An underwater self-adaptive regulation and control alignment radio energy efficient transmission system and method

Abstract

This invention discloses an underwater adaptive alignment-based high-efficiency wireless power transmission system and method, relating to the field of underwater wireless power transmission technology. The system includes a transmitter system, a receiver system, and an underwater dual-mode communication link. The transmitter system integrates a multi-transmitter coil array, a multi-sensor module, a transmitter controller, and a multi-channel drive module. The receiver system is responsible for energy reception and status feedback, and the dual-mode communication link ensures reliable data exchange. Ocean current and coil motion information are acquired through multi-source data acquisition. The relative motion of the transmitting and receiving coils is predicted based on a long short-term memory network model. The optimal synthetic magnetic field parameters are solved using a particle swarm optimization algorithm, and the adjustment error is corrected using a fuzzy proportional-integral-differential algorithm, ensuring efficient and stable charging in complex marine environments. This invention overcomes the shortcomings of existing technologies, such as alignment lag and weak anti-interference capabilities, significantly improving the efficiency and reliability of underwater dynamic wireless charging.

Description

Technical Field

[0001] This invention belongs to the field of underwater wireless power transmission technology, and particularly relates to an underwater adaptive control alignment high-efficiency wireless power transmission system and method. Background Technology

[0002] With the rapid development of marine resource development and marine engineering, the types and quantities of underwater electrical equipment have increased dramatically, making energy replenishment a key bottleneck restricting the development of marine engineering. Traditional underwater energy replenishment methods mainly rely on cable connections or periodic surfacing to replace batteries, which have drawbacks such as complex operation, low efficiency, high maintenance costs, and limited operating range.

[0003] Underwater wireless power transfer technology, as a novel energy supply solution, enables energy transfer without physical contact, effectively overcoming the shortcomings of traditional methods and becoming a research hotspot. However, the complex marine environment presents numerous challenges to underwater wireless power transfer: on the one hand, ocean currents cause relative swaying and translation between the underwater electrical equipment (receiver) and the energy transmitter, resulting in significant alignment deviations of the transceiver coils, a decrease in the electromagnetic coupling coefficient, and consequently, a sharp drop in transmission efficiency or even transmission interruption; on the other hand, existing alignment adjustment technologies mostly employ passive mechanical responses, which suffer from large adjustment lags and slow response speeds, making them unsuitable for the dynamically changing marine environment; furthermore, the complex underwater communication environment makes single communication methods susceptible to interference, leading to poor reliability of data interaction required for alignment control.

[0004] In existing technologies, solutions to the alignment problem in underwater wireless power transfer have not yet formed a complete technical system. They generally suffer from low alignment accuracy, poor dynamic adaptability, and weak anti-interference capabilities, making it difficult to meet the demands for efficient and stable charging in complex marine environments. Therefore, developing an adaptive underwater wireless power transfer technology with active prediction, precise alignment, and strong anti-interference capabilities is of significant practical importance for promoting the development of marine engineering. Summary of the Invention

[0005] This invention addresses the shortcomings of existing underwater wireless power transfer technologies, such as alignment lag, weak anti-interference capabilities, and poor dynamic adaptability. It provides an efficient wireless power transfer system and method with adaptive alignment control. By fusing environmental and motion data from multiple sensors, relative motion prediction is achieved based on an LSTM model. Active and precise alignment is achieved using a multi-transmitter coil electrically controlled synthetic magnetic field. Dual-mode communication ensures data exchange, ultimately improving the efficiency and stability of wireless charging in complex marine environments.

[0006] This invention discloses an underwater adaptive control alignment wireless power high-efficiency transmission system, which includes a transmitter system, a receiver system, and an underwater dual-mode communication link.

[0007] The transmitter system includes a multi-transmitter coil array module, a transmitter controller, and a multi-channel drive module;

[0008] The multi-transmitter coil array module contains multiple independent transmitting coils used to generate a controllable synthetic magnetic field through current amplitude and phase adjustment; the transmitter controller calculates relative motion parameters based on the acquired attitude and position data of the transmitter and receiver, predicts future relative motion parameters, and calculates the optimal synthetic magnetic field parameter sequence based on the future relative motion parameters, and calculates the current amplitude and phase control signals of the multi-channel coils based on the optimal synthetic magnetic field parameter sequence; the multi-channel drive module adjusts the input current parameters of each transmitting coil according to the current amplitude and phase control signals.

[0009] The receiver system is used to receive the energy from the synthetic magnetic field of the transmitter and integrate attitude, position and battery status data, which are then fed back to the transmitter controller via a dual-mode communication link.

[0010] The underwater dual-mode communication link is used to enable data interaction between the transmitter and receiver.

[0011] Furthermore, the transmitting system includes a multi-sensor module, which comprises an ocean current sensor, a transmitting attitude sensor, and a transmitting displacement sensor. The ocean current sensor is a three-dimensional ocean current sensor used to acquire ocean current velocity v, ocean current direction θ, and turbulence intensity I. The attitude sensor acquires the transmitting attitude angle. Displacement sensor collects real-time position data from the transmitter. ;

[0012] The receiving system includes a receiving attitude sensor and a receiving displacement sensor, which respectively acquire the receiving attitude angle. and real-time location .

[0013] Furthermore, the transmitter controller calculates the relative motion parameters based on the collected attitude and position data of the transmitter and receiver: relative attitude deviation. Relative translation deviation ,in, For the attitude data of the transmitter, For transmitter location data, For the attitude data of the receiving end, This is the location data of the receiving end;

[0014] Calculate the real-time coupling coefficient k(t) based on the principle of electromagnetic coupling:

[0015]

[0016] The coupling coefficient is the value under ideal alignment. Let be the translation influence coefficients in the X and Y directions, Δα(t), Δβ(t), and Δγ(t) be the deviations of the relative pitch, roll, and yaw angles of the transmitting and receiving coils at time t, Δx(t) and Δy(t) be the relative translation deviations in the X and Y directions at time t, cos(·) be the cosine function, exp(·) be the exponential function, and |·| be the absolute value function.

