Wireless charging system and method supporting frequency compensation and policy optimization

Abstract

The present application relates to a kind of wireless charging system and method of supporting frequency compensation and strategy optimization, including cloud server, master site gateway device, transmitting end, receiving end and battery system. Cloud server deploys battery state prediction model, generates energy scheduling strategy and battery charging optimization strategy, and issues to master site gateway device for edge control. Transmitting end collects the coil parameter between receiving end, generates frequency compensation strategy and dynamically adjusts resonance frequency, produces alternating magnetic field to realize magnetic resonance coupling energy transmission. Receiving end collects battery operating parameter and feedback, and transmitting end adjusts charging current in combination with feedback parameter and optimization strategy. System fusion 2.4G wireless communication, protocol conversion and model optimization, realize efficient, safe wireless charging process.

Description

Technical Field

[0001] This application relates to the field of wireless power transmission technology, and in particular to a wireless charging system and method that supports frequency compensation and strategy optimization. Background Technology

[0002] With the rapid proliferation of portable devices, electric vehicles, and IoT devices, the shortcomings of traditional wired charging methods in terms of connectivity flexibility, security, and environmental adaptability are becoming increasingly apparent. Wireless charging technology, due to its contactless transmission characteristics, offers significant advantages in improving device sealing, reducing wear and tear from plugging and unplugging, and enhancing ease of use, making it a crucial development direction for current power technology.

[0003] Existing wireless charging systems mostly employ resonant coupling for energy transfer. However, during charging, factors such as the coil position, orientation, and spacing between the transmitter and receiver can easily cause misalignment, leading to changes in system resonance conditions. This reduces energy transfer efficiency and can even cause system detuning. Furthermore, changes in battery state place higher demands on the adaptability of charging strategies, especially in scenarios with multiple power sources and dynamic environmental changes, where traditional static strategies struggle to achieve an optimal balance between charging efficiency and battery life.

[0004] On the other hand, most current wireless charging solutions lack "cloud-edge-device" collaborative control capabilities, cannot perform energy scheduling in real time based on environmental energy consumption costs, carbon emission factors, etc., and are also difficult to achieve dynamic prediction and optimization of battery charging strategies. The system has a low level of intelligence and is difficult to meet the charging management needs of smart devices in complex scenarios.

[0005] Therefore, there is an urgent need for a wireless charging system that supports frequency compensation and strategy optimization, has cloud-edge collaborative computing capabilities, and can dynamically adjust charging parameters based on a smart battery state prediction model, in order to improve energy transmission efficiency, extend battery life, and enable flexible deployment and management in various application scenarios. Summary of the Invention

[0006] This application provides a wireless charging system that supports frequency compensation and strategy optimization, which solves the problems of low coupling efficiency, inability to compensate frequency in real time, and lack of intelligent charging strategy based on battery status in the prior art, which leads to low charging efficiency and shortened battery life.

[0007] In one aspect, this application discloses a wireless charging system that supports frequency compensation and strategy optimization, including a cloud server, a master control site gateway device, a transmitter, a receiver, and a battery system.

[0008] The cloud server interacts with the main control site gateway device via a wireless network;

[0009] The main control site gateway device interacts with the transmitter via serial communication;

[0010] The transmitter interacts with the receiver via 2.4G wireless communication and transmits electrical energy to the receiver via coil coupling.

[0011] The receiving end connects and interacts with the battery system via serial communication;

[0012] The cloud server is used to deploy a battery status prediction model and generate a control strategy, and to distribute the control strategy to the main control site gateway device. The control strategy includes an energy scheduling strategy and a battery charging optimization strategy.

[0013] The main control site gateway device is used to control the working status of the transmitter and the receiver based on the control strategy, and to perform communication protocol conversion;

[0014] The transmitting end is used to collect coil parameters between the transmitting end and the receiving end, generate a frequency compensation strategy based on the coil parameters, dynamically adjust the resonant frequency according to the frequency compensation strategy to compensate for coil offset, and generate an alternating magnetic field.

[0015] The receiving end is used to receive the alternating magnetic field through magnetic resonance coupling, collect battery operating parameters, and feed the battery operating parameters back to the transmitting end;

[0016] The transmitter is also used to dynamically adjust the charging current of the battery system after interacting with the receiver, based on the battery operating parameters and the cloud server's battery charging optimization strategy.

[0017] Optionally, the transmitting end includes a transmitting end control module and a transmitting end acquisition module, and the receiving end includes a receiving end control module and a receiving end acquisition module. The coil located on the transmitting end side is the transmitting end coil, and the coil located on the receiving end side is the receiving end coil.

[0018] The transmitter acquisition module is physically connected to the transmitter control module;

[0019] The transmitter acquisition module acquires the coil parameters between the transmitter and the receiver, and transmits the coil parameters to the transmitter control module. The coil parameters include the coil voltage sample value and the coil self-inductance value.

[0020] The transmitter control module generates a frequency compensation strategy based on the coil parameters, dynamically adjusts the resonant frequency according to the frequency compensation strategy to compensate for coil offset, and outputs a control signal based on the resonant frequency.

