Charging pile double-frequency coupling wireless transmission device and efficiency compensation method

Abstract

The invention discloses a charging pile double-frequency coupling wireless transmission device and an efficiency compensation method, and the device comprises a transmitting end which comprises a low-frequency channel and a high-frequency channel; the receiving end comprises a receiving coil (L3) and a cooperative coil (L4) which are tightly coupled; the magnetic leakage intensity and electromagnetic radiation are reduced through the composite magnetic shielding layer; and a control module. The system at least has the following beneficial effects: the transmission efficiency is improved: through a dual-frequency cooperative transmission mechanism, a low-frequency channel can be activated by default when the system is started, and a basic energy transmission link is established by utilizing the strong magnetic field penetration characteristic of the low-frequency channel; and meanwhile, the load power demand of the receiving end is monitored in real time, and if the load power demand suddenly increases, the high-frequency channel is triggered to cooperatively work to realize local enhanced transmission. By means of the dual-frequency cooperative transmission mode, efficient energy transmission can be kept under the conditions of different loads and offsets; the system stability is enhanced; and electromagnetic radiation is reduced.

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: 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. 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. Composite magnetic shielding layer to reduce magnetic leakage intensity and electromagnetic radiation; 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.

[0006] Furthermore, 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. 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.

[0007] Furthermore, the magnetic shielding layer uses a composite shielding material of PC95 manganese-zinc ferrite and copper foil, and the shielding layer thickness is 0.3 - 0.5 mm, covering the back and sides of the receiving end.

[0008] Furthermore, the working steps of the control module include: Step 1: Data acquisition: The control module monitors data of the offset Δd, temperature T, and load current IL in real time through sensors and acquisition circuits. Step 2: Data processing: The acquired 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: According to the transmission efficiency monitored in real time, adjust the resonant capacitors (C1 / C2) and switching frequency to keep the system always working at the optimal coupling point.

[0009] In the second aspect, a method for compensating the efficiency of a charging pile includes the following steps: 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. 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. 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: Based on the real-time feedback of the coupling coefficient k, dynamically adjust the high-low frequency power ratio. 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 the efficiency loss. 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; 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.

[0010] Furthermore, the high-low frequency power ratio formula is: where 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 distribution loss, and α is the environmental correction factor.

[0011] Furthermore, the frequency ratio formula is: where 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.

[0012] Furthermore, the neural network in S7 predicting the attenuation trend of the coupling coefficient k includes the following steps: S7.1: Data collection and preprocessing: Collect data of 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; S7.2: LSTM network structure design: 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; 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 value of the coupling coefficient k within the next 10 ms. According to the prediction result, adjust the compensation capacitance C1 / C2 through the capacitance adjustment formula; The capacitance adjustment formula is:

[0013] where C new is the capacitance value adjusted according to the actual requirements, C nomrepresents 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: LSTM network training: S7.4: LSTM network prediction and compensation: After the training is completed, use the LSTM network to predict the coupling coefficient k value within the next 10 ms, and the prediction result is used to guide the operation and control of the system; Implement compensation: According to the prediction result, adjust the compensation capacitors C1 / C2 through the capacitance adjustment formula; S7.5: Model evaluation and optimization: Evaluate the model performance: Use the test data set to evaluate the prediction performance of the LSTM network; Optimize the model: According to the evaluation result, adjust and optimize the LSTM network; S7.6: Meet the standard and put into use: When the prediction accuracy rate reaches more than 90%, put it into the control module for use.

[0014] Furthermore, it also includes S8: Magnetic circuit optimization, and the magnetic circuit optimization steps include: S8.1: Segment guide 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 guides; S8.2: Magnetic force line path optimization: Adjust the guide excitation phase 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%.

[0015] Furthermore, it also includes S8.3: Magnetic shielding adaptive adjustment: 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 edge magnetic flux diffusion in the second aspect.

[0016] Compared with the prior art, the present application has at least the following beneficial effects: 1. Improve 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 use its strong magnetic field penetration characteristic to establish a basic energy transmission link; at the same time, it can monitor the load power demand of 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 cooperative transmission enables the present invention to maintain efficient energy transmission in the face of different loads and offset situations.

