CN119813562B - A follow-up wireless microwave charging method and system - Google Patents
A follow-up wireless microwave charging method and systemInfo
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- CN119813562B CN119813562B CN202510090576.XA CN202510090576A CN119813562B CN 119813562 B CN119813562 B CN 119813562B CN 202510090576 A CN202510090576 A CN 202510090576A CN 119813562 B CN119813562 B CN 119813562B
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Abstract
The invention relates to the technical field of wireless power transmission, and provides a following wireless microwave charging method and a following wireless microwave charging system, wherein the method is characterized in that a track prediction model is constructed to calculate movement track data of charging equipment in a T2 time period by collecting multiple groups of position coordinate information and speed information of the charging equipment in the T1 time period; according to the motion trail data, partitioning a microwave antenna into N subarray antenna units which are independently controlled, wherein each subarray antenna unit corresponds to a region section on a prediction trail, dynamically adjusting the phase and power parameters of each subarray antenna unit according to the real-time position of the charging equipment to form an adaptive wave beam, and carrying out regional gradient charging on the charging equipment through the adaptive wave beam. The method and the system not only solve the problem of charging alignment of the mobile equipment, but also improve the energy utilization efficiency through power gradient distribution.
Description
Technical Field
The invention relates to the technical field of wireless power transmission, in particular to a following wireless microwave charging method and system.
Background
With the popularization of mobile devices and the rapid development of portable electronic products, wireless charging technology is receiving more and more attention from academia and industry as a novel charging mode. Traditional wireless charging technologies mainly comprise near-field charging modes such as electromagnetic induction and magnetic resonance, and although the technologies show good charging efficiency in static charging scenes, the effective charging distance is usually limited in the centimeter range. In contrast, the microwave wireless energy transmission technology can realize energy transmission in the meter level and even in a longer distance by virtue of far-field transmission characteristics, and provides a new technical path for dynamic charging of mobile equipment. However, the conventional microwave wireless charging system still faces many challenges in practical application, firstly, the conventional directional microwave beam formation usually depends on a fixed beam forming algorithm, and is difficult to adapt to the rapid movement of the charging equipment, secondly, a single beam is limited in coverage area and uneven in energy distribution, so that the problem that energy in some areas is excessively concentrated and energy in other areas is insufficient is easily caused, and thirdly, the prior art lacks of accurate prediction capability on the movement track of the charging equipment, so that the energy transmission efficiency is low.
At present, scholars at home and abroad conduct a great deal of research on the microwave wireless charging technology, and various improvement schemes are proposed. The introduction of phased array antenna technology enables dynamic adjustment of microwave beams, but most of the existing schemes adopt an integral beam control mode, so that accurate energy delivery to different areas is difficult to achieve. Meanwhile, the traditional track tracking algorithm mainly focuses on the real-time position of the target, and neglects the predictive analysis of the motion trend, so that certain hysteresis exists in the response of the system, and the charging requirement in a mobile scene cannot be met. In addition, since the charging equipment is often in a moving state, the alignment accuracy of the microwave transmitting antenna and the receiving antenna is insufficient, so that the charging efficiency is low, obvious energy loss exists, the energy loss not only reduces the overall efficiency of the system, but also causes unnecessary electromagnetic interference to the surrounding environment.
Disclosure of Invention
The embodiment of the invention provides a following wireless microwave charging method, which can solve the problems of low charging efficiency and energy loss caused by insufficient alignment precision of a microwave transmitting antenna and a receiving antenna due to the fact that charging equipment is always in a moving state at least to a certain extent.
Other features and advantages of the invention will be apparent from the following detailed description, or may be learned by the practice of the invention.
According to one aspect of the present invention, there is provided a following wireless microwave charging method comprising:
Collecting multiple groups of position coordinate information and speed information of the charging equipment in a T1 time period, constructing a track prediction model, and calculating motion track data of the charging equipment in a T2 time period;
Dividing a microwave antenna into N subarray antenna units which are independently controlled according to the motion trail data, wherein each subarray antenna unit corresponds to a regional section on a prediction trail;
The control unit dynamically adjusts the phase and power parameters of each subarray antenna unit according to the real-time position of the charging equipment to form a self-adaptive wave beam;
and carrying out regional gradient charging on the charging equipment through the adaptive beam.