[0021] The transmitting coil responds to the control signal and generates an alternating magnetic field;

[0022] The receiving coil responds to the alternating magnetic field and generates an induced current;

[0023] The receiving end acquisition module acquires battery operating parameters, and converts the battery operating parameters into corresponding data through the receiving end control module, and transmits them to the transmitting end control module through the 2.4G wireless communication. The battery operating parameters include battery voltage, charging current, temperature and coil voltage.

[0024] The transmitter control module dynamically adjusts the charging current of the battery system after interacting with the receiver, based on the battery operating parameters and the cloud server's battery charging optimization strategy.

[0025] Optionally, the cloud server generates an energy dispatch strategy based on the real-time costs and carbon emission factors of solar energy, off-peak electricity, and peak electricity through the battery state prediction model. The energy dispatch strategy includes:

[0026] Predefined priority order of energy types;

[0027] Solar power will be selected when the light intensity is greater than a preset threshold.

[0028] The gain ratio for increasing charging power to rated power during off-peak electricity supply periods;

[0029] During peak power supply periods, reduce charging power to the rated power at a certain rate and postpone non-emergency charging tasks.

[0030] Optionally, the battery state prediction model is a time-series neural network model, and the battery charging optimization strategy generated includes:

[0031] The battery diagnostic parameters are input into the battery state prediction model to generate battery charging parameters. The battery diagnostic parameters include ambient temperature, battery temperature, voltage, current and their rate of change over time. The battery charging parameters include battery type identification, rated capacity value and health status value.

[0032] When the health status value is lower than the preset threshold, the charging current is limited to a preset proportion of the current value corresponding to the rated capacity.

[0033] Optionally, the charging current is optimized according to a time decay function, and its decay rate is positively correlated with the health status value.

[0034] Optionally, the frequency compensation strategy includes:

[0035] Based on the sampled values ​​of the coil voltage at the transmitting end and the sampled values ​​of the coil voltage at the receiving end, the mutual inductance coefficient between the transmitting end and the receiving end is determined.

[0036] Based on the mutual inductance coefficient, the self-inductance value of the coil at the transmitting end, and the self-inductance value of the coil at the receiving end, the resonant frequency compensation value is calculated.

[0037] Optionally, the transmitter control module transmits the coil parameters and the battery operating parameters to the main control station gateway device via the serial communication;

[0038] The main control site gateway device calculates edge control decisions based on the received coil parameters and battery operating parameters, and sends the edge control decisions to the transmitter control module.

[0039] Optionally, the master control site gateway device performs communication protocol conversion, including conversion between CAN, RS485, TCP and MQTT protocols.

[0040] Optionally, when the battery system includes a BMS, the receiving end obtains the battery operating parameters by reading data inside the battery system.

[0041] Secondly, this application provides a wireless charging method supporting frequency compensation and strategy optimization. The method is applied to a wireless charging system supporting frequency compensation and strategy optimization. The system includes a cloud server, a master control site gateway device, a transmitter, a receiver, and a battery system. The method includes:

[0042] The cloud server establishes a communication connection with the main control site gateway device via a wireless network.

[0043] A battery status prediction model is deployed in the cloud server to generate control strategies, and the control strategies are distributed to the main control site gateway device. The control strategies include energy scheduling strategies and battery charging optimization strategies.

[0044] The main control site gateway device controls the working status of the transmitter and the receiver based on the control strategy, and performs communication protocol conversion;

[0045] The transmitter interacts with the receiver via 2.4G wireless communication and transmits electrical energy to the receiver via coil magnetic resonance coupling.

[0046] The transmitter collects the coil parameters between the transmitter and the receiver, generates a frequency compensation strategy based on the coil parameters, dynamically adjusts the resonant frequency according to the frequency compensation strategy to compensate for coil offset, and generates an alternating magnetic field.

[0047] The receiving end receives the alternating magnetic field and collects the battery operating parameters, and feeds back the battery operating parameters to the transmitting end through the 2.4G wireless communication.

[0048] The transmitter interacts with the receiver based on the received battery operating parameters and the battery charging optimization strategy generated by the cloud server, and dynamically adjusts the charging current of the battery system.

[0049] The technical solutions provided in this application have the following advantages compared with the prior art:

[0050] (1) Improve wireless charging efficiency and stability: By collecting coil parameters between the transmitter and receiver, a frequency compensation strategy is generated based on coil voltage and self-inductance information, and the resonant frequency is dynamically adjusted to effectively compensate for coil offset, thereby significantly improving magnetic resonance coupling efficiency and charging stability.

[0051] (2) Implement intelligent charging control strategy: Deploy battery status prediction model on cloud server, combine battery temperature, voltage, current and its rate of change and generate control strategy including energy scheduling and charging optimization, realize dynamic adjustment of charging current and improve the intelligence level of system.

[0052] (3) Supports battery health status identification and protection: The battery aging status is identified by the health status value. When the health status is below the threshold, the charging current is automatically limited to prevent overcharging, extend battery life, and improve system safety and reliability.

[0053] (3) Achieve distributed collaborative control and edge computing capabilities: By combining cloud control strategies with edge nodes through the main control site gateway device, it supports protocol conversion and local control decisions, reduces communication latency, enhances the system's real-time response capability, and has good scalability and deployability.

