application provides a power distribution method suitable for a multiple-input multiple-output wireless power transmission system. The method can distribute power according to the power requirements of different devices, and the power distribution is accurate and the transmission efficiency is high. Iterative operation is adopted, and the real-time performance is high, and the method is easy to realize by a microcontroller or a computer, and has good engineering application value. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 An equivalent circuit model diagram of the input m-output wireless power transmission system of the present application is shown.

[0036] Figure 2 An optimal power distribution flowchart of the method of the present application is shown.

[0037] Figure 3 An input 2-output wireless power transmission system structure diagram of embodiment 2 of the present application is shown. DETAILED DESCRIPTION

[0038] The present application is further described below in combination with the drawings and embodiments.

[0039] In view of the deficiencies of the prior art, the present application provides a power distribution method suitable for a multiple-input multiple-output wireless power transmission system, takes the minimum transmission end current and the minimum power distribution error as the optimization target, adjusts the current amplitude and phase of each transmission end power source, and realizes power distribution to different loads. The method has accurate power distribution, is easy to realize, and has good engineering application value.

[0040] The principle of optimal power distribution of a multiple-input multiple-output wireless power transmission system is: according to the power requirement of each output device, taking the minimum current of the transmitting end and the minimum power distribution error as the optimization target, the power distribution of loads with different power requirements is realized by controlling the voltage amplitude and phase of each transmitting end power supply of the system.

[0041] An optimal power distribution method of a multiple-input multiple-output wireless power transmission system, comprising the following steps:

[0042] Step 1: Construct an equivalent circuit model of an n-input m-output wireless power transmission system, and determine the optimization target;

[0043] Step 1-1: As shown in the equivalent circuit model of the n-input m-output wireless power transmission system, i = 1, 2,..., n, v Figure 1 represents the excitation voltage phase of the i-th transmitting end, Z Ti represents the impedance in the i-th transmitting end loop, L Ti , C Ti , and C Ti represent the self-inductance and capacitance in the i-th transmitting end loop, respectively; j = 1, 2,..., m, Z Rj represents the impedance in the j-th receiving end loop, which includes the load, L Rj , C Rj , and C i,j represent the self-inductance and capacitance in the j-th receiving end loop, respectively; M T represents the mutual inductance between the i-th transmitting coil and the j-th receiving coil.

[0044] Step 1-2: During system operation, the actual power of each receiving end is closer to the target power , which means that the power distribution is more accurate. Therefore, the total error of power distribution is represented as the sum of the absolute errors of the power of each receiving end, which is the optimization target in power distribution:

[0045]

[0046] wherein, is the actual power of the receiving end, is the target power of the receiving end;

[0047] The total input power of the multiple-input multiple-output wireless power transmission system is composed of the transmitting loop loss and the total output power of the system. When the target power distribution is met, the total output power of the system is a fixed value, so when the transmitting loop loss is the smallest, the system transmission efficiency is the largest. Under normal circumstances, the self-impedance of all transmitting ends of the system is equal, denoted as r, then the transmitting loop loss P T is represented as:

[0048]

[0049] From equation (2), when the current amplitude |i Ti of each transmitting terminal is minimized, the transmitting loop loss P T is also minimized.

[0050] Step 2: Establish the objective function and constraint conditions.

[0051] Take the excitation voltage vectors [v T1 , v T2 …, v Tn ] of n transmitting terminals as decision variables, each of which includes voltage amplitude and phase value, i.e. The objective function is as follows: F1 represents the total error of power distribution, and F2 represents the loss of the transmitting loop:

[0052]

[0053] The constraint conditions are as follows:

[0054]

[0055] That is, the amplitude of the excitation voltage is constrained, and the maximum does not exceed the maximum output voltage V max of the excitation power supply, and the minimum is 0V.

[0056] Step 3: Optimal power distribution implementation.

[0057] A multi-objective genetic algorithm is used to achieve optimal power distribution of a multi-input multi-output wireless power transmission system. The specific implementation steps are as follows:

[0058] Step 3-1: Determine the relevant parameters.