In the invention, based on the previous proposal, the forgetting gate, the output gate, the candidate cell calculation, the cell state update and the output gate structure of the track prediction model;
the definition of the forgetting door is shown as the following formula:
the definition of the output gate is shown as follows:
The candidate cell state is calculated as follows:
the cell status update is represented by the formula:
the structure of the output door is shown as follows:
The final hidden state output is shown as follows:
wherein each parameter satisfies the following constraint:
δ,α,β,β,η,φ∈(0,1)
Wherein θ j is a trainable parameter, f t is a forgetting gate output value, range (0, 1), σ is a sigmoid activation function, W f is a forgetting gate weight matrix, h t-1 is a hidden state at the previous time, x t is a current input, b f is a forgetting gate bias term, δ is a time derivative weight coefficient, for adjusting state change sensitivity, I t is the time derivative of the hidden state, i t is the input gate output value, the range (0, 1), W i is the input gate weight matrix, b i is the input gate bias term, alpha is the historical information weight coefficient, m is the time step considered by the historical information, ω k is the weight of the kth historical moment, x t-k is the input before k steps,Is candidate cell state, W c is state weight matrix, b c is state bias term, beta is second order gradient weight coefficient,Is a second order gradient of hidden state, C t is the current cell state, by Hadamard product (multiplication element by element), gamma is the historical cell state weight coefficient, W i is the i-th historical state weight, mu i is the attenuation rate of the i-th time scale, deltat is the time step, o t is the output gate output value, W o is the output gate weight matrix, b o is the output gate bias term, eta is the state change rate weight coefficient,For the cell state change rate, h t is the hidden state output at the current moment, phi is the historical hidden state weight coefficient, p is the considered historical frame number, and v j is the adaptive weight of the j-th frame.
According to the method, based on the scheme, the motion track data calculate a plurality of space position points according to the output of the track prediction model, and connect the space position points through an interpolation algorithm to form a smooth continuous prediction track curve;
The spatial position points further comprise h t confidence coefficient output by the track prediction model at the t moment, and when the confidence coefficient is lower than a preset threshold value, data are collected again.
In the present invention, based on the foregoing scheme, the states of the sub-array antenna units include an active power supply unit, a standby function unit, and a sleep unit;
and cooperative control is performed among the subarray antenna units.
In the present invention, based on the foregoing aspect, the cooperative control includes judging whether to start or transition the state of the target sub-array antenna unit according to the distance between the charging device and the sub-array antenna unit;
And determining whether the target subarray antenna unit interacts with the subarray antenna unit according to a predicted track curve, if so, determining the target subarray antenna unit, if a plurality of subarray antenna units interact with the predicted track curve, calculating an overlapping area, and taking the subarray antenna unit with the largest overlapping area as a target subarray antenna unit.
In the present invention, based on the foregoing aspect, the cooperative control further includes:
When the abrupt change of the motion direction of the charging equipment is detected, the current position is taken as the center, and meanwhile, the adjacent subarray antenna units in the surrounding sectors are pre-activated, and power output is kept according to the coverage condition until effective track prediction is re-established or the motion trend of the charging equipment is obvious.
In the present invention, based on the foregoing scheme, the forming of the adaptive beam includes:
Calculating theoretical phase values of the subarray antenna units based on relative space vectors between the charging equipment and the subarray antenna units in each activation state;
Carrying out real-time correction on the theoretical phase by adopting a self-adaptive phase compensation algorithm to obtain a compensated actual phase value;
And performing coupling correction on the actual phase value to obtain a final phase control value, and forming an adaptive beam according to the phase control value and the power control among the subarray antenna units.
In the present invention, based on the foregoing scheme, the calculation of the compensated actual phase value is as follows:
Wherein, phi i,comp is the actual phase value after compensation, beta i (omega) is the compensation coefficient related to frequency, delta phi i is the accumulated value of historical phase errors, gamma i is the self-adaptive gain coefficient, the compensation strength is controlled, w k is the weight coefficient of each antenna unit, omega is the working angular frequency, N is the total number of antenna array units, phi i,theory is the ith theoretical phase value, t is the time variable, phi k,theory is the theoretical phase value of the kth antenna unit.
According to one aspect of the present invention, there is provided a following wireless microwave charging system comprising:
The acquisition module is used for acquiring a plurality of groups of position coordinate information and speed information of the charging equipment in the T1 time period;
The predicted track module is used for constructing a track predicted model according to the data of the acquisition module and calculating the movement track data of the charging equipment in the T2 time period;
the subarea charging module is used for dividing the microwave antenna into N subarray antenna units which are independently controlled according to the motion trail data, and each subarray antenna unit corresponds to one area section on the prediction trail;
The control unit is used for dynamically adjusting the phase and power parameters of each subarray antenna unit according to the real-time position of the charging equipment to form an adaptive wave beam;
and the charging module is used for carrying out regional gradient charging on the charging equipment through the self-adaptive beam.
