Wireless charging system and method based on multimode communication

Abstract

The invention relates to the technical field of wireless communication and power transmission, and discloses a wireless charging system and method based on multimode communication, and the system comprises an energy transmitting unit, an energy receiving unit, a multimode communication coordination unit and a dynamic power control unit. The multimode communication coordination unit dynamically selects an optimal communication link through a parallel link pre-establishment and seamless switching mechanism; and the dynamic power control unit accurately regulates and controls the transmitting power through a self-adaptive power mapping algorithm based on the real-time battery state and the coupling coefficient. According to the invention, high efficiency, high stability and high reliability in the wireless charging process are realized, and the service life of the battery is effectively prolonged.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication and power transmission technology, specifically relating to a wireless charging system and method based on multimode communication. Background Technology

[0002] Wireless power transfer technology, as an important branch of modern electronic device power supply systems, aims to achieve efficient, contactless power transfer from the transmitter to the receiver. Wireless charging systems based on electromagnetic induction or magnetic resonance principles have been widely applied in consumer electronics, smart homes, and IoT terminal devices, providing crucial technological support for overcoming the physical constraints of traditional wired charging.

[0003] Multimode communication-based wireless charging systems integrate multiple communication protocols such as near-field communication, Bluetooth, or Wi-Fi to achieve dynamic data interaction and power regulation between the transmitter and receiver during charging. The fundamental principle of such systems is to dynamically adjust energy transfer parameters based on real-time communication feedback between devices to optimize charging efficiency and ensure operational safety.

[0004] Existing technologies typically employ fixed communication mode switching logic or protocol selection mechanisms based on simple threshold judgments. However, in real-world mobile device charging scenarios, even minor changes in device position or relative displacement can easily trigger frequent switching between near-field communication (NFC) and protocols such as Bluetooth. Existing systems exhibit significant response latency and protocol renegotiation overhead during communication mode switching, preventing the system from accurately controlling power in real time in response to changes in device status.

[0005] This switching delay directly causes drastic fluctuations in charging power and unstable output, resulting not only in a sharp decline in overall charging efficiency, but also in the irreversible impact of frequent power jumps on the chemical system of the receiving battery, accelerating battery aging and potentially shortening its lifespan. Therefore, how to achieve seamless and rapid switching between multi-mode communication to maintain power stability and high efficiency in the wireless charging process has become a core technical challenge that urgently needs to be solved in this field. Summary of the Invention

[0006] The present invention aims to provide a wireless charging system and method based on multi-mode communication to solve the problems of drastic fluctuations in charging power, decreased efficiency and damaged battery life caused by communication mode switching delay in the prior art.

[0007] The technical solution of this invention is to construct a wireless charging system based on multimode communication. This system includes an energy transmitting unit, an energy receiving unit, a multimode communication coordination unit, and a dynamic power control unit. The energy transmitting unit comprises a high-frequency inverter circuit and a transmitting coil array for generating an alternating electromagnetic field. The energy receiving unit is integrated inside the receiving device and includes a receiving coil, a rectifier filter circuit, and a load management module for capturing electromagnetic energy and converting it into DC power to charge the battery. The multimode communication coordination unit consists of a dual-core communication processor deployed at both the transmitting and receiving ends, which embeds a near-field communication module, a Bluetooth Low Energy module, and a Wi-Fi Direct module. The dynamic power control unit is integrated within the transmitting end's main controller, receiving real-time status data from the multimode communication coordination unit and outputting precise power adjustment commands to the high-frequency inverter circuit.

[0008] The workflow of the multi-mode communication coordination unit is as follows: During system initialization, the near-field communication module establishes a connection first as the default link. The communication processor continuously monitors the signal strength and bit error rate of the near-field communication link. When the signal strength is lower than a first preset threshold or the bit error rate is higher than a second preset threshold, the communication processor does not immediately perform a communication mode switch, but instead initiates a parallel link pre-establishment mechanism. In the parallel link pre-establishment mechanism, the communication processor simultaneously activates the scanning and handshake processes of the Bluetooth Low Energy module and the Wi-Fi Direct module, while maintaining the existing connection of the near-field communication link. The communication processor performs millisecond-level polling and quantitative evaluation of the real-time communication quality indicators of the three parallel links. The communication quality indicators include signal reception strength, link latency, data throughput, and channel interference level. The evaluation algorithm adopts a weighted scoring model, assigning specific weights to each indicator and calculating the comprehensive quality score of each link. The communication processor selects the link with the highest comprehensive quality score as the main communication link and performs a seamless switching operation. During the seamless handover process, the communication processor first synchronizes the current session state and the power control parameters to be adjusted to the target link through the original link, and then completes the link switching within a 2-millisecond time window to ensure that the data stream received by the dynamic power control unit is uninterrupted.