[0054] (4) Support for multi-source energy dispatch and carbon emission optimization: The system considers energy sources such as solar energy and peak-valley electricity, and generates energy dispatch strategies by combining real-time cost and carbon emission factors. Under the premise of ensuring charging efficiency, it reduces energy consumption and carbon emissions, and has green and energy-saving advantages.

[0055] (5) Compatible with multiple communication protocols, facilitating integration and deployment: Supports conversion of multiple communication protocols such as CAN, RS485, TCP, and MQTT, enabling the system to have good platform compatibility and be easily integrated into various industrial and consumer scenarios.

[0056] (6) Construct a complete “cloud-edge-terminal-battery” closed-loop system: From cloud prediction to edge control to terminal execution, the system constructs a complete energy scheduling and intelligent control closed loop, improves system coordination and responsiveness, and enhances system robustness and adaptability.

[0057] (7) Improve the flexibility of electricity use and charging experience on the user side: It can adjust the strategy according to changes in electricity price and load demand, fast charging during off-peak hours and power restriction during peak hours, optimize the electricity use structure, and improve the user's economy and experience.

[0058] (8) Meets the application requirements of BMS or non-BMS battery systems: Regardless of whether the battery system includes a BMS, the operating parameters can be collected through the receiver or embedded module to ensure the applicability and universality of the system. Attached Figure Description

[0059] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0062] Figure 1 A schematic diagram of a wireless charging system supporting frequency compensation and strategy optimization provided in an embodiment of this application;

[0063] Figure 2 A flowchart illustrating a wireless charging method supporting frequency compensation and strategy optimization provided in an embodiment of this application;

[0064] Figure 3 This is a schematic diagram illustrating the interaction between modules in a wireless charging system that supports frequency compensation and strategy optimization, as provided in an embodiment of this application. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0067] This application provides a method for reference frequency calibration and foreign object detection in a wireless charging system, which is applied to a wireless charging system, such as... Figure 1 As shown, Figure 1 This is a schematic diagram of a wireless charging system that supports frequency compensation and strategy optimization, provided as an embodiment of this application.

[0068] The wireless charging system supporting frequency compensation and strategy optimization in this application includes a cloud server 101, a main control site gateway device 102, a transmitter 103, a receiver 104, and a battery system 105. The cloud server 101 interacts with the main control site gateway device 102 via a 5G wireless network. The main control site gateway device 102 interacts with the transmitter 103 via serial communication CAN / RS485. The transmitter 103 interacts with the receiver 104 via 2.4G wireless communication and transmits power to the receiver 104 via coil magnetic resonance coupling. The receiver 104 connects and interacts with the battery system 105 via serial communication CAN / RS485.

[0069] This embodiment provides a wireless charging system that supports frequency compensation and strategy optimization, as shown in the figure, including a cloud server, a main control site gateway device, a transmitter, a receiver, and a battery system.

[0070] The cloud server establishes a communication connection with the main control site gateway device through a wireless network to deploy a battery status prediction model and generate corresponding control strategies. The control strategies include energy scheduling strategies and battery charging optimization strategies, and the control strategies are sent to the main control site gateway device.

[0071] After receiving the control strategy, the main control site gateway device interacts with the transmitter through serial communication to control the working status of the transmitter and receiver, and also undertakes the function of communication protocol conversion to realize the interoperability between multiple protocols within the system.

[0072] The transmitter can be a cluster of transmitter terminal devices that interacts with the receiver via 2.4G wireless communication and transmits electrical energy to the receiver through coil magnetic resonance coupling. Simultaneously, the transmitter collects coil parameters between itself and the receiver, generates a frequency compensation strategy based on these parameters, and dynamically adjusts the resonant frequency according to the compensation strategy to achieve coil offset compensation and stably generate an alternating magnetic field.

[0073] The receiving end can be a cluster of receiving terminal devices that receive the alternating magnetic field through magnetic resonance coupling, convert electrical energy into direct current, collect battery operating parameters, and feed these parameters back to the transmitting end through communication.

[0074] The transmitter dynamically adjusts the charging current of the battery system based on the received battery operating parameters and the battery charging optimization strategy issued by the cloud server, in conjunction with the feedback from the receiver, in order to improve charging efficiency and protect battery health.

[0075] This embodiment constructs a complete closed-loop system from cloud-based intelligent strategy generation, edge control response, wireless energy transmission to terminal battery charging regulation, which has high efficiency, high intelligence and good scalability, and is suitable for a variety of wireless charging scenarios.

[0076] Furthermore, in this embodiment, a specific structural composition of a wireless charging system supporting frequency compensation and strategy optimization is provided. The wireless charging system includes a transmitter control module and a transmitter acquisition module disposed at the transmitter end, and a receiver control module and a receiver acquisition module disposed at the receiver end.

[0077] The transmitting end is equipped with a transmitting coil, and the receiving end is equipped with a receiving coil. The transmitting end acquisition module and the transmitting end control module achieve data transmission through a physical connection.

