Dual-frequency Coupled Wireless Transmission Device for Charging Pile and Efficiency Compensation Method

Abstract

The present application discloses a dual - frequency coupled wireless transmission device and an efficiency compensation method for a charging pile, including: a transmitting end, which includes a low - frequency channel and a high - frequency channel; a receiving end, which includes a tightly - coupled receiving coil (L3) and a cooperative coil (L4); a composite magnetic shielding layer, which reduces the leakage magnetic intensity and electromagnetic radiation; and a control module. The present application has at least the following beneficial effects: improving the transmission efficiency: through the dual - frequency cooperative transmission mechanism, the present invention can default to activate the low - frequency channel at the start of the system, and utilize its strong magnetic field penetration characteristic to establish a basic energy transmission link; at the same time, it real - time monitors the load power demand at the receiving end. If the load power demand suddenly increases, it triggers the high - frequency channel to work cooperatively to achieve local enhanced transmission. This way of dual - frequency cooperative transmission enables the present invention to maintain efficient energy transmission in the face of different loads and offset situations; enhancing the system stability; and reducing electromagnetic radiation.

Description

Technical Field

[0001] The present application belongs to the field of wireless charging technology, and specifically relates to a dual-frequency coupled wireless transmission device for a charging pile and an efficiency compensation method. Background Art

[0002] With the rapid development of the electric vehicle industry, wireless charging technology, as an important innovation in the field of electric vehicle charging, is gradually receiving widespread attention from the industry. Most traditional wireless charging systems use a single frequency for energy transmission. Although this method has achieved the convenience of wireless charging to a certain extent, its transmission efficiency and stability are often difficult to guarantee in the face of complex and changeable charging environments. Especially in the high-dynamic charging scenarios of electric vehicles, such as the offset of the vehicle parking position and the sudden change of load power demand, it is difficult for a single-frequency wireless charging system to maintain efficient energy transmission and low electromagnetic radiation.

[0003] In response to the above problems, the industry has been exploring more efficient and stable wireless charging solutions. As an emerging technical solution, dual-frequency coupling wireless transmission technology aims to achieve a composite magnetic field transmission effect of global coverage and local enhancement by combining low-frequency and high-frequency transmission modes, thereby improving the transmission efficiency and stability of the wireless charging system. However, how to achieve efficient energy transmission in dual-frequency coupling wireless transmission devices, how to adjust system parameters in real time to adapt to complex and changing charging environments, and how to reduce electromagnetic radiation are still key problems that need to be solved in the current field of wireless charging technology. Summary of the invention

[0004] The present application provides a dual-frequency coupled wireless transmission device for a charging pile and an efficiency compensation method, aiming to solve the problem of insufficient transmission efficiency and stability of existing wireless charging technologies under complex working conditions.

[0005] In a first aspect, a charging pile dual-frequency coupling wireless transmission device comprises:

[0006] The transmitting end includes a low-frequency channel and a high-frequency channel. The low-frequency channel is driven by a low-frequency resonant circuit to drive a first coil (L1) with strong magnetic field penetration characteristics. The high-frequency channel is driven by a high-frequency resonant circuit to drive a second coil (L2) to achieve local enhanced transmission. The switching circuit is an electronic switch array to dynamically switch between low-frequency / high-frequency excitation modes.

[0007] The receiving end includes a tightly coupled receiving coil (L3) and a cooperative coil (L4). L4 is tightly wound around the outside of L3. The cooperative coil (L4) forms a strong coupling with the transmitting end (L1 / L2). The coupling coefficient k between L3 and L4 is ≥ 0.4. The magnetic core adopts a "concave-convex magnetic core" layered structure.

[0008] Composite magnetic shielding layer to reduce magnetic leakage intensity and electromagnetic radiation;

[0009] The control module collects signals of transmission efficiency, load impedance and offset in real time, and has a built-in dual-frequency dynamic compensation algorithm. It adjusts the resonant capacitors (C1 / C2) and switching frequency according to the efficiency feedback to keep the transmission efficiency always working at the optimal coupling point.

[0010] Furthermore, for the coil structure of the low-frequency channel: the first coil (L1) adopts a rectangular closely wound coil structure, and its outer diameter is 1.5 - 2 times that of the receiving-end coil;

[0011] For the coil structure of the high-frequency channel: the second coil (L2) adopts a grouped series-wound coil structure, and the turn ratio of the inner and outer rings is 1:3. The coil interval is filled with a high-dielectric constant medium.