[0009] The dynamic power control unit performs power regulation based on real-time data from the received autonomous communication link. This real-time data includes the current voltage, current, and temperature of the receiving battery, as well as an estimated value of the coupling coefficient of the receiving coil. The dynamic power control unit embeds an adaptive power mapping algorithm, which dynamically calculates the optimal transmit power value based on the real-time data. The adaptive power mapping algorithm first constructs a multi-dimensional state space with battery voltage, current, temperature, and coupling coefficient as input variables. Then, through table lookup interpolation or a pre-defined neural network model, it maps the corresponding optimal transmit power value into this state space. The dynamic power control unit converts the calculated optimal transmit power value into a pulse-width modulation signal, directly driving the power switching devices in the high-frequency inverter circuit, thereby precisely controlling the alternating magnetic field strength of the transmitting coil.

[0010] In a preferred embodiment of the present invention, the multi-mode communication coordination unit also integrates a link quality prediction module. Based on historical communication data, the link quality prediction module uses time series analysis to predict the quality change trend of each communication link within the next 500 milliseconds. If the prediction shows that the overall quality score of the current primary communication link will drop below a third preset threshold within the next 200 milliseconds, while the predicted score of another backup link will significantly exceed that of the current primary link, the communication processor will initiate the parallel link pre-establishment and seamless switching process for the backup link in advance, achieving predictive and smooth switching of communication modes.

[0011] Furthermore, the energy transmission unit's transmitting coil array adopts a multi-coil topology, including one main transmitting coil and four auxiliary transmitting coils. The dynamic power control unit can also selectively activate specific auxiliary transmitting coils by controlling the relay array based on the receiving device location information provided by the multi-mode communication coordination unit, thereby achieving dynamic focusing on the electromagnetic energy transmission area and further improving coupling efficiency and charging stability.

[0012] In another preferred embodiment of the present invention, the adaptive power mapping algorithm of the dynamic power control unit uses a multilayer perceptron (MLP) trained with massive amounts of charging scenario data as the neural network model. The input layer of this MLP contains four neurons corresponding to four real-time input variables, the hidden layer contains 128 neurons and uses a modified linear unit (MRU) as the activation function, and the output layer consists of one neuron outputting the optimal transmit power value. The training process uses mean squared error as the loss function and iteratively optimizes the network weights and bias parameters using gradient descent.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: By introducing a multi-mode communication coordination unit and its parallel link pre-establishment mechanism and seamless switching technology, the delay and data interruption during communication mode switching are completely eliminated, ensuring the continuity and real-time performance of power control commands. The dynamic power control unit, combined with an adaptive power mapping algorithm, can accurately and quickly calculate the optimal transmit power based on multi-dimensional state variables, effectively suppressing fluctuations in charging power.

[0014] The introduction of the link quality prediction module enables predictive switching of communication modes, further enhancing the system's robustness in dynamic environments. The multi-coil topology and dynamic focusing mechanism of the transmitting coil array significantly improve the coupling efficiency of energy transfer. Through deep collaboration between communication and power control, the overall system achieves high efficiency, high stability, and high reliability in the wireless charging process, fundamentally avoiding damage to battery life caused by power step changes. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall technical solution architecture of the wireless charging system based on multi-mode communication proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the multi-mode communication coordination unit in this invention; Figure 3 This is a schematic diagram of the core principle framework of the adaptive power mapping algorithm of the dynamic power control unit in this invention; Figure 4 This is a logical flowchart of the parallel link pre-establishment and seamless switching mechanism in this invention; Figure 5 This is a schematic diagram illustrating the working principle of the multi-coil topology and dynamic focusing mechanism of the transmitting coil array in this invention. Detailed Implementation

[0016] Please refer to the attached document. Figures 1 to 5 This embodiment details the specific technical implementation of a wireless charging system based on multimode communication. The system aims to achieve efficient and stable wireless energy transmission through seamless multimode communication coordination and precise dynamic power control. The core components of the system include an energy transmitting unit, an energy receiving unit, a multimode communication coordination unit, and a dynamic power control unit. These units are coupled together through precise hardware layout and software logic to form a complete closed-loop control system.

[0017] The energy transmission unit is the physical basis for the system's energy transmission. This unit comprises a high-frequency inverter circuit and a transmitting coil array. The high-frequency inverter circuit consists of power switching devices with a full-bridge or half-bridge topology, such as insulated-gate bipolar transistors (IGBTs) or metal-oxide-semiconductor field-effect transistors (MOSFETs). These power switching devices receive pulse-width modulated signals from the dynamic power control unit and convert the DC input power into high-frequency alternating current.