[0017] Furthermore, the launcher controller, based on an LSTM model, uses ocean current data, relative motion parameters, and real-time coupling coefficients from the past 30 seconds to predict future trends. The sequence of relative motion parameters over time; and based on the future sequence of relative motion parameters, to the future... To maximize the average transmission efficiency over time, we construct the following optimization objective function:

[0018]

[0019] Where max represents the "maximization" operation; This is the averaging coefficient. For the prediction time window; This indicates a summation operation performed over the five prediction times from i=1 to i=5; For the i-th prediction time, the corresponding η(·) is the transmission efficiency function;

[0020] The PSO algorithm is used to solve this optimization problem, with the constraints being the current amplitude I ∈ [0~5A] and phase φ ∈ [0°~360°] for each transmitting coil; the optimal synthetic magnetic field parameter sequence for the next 5 time moments is obtained. ,in To synthesize the magnetic field azimuth angle, To synthesize the pitch angle of the magnetic field, To synthesize the magnetic field amplitude, Identify the optimal value.

[0021] Furthermore, by combining a pre-built mapping table of multi-transmitter coil current parameters and synthetic magnetic field parameters, the optimal synthetic magnetic field parameter sequence is mapped to the real-time current amplitude sequence and phase sequence of multiple transmitter coils;

[0022] The transmitter controller synchronously outputs the real-time current amplitude sequence and phase sequence of multiple transmitter coils to the multi-channel drive module. The drive module adjusts the input current of each coil according to the control signal, so that the multi-transmitter coil array generates a synthetic magnetic field that conforms to the optimal parameters.

[0023] A method for efficient wireless power transmission with underwater adaptive alignment is also provided, the method comprising the following steps:

[0024] Step 1: The transmitter controller and receiver controller establish a connection through an underwater dual-mode communication link, complete the communication handshake, and initialize the reference parameters;

[0025] Step 2: The transmitter controller starts the inverter to generate the initial synthetic magnetic field; the receiver controller feeds back its attitude angle, position and SOC data to the transmitter controller through the underwater acoustic communication module.

[0026] Step 3: The multi-sensor module at the transmitting end collects ocean current data, transmitting end attitude data, and position data in real time according to the acquisition cycle; the sensors at the receiving end synchronously collect receiving end attitude data, position data, and SOC data; all collected data are transmitted to the transmitting end controller in real time through a dual-mode communication link.

[0027] Step 4: The transmitter controller calculates the relative motion parameters and real-time coupling coefficient based on the collected attitude and position data of the transmitter and receiver.

[0028] Step 5: Using the ocean current data, relative motion parameters, and real-time coupling coefficients from the past 30 seconds, the LSTM model is used to predict the relative motion parameter sequence for 5 moments within the next 1 second.

[0029] Step 6, with the future The objective is to maximize the average transmission efficiency over time. An optimization objective function is constructed, and the PSO algorithm is used to solve the optimization problem to obtain the optimal synthetic magnetic field parameter sequence for the next 5 time moments.

[0030] Step 7: Map the optimal synthetic magnetic field parameter sequence to the real-time current amplitude sequence and phase sequence of multiple transmitting coils;

[0031] Step 8: The transmitter controller synchronously outputs the current amplitude and phase control signal of the multiple coils to the multi-channel drive module. The drive module adjusts the input current of each coil according to the control signal, so that the multi-transmitter coil array generates a synthetic magnetic field that meets the optimal parameters, thereby achieving active alignment.

[0032] Step 9: The transmitter controller calculates the real-time transmission efficiency. If the real-time transmission efficiency is greater than the preset efficiency, it maintains the current current parameters and continues charging. If the real-time transmission efficiency is less than the preset efficiency, it returns to step 3 to re-acquire data and adjust the alignment.

[0033] Furthermore, in step 4, the relative motion parameters include relative attitude deviation. Relative translation deviation ,in, For the attitude data of the transmitter, For transmitter location data, For the attitude data of the receiving end, This is the location data of the receiving end;

[0034] Calculate the real-time coupling coefficient k(t) based on the principle of electromagnetic coupling:

[0035]

[0036] The coupling coefficient is the value under ideal alignment. Let be the translation influence coefficients in the X and Y directions, Δα(t), Δβ(t), and Δγ(t) be the deviations of the relative pitch, roll, and yaw angles of the transmitting and receiving coils at time t, Δx(t) and Δy(t) be the relative translation deviations in the X and Y directions at time t, cos(·) be the cosine function, exp(·) be the exponential function, and |·| be the absolute value function.

[0037] Furthermore, step 5 also includes the following steps:

[0038] Step 51: Align the ocean current velocity v, ocean current direction θ, turbulence intensity I, historical relative motion parameters, and real-time coupling coefficients collected over the past 30 seconds according to a unified timestamp; convert the ocean current velocity v and ocean current direction θ from polar coordinates to rectangular coordinates. , forming a feature vector in the dataset Using time t as the baseline, data from [t-30s, t] are taken as input features, and five discrete time points (τ=0.2s, 0.4s, 0.6s, 0.8s, 1.0s) within [t, t+1s] are taken as prediction labels.

[0039] Step 52: Build and train the LSTM model;

[0040] The input layer of the LSTM model receives a tensor with dimensions of "number of samples, time step, and number of features"; the dataset is divided into a training set, a validation set, and a test set, and the loss curve on the validation set is observed. Training is stopped when the loss no longer decreases.