[0012] Furthermore, the magnetic shielding layer uses a composite shielding material of PC95 Mn-Zn ferrite and copper foil, and the shielding layer thickness is 0.3 - 0.5 mm, covering the back and side of the receiving end.

[0013] Furthermore, the working steps of the control module include:

[0014] Step 1: Data acquisition: The control module monitors data of offset Δd, temperature T, and load current IL in real time through sensors and acquisition circuits;

[0015] Step 2: Data processing: After the acquired data is preprocessed, it is sent to the processor for further processing. The processor analyzes and calculates the data to obtain the current state and working parameters of the system;

[0016] Step 3: Input the working parameters into the dynamic compensation algorithm: Adjust the resonant capacitors (C1 / C2) and switching frequency according to the real-time monitored transmission efficiency to keep the system always working at the optimal coupling point.

[0017] In the second aspect, a method for compensating the efficiency of a charging pile includes the following steps:

[0018] S1: Dual-frequency collaborative transmission: Initial energy transmission of the low-frequency channel: When the system starts, the low-frequency channel is default activated, and its strong magnetic field penetration characteristic is used to establish a basic energy transmission link;

[0019] S2: Dynamic load detection: Monitor the load power demand of the receiving end in real time. If the load power demand suddenly increases, trigger the high-frequency channel to work collaboratively;

[0020] S3: Start the offset compensation mechanism: Detect the receiving-end offset Δd through a Hall sensor. When Δd > 5 cm, gradually increase the power ratio of the high-frequency channel to form a composite magnetic field of "low-frequency global coverage + high-frequency local enhancement";

[0021] S4: High-frequency channel adaptive power allocation: Based on the real-time feedback of the coupling coefficient k, dynamically adjust the high-frequency and low-frequency power ratios;

[0022] S5: Pulse width modulation optimization: Use variable duty cycle PWM to control the high-frequency inverter, and increase the high-frequency pulse width during offset to compensate for efficiency losses;

[0023] S6: Dynamic parameter adjustment: Calculate the optimal frequency ratio in real time based on the load change, and adjust the inverter duty cycle through a PID controller;

[0024] S7: Use a neural network to predict the attenuation trend of the coupling coefficient k, and adjust the compensation capacitance value in advance to suppress efficiency fluctuations.

[0025] Furthermore, the high-frequency and low-frequency power ratio formula is:

[0026] Among them, P high is the allocated power in the high-frequency band, P low is the allocated power in the low-frequency band, k high is the coupling coefficient in the high-frequency band, k low is the coupling coefficient in the low-frequency band, R L is the return loss, R loss is the allocation loss, and α is the environmental correction factor.

[0027] Furthermore, the frequency ratio formula is:

[0028] Among them, f high is the high-frequency resonance frequency, f low is the low-frequency resonance frequency, k is the coupling coefficient, L is the inductance value, R L is the return loss, R loss is the allocation loss.

[0029] Furthermore, the neural network in S7 predicting the attenuation trend of the coupling coefficient k includes the following steps:

[0030] S7.1: Data collection and preprocessing: Collect data on parameters such as historical coupling coefficient k, offset Δd, temperature T, load current I_L, etc., clean the collected data to remove outliers and missing values, and then normalize the data to scale it to a unified range;

[0031] S7.2: LSTM network structure design:

[0032] Input layer design: According to the results of data preprocessing, determine the number of nodes in the input layer, that is, the parameter dimensions of the historical coupling coefficient k, offset Δd, temperature T, load current I_L, and use the preprocessed data as the input of the input layer;

[0033] Hidden layer design: adopt 3-layer LSTM network structure;

[0034] Output layer: The number of nodes in the output layer is set to 1, which is used to predict the coupling coefficient k value within the next 10ms. According to the prediction result, the compensation capacitor C1 / C2 is adjusted through the capacitor adjustment formula;

[0035] The capacitance adjustment formula is:

[0036] Among them, C new C is the capacitance value adjusted according to actual needs. nom It represents the rated capacitance value marked on the capacitor, that is, the design value of the capacitor under ideal conditions, and β is the capacitance adjustment gain coefficient;

[0037] S7.3: LSTM network training:

[0038] S7.4: LSTM network prediction and compensation: After training, the LSTM network is used to predict the coupling coefficient k value within the next 10ms. The prediction results are used to guide the operation and control of the system.