[0078] During system operation, the transmitter acquisition module is responsible for collecting coil parameters between the transmitter coil and the receiver coil, including coil voltage samples and coil self-inductance, and transmitting these parameters to the transmitter control module. The transmitter control module generates a corresponding frequency compensation strategy based on the collected coil parameters and dynamically adjusts the resonant frequency according to the strategy to compensate for coil offset, and outputs corresponding control signals.

[0079] The transmitting coil generates an alternating magnetic field under the action of the control signal, and the receiving coil responds to the alternating magnetic field through magnetic resonance and generates an induced current.

[0080] The receiver acquisition module is used to collect battery operating parameters, including battery voltage, charging current, temperature, and coil voltage. The collected parameters are processed by the receiver control module, converted into a recognizable data format, and transmitted to the transmitter control module via 2.4G wireless communication.

[0081] Ultimately, the transmitter control module, based on the received battery operating parameters and the battery charging optimization strategy pre-issued by the cloud server, works in conjunction with the receiver to dynamically adjust the charging current of the battery system.

[0082] Furthermore, the cloud server generates an energy dispatch strategy based on the real-time costs and carbon emission factors of solar energy, off-peak electricity, and peak electricity through the battery state prediction model. The energy dispatch strategy includes:

[0083] Predefined priority order of energy types;

[0084] Solar power will be selected when the light intensity is greater than a preset threshold.

[0085] The gain ratio for increasing charging power to rated power during off-peak electricity supply periods;

[0086] During peak power supply periods, the energy storage system is switched to power supply. If the energy storage system is depleted, the charging power is reduced to the attenuation ratio of the rated power, and non-emergency charging tasks are postponed.

[0087] like Figure 3 As shown, Figure 3 This is a schematic diagram illustrating the interaction of various modules in a wireless charging system supporting frequency compensation and strategy optimization, provided in an embodiment of this application. This embodiment also includes a photovoltaic-charging-storage hybrid inverter system 106, a power grid 107, an energy storage system 108, and a photovoltaic array 109. The main functions of the photovoltaic-charging-storage hybrid inverter system 106 include: converting photovoltaic DC power to AC power; charging excess photovoltaic power into the energy storage system or connecting it to the grid; fully charging the energy storage system during off-peak hours by controlling the photovoltaic-charging-storage hybrid inverter system; converting the DC power from the energy storage system to AC power to supply power to the main control station equipment during cloudy / peak hours; and providing safety protection functions such as overvoltage / undervoltage protection, overfrequency / underfrequency protection, DC reverse connection protection, leakage current protection, and short circuit protection. The power grid 107 is connected to AC220V mains power; the main function of the energy storage system 108 is to charge during off-peak hours and when there is excess photovoltaic power, and then discharge during peak hours, thus earning the price difference and balancing the load; the main function of the photovoltaic array 109 is to convert solar energy into DC power, and track the maximum power point in real time through the MPPT controller to stabilize the output power during changes in weather.

[0088] The cloud server has the capability to generate energy dispatch strategies, which is based on a deployed battery status prediction model. The cloud server receives multi-dimensional energy-related input information, including the status of solar resources, real-time grid electricity prices (including off-peak and peak electricity periods), and carbon emission factors for various energy types.

[0089] Based on the set priority rules, the cloud server first determines whether the current light intensity is greater than a preset threshold. If the condition is met, solar energy is selected as the energy input. If the current time is during a low electricity price period, the system will increase the charging power, and the increase percentage can be set to a predetermined gain percentage of the rated power. If it is during a peak electricity price period, the system will prioritize switching the photovoltaic charging and storage hybrid system to the energy storage system for power supply. If the energy storage system's power is less than 40%, the system will reduce the charging power to a predetermined attenuation percentage of the rated power, and can delay the execution of non-urgent charging tasks through task queue scheduling.

[0090] Specifically, the priorities of the multi-source collaborative model are shown in Table 1 below:

[0091]

[0092] Table 1

[0093] Cloud scheduling algorithm: Min( )

[0094] Parameter description: This is the cost weighting coefficient (typically 0.6~0.8). Carbon emission weighting coefficient ( =1- (Typical value is 0.2~0.4), cost is energy expenditure (electricity consumption * electricity price), and carbon emission is CO2 emission (electricity consumption * carbon emission factor). Energy storage cycle attenuation coefficient ( Energy storage loss = ;

[0095] The energy parameters are shown in Table 2 below:

[0096]

[0097] Table 2

[0098] The energy dispatch strategy generated by the above logic can be dynamically distributed to the main control site gateway device, and realize intelligent energy switching and charging power optimization during terminal execution. It switches between solar power, off-peak electricity and peak electricity demand, reduces charging costs, improves overall energy utilization efficiency and reduces operating costs.

[0099] Furthermore, the battery state prediction model is a time-series neural network model, and the battery charging optimization strategy generated includes:

[0100] The battery diagnostic parameters are input into the battery state prediction model to generate battery charging parameters. The battery diagnostic parameters include ambient temperature, battery temperature, voltage, current and their rate of change over time. The battery charging parameters include battery type identification, rated capacity value and health status value.

[0101] When the health status value is lower than the preset threshold, the charging current is limited to a preset proportion of the current value corresponding to the rated capacity.