In the technical scheme of the invention, the antenna parameters are adjusted in advance through track prediction by adopting a mode of combining neural network prediction with subarray antenna subarea dynamic power supply, subarray antenna is utilized to realize subarray gradient charging, the alignment deviation is controlled within 3 degrees, and the charging efficiency is improved to 92%. The scheme not only solves the problem of charging alignment of the mobile equipment, but also improves the energy utilization efficiency through power gradient distribution.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 schematically shows a flow chart of a method of follow-up wireless microwave charging in one embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many different forms and should not be construed as limited to the examples set forth herein, but rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.
The implementation details of the technical scheme of the invention are explained in detail as follows:
fig. 1 shows a flow chart of a following wireless microwave charging method according to an embodiment of the invention. Referring to fig. 1, it is shown that:
S1, acquiring multiple groups of position coordinate information and speed information of charging equipment in a T1 time period, and calculating motion trail data of the charging equipment in a T2 time period based on a neural network prediction algorithm;
And acquiring multiple sets of position coordinate information and speed information of the charging equipment in a T1 time period (particularly 10 seconds), wherein the position coordinate information comprises three-dimensional space coordinates (X, Y, Z) of the charging equipment, the position coordinate information is acquired through a GPS positioning module arranged on the charging equipment, the positioning accuracy is better than 0.1 meter, the speed information comprises speed components (Vx, vy and Vz) of the charging equipment in three directions, the speed information is acquired through nine-axis inertial sensors arranged on the charging equipment, and the acquisition frequency is one set of data every 50 milliseconds.
The acquired position coordinate information and the acquired speed information are subjected to data noise reduction processing by a Kalman filtering algorithm, data jitter caused by environmental interference is filtered, smoothed motion parameters are obtained, the position coordinate information and the speed information subjected to noise reduction processing are arranged according to time sequence to construct an input data matrix,
The input data matrix is input into a pre-trained long-short-term memory neural network model, the neural network model comprises a three-layer hidden layer structure, 64 neurons are arranged on each layer, model training is carried out by adopting an Adam optimizer, the learning rate is set to be 0.001, and the time sequence characteristic analysis and the nonlinear mapping of historical data are carried out.
The long-term and short-term memory neural network model comprises a forgetting gate, an output gate, candidate cell calculation, cell state update and an output gate structure.
The definition of the forgetting gate is as follows:
the definition of the output gate is shown as follows:
The candidate cell state is calculated as follows:
the cell status update is represented by the formula:
the structure of the output door is shown as follows:
The final hidden state output is shown as follows:
wherein each parameter satisfies the following constraint:
δ,α,β,γ,η,φ∈(0,1)
Wherein θ j is a trainable parameter, f t is a forgetting gate output value, range (0, 1), σ is a sigmoid activation function, W f is a forgetting gate weight matrix, h t-1 is a hidden state at the previous time, x t is a current input, b f is a forgetting gate bias term, δ is a time derivative weight coefficient, for adjusting state change sensitivity, I t is the time derivative of the hidden state, i t is the input gate output value, the range (0, 1), W i is the input gate weight matrix, b i is the input gate bias term, alpha is the historical information weight coefficient, m is the time step considered by the historical information, ω k is the weight of the kth historical moment, x t-k is the input before k steps,Is candidate cell state, W c is state weight matrix, b c is state bias term, beta is second order gradient weight coefficient,Is a second order gradient of hidden state, C t is the current cell state, by Hadamard product (multiplication element by element), gamma is the historical cell state weight coefficient, W i is the i-th historical state weight, mu i is the attenuation rate of the i-th time scale, deltat is the time step, o t is the output gate output value, W o is the output gate weight matrix, b o is the output gate bias term, eta is the state change rate weight coefficient,For the cell state change rate, h t is the hidden state output at the current moment, phi is the historical hidden state weight coefficient, p is the considered historical frame number, and v j is the adaptive weight of the j-th frame.
Further, the output of the final LSTM model is a six-dimensional vector, and the six-dimensional vector is converted into a spatial position point, that is, motion track data of the charging device in a predicted future T2 time period (specifically, 3 seconds), the motion track data predicts 30 spatial position points with 100 ms as an interval, and the predicted position points are connected through a cubic spline interpolation algorithm, so as to form a smooth continuous predicted track curve, that is:
model output for time point t Converted to spatial location points P t＝(xt,yt,zt), wherein the calculation of each spatial location point is shown by the following equation:
wherein the acceleration term is obtained by adjacent velocity differences:
Wherein P i is the three-dimensional space position coordinate at the ith moment, P i-1 is the position coordinate representing the previous moment, Is the velocity component in three directions,Is the acceleration component in three directions,Is a time interval.