[0039] Implement compensation: According to the prediction results, adjust the compensation capacitor C1 / C2 through the capacitor adjustment formula;

[0040] S7.5: Model Evaluation and Optimization: Evaluate model performance: Use the test dataset to evaluate the prediction performance of the LSTM network;

[0041] Optimize the model: adjust and optimize the LSTM network based on the evaluation results;

[0042] S7.6: Meet the standard and put into use: When the prediction accuracy reaches more than 90%, the control module is put into use.

[0043] Furthermore, it also includes S8: magnetic circuit optimization, and the magnetic circuit optimization step includes:

[0044] S8.1: Dynamic activation of segmented rails: Use COMSOL to simulate the magnetic field distribution in real time, identify the receiving end position, and activate the nearest three segments of the transmitting rails;

[0045] S8.2: Optimization of magnetic field path: adjust the guide rail excitation phase so that the direction of the synthetic magnetic field is always aligned with the receiving end, and the coupling coefficient k fluctuates ≤5%.

[0046] Furthermore, it also includes S8.3: Adaptive adjustment of magnetic shielding: Active shielding coil control: deploying auxiliary shielding coils at the edge of the receiving end, injecting reverse current according to the leakage magnetic detection signal to offset the leakage magnetic field, and using gradient permeability ferrite to reduce the second aspect of edge flux diffusion.

[0047] Compared with the prior art, the present application has at least the following beneficial effects:

[0048] 1. Improve transmission efficiency: Through the dual-frequency collaborative transmission mechanism, the present invention can default to activate the low-frequency channel when the system starts, and utilize its strong magnetic field penetration characteristics to establish a basic energy transmission link; at the same time, it can monitor the load power demand at the receiving end in real time. If the load power demand suddenly increases, it will trigger the high-frequency channel to work together to achieve local enhanced transmission. This way of dual-frequency collaborative transmission enables the present invention to maintain efficient energy transmission in the face of different loads and offset situations.

[0049] 2. Enhance system stability: The present invention incorporates a dual-frequency dynamic compensation algorithm, which can dynamically adjust the resonant capacitor and switching frequency according to the real-time monitored transmission efficiency, load impedance, and offset signal, so that the system always operates at the optimal coupling point. In addition, the present invention also uses a neural network to predict the attenuation trend of the coupling coefficient and adjusts the compensation capacitance value in advance to suppress efficiency fluctuations. These measures together enhance the stability and robustness of the system.

[0050] 3. Reduce electromagnetic radiation: The present invention adopts a composite magnetic shielding layer structure, as well as technical means such as segmented guide rail dynamic activation and magnetic path optimization, effectively reducing the magnetic leakage intensity and electromagnetic radiation. This not only meets the requirements of relevant electromagnetic radiation standards, but also improves the safety and environmental protection of the wireless charging system.

[0051] 4. Wide range of applications: The dual-frequency coupled wireless transmission device and efficiency compensation method for charging piles of the present invention are applicable to the wireless charging scenario of high-dynamic electric vehicles, and can maintain high efficiency and low electromagnetic radiation under complex working conditions such as vehicle parking position offset and sudden change in load power demand. This makes the present invention have broad application prospects and market value in the field of wireless charging of electric vehicles. Brief Description of the Drawings

[0052] Figure 1 It is a connection schematic diagram of a dual-frequency coupled wireless transmission device for a charging pile provided by an embodiment of the present application;

[0053] Figure 2 It is a flowchart of an efficiency compensation method for a charging pile provided by an embodiment of the present application. Detailed Description of the Embodiment

[0054] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments.

[0055] Such as Figure 1 and Figure 2As shown, the present application provides a dual - frequency coupled wireless transmission device and efficiency compensation method for a charging pile, including a dual - frequency coupled wireless transmission device for a charging pile, which includes a transmitting end and a receiving end. Among them, the transmitting end includes a low - frequency channel and a high - frequency channel. The low - frequency channel drives the first coil (L1) by a low - frequency resonant circuit, having strong magnetic field penetration characteristics. The high - frequency channel drives the second coil (L2) by a high - frequency resonant circuit to achieve local enhanced transmission. The switching circuit is based on an electronic switch array of MOSFETs to dynamically switch between low - frequency / high - frequency excitation modes;

[0056] Specifically, the driving circuit of the low - frequency channel: The low - frequency channel is driven by a low - frequency resonant circuit (20 - 100 kHz), which consists of a high - precision oscillator, a power amplifier, and a matching network, and can generate stable and efficient low - frequency current.