[0059] For m receiving terminals, according to different power requirements of the receiving load, the target power value of each receiving terminal is determined Set the required parameters of the algorithm: the maximum number of iterations G max , the population size q, the mutation probability cp, the crossover recombination probability mp, and the target function improvement termination threshold T.

[0060] Step 3-2: Initialize n excitation voltages, randomly generate a set of excitation voltage sequences [v T1 , v T2 …, v Tn ] × q with a quantity of q, randomly assign initial voltage values and initial phase values to the excitation voltages as the parent population, and set the iteration number identifier G = 1.

[0061] Step 3-3: For the parent population of excitation voltage sequence, mutation operation is performed with probability cp; crossover recombination operation is performed with probability mp, to generate new excitation voltage sequence as the child population;

[0062] Step 3-4: The parent population and the child population are merged to form a new population, and the target functions F1 and F2 corresponding to the wireless power transmission system operating at a given excitation voltage are calculated according to formula (3), and the merged population is sorted according to the values of F1 and F2, and the current optimal q excitation voltage sequences are selected as the new parent population;

[0063] Step 3-5: Determine whether the algorithm iteration termination condition is met: 1. The iteration number G reaches the upper limit G max ; 2. The improvement value of the target function in consecutive multiple (can be set) iterations is less than the set threshold T; any one of the above two conditions is met, and step 3-6 is executed, otherwise jump to step 3-3, and update the iteration number identifier, so that G is increased by 1;

[0064] Step 3-6: From the above step, q solutions are obtained, the solution that minimizes the target function F1 is selected as the optimal solution, and the n excitation voltage sources output corresponding excitation voltage amplitude and phase value according to the optimal solution, to realize optimal power distribution. Specific embodiments:

[0066] 2 input 2 output wireless power transmission system as shown in the attached Figure 3 The system transmitting end includes: a DC voltage source, a high-frequency excitation circuit composed of a full-bridge inverter circuit, a microprocessor (MCU), wherein the MCU can generate 2-way duty cycle and phase adjustable PWM wave to drive the full-bridge inverter circuit to generate high-frequency alternating excitation, and the MCU is responsible for collecting the current of the transmitting end and performing Zigbee communication (for receiving the load power information feedback from the system receiving end). The receiving end includes: AC / DC rectifier circuit, load, voltage and current acquisition module and Zigbee communication module, after the voltage and current acquisition module of the receiving end collects the voltage and current information of the load, the information is sent to the MCU of the transmitting end through the Zigbee network. In operation, the MCU of the receiving end calculates the excitation voltage parameters required to meet the power distribution according to the power demand of the load end, generates a PWM signal with a certain duty cycle and phase to drive the high-frequency excitation circuit for wireless power transmission, and meets the power demand of each load.

[0067] The system is used to transmit power to two loads and perform power distribution.

[0068] After starting the system, the microprocessor first sets the initial parameters of the optimization algorithm, including: the maximum iteration number G max(200 times in this example), population quantity q (10 in this example), mutation probability cp (0.5 in this example), crossover recombination probability mp (0.7 in this example), target function improvement termination threshold T (0.01 in this example), and the target power is determined according to the power value required by the load.

[0069] In this example, the load 1 is assigned 10W power and the load 2 is assigned 3W power, and the target power is 10W and 3W respectively.

[0070] The microprocessor runs according to the flow attached Figure 2 , executes the multi-objective optimization algorithm proposed in this patent, converges to the optimal solution through iterative operation, and outputs two groups of PWM signals that can meet the power distribution requirements and minimize the transmitting end current. The two groups of PWM signals with independent duty cycles and phases pass through the high-frequency excitation circuit and then transmit power to the load end through the transmitting coil, outputting 10W and 3W power to the two loads respectively.

[0071] The target power is specified again as 10W and 10W respectively, and the wireless power transmission system is started to distribute 10W and 10W power to the two loads respectively.

[0072] Finally, the target power is specified as 1W and 10W respectively, and the wireless power transmission system is started to distribute 1W and 10W power to the two loads respectively.