[0018] The typical frequency range of high-frequency alternating current is 85 kHz to 205 kHz, with specific values ​​set according to system design standards and electromagnetic compatibility requirements. The transmitting coil array is wound with Litz wire, and its inductance, together with the resonant capacitor, forms a series or parallel resonant network to improve energy transmission efficiency. The transmitting coil array is directly connected to the output of the high-frequency inverter circuit, generating an alternating electromagnetic field under the drive of the high-frequency alternating current.

[0019] The energy receiving unit is integrated within the powered device, such as a smartphone, tablet, or wearable device. This unit is responsible for capturing the alternating electromagnetic field generated by the energy transmitting unit and converting it into direct current (DC) power that can be used by a battery. The energy receiving unit includes a receiving coil, a rectifier and filter circuit, and a load management module. The receiving coil is also wound with Litz wire, and its resonant frequency is matched to the resonant frequency of the transmitting coil array to achieve maximum energy coupling. The AC voltage induced in the receiving coil is fed to the rectifier and filter circuit.

[0020] The rectifier and filter circuit typically consists of a full-bridge rectifier and a π-type filter network, converting high-frequency alternating current (AC) into smooth direct current (DC). The load management module (LLM) is responsible for secondary regulation and distribution of the rectified DC power. The LLM integrates a voltage conversion circuit, a current sampling resistor, a temperature sensor, and a battery management chip. The voltage conversion circuit adjusts the output voltage and current curves according to the battery's charging characteristics. The current sampling resistor monitors the current value in the charging circuit in real time and converts it into a voltage signal for the analog-to-digital converter (ADC) to read.

[0021] A temperature sensor is mounted close to the battery surface to accurately detect temperature changes during charging. The battery management chip integrates voltage, current, and temperature data and performs charging status determination, charging stage switching, and safety protection logic.

[0022] The multi-mode communication coordination unit is the core of the system for achieving intelligent communication and seamless handover. Please refer to the appendix. Figure 2 This unit consists of dual-core communication processors deployed at both the energy transmitter and receiver ends. The dual-core communication processors employ a heterogeneous computing architecture, with one core dedicated to processing and scheduling the communication protocol stack, and the other core responsible for real-time analysis and decision-making regarding communication quality data. The multi-mode communication coordination unit embeds three independent communication modules: a near-field communication module, a Bluetooth Low Energy module, and a Wi-Fi Direct module. The near-field communication module operates in the 13.56 MHz frequency band, with an extremely short communication distance, typically within a few centimeters, and features rapid connection establishment and extremely low power consumption.

[0023] The Bluetooth Low Energy module operates in the 2.4 GHz industrial, scientific, and medical band, supporting both point-to-point and broadcast communication modes, providing a balanced power consumption and data transfer rate at medium distances. The Wi-Fi Direct module also operates in the 2.4 GHz or 5 GHz band. It does not rely on traditional wireless access points but establishes a direct wireless link between the power transmitter and receiver, providing the highest data transfer rate and relatively long communication distance.

[0024] The multi-mode communication coordination unit operates according to a sophisticated state machine logic. During system power-on initialization, the near-field communication module is activated as the default preferred communication link. The dual-core communication processor controls the near-field communication module to execute the device discovery and handshake process, quickly establishing a data connection upon detecting a valid powered device. Once the connection is established, the communication processor enters a continuous communication link monitoring state. The monitoring process occurs in millisecond cycles, and for the currently active near-field communication link, the communication processor collects its signal strength indicator value and bit error rate in real time. The signal strength indicator value is obtained through the receiving signal strength indicator circuit, and its value directly reflects the electromagnetic field strength and communication stability of the link. The bit error rate is calculated after verifying the transmitted data packets using cyclic redundancy check or forward error correction technology.

[0025] When the signal strength indicator value of the near-field communication link is detected to be lower than a first preset threshold, or the bit error rate is detected to be higher than a second preset threshold, the communication processor does not immediately execute a forced switch to the communication mode. Instead, it initiates a mechanism called parallel link pre-establishment. Please refer to the appendix. Figure 4 In the parallel link pre-establishment mechanism, the communication processor maintains the existing near-field communication link connection and data exchange while simultaneously activating the Bluetooth Low Energy module and the Wi-Fi Direct module. The Bluetooth Low Energy module begins device scanning and connection requests. The Wi-Fi Direct module initiates service discovery protocol and port listening, preparing to establish a direct wireless connection. At this point, all three communication links of the system—near-field communication, Bluetooth Low Energy, and Wi-Fi Direct—are either active or in standby mode.