[0057] The coil structure of the low - frequency channel: The first coil (L1) adopts a rectangular closely - wound coil structure, with an outer diameter 1.5 - 2 times that of the receiving - end coil. This structure helps to enhance the focusing effect of the magnetic field, and an embedded ferrite core is used to enhance the magnetic field focusing;

[0058] To reduce electromagnetic interference and thermal loss, the low - frequency channel part adopts a multi - layer shielding structure and is equipped with a heat dissipation system to ensure long - term stable operation;

[0059] Specifically, the driving circuit of the high - frequency channel: The second coil (L2) is driven by a high - frequency resonant circuit (6.78 - 13.56 MHz), which can generate high - frequency current;

[0060] The coil structure of the high - frequency channel: The second coil (L2) adopts a grouped series - wound coil structure, with the turn ratio of the inner and outer rings being 1:3. This structure helps to reduce high - frequency loss;

[0061] The dielectric filling of the high - frequency channel: The coil is filled with a high - dielectric - constant dielectric (such as barium titanate ceramic) at intervals, further improving the efficiency of high - frequency transmission;

[0062] The function of the switching circuit: Based on an electronic switch array of MOSFETs, it can dynamically switch between low - frequency / high - frequency excitation modes. By flexibly switching the excitation mode, dual - frequency collaborative transmission is achieved, improving the transmission efficiency and stability;

[0063] The receiving end includes a dual - frequency pickup coil structure: It includes a tightly - coupled receiving coil (L3) and a collaborative coil (L4). L4 is closely wound outside L3. The collaborative coil (L4) forms a strong coupling with the transmitting end (L1 / L2); The coupling coefficient k of L3 and L4 is k≥0.4, ensuring efficient energy transmission; The magnetic core adopts a "concave - convex magnetic core" layered structure, further optimizing the magnetic field distribution and coupling effect;

[0064] The composite magnetic shielding layer uses a composite shielding material of PC95 manganese-zinc ferrite and copper foil. The thickness of the shielding layer is 0.3 - 0.5 mm, covering the back and sides of the receiving end. The magnetic leakage is reduced by ≥30%, which not only ensures the shielding effect but also reduces the weight of the device, effectively reducing the magnetic leakage intensity and electromagnetic radiation, and improving the safety of the system.

[0065] The control module collects signals of transmission efficiency, load impedance, and offset in real time;

[0066] The control module is built-in with a dual-frequency dynamic compensation algorithm, which adjusts the resonant capacitors (C1 / C2) and switching frequency according to the efficiency feedback, making the system always operate at the optimal coupling point;

[0067] The control module can use neural networks to predict the attenuation trend of the coupling coefficient k, and adjust the compensation capacitance value in advance to suppress the efficiency fluctuation;

[0068] Specifically, the control module is the core part of the dual-frequency coupled wireless transmission device, responsible for the monitoring, adjustment, and optimization of the system to ensure the high efficiency and stability of wireless energy transmission. The following are the working steps of the control module design:

[0069] Step 1: Data acquisition

[0070] Real-time monitoring:

[0071] The control module, through high-precision sensors and acquisition circuits, monitors data such as offset Δd, temperature T, and load current I_L in real time. These data are important indicators for evaluating the system performance and are crucial for the optimization and adjustment of the system;

[0072] Step 2: Data processing: The collected data is preprocessed through filtering, amplification, etc., and then sent to the microprocessor for further processing. The microprocessor analyzes and calculates the data to obtain the current state and working parameters of the system.

[0073] Step 3: Input the working parameters into the dynamic compensation algorithm

[0074] Algorithm principle: The dynamic compensation algorithm is a closed-loop control algorithm based on the system efficiency feedback. It adjusts the resonant capacitors (C1 / C2) and switching frequency according to the real-time monitored transmission efficiency, making the system always operate at the optimal coupling point.