And simultaneously calculating the confidence coefficient of each point on the predicted track curve, and triggering a data re-acquisition mechanism when the predicted point with the confidence coefficient lower than 85% appears, so as to ensure the accuracy of track prediction. More specifically, the state prediction confidence assessment is shown in the following formula:
Wherein κ 1,κ2,κ3 is a dynamically adjusted weight coefficient:
wherein h t-ht-1 is the state difference, |h t | is the first derivative norm, For the second derivative norm, ρ i is the weight dynamic adjustment coefficient.
It should be noted that in a mobile wireless charging scenario, the charging device often exhibits variable motion characteristics, including sudden motion speed changes, multi-scale features of motion trajectories (short-term obstacle avoidance and direction change and long-term path planning), and trajectory fluctuations caused by environmental interference, where these features result in that the conventional LSTM model cannot accurately predict the motion trajectories of the charging device, so as to affect real-time alignment of microwave antennas. Therefore, the embodiment improves the track prediction aspect of the charging equipment, and is better in terms of quick change response, and introduces a time derivative term into the forgetting gate to enable the model to quickly respond to the change of speed, so that the state prediction delay is reduced from 150ms to 35ms, and the real-time alignment requirement of the antenna is met.
Preferably, the cell state update introduces multi-time scale historical information, so that short-term (0.1 s level) and long-term (3 s level) motion characteristics can be captured at the same time, the track prediction accuracy is remarkably improved, and the track fluctuation caused by environmental interference is effectively filtered by adding exponential decay weighting of historical data in an input gate relative to the track fluctuation problem.
S2, partitioning a microwave antenna into N subarray antenna units which are independently controlled according to the motion track data, wherein each subarray antenna unit corresponds to a region section on a predicted track;
Based on the space distribution characteristics of the two-dimensional projection track, the whole antenna array is divided into N subarray antenna units (the value range of N is between 4 and 16), each subarray antenna unit is composed of M multiplied by M antenna array elements (the value range of M is between 4 and 8), and an overlapping area of 2 array elements is arranged between adjacent subarray antenna units and used for ensuring the continuity of beam transition.
Further, each subarray antenna unit is functionally partitioned, the subarray antenna units with movement tracks passing through the area are set as active energy supply units, the subarray antenna units adjacent to the movement tracks are set as standby energy supply units, the rest subarray antenna units are set as sleep states, the active energy supply units realize beam forming by adopting a phased array technology, the standby energy supply units keep a low-power standby state, and the standby energy supply units can be switched to the active energy supply state within 100 milliseconds.
Further, based on the track prediction data, calculating the beam coverage of each subarray antenna unit, wherein the beam coverage is determined by two parameters, namely the beam width and the beam direction, the beam width is dynamically adjusted according to the movement speed of the charging equipment, the faster the speed is, the larger the beam width is, the slower the speed is, the smaller the beam width is, and the effective coverage can be kept under different movement states, and the beam direction is determined according to the average direction of the predicted track section of the charging equipment in the subarray antenna unit coverage area.
And simultaneously, the transmitting power of each sub-array antenna unit is dynamically distributed, the sub-array antenna unit close to the charging equipment obtains higher power distribution proportion, and the sub-array antenna unit far away from the charging equipment obtains lower power distribution proportion, so that the gradient utilization of energy is realized.
More specifically, the cooperative control mechanism between the subarray antenna elements is specifically as follows:
Based on the real-time position information and predicted track data of the charging equipment, continuously calculating the distance between the charging equipment and the coverage area boundary of the currently activated subarray antenna unit, determining the next target subarray antenna unit to be entered according to the predicted track when the distance is smaller than a preset first critical distance threshold (particularly, 30% of the coverage area radius), starting a preheating program of the target subarray antenna unit when the distance is further reduced to a second critical distance threshold (particularly, 20% of the coverage area radius), switching the working state of the target subarray antenna unit from dormancy to standby, and simultaneously precharging an energy storage unit, and gradually increasing the output power of the target subarray antenna unit when the distance reaches a third critical distance threshold (particularly, 15% of the coverage area radius), wherein the initial power is set to be 30% of the nominal power and gradually increased at a rate of 5% every 100 milliseconds.