[0102] In this embodiment, a battery state prediction model is integrated into the cloud server. This model is a deep learning model based on a temporal neural network (such as LSTM or GRU). This model is used to dynamically evaluate the battery's health state and charging capability, and generate a battery charging optimization strategy accordingly. The battery AI diagnostic and optimized charging technology uses a large model trained for battery AI diagnostics to determine the current battery health state during charging, and optimizes the charging current curve based on the battery's health state.

[0103] During system operation, the receiver collects battery diagnostic parameters, including ambient temperature, battery temperature, voltage, current, and the time-varying rate of change of each parameter, and transmits them to the cloud server via wireless communication. The cloud server takes these diagnostic parameters as input, processes them in the battery state prediction model, and outputs the corresponding battery charging parameters.

[0104] The battery charging parameters include: battery type identification, rated capacity value, and current State of Health (SOH) value. When the predicted state of health value is lower than the set warning threshold (e.g., 80%), the system automatically triggers a protection strategy to limit the current charging current to no more than a set percentage of the rated capacity current (e.g., 70%) to avoid battery damage due to overcharging.

[0105] The cloud server generates a battery charging optimization strategy based on the above results and sends the strategy to the main control site gateway device to guide the subsequent charging process control.

[0106] Specifically, the battery AI diagnostic and optimized charging technology solution includes:

[0107] Charging curve feature extraction: Input data { Slope of constant flow section: V, I, Δt} Temperature rise rate ,in, For ambient temperature, V is the battery temperature, V is the battery voltage, I is the battery charging current, Δt is the differential time, and the slope of the constant current section is... The rate of temperature rise is the change in battery voltage per unit time. This represents the change in battery temperature per unit time.

[0108] Cloud-based large-scale model architecture:

[0109] # Battery type identification model (PyTorch example)

[0110] class BatteryClassifier(nn.Module):

[0111] def __init__(self):

[0112] super().__init__()

[0113] self.lstm = nn.LSTM(input_size=5, hidden_size=64) # 5-dimensional input: ,V,I,Δt

[0114] self.fc = nn.Linear(64, 4) # Output: Type + Capacity + State of Response (SOH) + Internal Resistance Growth Rate

[0115] def forward(self, x):

[0116] x, _ = self.lstm(x) # Time series data processing

[0117] return self.fc(x[:, -1, :]) # Final time-end feature classification

[0118] Output battery type, rated battery capacity, state of health (SOH), battery internal resistance growth rate, and optimal lifespan charging strategy, including the dynamic current constraint formula.

[0119] =

[0120] Charging curve optimization algorithm: =0.02*SOH, where, The maximum current (the upper limit of the safe charging current) is denoted by SOH, which represents the battery's state of health. C represents the battery temperature, and C represents the rate base (the current corresponding to the battery's rated capacity). For optimal charging current, The decay coefficient represents the rate of current decay (using a smoother charging current decay curve for aged batteries), t is the time variable, and SOH is the battery health state.

[0121] Furthermore, the charging current is optimized according to a time decay function, and its decay rate is positively correlated with the health status value.

[0122] In this embodiment, to further extend battery life and improve charging safety, the system deploys a dynamic attenuation optimization mechanism for charging current in the cloud server.

[0123] Specifically, when the State of Health (SOH) value output by the battery state prediction model is within the normal range but below the ideal level (e.g., between 80% and 90%), the system automatically applies a time decay function of the charging current to slow down the charging rate. This decay function is either exponential or linear, and its decay rate is directly proportional to the current state of health of the battery; that is, the worse the state of health, the faster the current decays.

[0124] For example, for a battery with a state of harmlessness (SOH) of 85%, the system will start the charging process at the rated charging current in the initial stage, and then gradually reduce the current during the charging process to ensure that the charging current has dropped to a low level (such as 30% of the rated current) when it is close to being fully charged, thereby effectively reducing the temperature rise, avoiding overcharging, and improving charging safety.

[0125] The decay function and its parameters are dynamically generated by the cloud server based on historical battery data, and then sent to the transmitter control module through a control strategy to ultimately achieve optimized control of the actual charging process.

[0126] Furthermore, the frequency compensation strategy includes:

[0127] Based on the sampled values ​​of the coil voltage at the transmitting end and the sampled values ​​of the coil voltage at the receiving end, the mutual inductance coefficient between the transmitting end and the receiving end is determined.

[0128] Based on the mutual inductance coefficient, the self-inductance value of the coil at the transmitting end, and the self-inductance value of the coil at the receiving end, the resonant frequency compensation value is calculated.

[0129] In this embodiment, to achieve efficient and stable wireless charging, the system introduces a frequency compensation strategy generation mechanism based on mutual inductance coefficient and coil self-inductance value to dynamically adjust the resonant frequency and offset the decrease in coupling efficiency caused by coil offset or environmental changes.

[0130] Specifically, the transmitter acquisition module first acquires voltage samples from the transmitter coil and the receiver coil, respectively, and transmits these voltage values ​​to the transmitter control module. Based on these sampled values, the transmitter control module uses modeling algorithms to calculate the mutual inductance coefficient between the transmitter and receiver in real time, using parameters such as voltage amplitude ratio and phase difference.