[0075] Implementation method: The algorithm is implemented through the microprocessor. According to the preset control strategies and parameters, it adjusts the resonant capacitors and switching frequency in real time. During the adjustment process, the algorithm considers factors such as the load change and offset situation of the system to ensure the stability and high efficiency of the system.

[0076] The dynamic compensation algorithm can adaptively adjust system parameters, improve transmission efficiency, suppress efficiency fluctuations caused by factors such as load changes and offsets, and ensure the stable operation of the system.

[0077] Intelligent Prediction and Adjustment

[0078] Neural network prediction: The control module has a built-in neural network model for predicting the attenuation trend of the coupling coefficient k. Through learning and training, the neural network can accurately predict changes in the coupling coefficient, providing a basis for system adjustment.

[0079] Early adjustment: According to the prediction results of the neural network, the control module will adjust the compensation capacitance value in advance to suppress efficiency fluctuations. This early adjustment method can more effectively stabilize system performance and improve transmission efficiency.

[0080] Intelligent management: The control module also has an intelligent management function, which can automatically adjust control strategies and parameters according to the operating state and working environment of the system, enabling the system to more flexibly respond to various changes and challenges and maintain efficient and stable operation.

[0081] In one embodiment, as Figure 2 shown, there is also provided an efficiency compensation method, including the following steps:

[0082] S1: Dual-frequency collaborative transmission: Initial energy transmission of the low-frequency channel (20 - 100 kHz): When the system starts, the low-frequency channel is default activated, and its strong magnetic field penetration characteristic is used to establish a basic energy transmission link;

[0083] S2: Dynamic load detection: Real-time monitor the load power demand at the receiving end (through current / voltage sensors). If the load power demand suddenly increases (ΔP > 15% of the rated power), then trigger the high-frequency channel (6.78 - 13.56 MHz) to work collaboratively;

[0084] S3: Start the offset compensation mechanism: Detect the receiving end offset Δd through a Hall sensor. When Δd > 5 cm, gradually increase the high-frequency channel power ratio to form a composite magnetic field of "low-frequency global coverage + high-frequency local enhancement";

[0085] S4: Adaptive power distribution of the high-frequency channel (6.78 - 13.56 MHz): Based on the real-time feedback of the coupling coefficient k, dynamically adjust the high and low frequency power ratios:

[0086] where, P high is the allocated power of the high-frequency band, P low is the allocated power of the low-frequency band, k high is the coupling coefficient of the high-frequency band, k low$k$ is the coupling coefficient in the low - frequency band, $RL$ is the return loss, $R_{loss}$ is the distribution loss, and $\alpha$ is the environmental correction factor (such as temperature, metal interference);

[0087] S5: Pulse - width modulation (PWM) optimization: Use variable - duty - cycle PWM to control the high - frequency inverter, and increase the high - frequency pulse width during offset to compensate for efficiency losses;

[0088] S6: Dynamic parameter adjustment:

[0089] Calculate the optimal frequency ratio in real - time based on the load change , and adjust the duty - cycle of the inverter through a PID controller. Among them, $f$ high is the high - frequency resonance frequency, $f$ low is the low - frequency resonance frequency, $k$ is the coupling coefficient, $L$ is the inductance value, $R$ L is the return loss, $R$ loss is the distribution loss;

[0090] PID closed - loop control:

[0091] Proportional term (P): Respond quickly to sudden load changes;

[0092] Integral term (I): Eliminate steady - state errors;

[0093] Derivative term (D): Suppress high - frequency oscillations.

[0094] The PID output directly adjusts the inverter switching frequency to ensure that the system always operates at the optimal resonance point;

[0095] S7: Use a neural network to predict the attenuation trend of the coupling coefficient $k$, and adjust the compensation capacitance value in advance to suppress efficiency fluctuations. The steps for the neural network to predict the attenuation trend of the coupling coefficient $k$ are as follows:

[0096] S7.1: Data collection and pre - processing: Collect data on historical coupling coefficient $k$, offset $\Delta d$, temperature $T$, load current $I_L$, etc. These data should cover the operating states of the system under different working conditions and different environmental conditions to ensure that the LSTM network can learn the comprehensive system characteristics;

[0097] Data pre - processing: Clean the collected data to remove outliers, missing values, etc.; normalize the data and scale it to a unified range to improve the training efficiency and prediction accuracy of the LSTM network;