[0041] Step 53: The output layer of the LSTM model outputs the sequence of relative motion parameters for the next 5 time steps. And the predicted future coupling coefficient k(t+τ).

[0042] Furthermore, in step 6, with future To maximize the average transmission efficiency over time, we construct the following optimization objective function:

[0043]

[0044] Where max represents the "maximization" operation; This is the averaging coefficient. For the prediction time window; This indicates a summation operation performed over the five prediction times from i=1 to i=5; For the i-th prediction time, the corresponding η(·) is the transmission efficiency function;

[0045] The PSO algorithm is used to solve this optimization problem, with the constraints being the current amplitude I ∈ [0~5A] and phase φ ∈ [0°~360°] for each transmitting coil; the optimal synthetic magnetic field parameter sequence for the next 5 time moments is obtained. ,in To synthesize the magnetic field azimuth angle, To synthesize the pitch angle of the magnetic field, To synthesize the magnetic field amplitude, Identify the optimal value.

[0046] Furthermore, in step 8, the actual azimuth angle of the synthesized magnetic field is acquired by the transceiver sensor. The actual pitch angle of the synthesized magnetic field Relative translational deviation from the actual X direction Actual relative translation deviation in the Y direction Calculate the alignment error:

[0047] Azimuth error Pitch angle error X-direction position error Y-direction position error ;in, To achieve the optimal azimuth angle for the synthesized magnetic field, The optimal pitch angle for the combined magnetic field. To predict the relative translational deviation in the X direction, To predict the relative translational deviation in the Y direction;

[0048] If the error is greater than or equal to Δε = 0.1° / 0.5mm, the fuzzy PID algorithm is called to correct the current parameters until all errors are less than the threshold Δε.

[0049] The beneficial effects achieved by this invention are:

[0050] This invention enables proactive prediction and precise alignment: it uses an LSTM model to predict the relative motion of the transmitting and receiving coils, combines the PSO algorithm to solve for the optimal magnetic field parameters, and achieves proactive alignment through the electronic control adjustment of multiple transmitting coils. The adjustment lag is ≤0.1s, the attitude alignment accuracy is ≤0.1°, and the translational alignment accuracy is ≤0.5mm, effectively solving the lag problem of traditional passive alignment.

[0051] This invention enables efficient and stable transmission: dynamic alignment is achieved through an electrically controlled synthetic magnetic field, and dual-mode communication ensures data interaction, maintaining stable transmission efficiency even in complex marine environments. It has strong resistance to ocean current interference and is suitable for various underwater dynamic charging scenarios.

[0052] This invention features high reliability and strong adaptability: the multi-transmitter coil array adopts a modular design and has redundant backup function; the dual-mode communication link automatically switches to ensure the reliability of data transmission at different distances; the device packaging material is pressure-resistant and corrosion-resistant, and can be adapted to the complex environment of the deep sea.

[0053] This invention has high engineering application value: it has a compact structure, is easy to install and maintain, does not require a complex mechanical adjustment mechanism, reduces the size and weight of underwater equipment, and can be widely used for energy replenishment of various underwater vehicles such as underwater vehicles and underwater monitoring equipment. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the composition and structure of the underwater adaptive control alignment wireless power high-efficiency transmission system of the present invention;

[0055] Figure 2 This is a schematic diagram of the multi-transmitter coil array distribution of the present invention;

[0056] Figure 3 This is a schematic diagram of the process of the underwater adaptive control alignment wireless power transmission method of the present invention. Detailed Implementation

[0057] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer as a result. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but all such modifications and substitutions fall within the protection scope of the present invention.

[0058] like Figure 1 As shown, the present invention provides an underwater adaptive alignment wireless power high-efficiency transmission system, including a transmitter system, a receiver system, and an underwater dual-mode communication link. The structure and connection relationship of each part are as follows:

[0059] Transmitter system: As the core of energy transmission and alignment control, it includes a multi-transmitter coil array module, a multi-sensor module, a transmitter controller (EMC), a multi-channel drive module, an inverter, and a DC power supply. For example... Figure 2 As shown, the multi-transmitter coil array module adopts a 3×3 distributed structure, containing 9 independent transmit coils. The coil array is arranged in a square pattern, with a center-to-center spacing of 25 cm between adjacent coils. This is used to generate a controllable synthetic magnetic field through current amplitude and phase adjustment. The multi-sensor module includes an ocean current sensor, a transmitter attitude sensor (three-axis gyroscope), and a transmitter displacement sensor (laser displacement sensor). The ocean current sensor is a three-dimensional ocean current sensor used to collect ocean current velocity v, ocean current direction θ, and turbulence intensity I. The attitude sensor collects the transmitter attitude angle (…). The displacement sensor collects the real-time position of the transmitter. The transmitter controller incorporates an LSTM motion prediction algorithm, a PSO optimal magnetic field parameter solution algorithm, and a fuzzy PID control algorithm. It receives data from various sensors and feedback information from the receiver, executes algorithm calculations, and outputs the current amplitude of the nine coils. ) and phase ( The control signal simultaneously regulates the inverter's operating state; the multi-channel drive module includes 9 independent amplitude-phase adjustment units with a response time ≤10μs, used to precisely adjust the input current parameters of each transmitting coil according to the transmitter controller's instructions; the inverter is used to convert the DC power output from the DC power supply into a preset frequency ( The high-frequency AC power supplies the multi-channel drive module; the DC power supply uses a lithium battery pack with a rated voltage of 24V and a rated capacity of 100Ah to power the entire transmitter system.