More specifically, the logic for determining the next incoming target sub-array antenna element according to the predicted trajectory is as follows:
the method comprises the steps of taking adjacent subarray antenna units with the coverage area boundary of a current subarray antenna unit along the direction of a predicted track as candidate units, calculating the size of the overlapping area of the track and the coverage area of each candidate unit when the predicted track is overlapped with the coverage area of the adjacent candidate units, determining the candidate unit as a target subarray antenna unit if the predicted track is overlapped with only one candidate unit, and selecting the candidate unit with the largest overlapping area as the target subarray antenna unit if the predicted track is overlapped with a plurality of candidate units at the same time.
The size of the overlapping area of the calculated track and each candidate unit coverage area comprises:
And calculating the overlapping area of the track and the coverage area of the candidate unit, calculating the intersection area of the circle and the coverage area of the candidate unit by taking the position point as the center of a circle and taking the radius of the receiving surface of the charging equipment as the radius when the space position point falls in the coverage area of the candidate unit, accumulating the intersection areas corresponding to the position points if a plurality of continuous space position points are positioned in the same coverage area of the candidate unit to obtain the total overlapping area of the predicted track and the candidate unit, and selecting the candidate unit with the largest overlapping area as the target subarray antenna unit after the overlapping area of all the candidate units is calculated.
Particularly, if the actual motion state of the charging equipment is detected to have a severe change, so that the predicted track model cannot respond in time, the moving speed of the charging equipment is detected, when the moving speed mutation (the speed change rate exceeds 30% of the current speed) is detected, 3 subarray antenna units adjacent in the direction are triggered in advance to enter a standby state based on the current speed vector direction, when the moving direction mutation (the direction change exceeds 30 degrees) of the charging equipment is detected, the current position is taken as the center, meanwhile, the subarray antenna units adjacent in the surrounding 60-degree sectors are pre-activated, and the power output is kept according to the coverage condition until the effective track prediction is re-established or the motion trend of the charging equipment is obvious.
When the charging equipment completely leaves the coverage area of the current subarray antenna unit, the output power of the charging equipment is gradually reduced at the rate of 10% every 100 milliseconds, when the power is reduced to below 20%, the working state of the charging equipment is switched to standby, after continuous monitoring for 60 seconds, if the charging equipment is confirmed not to return, the charging equipment is switched to a dormant state to reduce the energy consumption, and if the charging equipment is detected to return during standby, the system quickly restores the working state of the subarray antenna unit and restarts the energy supply flow.
S3, the control unit dynamically adjusts the phase and power parameters of each subarray antenna unit according to the real-time position of the charging equipment to form a self-adaptive wave beam;
The control unit calculates and dynamically adjusts the phase parameters for each active sub-array antenna unit according to the calculated space vector information, so as to realize accurate beam forming, wherein the space vector information is the relative space vector between the charging equipment and each active sub-array antenna unit, and comprises the relative distance and the relative angle, and the calculation of the space vector information is a technology well known to the person skilled in the art, so that the embodiment is not excessively described.
Further, a theoretical phase value phi i,theory of each sub-array antenna unit is calculated based on the space vector information, as shown in the following formula:
Wherein, (x i,yi) is the coordinate position of the ith antenna unit on the array plane, (theta, phi) is the pitch angle and azimuth angle of the target direction, alpha i is the amplitude weighting coefficient, d i,target is the distance from the antenna unit to the target, R max is the maximum effective coverage radius of the antenna array, and lambda is the working wavelength. Nonlinear term added newly in For optimizing the phase control of the edge region.
Preferably, in the calculation of the basic phase, the theoretical phase value is calculated according to the space geometrical relationship, but when the calculation is performed by adopting the traditional method, the phenomenon that mutation easily occurs in the edge area is found, namely when equipment enters an uncovered area from a covered area, the phase change is severe, the mutation can cause unstable wave beams and influence the charging effect, so in the calculation of the basic phase, the nonlinear term is added to smooth the mutation easily occurring in the edge area, namely preliminary compensation is performed, gradual transition is provided in the edge area, and the wave beams of the subarray antenna units tend to be stable.
Further, the adaptive phase compensation algorithm is adopted to correct in real time, and the actual phase value phi i,theory after compensation is calculated as follows:
Wherein, beta i (omega) is a frequency-dependent compensation coefficient, which is dynamically adjusted along with the working frequency, delta phi i is a historical phase error accumulation value, gamma i is an adaptive gain coefficient, the compensation strength is controlled, w k is a weight coefficient of each antenna unit, omega is the working angular frequency, and N is the total number of antenna array units.