[0098] S7.2: LSTM network structure design. Among them, input - layer design: According to the results of data pre - processing, determine the number of nodes in the input layer, that is, the dimensions of parameters such as historical coupling coefficient $k$, offset $\Delta d$, temperature $T$, load current $I_L$, etc., and use the pre - processed data as the input of the input layer;

[0099] Hidden layer design: Adopt a 3-layer LSTM network structure. The number of LSTM units in each layer can be adjusted according to the complexity of the system and prediction requirements. The LSTM network can process time series data and control and update the cell state through its internal forget gate, input gate, and output gate, so as to achieve long-term prediction of the system state;

[0100] Output layer: The number of output layer nodes is set to 1, which is used to predict the coupling coefficient k value within the next 10 ms. According to the prediction result, the compensation capacitors C1 / C2 are adjusted through the capacitance adjustment formula;

[0101] The capacitance adjustment formula is:

[0102]

[0103] where C new is the capacitance value adjusted according to the actual demand, and C nom represents the rated capacitance value marked on the capacitor, that is, the design value of the capacitor under ideal conditions, and β is the capacitance adjustment gain coefficient (calibrated through experiments);

[0104] S7.3: LSTM network training:

[0105] S7.3.1: Select the training algorithm and optimizer:

[0106] Select the backpropagation through time (BPTT) algorithm and the Adam optimizer to update the weights and biases of the LSTM network;

[0107] S7.3.2: Set the training parameters: Determine the training parameters such as the number of epochs and batch size. These parameters will affect the training efficiency and prediction performance of the LSTM network.

[0108] S7.3.3: Conduct training: Input the preprocessed data into the LSTM network for training. During the training process, monitor the change of the loss function and adjust the training parameters to optimize the network performance;

[0109] S7.4: LSTM network prediction and compensation:

[0110] Make predictions: After the training is completed, use the LSTM network to predict the coupling coefficient k value within the next 10 ms. The prediction result can be used as a priori information of the system state to guide the operation and control of the system.

[0111] Implement compensation: According to the prediction result, adjust the compensation capacitors C1 / C2 through the capacitance adjustment formula. The adjustment of the compensation capacitors can be carried out in real time to compensate for the efficiency loss caused by the change of the system state.

[0112] S7.5: Model Evaluation and Optimization

[0113] Evaluate the model performance: Use the test dataset to evaluate the prediction performance of the LSTM network. The evaluation metrics can include prediction accuracy, mean squared error (MSE), etc.

[0114] Optimize the model: According to the evaluation results, adjust and optimize the structure, training parameters, etc. of the LSTM network. Through continuous iteration and optimization, improve the prediction performance and compensation effect of the LSTM network.

[0115] Meet the standard and put into use: When the prediction accuracy reaches more than 90%, put it into the control module for use.

[0116] S8: Magnetic Circuit Optimization and Anti-Offset Control:

[0117] S8.1 Segmented Guide Rail Dynamic Activation: Finite Element Simulation Feedback: Through real-time simulation of the magnetic field distribution by COMSOL, identify the position of the receiving end, and activate the nearest 3 transmitting guide rails (each segment length = 1.2 times the receiving end coil);

[0118] S8.2: Magnetic Circuit Path Optimization: Adjust the excitation phase of the guide rail so that the direction of the synthetic magnetic field is always aligned with the receiving end, and the fluctuation of the coupling coefficient k ≤ 5%.

[0119] S8.3: Magnetic Shielding Adaptive Adjustment: Active Shielding Coil Control: Deploy auxiliary shielding coils at the edge of the receiving end, and inject reverse current according to the magnetic leakage detection signal (B < 27 μT) to cancel the leakage magnetic field;

[0120] Material Optimization: Adopt gradient permeability ferrite (μ_r gradually changes from 1000 to 3000) to reduce the edge magnetic flux diffusion.

[0121] Experimental Verification and Effect

[0122] Index Traditional single - frequency system The solution of the present invention Improvement amplitude Maximum offset tolerance 10 cm 20 cm 100% Efficiency fluctuation ( Δη ) ±15% ±3% 80% Magnetic leakage intensity (@1 m) 35 μT 22 μT 37% Dynamic response time 50 ms 5 ms 90%

[0123] Conclusion: Through dual-frequency collaborative transmission, intelligent parameter adjustment and magnetic circuit optimization, the system can still maintain high efficiency (>90%) and low electromagnetic radiation under complex working conditions, and is suitable for high-dynamic electric vehicle wireless charging scenarios.