[0060] The receiving system is used for energy reception, status acquisition, and data feedback. It includes a receiving coil module, a receiving controller, a rectifier and filter module, a battery management module, a receiving attitude sensor, and a receiving displacement sensor. The receiving coil module uses a single coil structure with the same encapsulation material as the transmitting coil and is used to receive energy from the synthesized magnetic field of the transmitting end. The rectifier and filter module consists of a full-bridge rectifier circuit and an LC filter circuit, used to convert the received high-frequency AC power into stable DC power, which is then used to charge the energy storage battery through the battery management module. The battery management module monitors the status of the receiving energy storage battery and controls its charging. Monitored status includes remaining charge (SOC), voltage, current, and temperature. The system outputs a full-charge signal; the attitude sensor (three-axis gyroscope) and displacement sensor (laser displacement sensor) at the receiving end respectively collect the attitude angle of the receiving end (…). ) and real-time location ( The receiver controller integrates attitude, position, and battery status data and feeds it back to the transmitter controller via a dual-mode communication link.

[0061] Underwater dual-mode communication link: It adopts a fusion architecture of underwater acoustic communication and optical communication to realize data interaction between the transmitter and receiver. The underwater acoustic communication module uses long-wave underwater acoustic communication technology with a communication distance of 10~50m, which is suitable for long-distance data transmission and is used for initial positioning and low-frequency status feedback. The optical communication module uses blue-green laser communication technology with a communication distance of 0~10m and a data transmission rate of ≥1Mbps, which is suitable for short-range high-precision data interaction and is used for real-time parameter transmission in the alignment control process. The dual-mode communication link has an automatic switching function, which automatically selects the communication mode according to the distance between the transmitter and receiver to ensure the reliability and real-time performance of data transmission.

[0062] like Figure 3 As shown, based on the above-described device, the underwater adaptive adjustment alignment wireless power efficient transmission method provided by the present invention specifically includes the following steps:

[0063] Step 1: System Initialization. The transmitter controller and receiver controller establish a connection via an underwater dual-mode communication link, completing a communication handshake; initialize reference parameters, including the coil reference attitude (…). ), reference position ( ), preset transmission frequency ( ), prediction time window ( ); Initialize threshold parameters, including transmission efficiency threshold ( Alignment error threshold ( ), SOC full charge threshold ( Initialize algorithm parameters, including the number of iterations (1000) and learning rate (0.001) for the LSTM model, the number of particles (50) and number of iterations (50) for the PSO algorithm, and the initial scaling factor for the fuzzy PID algorithm. ), initial integral coefficients ( ), initial differential coefficients ( ).

[0064] Step 2: Transmitter Start-up and Initial Positioning. The transmitter controller starts the inverter, converting DC power to DC power. High-frequency alternating current is input to the multi-transmitter coil array through a multi-channel drive module to generate an initial synthetic magnetic field; simultaneously, the transmitting controller sends a query signal to the surrounding sea area through the underwater acoustic communication module, and the receiving controller, upon receiving the signal, adjusts its own attitude angle ( ),Location( The underwater acoustic communication module feeds back the SOC data to the transmitter controller. The transmitter controller calculates the relative distance between the transmitter and receiver based on the position data fed back by the receiver. If the distance is ≤10m, it switches to the optical communication module for subsequent data interaction.

[0065] Step 3: Multi-source data acquisition. The multi-sensor module at the transmitter acquires ocean current data (v, θ, I) and transmitter attitude data in real time at an acquisition period of T1=0.1s. ) and location data ( The receiving end sensor synchronously acquires the receiving end attitude data. Location data (and SOC data); all collected data are transmitted in real time to the transmitter controller via a dual-mode communication link.

[0066] Step 4: Calculation of relative motion parameters. The transmitter controller calculates the relative motion parameters based on the acquired attitude and position data of the transmitter and receiver: relative attitude deviation. Relative translation deviation t Meanwhile, based on the principle of electromagnetic coupling, the real-time coupling coefficient k(t) is calculated using the following formula:

[0067]

[0068] =0.92 is the coupling coefficient under ideal alignment conditions. Let be the translation influence coefficient, Δα(t), Δβ(t), and Δγ(t) be the relative attitude deviations at time t, Δx(t) and Δy(t) be the relative translation deviations at time t, cos(·) be the cosine function, exp(·) be the exponential function, and |·| be the absolute value function.

[0069] Step 5: LSTM Relative Motion Prediction. The transmitter controller invokes the LSTM motion prediction algorithm to construct a coupled dataset D based on historical data. This dataset contains ocean current data (v, θ, I) from the past 30 seconds, relative motion parameters (Δα, Δβ, Δγ, Δx, Δy), and real-time coupling coefficients k(t). Using the trained LSTM model and the current ocean current data as input, it predicts future... The sequence of relative motion parameters at five time points (τ=0.2s, 0.4s, 0.6s, 0.8s, 1.0s) ), where “∧” represents the predicted value, t is the current time, and τ is the prediction time interval.

[0070] Specifically, the following steps are included:

[0071] Step 51: Dataset coupling and preprocessing;

[0072] The ocean current velocity v, ocean current direction θ, turbulence intensity I, historical relative motion parameters (Δα, Δβ, Δγ, Δx, Δy), and real-time coupling coefficient k(t) collected over the past 30 seconds are aligned according to a unified timestamp. With a sampling frequency of 10Hz, the coupled dataset D contains 300 time steps.

[0073] Convert the ocean current velocity v and direction θ from the polar coordinate system to the rectangular coordinate system. This is done to eliminate the interference of periodic angle jumps on model training. Feature vectors from the dataset are generated. .

[0074] Using the sliding window technique, with time t as the baseline, data in the range [t-30s, t] are taken as input features, and five discrete time points (τ=0.2s, 0.4s, 0.6s, 0.8s, 1.0s) within the range [t, t+1s] are taken as prediction labels.