[0017] 2. Enhance system stability: The present invention incorporates a dual-frequency dynamic compensation algorithm that can dynamically adjust the resonant capacitance and switching frequency according to the real-time monitored transmission efficiency, load impedance, and offset signals, enabling the system to always operate 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 jointly enhance the stability and robustness of the system.

[0018] 3. Reduce electromagnetic radiation: The present invention adopts a composite magnetic shielding layer structure, as well as technical means such as segmented rail dynamic activation and magnetic path optimization, effectively reducing the leakage magnetic 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.

[0019] 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 high-dynamic wireless charging scenario of 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 gives the present invention broad application prospects and market value in the field of wireless charging for electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic connection diagram of a dual-frequency coupled wireless transmission device for a charging pile provided by an embodiment of the present application; Figure 2 It is a schematic flowchart of an efficiency compensation method for a charging pile provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0022] As Figure 1 and Figure 2 shown, the present application provides a dual-frequency coupled wireless transmission device and an 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 and has 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; Specifically, the drive 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.

[0023] 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 coil. This structure helps enhance the focusing effect of the magnetic field, and an embedded ferrite core is used to enhance the magnetic field focusing; To reduce electromagnetic interference and heat 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; Specifically, the drive 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; 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 reduce high-frequency losses; Dielectric filling of the high-frequency channel: The coil interval is filled with a high-dielectric-constant dielectric (such as barium titanate ceramic), which further improves the efficiency of high-frequency transmission; Function of the switching circuit: An electronic switch array based on MOSFET can dynamically switch between low-frequency / high-frequency excitation modes. By flexibly switching the excitation modes, dual-frequency collaborative transmission is achieved, improving the transmission efficiency and stability; 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 tightly wound outside L3, and the collaborative coil (L4) forms a strong coupling with the transmitting end (L1 / L2); The coupling coefficient k between 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; 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 reduction is ≥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.

[0024] The control module collects signals of transmission efficiency, load impedance, and offset in real time; The control module has a built-in 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; 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 efficiency fluctuations; 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: Step 1: Data acquisition Real-time monitoring: The control module monitors data such as the offset Δd, temperature T, and load current I_L in real time through high-precision sensors and acquisition circuits. These data are important indicators for evaluating the system performance and are crucial for the optimization and adjustment of the system. Step 2: Data processing: After the collected data are preprocessed through filtering, amplification, etc., they are 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.

[0025] Step 3: Input the working parameters into the dynamic compensation algorithm Principle of the algorithm: 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, so that the system always operates at the optimal coupling point.

[0026] Implementation method: The algorithm is implemented by the microprocessor. According to the preset control strategy and parameters, it adjusts the resonant capacitors and switching frequency in real time. During the adjustment process, the algorithm takes into account factors such as the load change and offset of the system to ensure the stability and high efficiency of the system.

[0027] The dynamic compensation algorithm can adaptively adjust the system parameters, improve the transmission efficiency, suppress the efficiency fluctuations caused by factors such as load change and offset, and ensure the stable operation of the system.

[0028] Intelligent prediction and adjustment 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 the change of the coupling coefficient, providing a basis for the adjustment of the system.

[0029] Early adjustment: According to the prediction result of the neural network, the control module will adjust the compensation capacitance value in advance to suppress the efficiency fluctuation. This early adjustment method can more effectively stabilize the system performance and improve the transmission efficiency.

[0030] Intelligent management: The control module also has an intelligent management function, which can automatically adjust the control strategy and parameters according to the operating state and working environment of the system, enabling the system to more flexibly cope with various changes and challenges and maintain efficient and stable operation.