[0124] The following also gives a specific application example:

[0125] The size of the transmitting end L1 is 60 cm × 40 cm (low frequency), the size of L2 is 30 cm × 30 cm (high frequency), and the receiving end L3 / L4 adopts a 20 cm × 20 cm double-layer concave-convex magnetic core;

[0126] The control module is centered around STM32H7, with a sampling frequency of 1 kHz and a dynamically adjustable C1 / C2 range of 10 - 100 nF;

[0127] Experiments show that when the lateral offset is 15 cm, the transmission efficiency is increased from 75% of the traditional single - frequency system to 89%, and the magnetic leakage intensity ≤ 25 μT (meeting the standard of GB / T 38775.4).

[0128] The technical features of the above - mentioned embodiments can be combined arbitrarily. For the sake of brevity in description, not all possible combinations of the technical features in the above - mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

Claims

1. A dual - frequency coupled wireless transmission device for a charging pile, characterized in that, Including: A transmitting end, including a low-frequency channel and a high-frequency channel. The low-frequency channel drives a first coil (L1) by a low-frequency resonant circuit, which has strong magnetic field penetration characteristics. The high-frequency channel drives a second coil (L2) by a high-frequency resonant circuit to achieve local enhanced transmission. The switching circuit is an electronic switch array for dynamically switching the low-frequency / high-frequency excitation mode; A receiving end, including a tightly coupled receiving coil (L3) and a cooperative coil (L4). The cooperative coil is tightly wound outside the receiving coil. The cooperative coil forms a strong coupling with the transmitting end. The coupling coefficient k between the receiving coil and the cooperative coil is k≥0.4, and the magnetic core adopts a "concave-convex magnetic core" hierarchical structure; A composite magnetic shielding layer to reduce the leakage magnetic field intensity and electromagnetic radiation; A control module, which collects signals of transmission efficiency, load impedance and offset in real time, and has a built-in dual-frequency dynamic compensation algorithm. It adjusts the resonant capacitance and switching frequency according to the efficiency feedback to make the transmission efficiency always work at the optimal coupling point. Among them, the dual-frequency dynamic compensation algorithm includes the following steps: S1: Dual-frequency cooperative transmission: Initial energy transmission of the low-frequency channel: When the system starts, the low-frequency channel is default activated, and a basic energy transmission link is established by using its strong magnetic field penetration characteristics; S2: Dynamic load detection: Monitor the load power demand of the receiving end in real time. If the load power demand suddenly increases, trigger the high-frequency channel to work cooperatively; S3: Start the offset compensation mechanism: Detect the receiving end offset Δd through a Hall sensor. When Δd > 5 cm, gradually increase the power ratio of the high-frequency channel to form a composite magnetic field of "low-frequency global coverage + high-frequency local enhancement"; S4: Adaptive power distribution of the high-frequency channel: Dynamically adjust the high-low frequency power ratio based on the real-time feedback of the coupling coefficient k; S5: Pulse width modulation optimization: Use variable duty cycle PWM to control the high-frequency inverter, and increase the high-frequency pulse width during offset to compensate for efficiency losses; S6: Dynamic parameter adjustment: Calculate the optimal frequency ratio in real time based on load changes, and adjust the inverter duty cycle through a PID controller; S7: Use a neural network to predict the attenuation trend of the coupling coefficient k, and adjust the compensation capacitance value in advance to suppress efficiency fluctuations.

2. The dual-frequency coupled wireless transmission device for charging piles according to claim 1, wherein Coil structure of the low-frequency channel: The first coil adopts a rectangular closely wound coil structure, and the outer diameter is 1.5-2 times that of the receiving end coil; Coil structure of the high-frequency channel: The second coil adopts a grouped series-wound coil structure, and the turn ratio of the inner and outer rings is 1:

3. The coil interval is filled with a high dielectric constant medium.

3. The dual-frequency coupled wireless transmission device for charging piles according to claim 1, wherein The magnetic shielding layer adopts a PC95 Mn-Zn ferrite and copper foil composite shielding material, and the shielding layer thickness is 0.3-0.5 mm, covering the back and side of the receiving end.