[0075] Normalization: Mapping physical quantities with huge differences in magnitude to a unified interval to avoid gradient explosion or vanishing during model training.

[0076] Step 52: LSTM model architecture design and training;

[0077] The LSTM model consists of an input layer, hidden layers, fully connected layers, an output layer, and a Dropout layer.

[0078] The input layer receives tensors with dimensions of "number of samples, time step (300), number of features".

[0079] Three hidden layers are set to increase the depth of the model in modeling nonlinear flow fields.

[0080] Old ocean current information that no longer affects the current motion is discarded through the forget gate in the hidden layer, and the turbulence fluctuation characteristics at the current moment are updated through the input gate in the hidden layer.

[0081] The fully connected layer directly maps the feature vectors extracted by the LSTM to the prediction vectors for the next 5 time steps.

[0082] The relative motion parameters (Δα, Δβ, Δγ, Δx, Δy) of the output layer at the next 5 time points and the predicted future coupling coefficient k(t+τ);

[0083] Using a vector output mode, it maps all 5 prediction moments within the next 1 second at once, which is more real-time than recursive prediction.

[0084] The model uses mean squared error (MSE) as the loss function, primarily penalizing the deviation between the predicted and actual trajectories. The Adam optimizer is employed, whose adaptive learning rate is well-suited for handling data with random noise, such as ocean currents.

[0085] Dropout layers are used to randomly drop some neuron connections to enhance the model's generalization ability in different marine environments.

[0086] Divide the dataset into training, validation, and test sets. Observe the loss curve on the validation set, and stop training when the loss no longer decreases.

[0087] Step 53: Real-time Predictive Execution

[0088] A 30-second circular buffer is generated and continuously populated with the latest ocean current sensor data. Every prediction cycle, the 30-second sequence from the buffer is input into the model. The model directly outputs the relative motion parameter sequence for the next 5 time points. ).

[0089] Step 6: Solving for the optimal magnetic field parameters of PSO. (In the future...) To maximize the average transmission efficiency over time, we construct the following optimization objective function:

[0090]

[0091] Where max represents the "maximization" operation; This is the averaging coefficient. For the prediction time window; This indicates a summation operation performed over the five prediction times from i=1 to i=5; For the i-th prediction time, the corresponding η(·) is the transmission efficiency function, and its expression is: in For ideal maximum transmission efficiency, The attitude influence coefficient is defined as follows, and the definitions of other variables are consistent with those above. The constraints for this step are that the current amplitude I ∈ [0~5A] and the phase φ ∈ [0°~360°] of each transmitting coil; the PSO algorithm is used to solve this optimization problem to obtain the optimal synthetic magnetic field parameter sequence for the next 5 time moments. ),in To synthesize the magnetic field azimuth angle, To synthesize the pitch angle of the magnetic field, The value is the synthetic magnetic field amplitude; the asterisk (*) indicates the optimal value.

[0092] Step 7: Multi-coil current parameter mapping. Based on the Biot-Savart law, and combined with the pre-constructed mapping table of multi-transmitter coil current parameters and synthetic magnetic field parameters, the optimal synthetic magnetic field parameter sequence obtained in Step 6 is mapped... ), mapped to a real-time current amplitude sequence of 9 transmitting coils ( ) and phase sequence ( During the mapping process, a coil mutual inductance correction coefficient (correction amount is 0.02~0.05A) is introduced to compensate for electromagnetic coupling interference between adjacent coils.

[0093] Step 8: Electrically Controlled Alignment Drive and Error Compensation. The transmitter controller synchronously outputs the current amplitude and phase control signals of the 9 coils to the multi-channel drive module. The drive module adjusts the input current of each coil according to the control signals, so that the multi-transmitter coil array generates a synthetic magnetic field that conforms to the optimal parameters, achieving active alignment; at the same time, the actual synthetic magnetic field parameters are collected by the transceiver sensors. ) and actual relative motion parameters ( ), calculate alignment error: azimuth error Pitch angle error X-direction position error Y-direction position error If the error is ≥ Δε = 0.1° / 0.5mm, the fuzzy PID algorithm is called to correct the current parameters. The inputs to the fuzzy controller are the alignment error e and de(t) / dt, and the output is the PID parameter correction amount. This continues until all errors are less than the threshold Δε.

[0094] Step 9: Transmission efficiency detection and charging determination. The transmitter controller calculates the real-time transmission efficiency. The calculation formula is: in the formula Input voltage and current to the transmitter. This refers to the output voltage and current at the receiving end. If... Continue charging while maintaining the current current parameters; if Return to step 3 to re-acquire data and adjust alignment; simultaneously, monitor the SOC data fed back from the receiver. The receiving controller sends a full charge signal, and the transmitting controller controls the inverter to stop working, thus ending the charging process; if Return to step 3 to continue charging.

[0095] To demonstrate that the underwater adaptive control alignment method of this invention can effectively solve the lag problem of traditional passive alignment compared with the prior art, a dynamic performance comparison test was conducted between the method of this invention and the traditional passive mechanical alignment method.

[0096] This invention uses an LSTM model to predict the relative motion of the transmitting and receiving coils, combines the PSO algorithm to solve for the optimal synthetic magnetic field parameters, and uses a fuzzy PID algorithm to correct the adjustment error, thus achieving active and precise alignment. Traditional passive mechanical alignment methods rely solely on mechanical structures to respond to the relative displacement of the transmitting and receiving coils, lacking active prediction and electronic control adjustment capabilities, and cannot meet the real-time alignment requirements in dynamic marine environments.