Preferably, the phase error between the actual phase value of each antenna unit and the smoothed theoretical phase value is continuously measured, and comprehensive compensation is performed through historical error compensation and real-time dynamic compensation, namely, the historical error compensation calculation calculates a long-term error compensation value through accumulated phase error and frequency compensation coefficient, and the real-time dynamic compensation calculation calculates the distance from the antenna unit to the target equipment, and the weight coefficient is dynamically calculated, and the closer the distance is, the larger the weight is. Then, the real-time compensation value is calculated by combining the adaptive gain coefficient gamma i (initial value 0.3, which is dynamically adjusted according to the system stability). And then, the historical error compensation and the real-time dynamic compensation are overlapped to form a final compensation value, and the compensation is implemented through a phase control circuit. The layered compensation mechanism of the embodiment can process accumulated errors existing in a system for a long time, and can rapidly respond to transient disturbance caused by environmental change, so that high precision (error <0.5 degrees) and rapid response (< 1 millisecond) of phase control are realized. The whole self-adaptive process is continuous closed-loop control, and through continuous monitoring, calculation and execution, the beam is ensured to be always and accurately directed to the target equipment, and a stable and reliable energy transmission channel is provided for wireless charging.
Further considering the electromagnetic coupling effect between antenna units, introducing a dynamic correction coefficient mu ij:
Wherein η 0 is a standard coupling coefficient, d ij is an antenna unit pitch, κ is a frequency correction factor, and f 0 is a center frequency. The correction factor is adaptively adjusted with the antenna element spacing and operating frequency. The final phase control value phi i,final is:
It should be noted that the dynamic correction coefficient describes the degree of interaction of the electromagnetic field between any two antenna units in the wireless charging system. When an electromagnetic wave propagates in the antenna array, each antenna element radiates not only the electromagnetic wave but also the radiation field of neighboring elements, which is attenuated as the distance between the elements increases, and exhibits a periodic phase change characteristic. Meanwhile, due to the frequency dispersion characteristic of the antenna material, the current distribution on the metal surface can change at a higher frequency, so that the coupling strength is enhanced. The dynamic correction coefficient accurately predicts and compensates mutual interference among the units by establishing a quantitative relation among the space distance, the working frequency and the coupling strength among the antenna units, thereby accurately controlling the radiation characteristic of each antenna unit in the actual wireless charging process and ensuring the formation of stable and efficient focused beams.
Finally, by accumulating the phase differences (weighted by the coupling coefficients) of all adjacent cells, the driving phase actually required by each cell is accurately calculated, compensating for the phase distortion due to the coupling between cells. Similarly, the theoretical phase value of the array antenna unit is smoothed in the initial calculation process, so that the radiation phase of each unit can be accurately controlled under the condition that the charging equipment enters the array antenna unit and strong coupling interference exists, the expected beam direction and shape are maintained, charging power is maintained, the emitted fluctuation is ensured to be continuously followed, and the overall focusing precision and energy transmission efficiency are improved.
The method comprises the steps of starting smooth transition control of energy when charging equipment enters an overlapping coverage area of two subarray antenna units, adjusting output power proportion of the two subarray antenna units in real time through a dynamic power distribution algorithm, maintaining higher power output (such as 70%) of a current subarray antenna unit and lower power output (such as 30%) of a target subarray antenna unit if the charging equipment is located at a starting section of the overlapping area, enabling the power proportion of the two subarray antenna units to adopt a nonlinear dynamic adjustment strategy when the charging equipment moves in the overlapping area, enabling a power adjustment curve to follow S-shaped change characteristics, ensuring that total energy received by the charging equipment is kept constant and smooth in change, and improving the power proportion of the target subarray antenna unit to 70% and reducing the power proportion of the current subarray antenna unit to 30% if the charging equipment is located at a terminating section of the overlapping area.
And then, the control unit sends the calculated phase and power parameters to the phase shift phase device and the power amplifier of each subarray antenna unit through a high-speed digital bus to carry out self-adaptive charging.
And S4, carrying out regional gradient charging on the charging equipment through the self-adaptive beam, wherein the subarray antenna units close to the equipment position output larger power, and the subarray antenna units far away from the equipment position output smaller power.
And after the antenna array completes initial phase control, carrying out regional gradient charging on all antenna units. The antenna array is divided into an inner circle region (0-20 cm), an intermediate region (20-40 cm) and an outer circle region (40-60 cm) based on the position information of the charging device. For antenna elements in the inner loop region, the output power of these elements is adjusted to a higher level (85-100% of rated power) due to the closest distance to the device.
When extended outwards, the power output of the antenna elements of each region is correspondingly reduced according to the increase of the space distance. In particular, when dividing the antenna elements into intermediate regions, the output power of these elements is adjusted to 65-80% of the rated power. If the antenna units belong to the outermost ring region, the output power of the units is further reduced to 40-60% of rated power.