[0031] In one embodiment, as Figure 2 shown, there is also provided an efficiency compensation method, including the following steps: 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. S2: Dynamic load detection: Continuously 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), trigger the high-frequency channel (6.78 - 13.56 MHz) to work in coordination; S3: Activate the offset compensation mechanism: Detect the offset Δd at the receiving end 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 (6.78 - 13.56 MHz): Based on the real-time feedback of the coupling coefficient k, dynamically adjust the high and low-frequency power ratio: 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 is the coupling coefficient of the low-frequency band, RL is the return loss, Rloss is the distribution loss, and α is the environmental correction factor (such as temperature, metal interference); 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; 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. 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; PID closed-loop control: Proportional term (P): Quickly respond to sudden load changes; Integral term (I): Eliminate steady-state errors; Derivative term (D): Suppress high-frequency oscillations.

[0032] The PID output directly adjusts the inverter switching frequency to ensure that the system always operates at the optimal resonance point; 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 include the following: 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. This data should cover the operating states of the system under different working conditions and environmental conditions to ensure that the LSTM network can learn comprehensive system characteristics; Data preprocessing: Clean the collected data to remove outliers, missing values, etc.; perform normalization on the data to scale it to a unified range to improve the training efficiency and prediction accuracy of the LSTM network; S7.2: LSTM network structure design. Among them, input layer design: According to the results of data preprocessing, determine the number of nodes in the input layer, that is, the dimensions of parameters such as historical coupling coefficient k, offset Δd, temperature T, load current I_L, etc., and use the preprocessed data as the input of the input layer; 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; 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, adjust the compensation capacitor C1 / C2 through the capacitance adjustment formula; The capacitance adjustment formula is:

[0033] where C new is the capacitance value adjusted according to actual needs, 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 by experiment); S7.3: LSTM network training: S7.3.1: Select training algorithm and optimizer: Select the backpropagation through time (BPTT) algorithm and the Adam optimizer to update the weights and biases of the LSTM network; S7.3.2: Set training parameters: Determine 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.

[0034] 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; S7.4: LSTM network prediction and compensation: Make predictions: After training is completed, use the LSTM network to predict the coupling coefficient k value within the next 10 ms. The prediction results can be used as a priori information about the system state to guide the operation and control of the system.

[0035] Implement compensation: According to the prediction results, 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 changes in the system state.

[0036] S7.5: Model evaluation and optimization 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 square error (MSE), etc.

[0037] 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.

[0038] Meet the standard and put into use: When the prediction accuracy reaches over 90%, put it into use in the control module.

[0039] S8: Magnetic circuit optimization and anti-offset control: S8.1 Segment guide 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). S8.2: Magnetic force line 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 coupling coefficient k fluctuation ≤ 5%.

[0040] 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. Material optimization: Use gradient permeability ferrite (μ_r gradually changes from 1000 to 3000) to reduce the edge magnetic flux diffusion.

[0041] Experimental verification and effect 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% 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.

[0042] The following also gives a specific application example: The size of the transmitting end L1 is 60 cm × 40 cm (low frequency), and the size of L2 is 30 cm × 30 cm (high frequency). The receiving end L3 / L4 uses a double-layer concave-convex magnetic core with a size of 20 cm × 20 cm. The control module is centered on STM32H7, with a sampling frequency of 1 kHz, and dynamically adjusts the range of C1 / C2 to be 10 - 100 nF. 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).

[0043] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

Claims

1. A dual-frequency coupling wireless transmission device for a charging pile, characterized in that: include: 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. 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. Composite magnetic shielding layer to reduce magnetic leakage intensity and electromagnetic radiation; The control module collects transmission efficiency, load impedance and offset signals in real time, and has a built-in dual-frequency dynamic compensation algorithm. It adjusts the resonant capacitor (C1 / C2) and switching frequency according to efficiency feedback, so that the transmission efficiency always works at the optimal coupling point.

2. The charging pile dual-frequency coupling wireless transmission device according to claim 1, characterized in that: Coil structure of the low-frequency channel: The first coil (L1) adopts a rectangular densely wound coil structure, and its outer diameter is 1.5-2 times that of the receiving end coil; Coil structure of the high-frequency channel: The second coil (L2) adopts a grouped series-wound coil structure with an inner and outer ring turns ratio of 1:3, and the coil intervals are filled with a high dielectric constant medium.