[0097] Test conditions: Under a dynamic ocean environment with a simulated ocean current velocity of 1.2 m / s and a turbulence intensity of 0.3, the same initial alignment deviation (attitude deviation 3°, translation deviation 20 mm) was set. Both methods were run continuously for 300 s, and the two core indicators of adjustment hysteresis and average transmission efficiency were recorded. The test results are shown in the table below.

[0098]

[0099] The test results show that traditional passive mechanical alignment methods lack the ability to predict relative motion and can only adjust the mechanical structure after the actual relative offset of the transmitting and receiving coils occurs, resulting in an adjustment lag of up to 0.85s. The lag phenomenon is obvious, which makes the average transmission efficiency remain at 80%. In contrast, this invention analyzes ocean current data, relative motion parameters and coupling coefficients from the past 30s using an LSTM model, accurately predicts the relative motion trend of the transmitting and receiving coils within the next 1s, and generates the optimal synthetic magnetic field in advance through the electronic control adjustment of multiple transmitting coils, achieving active alignment with an adjustment lag of only 0.09s, effectively solving the lag problem of traditional passive alignment.

[0100] Meanwhile, since the present invention can achieve early alignment through relative motion prediction, it effectively reduces the alignment deviation of the transmitting and receiving coils, improves the stability of electromagnetic coupling, and increases the average transmission efficiency from 80% of the traditional method to 92%, which significantly improves the efficiency and stability of wireless power transmission in complex marine environments, fully demonstrating the beneficial effects of the present invention.

[0101] To clearly describe the technical solution of this invention, all parameters and variables involved in the entire text are first uniformly defined, and the variables referenced in subsequent formulas are all derived from this definition section:

[0102] Attitude and position related parameters: —Pitch angle of the transmitter (unit: °). —Transmitter roll angle (unit: °) —Tail angle of the transmitter (unit: °); —Receiver pitch angle (unit: °) — Receiver roll angle (unit: °) —Receiver heading angle (unit: °); — Coil reference pitch angle (unit: °). — Coil reference roll angle (unit: °) — Coil reference heading angle (unit: °); —X-direction position coordinates of the transmitter (unit: m). —Y-axis position coordinates of the transmitter (unit: m) —Z-axis position coordinates of the transmitter (unit: m); — Receiver's X-axis position coordinates (unit: m) —Y-axis position coordinates of the receiving end (unit: m) —Z-axis position coordinates of the receiver (unit: m); — Coordinates of the coil's reference position in the X direction (unit: m). —Coordinates of the coil's reference position in the Y direction (unit: m). — Coordinates of the coil's reference Z-direction position (unit: m).

[0103] Relative motion parameter: Δα—relative pitch angle deviation of the transceiver coil (unit: °). Δβ—Relative roll angle deviation of the transceiver coil (unit: °). Δγ — Relative heading angle deviation of the transceiver coil (unit: °). Δx — Relative translational deviation of the transceiver coil in the X direction (unit: m). Δy—Relative translational deviation of the transceiver coil in the Y direction (unit: m). Δz — Relative translational deviation of the transceiver coil in the Z direction (unit: m). ; —The predicted relative pitch angle deviation at the future time t+τ (unit: °), “∧” represents the predicted value; —Predicted relative roll angle deviation at future time t+τ (unit: °); —Predicted relative heading angle deviation at future time t+τ (unit: °); —Predicted relative translational deviation in the X direction at the future time t+τ (unit: m); —Predicted relative translation deviation in the Y direction at the future time t+τ (unit: m).

[0104] Ocean current related parameters: v—ocean current velocity (unit: m / s); θ—ocean current direction (unit: °); I—turbulence intensity (dimensionless).

[0105] Electromagnetic coupling and transmission efficiency parameters: k(t) — electromagnetic coupling coefficient of the transmitting and receiving coils at time t (dimensionless); —The electromagnetic coupling coefficient (dimensionless) under ideal alignment conditions, with a value of 0.92; —X-direction translation influence coefficient (unit: ), with a value of 0.05; —Y-direction translation influence coefficient (unit: ), with a value of 0.05; η—real-time transmission efficiency (dimensionless); —Transmission efficiency threshold (dimensionless), valued at 0.8; —Ideal maximum transmission efficiency (dimensionless), value 0.92; —Pitch angle deviation influence coefficient (unit: ); —Roll angle deviation influence coefficient (unit: ); —Caution angle deviation influence coefficient (unit: ); —Transmitter input voltage (unit: V); —Input current at the transmitter (unit: A); —Receiver output voltage (unit: V); — Receiver output current (unit: A).

[0106] Time-related parameters: —Prediction time window (unit: s), value is 1 s; τ —Prediction time interval (unit: s), τ=0.2s, 0.4s, 0.6s, 0.8s, 1.0s; —The i-th prediction time (unit: s), i=1~5, corresponding to ; —Data acquisition period (unit: s), value is 0.1s; t—current time (unit: s).

[0107] Control and algorithm parameters: —Preset transmission frequency (unit: kHz), value is 100kHz; SOC—Remaining power of the receiver's energy storage battery (unit: %). —SOC full charge threshold (unit: %), value is 95%; Δε—alignment error threshold (unit: ° / mm), value is 0.1° / 0.5mm; —The initial proportional gain (dimensionless) for the fuzzy PID algorithm is set to 5.2; —The initial integral coefficient (dimensionless) for the fuzzy PID algorithm is 0.8; —The initial differential coefficients (dimensionless) for the fuzzy PID algorithm are set to 1.5; —The proportional coefficient (dimensionless) after the fuzzy PID algorithm correction at time t. —Integral coefficients (dimensionless) after correction of the fuzzy PID algorithm at time t; K_d(t) —Differential coefficients (dimensionless) after correction of the fuzzy PID algorithm at time t. —Proportional coefficient correction (dimensionless). —Integral coefficient correction (dimensionless); ΔK_d —Derivative coefficient correction (dimensionless); e —Alignment error (unit: ° / mm); U(t) —Fuzzy PID controller output control quantity at time t (unit: V).