If the position of the charging equipment is detected to be deviated, the system divides the power areas again and updates the power distribution of all antenna units in each area. The system updates the power allocation scheme synchronously, especially when the final phase of each cell needs to be recalculated. If the temperature in a certain area is abnormally increased, the system automatically reduces the power output of the area, so that the safety and stability of the charging process are ensured.
More specifically, after the regional gradient charging is performed according to the coupling coefficient, if the charging equipment is detected to enter the overlapping coverage area of the adjacent subarrays, an energy smooth transition control mechanism is superposed on the basis of keeping the gradient charging. At this time, the original gradient charging scheme is used as a reference power configuration, and the smooth transition control is dynamically adjusted on the basis:
When the charging device just enters the initial section of the overlapping area, the antenna units of the current subarray (i.e. the area where the device is located) maintain a higher gradient charging power configuration, and the output power proportion is set to be 70%. While the target sub-array (i.e., the area into which the device is to enter) should originally output lower power according to the distance gradient, the system will adjust it to 30% of the transition power in preparation for receiving the charging device.
If the charging device moves continuously in the overlapping area, the system keeps the gradient charging characteristics in each area, and the power ratio of two adjacent subarrays can be dynamically adjusted according to the S-shaped curve. The nonlinear adjustment strategy ensures the smooth transition of the total energy received by the equipment and avoids the fluctuation of the charging efficiency caused by power mutation.
When the charging device approaches the end section of the overlapping area, the antenna units of the target subarray gradually rise to the gradient charging power level corresponding to the area, and the power proportion is increased to 70%. At the same time, the power ratio of the current sub-array is reduced to 30%, but the internal power gradient distribution characteristic is still maintained.
In particular, when multiple overlapping regions occur, the system coordinates both the gradient charging and the smooth transition control mechanisms, ensuring that the charging device can obtain stable and continuous charging energy at any location. The system dynamically adjusts the power configuration of each region by monitoring the change of the coupling coefficient in real time, so that gradient charging and energy smooth transition are organically combined, and the continuity and stability of the charging process are ensured together.
The cooperation of the dual control mechanism ensures not only the efficient charging based on the coupling characteristic, but also the stable transition of the equipment during the regional switching.
A follow-up wireless microwave charging system according to one embodiment of the invention comprises:
The acquisition module is used for acquiring a plurality of groups of position coordinate information and speed information of the charging equipment in the T1 time period;
The predicted track module is used for constructing a track predicted model according to the data of the acquisition module and calculating the movement track data of the charging equipment in the T2 time period;
the subarea charging module is used for dividing the microwave antenna into N subarray antenna units which are independently controlled according to the motion trail data, and each subarray antenna unit corresponds to one area section on the prediction trail;
The control unit is used for dynamically adjusting the phase and power parameters of each subarray antenna unit according to the real-time position of the charging equipment to form an adaptive wave beam;
and the charging module is used for carrying out regional gradient charging on the charging equipment through the self-adaptive beam.
In the technical scheme of the invention, the antenna parameters are adjusted in advance through track prediction by adopting a mode of combining neural network prediction with subarray antenna subarea dynamic power supply, subarray antenna is utilized to realize subarray gradient charging, the alignment deviation is controlled within 3 degrees, and the charging efficiency is improved to 92%. The scheme not only solves the problem of charging alignment of the mobile equipment, but also improves the energy utilization efficiency through power gradient distribution.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
According to one aspect of the present invention, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in the various alternative implementations described above.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (5)
1. A following wireless microwave charging method, comprising:
Collecting multiple groups of position coordinate information and speed information of the charging equipment in a T1 time period, constructing a track prediction model, and calculating motion track data of the charging equipment in a T2 time period;
Dividing a microwave antenna into N subarray antenna units which are independently controlled according to the motion trail data, wherein each subarray antenna unit corresponds to a regional section on a prediction trail;
The control unit dynamically adjusts the phase and power parameters of each subarray antenna unit according to the real-time position of the charging equipment to form a self-adaptive wave beam;
carrying out regional gradient charging on the charging equipment through the adaptive beam;
forgetting gate, input gate, candidate cell calculation, cell state update and output gate structure of the track prediction model;
the definition of the forgetting door is shown as the following formula:
the definition of the input gate is shown as follows:
The candidate cell state is calculated as follows:
the cell status update is represented by the formula:
the structure of the output door is shown as follows:
The final hidden state output is shown as follows:
wherein each parameter satisfies the following constraint:
Wherein, the In order for the parameters to be trainable,To forget the gate output value, range (0, 1),For the sigmoid activation function,In order to forget the gate weight matrix,In order to be in the hidden state at the previous moment,For the current input to be entered,In order to forget the door bias term,For the time derivative weight coefficient, for adjusting the state change sensitivity,Is the time derivative of the hidden state,To input the gate output value, range (0, 1),In order to input the gate weight matrix,For the input of the gate bias term,As the weight coefficient of the history information,The time step considered for the history information,As the weight of the kth historic moment,For the input before the step k,In order to be a candidate cell state,In the form of a state weight matrix,For the state-bias term,Is the second-order gradient weight coefficient,Is a second order gradient of the hidden state,For the current state of the cell,In the form of a Hadamard product,As a weight coefficient for the state of the cells in the history,For the i-th historical state weight,For the decay rate of the ith time scale,For the time step size of the time step,In order to output the gate output value,In order to output the gate weight matrix,In order to output the gate bias term,As the state-change rate weighting factor,In order to be a rate of change of the state of the cell,For the hidden state output at the current moment,For the historical hidden state weight coefficient,For the number of historical frames to be considered,The adaptive weight of the j-th frame;
The state of the subarray antenna unit comprises an active energy supply unit, a standby functional unit and a dormancy unit;
The subarray antenna units are cooperatively controlled;
The cooperative control comprises judging whether to start or transition the state of the target subarray antenna unit according to the distance between the charging equipment and the subarray antenna unit;
Determining whether the target subarray antenna unit interacts with the subarray antenna unit according to a predicted track curve, if so, determining the target subarray antenna unit, if a plurality of subarray antenna units interact with the predicted track curve, calculating an overlapping area, and taking the subarray antenna unit with the largest overlapping area as the target subarray antenna unit;
the cooperative control further includes:
If the actual motion state of the charging equipment is detected to be changed severely, the predicted track model cannot respond timely, the moving speed of the charging equipment is detected, when the moving speed mutation is detected, a plurality of subarray antenna units adjacent to the moving speed is triggered in advance to enter a standby state based on the current speed vector direction, when the moving direction mutation of the charging equipment is detected, the current position is taken as the center, and meanwhile, the adjacent subarray antenna units in surrounding sectors are pre-activated, and power output is kept according to the coverage condition until effective track prediction is reestablished.
2. The following wireless microwave charging method according to claim 1, wherein the motion trajectory data calculates a plurality of spatial position points according to the output of the trajectory prediction model, and connects the spatial position points through an interpolation algorithm to form a smooth continuous predicted trajectory curve;
the spatial position point also comprises the output of the track prediction model at the moment t Confidence, and re-acquiring data when the confidence is lower than a preset threshold.
3. The method of claim 1, wherein the forming of the adaptive beam comprises:
Calculating theoretical phase values of the subarray antenna units based on relative space vectors between the charging equipment and the subarray antenna units in each activation state;
Carrying out real-time correction on the theoretical phase by adopting a self-adaptive phase compensation algorithm to obtain a compensated actual phase value;
And performing coupling correction on the actual phase value to obtain a final phase control value, and forming an adaptive beam according to the phase control value and the power control among the subarray antenna units.
4. A following wireless microwave charging method according to claim 3, wherein the calculation of the compensated actual phase value is as follows:
Wherein, the In order to compensate for the actual phase value after compensation,Is a frequency dependent compensation coefficient, dynamically adjusts with the operating frequency,For the accumulated value of the historical phase error,For the adaptive gain factor, the compensation strength is controlled,For the weight coefficient of each antenna element,In order for the operating angular frequency to be a function of,For the total number of antenna array elements,For the ith theoretical phase value, t is the time variable,Is the theoretical phase value of the kth antenna element.
5. A follow-up wireless microwave charging system for use in a method of charging a wireless microwave according to any one of claims 1-4, comprising:
The acquisition module is used for acquiring a plurality of groups of position coordinate information and speed information of the charging equipment in the T1 time period;
The predicted track module is used for constructing a track predicted model according to the data of the acquisition module and calculating the movement track data of the charging equipment in the T2 time period;
the subarea charging module is used for dividing the microwave antenna into N subarray antenna units which are independently controlled according to the motion trail data, and each subarray antenna unit corresponds to one area section on the prediction trail;
The control unit is used for dynamically adjusting the phase and power parameters of each subarray antenna unit according to the real-time position of the charging equipment to form an adaptive wave beam;
and the charging module is used for carrying out regional gradient charging on the charging equipment through the self-adaptive beam.
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| CN117148985A (en) * | 2023-09-04 | 2023-12-01 | 上海复气东辇科技发展有限公司 | A wireless charging method that matches mouse movement |
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