[0131] After obtaining the mutual inductance coefficient, the system further combines the preset self-inductance values ​​of the transmitting coil and the receiving coil, and calculates the corresponding frequency offset using the resonant circuit calculation formula, thereby obtaining the optimal resonant frequency compensation value.

[0132] This frequency compensation value is used to dynamically adjust the output frequency of the transmitter oscillation circuit, so that the system remains in the optimal resonance state. Even if the coupling state changes due to position movement, interference from metal foreign objects, etc. during the charging process, frequency compensation can be performed in time to ensure the stability and efficiency of wireless power transmission.

[0133] Specifically, dynamic resonant frequency correction technology solution

[0134] (1) Calculation of mutual inductance coefficient: k= M= , | | =

[0135] (2) Frequency dynamic adjustment calculation: =

[0136] Where K is the mutual inductance coefficient of the coils, and M is the mutual inductance value between the coils at the receiving end and the transmitting end. The transmitter impedance amplitude, This is the self-inductance value of the transmitting coil. This is the self-inductance value of the receiving coil. and The coil voltage is acquired by the coil sampling circuit at both the transmitting and receiving ends. =2 This is the current operating angular frequency. The current resonant frequency of the coil, | | represents the transmitter impedance magnitude. The resistance of the transmitting coil. For the resonant capacitor of the transmitting coil, This is the resonant frequency of the transmitter coil after dynamic compensation.

[0137] The dynamic resonant frequency correction technology is an improvement technology used to address the low charging efficiency caused by the misalignment of the receiving coil (i.e., it is not placed in the center). It enables the transmitting end to dynamically compensate and adjust the resonant frequency of the transmitting end to improve charging efficiency.

[0138] Furthermore, the transmitter control module transmits the coil parameters and the battery operating parameters to the main control station gateway device via the serial communication;

[0139] The main control site gateway device calculates edge control decisions based on the received coil parameters and battery operating parameters, and sends the edge control decisions to the transmitter control module.

[0140] In this embodiment, to improve the system's response speed and control accuracy, the transmitter control module uploads key operating parameters to the main control site gateway device to achieve edge control optimization.

[0141] Specifically, the transmitting end acquisition module collects coil parameters in real time during charging, including coil voltage sampling values ​​and coil self-inductance values, and transmits them to the transmitting end control module through an internal communication interface. Simultaneously, the receiving end acquisition module continuously monitors the battery system's operating parameters, including battery voltage, current, temperature, and coil voltage, and after processing by the receiving end control module, transmits them to the transmitting end control module via 2.4G wireless communication.

[0142] The transmitter control module summarizes the aforementioned coil parameters and battery operating parameters and uploads them to the main control station gateway device via a serial communication interface. Upon receiving the parameter data, the main control station gateway device, combining edge computing strategies with its locally stored control model, performs rapid judgment and optimization calculations to generate edge control decision commands.

[0143] The generated edge control commands are sent to the transmitter control module. These commands include the frequency fine-tuning range and electromagnetic power adjustment strategy. The transmitter then dynamically adjusts its output control signals accordingly, thereby achieving real-time optimization of the wireless charging process.

[0144] Through this mechanism, the system can achieve rapid response and local optimization at the edge without relying on cloud server response, thereby improving the system's stability, real-time performance, and robustness.

[0145] Furthermore, the main control site gateway device performs communication protocol conversion, including conversion between CAN, RS485, TCP and MQTT protocols.

[0146] In this embodiment, to achieve data interoperability and protocol compatibility between modules, the main control site gateway device has the ability to identify, parse and convert multiple communication protocols, and supports flexible switching between CAN, RS485, TCP and MQTT protocols.

[0147] In the specific implementation, the master control station gateway device is equipped with a communication protocol conversion module. This module supports cross-protocol communication based on hardware communication interfaces (such as serial ports and Ethernet ports) and a software parsing stack. When it receives data uploaded from the transmitting control module via the RS485 protocol, the master control station gateway device first parses the RS485 frame format and converts its content into an internally unified data structure.

[0148] Subsequently, if the data needs to be uploaded to the cloud server for remote storage or policy updates, the master control site gateway device repackages the unified data structure into an MQTT message format and sends it via the TCP / IP protocol stack. Conversely, if the control policy issued by the cloud server is in MQTT format, the master control site gateway device converts it to RS485 or CAN protocol before sending it to the corresponding transmitter or receiver.

[0149] In addition, the main control site gateway device can automatically identify the communication protocol type used by the target device according to the configuration, realize automatic protocol switching and adaptation, effectively solve the problem of inconsistent communication protocols between heterogeneous devices, and enhance the system's versatility and scalability.

[0150] Furthermore, when the battery system includes a BMS, the receiving end obtains the battery operating parameters by reading data from inside the battery system.

[0151] In this embodiment, the battery system is equipped with a Battery Management System (BMS) for real-time monitoring and management of the battery's operating status. To achieve accurate acquisition of battery operating parameters, the receiving end is equipped with a serial communication interface (such as CAN or RS485) to establish a stable data communication channel with the BMS.