[0073] The results of the three power distribution are shown in Table 1. It can be seen that the multi-input multi-output wireless power transmission system can perform power distribution transmission using the power distribution algorithm of the present application, and the maximum distribution error of power distribution is 0.27W, which can meet the application requirements of actual engineering.

[0074] Table 1: Results of three power distribution (unit: W)

[0075]

Claims

1. A method for optimal power allocation in a multiple-input multiple-output wireless power transfer system, the method comprising: The method comprises the following steps: Step 1: Constructing an equivalent circuit model of the n-input m-output wireless power transmission system and determining an optimization target; Step 1-1: In the i-th transmitting loop of the equivalent circuit model of the n-input m-output wireless power transfer system, i = 1, 2, …, n, v Ti represents the excitation voltage phasor of the i-th transmitting end, Z Ti represents the impedance in the i-th transmitting loop, L Ti , C Ti respectively represent the self-inductance and the capacitance in the i-th transmitting loop; in the j-th receiving loop, j = 1, 2, …, m, Z Rj represents the impedance in the j-th receiving loop, which contains the load, L Rj , C Rj respectively represent the self-inductance and the capacitance in the j-th receiving loop; M i,j represents the mutual inductance between the i-th transmitting coil and the j-th receiving coil; Step 1-2: Expressing the total error of power distribution as the sum of the absolute errors of the power of each receiving end as the optimization target in power distribution: wherein, Pact is the actual power at the receiving end, Ptarget is the target power at the receiving end; The self-impedance of all the transmitting ends of the system is equal, denoted as r, then the transmitting loop loss P T is expressed as: From (2), it is known that the transmission loop loss P Ti can be achieved when the sum of the squares of the current amplitudes |i T is minimum. Step 2: Establishing a target function and constraint conditions; Let the n excitation voltage vectors [v T1 ,v T2 …,v Tn ] of the n transmitting ends be decision variables, each of which includes a voltage amplitude and a phase value, i.e. The objective function is as follows: F1 represents the total error of power distribution, and F2 represents the loss of the transmitting loop: The constraint conditions are as follows: That is, the amplitude of the excitation voltage is constrained, and the maximum does not exceed the maximum output voltage V max of the excitation power supply, and the minimum is 0 V. Step 3: Optimal power distribution implementation; The optimal power distribution of the multi-input multi-output wireless power transmission system is implemented by using a multi-objective genetic algorithm, and the specific implementation steps are as follows: Step 3-1: Determining relevant parameters; For m receiving ends, target power values of the receiving ends are determined according to different power requirements of receiving loads Set parameters required by the algorithm: maximum iteration number G max , population number q, mutation probability cp, crossover recombination probability mp, and target function improvement termination threshold T Step 3-2: initialize n excitation voltages, randomly generate a set of q number of excitation voltage sequences [v T1 ,v T2 …,v Tn ]×q, randomly assign initial voltage values and initial phase values to the excitation voltages as the parent population, let the iteration number identifier G = 1; Step 3-3: For the excitation voltage sequence in the parent population, mutation operation is performed with a probability cp, and crossover recombination operation is performed with a probability mp to generate a new excitation voltage sequence as the child population; Step 3-4: Merging the parent population and the child population to form a new population, calculating the target functions F1 and F2 corresponding to the given excitation voltage of the wireless power transmission system according to formula (3), and performing fast non-dominated sorting and crowding degree sorting on the merged population according to the values of F1 and F2, and selecting the current optimal q excitation voltage sequences as the new parent population; Step 3-5: Determine whether the algorithm iteration termination condition is met:

1. The iteration number G reaches the upper limit G max ; 2. The improvement value of the objective function in the iteration in the continuous set number of times is less than the set threshold T; any one of the above two conditions is met, which is considered to reach the termination condition, and step 3-6 is executed, otherwise, jump to step 3-3, and update the iteration number identifier, so that G is increased by 1; Step 3-6: Selecting the solution that minimizes the target function F1 from the q solutions obtained in the above steps as the optimal solution, and outputting the corresponding excitation voltage amplitude and phase value according to the optimal solution, thereby realizing optimal power distribution.

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