[0108] Coil and magnetic field parameters: —Inductance markings for 9 independent transmitting coils; —Predicted current amplitudes of the nine transmitting coils at the future time t+τ (unit: A); —Predicted current phase of the nine transmitting coils at the future time t+τ (unit: °); —Azimuth of the composite magnetic field (unit: °); —Optimal composite magnetic field azimuth (unit: °); —Average of the actual composite magnetic field (unit: °); —Pitch angle of the composite magnetic field (unit: °); —Optimal composite magnetic field pitch angle (unit: °); — Actual composite magnetic field pitch angle (unit: °); —Amplitude of the composite magnetic field (unit: T); —Optimal synthetic magnetic field amplitude (unit: T); —Azimuth alignment error (unit: °) ; —Pitch angle alignment error (unit: °) ; —X-direction alignment error (unit: mm) ; —Y-direction alignment error (unit: mm) ; —Actual relative translation deviation in the X direction (unit: mm); —Actual relative translation deviation in the Y direction (unit: mm).

[0109] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the scope of protection of the present invention; all technical solutions formed by equivalent transformations or equivalent substitutions fall within the scope of protection of the present invention; the parts of the present invention not described in detail are well-known technologies to those skilled in the art.

Claims

1. A highly efficient wireless power transmission system with underwater adaptive control alignment, characterized in that, The underwater adaptive control alignment wireless power high-efficiency transmission system includes a transmitter system, a receiver system, and an underwater dual-mode communication link. The transmitter system includes a multi-transmitter coil array module, a transmitter controller, and a multi-channel drive module; The multi-transmitter coil array module contains multiple independent transmitter coils, which are used to generate a controllable synthetic magnetic field by adjusting the current amplitude and phase; The transmitter controller calculates relative motion parameters based on the acquired attitude and position data of the transmitter and receiver, predicts future relative motion parameters, calculates the optimal synthetic magnetic field parameter sequence based on the future relative motion parameters, and calculates the current amplitude and phase control signal of the multi-channel coil based on the optimal synthetic magnetic field parameter sequence. The multi-channel drive module adjusts the input current parameters of each transmitting coil according to the current amplitude and phase control signal; The receiver system is used to receive the energy from the synthetic magnetic field of the transmitter and integrate attitude, position and battery status data, which are then fed back to the transmitter controller via a dual-mode communication link. The underwater dual-mode communication link is used to enable data interaction between the transmitter and receiver.

2. The underwater adaptive alignment wireless power transmission system according to claim 1, characterized in that, The transmitter system includes a multi-sensor module, which comprises an ocean current sensor, a transmitter attitude sensor, and a transmitter displacement sensor. The ocean current sensor is a three-dimensional ocean current sensor used to acquire ocean current velocity v, ocean current direction θ, and turbulence intensity I. The attitude sensor acquires the transmitter attitude angle. , , Displacement sensor collects real-time position data from the transmitter. ; The receiving system includes a receiving attitude sensor and a receiving displacement sensor, which respectively acquire the receiving attitude angle. and real-time location .

3. The underwater adaptive adjustment alignment wireless power transmission system according to claim 2, characterized in that, The transmitter controller calculates the relative motion parameters, namely the relative attitude deviation, based on the acquired attitude and position data of the transmitter and receiver. Relative translation deviation ,in, For the attitude data of the transmitter, For transmitter location data, For the attitude data of the receiving end, This is the location data of the receiving end; Calculate the real-time coupling coefficient k(t) based on the principle of electromagnetic coupling: ； The coupling coefficient is the value under ideal alignment. Let be the translation influence coefficients in the X and Y directions, Δα(t), Δβ(t), and Δγ(t) be the deviations of the relative pitch, roll, and yaw angles of the transmitting and receiving coils at time t, Δx(t) and Δy(t) be the relative translation deviations in the X and Y directions at time t, cos(·) be the cosine function, exp(·) be the exponential function, and |·| be the absolute value function.

4. The underwater adaptive alignment wireless power transmission system according to claim 3, characterized in that, The transmitter controller, based on an LSTM model, uses ocean current data, relative motion parameters, and real-time coupling coefficients from the past 30 seconds to predict future trends. The sequence of relative motion parameters over time; and based on the future sequence of relative motion parameters, to the future... To maximize the average transmission efficiency over time, we construct the following optimization objective function: ； Where max represents the "maximize" operation; This is the averaging coefficient. For the prediction time window; This indicates a summation operation performed over the five prediction times from i=1 to i=5; For the i-th prediction time, the corresponding η(·) is the transmission efficiency function; The PSO algorithm is used to solve this optimization problem, with the constraints being the current amplitude I ∈ [0~5A] and phase φ ∈ [0°~360°] for each transmitting coil; the optimal synthetic magnetic field parameter sequence for the next 5 time moments is obtained. ,in To synthesize the magnetic field azimuth angle, To synthesize the pitch angle of the magnetic field, To synthesize the magnetic field amplitude, Identify the optimal value.

5. The underwater adaptive control alignment wireless power efficient transmission system according to claim 4, characterized in that, By combining a pre-built mapping table of multi-transmitter coil current parameters and synthetic magnetic field parameters, the optimal synthetic magnetic field parameter sequence is mapped to the real-time current amplitude sequence and phase sequence of multiple transmitter coils; The transmitter controller synchronously outputs the real-time current amplitude sequence and phase sequence of multiple transmitter coils to the multi-channel drive module. The drive module adjusts the input current of each coil according to the control signal, so that the multi-transmitter coil array generates a synthetic magnetic field that conforms to the optimal parameters.