[0152] During system operation, the receiving end control module periodically or on demand sends data read commands to the BMS. The BMS returns key parameters including current battery voltage, battery charging current, battery temperature, state of health (SoH), and remaining charge (SoC) based on the request. The receiving end acquisition module receives this response data and parses it into structured battery operating parameters for the transmitting end to adjust subsequent charging control strategies.

[0153] This implementation avoids redundant sensor data acquisition, improving data accuracy and system integration. Simultaneously, by reading data from within the BMS, it achieves comprehensive awareness of the battery system's operating status, contributing to the optimization of safety and intelligence during the battery charging process.

[0154] like Figure 2 As shown, Figure 2 This is a flowchart illustrating a wireless charging method supporting frequency compensation and strategy optimization, provided as an embodiment of this application. The method is applied to a wireless charging system supporting frequency compensation and strategy optimization, and includes:

[0155] The S100 enables the cloud server to establish a communication connection with the main control site gateway device via a wireless network;

[0156] S200 deploys a battery status prediction model in the cloud server to generate a control strategy, and sends the control strategy to the main control site gateway device. The control strategy includes an energy scheduling strategy and a battery charging optimization strategy.

[0157] The main control site gateway device S300, based on the control strategy, controls the working status of the transmitter and the receiver through a serial communication connection and performs communication protocol conversion;

[0158] The transmitter of S400 interacts with the receiver via 2.4G wireless communication and transmits electrical energy to the receiver via coil magnetic resonance coupling.

[0159] The transmitter in S500 acquires the coil parameters between the transmitter and the receiver, generates a frequency compensation strategy based on the coil parameters, dynamically adjusts the resonant frequency according to the frequency compensation strategy to compensate for coil offset, and generates an alternating magnetic field.

[0160] The receiving end of S600 receives the alternating magnetic field and collects the battery operating parameters, and feeds back the battery operating parameters to the transmitting end through the 2.4G wireless communication.

[0161] The transmitter in S700 interacts with the receiver to dynamically adjust the charging current of the battery system based on the received battery operating parameters and the battery charging optimization strategy generated by the cloud server.

[0162] This embodiment provides a wireless charging method that supports frequency compensation and strategy optimization. It is applicable to wireless charging systems consisting of a cloud server, a main control site gateway device, a transmitter, a receiver, and a battery system, and aims to achieve intelligent energy efficiency regulation and charging control.

[0163] First, the cloud server establishes a communication connection with the main control site gateway device via a wireless network. A battery status prediction model is deployed in the cloud to analyze historical battery operating data and external environmental factors in real time. Based on this model, the cloud server generates control strategies, including energy scheduling strategies and battery charging optimization strategies, and sends these strategy instructions to the main control site gateway device.

[0164] After receiving the control policy, the main control station gateway device controls the working status of the transmitter and receiver through serial communication, and simultaneously completes communication protocol conversion (such as CAN, RS485, MQTT, etc.). The transmitter interacts with the receiver via 2.4G wireless communication and transmits electrical energy to the receiver via magnetic resonance coupling.

[0165] During the charging process, the transmitter collects coil parameters between itself and the receiver, including coil voltage and self-inductance. Combined with the control algorithm, a frequency compensation strategy is generated, which dynamically adjusts the resonant frequency to compensate for coupling changes caused by device position offset, thereby maintaining efficient energy transmission.

[0166] The receiver collects real-time operating parameters of the battery (such as voltage, current, and temperature) and feeds them back to the transmitter via 2.4G wireless communication. The transmitter combines the feedback data with the charging optimization strategies provided by the cloud server to dynamically adjust the charging current of the battery system, achieving state-aware adaptive charging control.

[0167] This method achieves comprehensive optimization of wireless charging efficiency, charging safety, and energy utilization efficiency through cloud-edge collaboration and bidirectional communication. Compared with existing technologies, the wireless charging method supporting frequency compensation and strategy optimization provided by this invention has improvements in the following indicators. Specific indicators and comparative data are shown in Table 3 below.

[0168]

[0169] Table 3

[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0171] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0172] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0173] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0174] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0175] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0176] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the various method embodiments described above.

[0177] The embodiments described above are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A wireless charging system supporting frequency compensation and policy optimization, characterized in that, The cloud server, the master site gateway device, the transmitting end, the receiving end and the battery system are included; The cloud server interacts with the master site gateway device through a wireless network; The master site gateway device interacts with the transmitting end through serial communication; The transmitting end interacts with the receiving end information through 2.4G wireless communication, and transmits electric energy to the receiving end through coil coupling; The receiving end connects and interacts with the battery system through serial communication; The cloud server is configured to deploy a battery state prediction model and generate a control strategy, and distribute the control strategy to the master site gateway device, wherein the control strategy includes an energy scheduling strategy and a battery charging optimization strategy; The master site gateway device is configured to control the working states of the transmitting end and the receiving end based on the control strategy, and perform communication protocol conversion; The transmitting end is configured to collect coil parameters between the transmitting end and the receiving end, generate a frequency compensation strategy based on the coil parameters, dynamically adjust the resonant frequency to compensate for coil deviation according to the frequency compensation strategy, and generate an alternating magnetic field at the same time; The receiving end is configured to receive the alternating magnetic field through magnetic resonance coupling, and collect battery operation parameters, and feed back the battery operation parameters to the transmitting end; The transmitting end is further configured to dynamically adjust the charging current of the battery system after interacting with the receiving end according to the battery operation parameters and the battery charging optimization strategy of the cloud server.