3. The charging pile dual-frequency coupling wireless transmission device according to claim 1, characterized in that: The magnetic shielding layer uses PC95 manganese-zinc ferrite and copper foil composite shielding material. The shielding layer thickness is 0.3-0.5mm, covering the back and sides of the receiving end.

4. The charging pile dual-frequency coupling wireless transmission device 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 offset Δd, temperature T, and load current I_L in real time through sensors and acquisition circuits; Step 2: Data processing: After preprocessing, the collected data 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; Step 3: Input working parameters into dynamic compensation algorithm: According to the real-time monitored transmission efficiency, adjust the resonant capacitor (C1 / C2) and switching frequency to make the system always work at the optimal coupling point.

5. A charging pile efficiency compensation method, characterized in that: The following steps are involved: S1: Dual-frequency cooperative transmission: Initial energy transmission through low-frequency channel: When the system starts, the low-frequency channel is activated by default, and its strong magnetic field penetration characteristics are used to establish a basic energy transmission link; S2: Dynamic load detection: real-time monitoring of the load power demand at the receiving end. If the load power demand increases suddenly, the high-frequency channel will be triggered to work together. S3: Start the offset compensation mechanism: The Hall sensor detects the offset Δd of the receiving end. When Δd > 5 cm, the high-frequency channel power ratio is gradually increased to form a composite magnetic field of "low-frequency global coverage + high-frequency local enhancement"; S4: Adaptive power allocation of high-frequency channels: Based on real-time feedback of the coupling coefficient k, the high-frequency and low-frequency power ratios are dynamically adjusted; 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 loss; S6: Dynamic parameter adjustment: Calculate the optimal frequency ratio in real time based on load changes and adjust the inverter duty cycle through the PID controller; S7: Use neural network to predict the attenuation trend of the coupling coefficient k, adjust the compensation capacitor value in advance, and suppress efficiency fluctuations.

6. The charging pile efficiency compensation method according to claim 5, characterized in that: The formula for the ratio of high and low frequency power is: 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 distribution loss, and α is the environmental correction factor.

7. The charging pile efficiency compensation method according to claim 5, characterized in that: The frequency ratio formula is: Among them, f high is the high frequency resonant frequency, f low is the low-frequency resonant frequency, k is the coupling coefficient, L is the inductance value, R L is the return loss, R loss To allocate losses.

8. The charging pile efficiency compensation method according to claim 5, characterized in that: The neural network in S7 predicts the decay trend of the coupling coefficient k including the following steps: S7.1: Data collection and preprocessing: Collect data on historical coupling coefficient k, offset Δd, temperature T, load current I_L and other parameters, clean the collected data, remove outliers and missing values, and then normalize the data to scale it to a unified range; S7.2: LSTM network structure design: 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, and load current I_L, and use the preprocessed data as the input of the input layer; Hidden layer design: adopts 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 10ms. According to the prediction result, the compensation capacitor C1 / C2 is adjusted through the capacitor adjustment formula; The capacitance adjustment formula is: 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; S7.3: LSTM network training: 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. Implement compensation: According to the prediction results, adjust the compensation capacitor C1 / C2 through the capacitor adjustment formula; S7.5: Model Evaluation and Optimization: Evaluate model performance: Use the test dataset to evaluate the prediction performance of the LSTM network; Optimize the model: adjust and optimize the LSTM network based on the evaluation results; S7.6: Meet the standard and put into use: When the prediction accuracy reaches more than 90%, the control module is put into use.

9. The charging pile efficiency compensation method according to claim 5, characterized in that: It also includes S8: magnetic circuit optimization, the magnetic circuit optimization steps include: 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; 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%.

10. The charging pile efficiency compensation method according to claim 9, characterized in that: It also includes S8.3: Adaptive adjustment of magnetic shielding: Active shielding coil control: Auxiliary shielding coils are deployed at the edge of the receiving end, and reverse current is injected according to the leakage magnetic detection signal to offset the leakage magnetic field. Gradient permeability ferrite is used to reduce edge flux diffusion.

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