4. The dual-frequency coupled wireless transmission device for charging piles according to claim 1, characterized in that The working steps of the control module include: Step 1: Data acquisition: The control module monitors the data of the offset Δd, temperature T, and load current I_L in real time through sensors and acquisition circuits; Step 2: Data processing: The collected data is preprocessed and then sent to the processor for further processing. The processor analyzes and calculates the data to obtain the current state and working parameters of the system; Step 3: Input the working parameters into the dynamic compensation algorithm: Adjust the resonant capacitance and switching frequency according to the real-time monitored transmission efficiency to make the system always work at the optimal coupling point.

5. The dual-frequency coupled wireless transmission device for charging pile according to claim 1, wherein The high-low frequency power ratio formula is: Among them, P high is the allocated power in the high-frequency band, and P low is the allocated power in the low-frequency band. k high is the coupling coefficient in the high-frequency band, and k low is the coupling coefficient in the low-frequency band. R L is the return loss, and R loss is the distribution loss. α is the environmental correction factor.

6. The dual-frequency coupled wireless transmission device for charging piles according to claim 1, characterized in that, The frequency ratio formula is as follows: Among them, f high is the high-frequency resonance frequency, f low is the low-frequency resonance frequency, k is the coupling coefficient, L is the inductance value, R L is the return loss, R loss is the distribution loss.

7. The dual-frequency coupled wireless transmission device for a charging pile according to claim 1, wherein The attenuation trend of the coupling coefficient k predicted by the neural network in S7 includes the following steps: S7.1: Data collection and preprocessing: Collect data on historical coupling coefficient k, offset Δd, temperature T, and load current IL parameters. Clean the collected data to remove outliers and missing values, and then normalize the data to scale it to a unified range. S7.2: Design of the LSTM network structure: Input layer design: According to the results of data preprocessing, determine the number of nodes in the input layer, that is, the parameter dimensions of historical coupling coefficient k, offset Δd, temperature T, and load current IL, and use the preprocessed data as the input to the input layer. Hidden layer design: Adopt a 3-layer LSTM network structure. Output layer: The number of nodes in the output layer is set to 1, which is used to predict the coupling coefficient k value within the next 10 ms. According to the prediction results, adjust the compensation capacitors C1 / C2 through the capacitance adjustment formula. The capacitance adjustment formula is as follows: Among them, C new is the capacitance value adjusted according to actual requirements, C nom represents the rated capacitance value marked on the capacitor, that is, the design value of the capacitor under ideal conditions, and β is the capacitance adjustment gain coefficient; S7.3: Training of the LSTM network: S7.4: Prediction and compensation of the LSTM network: After training is completed, use the LSTM network to predict the coupling coefficient k value within the next 10 ms, and the prediction results are used to guide the operation and control of the system. Implement compensation: According to the prediction results, adjust the compensation capacitors C1 / C2 through the capacitance adjustment formula. S7.5: Model evaluation and optimization: Evaluate the model performance: Evaluate the prediction performance of the LSTM network using the test data set. Optimize the model: According to the evaluation results, adjust and optimize the LSTM network. S7.6: Meet the standard and put into use: When the prediction accuracy reaches more than 90%, put it into the control module for use.

8. The dual-frequency coupled wireless transmission device for a charging pile according to claim 1, wherein The dual-frequency dynamic compensation algorithm also includes S8: Magnetic circuit optimization. The magnetic circuit optimization steps include: S8.1: Segment-based guide rail dynamic activation: Through real-time simulation of the magnetic field distribution by COMSOL, identify the position of the receiving end, and activate the nearest 3 transmitting guide rails. S8.2: Optimization of the magnetic force line path: Adjust the excitation phase of the guide rail so that the direction of the synthetic magnetic field is always aligned with the receiving end, and the fluctuation of the coupling coefficient k ≤ 5%.

9. The dual-frequency coupled wireless transmission device for a charging pile according to claim 8, characterized in that, It also includes S8.3: Adaptive adjustment of magnetic shielding: Active shielding coil control: Deploy auxiliary shielding coils at the edge of the receiving end, inject reverse current according to the magnetic leakage detection signal to cancel the leakage magnetic field, and use gradient permeability ferrite to reduce the diffusion of edge magnetic flux.

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