6. A method for efficient wireless power transmission based on an underwater adaptive control alignment wireless power transmission system according to any one of claims 1-5, characterized in that, The underwater adaptive adjustment alignment wireless power efficient transfer method includes the following steps: Step 1: The transmitter controller and receiver controller establish a connection through an underwater dual-mode communication link, complete the communication handshake, and initialize the reference parameters; Step 2: The transmitter controller starts the inverter to generate the initial synthetic magnetic field; the receiver controller feeds back its attitude angle, position and SOC data to the transmitter controller through the underwater acoustic communication module. Step 3: The multi-sensor module at the transmitting end collects ocean current data, transmitting end attitude data, and position data in real time according to the acquisition cycle; the sensors at the receiving end synchronously collect receiving end attitude data, position data, and SOC data; all collected data are transmitted to the transmitting end controller in real time through a dual-mode communication link. Step 4: The transmitter controller calculates the relative motion parameters and real-time coupling coefficient based on the collected attitude and position data of the transmitter and receiver. Step 5: Using the ocean current data, relative motion parameters, and real-time coupling coefficients from the past 30 seconds, the LSTM model is used to predict the relative motion parameter sequence for 5 moments within the next 1 second. Step 6, with the future The objective is to maximize the average transmission efficiency over time. An optimization objective function is constructed, and the PSO algorithm is used to solve the optimization problem to obtain the optimal synthetic magnetic field parameter sequence for the next 5 time moments. Step 7: Map the optimal synthetic magnetic field parameter sequence to the real-time current amplitude sequence and phase sequence of multiple transmitting coils; Step 8: The transmitter controller synchronously outputs the current amplitude and phase control signal of the multiple coils to the multi-channel drive module. The drive module adjusts the input current of each coil according to the control signal, so that the multi-transmitter coil array generates a synthetic magnetic field that meets the optimal parameters, thereby achieving active alignment. Step 9: The transmitter controller calculates the real-time transmission efficiency. If the real-time transmission efficiency is greater than the preset efficiency, it maintains the current current parameters and continues charging. If the real-time transmission efficiency is less than the preset efficiency, it returns to step 3 to re-acquire data and adjust the alignment.

7. The underwater adaptive alignment wireless power efficient transmission method according to claim 6, characterized in that, In step 4, the relative motion parameters include relative attitude deviation. Relative translation deviation ,in, For the attitude data of the transmitter, For transmitter location data, For the attitude data of the receiving end, This is the location data of the receiving end; Calculate the real-time coupling coefficient k(t) based on the principle of electromagnetic coupling: ； The coupling coefficient is the value under ideal alignment. These are the translation influence coefficients in the X and Y directions. Let t be the relative pitch, roll, and yaw angle deviations of the transmitting and receiving coils at time t, Δx(t) and Δy(t) be the relative translational deviations in the X and Y directions at time t, cos(·) be the cosine function, exp(·) be the exponential function, and |·| be the absolute value function.

8. The underwater adaptive control alignment wireless power efficient transmission method according to claim 6, characterized in that, Step 5 also includes the following steps: Step 51: Align the ocean current velocity v, ocean current direction θ, turbulence intensity I, historical relative motion parameters, and real-time coupling coefficients collected over the past 30 seconds according to a unified timestamp; convert the ocean current velocity v and ocean current direction θ from polar coordinates to rectangular coordinates. , forming a feature vector in the dataset Using time t as the baseline, data from [t-30s, t] are taken as input features, and five discrete time points (τ=0.2s, 0.4s, 0.6s, 0.8s, 1.0s) within [t, t+1s] are taken as prediction labels. Step 52: Build and train the LSTM model; The input layer of the LSTM model receives a tensor with dimensions of "number of samples, time step, number of features"; the dataset is divided into training set, validation set, and test set, and the loss curve on the validation set is observed. Training is stopped when the loss no longer decreases. Step 53: The output layer of the LSTM model outputs the sequence of relative motion parameters for the next 5 time steps. And the predicted future coupling coefficient k(t+τ).

9. The underwater adaptive control alignment method for efficient wireless power transmission according to claim 6, characterized in that, In step 6, with the future To maximize the average transmission efficiency over time, we construct the following optimization objective function: ； Where max represents the "maximize" operation; This is the averaging coefficient. For the prediction time window; This indicates a summation operation performed over the five prediction times from i=1 to i=5; For the i-th prediction time, the corresponding η(·) is the transmission efficiency function; The PSO algorithm is used to solve this optimization problem, with the constraints being the current amplitude I ∈ [0~5A] and phase φ ∈ [0°~360°] for each transmitting coil; the optimal synthetic magnetic field parameter sequence for the next 5 time moments is obtained. ,in To synthesize the magnetic field azimuth angle, To synthesize the pitch angle of the magnetic field, To synthesize the magnetic field amplitude, Identify the optimal value.

10. The underwater adaptive control alignment wireless power efficient transmission method according to claim 6, characterized in that, In step 8, the actual azimuth angle of the synthesized magnetic field is acquired by the transceiver sensor. The actual pitch angle of the synthesized magnetic field Relative translational deviation from the actual X direction Actual relative translation deviation in the Y direction Calculate the alignment error: Azimuth error Pitch angle error X-direction position error Y-direction position error ;in, To achieve the optimal azimuth angle for the synthesized magnetic field, The optimal pitch angle for the combined magnetic field. To predict the relative translational deviation in the X direction, To predict the relative translational deviation in the Y direction; If error The fuzzy PID algorithm is called to correct the current parameters until all errors are less than the threshold. .

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