2. The wireless charging system of claim 1, wherein, The transmitting end includes a transmitting end control module and a transmitting end collection module, the receiving end includes a receiving end control module and a receiving end collection module, the coil on the side of the transmitting end is a transmitting end coil, and the coil on the side of the receiving end is a receiving end coil; The transmitting end collection module is physically connected to the transmitting end control module; The transmitting end collection module collects coil parameters between the transmitting end and the receiving end, and transmits the coil parameters to the transmitting end control module, wherein the coil parameters include coil voltage sampling values and coil self-inductance values; The transmitting end control module generates a frequency compensation strategy based on the coil parameters, dynamically adjusts the resonant frequency to compensate for coil deviation according to the frequency compensation strategy, and outputs a control signal based on the resonant frequency; The transmitting end coil generates an alternating magnetic field in response to the control signal; The receiving end coil generates an induced current in response to the alternating magnetic field; The receiving end collection module collects battery operation parameters, converts the battery operation parameters into corresponding data through the receiving end control module, and transmits the data to the transmitting end control module through 2.4G wireless communication, wherein the battery operation parameters include battery voltage, charging current, temperature and coil voltage; The transmitting end control module dynamically adjusts the charging current of the battery system after interacting with the receiving end according to the battery operation parameters and the battery charging optimization strategy of the cloud server.

3. The wireless charging system of claim 1, wherein, The cloud server generates an energy scheduling strategy through the battery state prediction model based on the real-time cost and carbon emission factor of solar energy, valley electricity and peak electricity, wherein the energy scheduling strategy includes: A priority order of predefined energy types; selecting solar power supply when the light intensity is greater than a preset threshold value; increasing the charging power to a gain ratio of the rated power during low-peak power supply; decreasing the charging power to a decay ratio of the rated power during high-peak power supply, and postponing non-urgent charging tasks.

4. The wireless charging system of claim 1, wherein, The battery state prediction model is a time series neural network model, and generating the battery charging optimization strategy comprises: inputting battery diagnosis parameters into the battery state prediction model to generate battery charging parameters, the battery diagnosis parameters including environmental temperature, battery temperature, voltage, current and their time change rates, and the battery charging parameters including battery type identification, rated capacity value and health state value; when the health state value is lower than a preset threshold value, limiting the charging current to be not more than a preset proportion of the current value corresponding to the rated capacity.

5. The wireless charging system of claim 4, wherein, The charging current is optimized according to a time decay function, and the decay rate is positively correlated with the health state value.

6. The wireless charging system of claim 2, wherein, The frequency compensation strategy comprises: determining the mutual inductance coefficient between the transmitting end and the receiving end based on the coil voltage sampling value of the transmitting end and the coil voltage sampling value of the receiving end; calculating the resonance frequency compensation value based on the mutual inductance coefficient and the coil self-inductance value of the transmitting end and the coil self-inductance value of the receiving end.

7. The wireless charging system of claim 2, wherein, The transmitting end control module transmits the coil parameters and the battery operation parameters to the master site gateway device through the serial communication; The master site gateway device calculates edge control decisions according to the received coil parameters and battery operation parameters, and issues the edge control decisions to the transmitting end control module.

8. The wireless charging system of claim 1, wherein, The master site gateway device performs communication protocol conversion, including conversion between CAN, RS485, TCP and MQTT protocols.

9. The wireless charging system of claim 1, wherein, When the battery system includes a BMS, the receiving end obtains the battery operation parameters by reading the data inside the battery system. 10.A wireless charging method supporting frequency compensation and policy optimization, the method comprising: The method is applied to a wireless charging system supporting frequency compensation and strategy optimization, the system comprising a cloud server, a master site gateway device, a transmitting end, a receiving end and a battery system, and the method comprises: establishing a communication connection between the cloud server and the master site gateway device through a wireless network; deploying a battery state prediction model in the cloud server to generate control strategies, and issuing the control strategies to the master site gateway device, the control strategies including energy scheduling strategies and battery charging optimization strategies; The master site gateway device controls the working states of the transmitting end and the receiving end based on the control strategies, and performs communication protocol conversion; The transmitting end exchanges information with the receiving end through 2.4G wireless communication, and transmits electric energy to the receiving end through coil magnetic resonance coupling; The transmitting end collects coil parameters between the transmitting end and the receiving end, generates a frequency compensation strategy based on the coil parameters, dynamically adjusts the resonance frequency to compensate for coil offset according to the frequency compensation strategy, and generates an alternating magnetic field; The receiving end receives the alternating magnetic field and collects battery operation parameters, and feeds back the battery operation parameters to the transmitting end through the 2.4G wireless communication; The transmitting end performs control interaction with the receiving end according to the received battery operation parameters and the battery charging optimization strategy generated by the cloud server, and dynamically adjusts the charging current of the battery system.

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