[0026] The communication processor then performs millisecond-level polling and quantification evaluation of the communication quality of these three parallel links. The communication quality metrics collected for evaluation include signal strength, link latency, data throughput, and channel interference level. Signal strength is represented by a signal strength indicator value for near-field communication links, and by a received signal strength indicator for Bluetooth Low Energy and Wi-Fi Direct. Link latency is determined by sending and receiving timestamp request and response packets and calculating their round-trip time. Data throughput is measured by the amount of data successfully transmitted within a fixed time window. Channel interference level is evaluated by monitoring background noise power or carrier sensing results in a specific frequency band.

[0027] The communication processor employs a weighted scoring model to comprehensively quantify the communication quality of each link. This model assigns a specific weight coefficient to each communication quality indicator. For example, signal reception strength might be assigned a weight of 0.3, link delay a weight of 0.25, data throughput a weight of 0.25, and channel interference level a weight of 0.2. Based on real-time collected indicator data and a preset scoring scale, the communication processor calculates the score for each link on each indicator, multiplies it by the corresponding weight, and sums the results to obtain the overall quality score for that link. The calculation process can be expressed mathematically as follows: ; in, The overall quality score representing the link. , , , These represent the weighting coefficients for signal reception strength, link delay, data throughput, and channel interference level, respectively. , , , This represents the normalized score of the corresponding indicator. The communication processor compares the overall quality scores of the three links and selects the link with the highest score as the new main communication link.

[0028] After establishing the primary communication link, the communication processor performs a seamless handover operation. This seamless handover process ensures that the data stream received by the dynamic power control unit is uninterrupted. The communication processor first synchronizes the current communication session state, including the sequence number, encryption context, and power control parameters to be sent, to the target link (e.g., Bluetooth Low Energy) via the existing near-field communication link. The synchronization process uses data packet transmission with an acknowledgment mechanism to ensure that all critical status information is successfully transmitted. Subsequently, within a very short 2-millisecond time window, the communication processor completes the data stream switch from the original link to the target link. This handover involves interrupt redirection in the underlying driver and context switching in the protocol stack. Throughout this process, the dynamic power control unit only perceives a seamless change in the data source, without any data packet loss or timing disruptions.

[0029] The dynamic power control unit (Dynamic Power Control Unit) is the decision-making center for the system to achieve precise power output. This unit is typically integrated within the main controller of the energy transmitter, running as a high-priority software task or a dedicated coprocessor. The Dynamic Power Control Unit continuously receives real-time data from the main communication link established by the multi-mode communication coordination unit. This real-time data includes key status information from the energy receiver, specifically the current voltage of the receiver battery, the battery charging current, the battery temperature, and the coupling coefficient between the receiving and transmitting coils estimated using the principle of electromagnetic induction.

[0030] The core of the dynamic power control unit is its embedded adaptive power mapping algorithm. Please refer to the appendix. Figure 3 The algorithm dynamically calculates the optimal transmit power value based on received multidimensional real-time data. The adaptive power mapping algorithm first logically constructs a multidimensional state space. The dimensions of this state space correspond one-to-one with the input variables: battery voltage, battery current, battery temperature, and coupling coefficient. Each dimension is divided into several discrete intervals or grid points.

[0031] The algorithm uses a lookup table interpolation method to find the optimal transmit power value in the multidimensional state space. A large lookup table is pre-stored in the system before shipment or during initialization. This lookup table defines the experimentally verified optimal transmit power value for a large number of typical operating conditions, i.e., specific combinations of voltage, current, temperature, and coupling coefficients. When the dynamic power control unit receives a set of real-time data, it first determines the grid region in the state space where this data resides. Then, the algorithm selects multiple known operating points around this region and uses linear interpolation, bilinear interpolation, or higher-order interpolation methods to calculate the optimal transmit power value under the current operating condition based on the relative positions of the real-time data and these known points. The interpolation process ensures the smoothness and continuity of the power output, avoiding abrupt changes.

[0032] The dynamic power control unit converts the calculated optimal transmit power value into specific control commands. This conversion process typically involves mapping the power value to a pulse-width modulation (PWM) signal with a specific duty cycle and frequency. The PWM signal is generated by a hardware timer in the main controller, and its duty cycle directly determines the on-time of the power switching devices in the high-frequency inverter circuit, thereby controlling the average power flowing to the transmit coil array. The frequency of the PWM signal is consistent with the system's operating resonant frequency. The generated PWM signal is directly output to the driver chip of the high-frequency inverter circuit, which then precisely controls the switching state of the insulated-gate bipolar transistor (IGBT) or metal-oxide-semiconductor (MOSFET), ultimately achieving precise control over the alternating magnetic field strength generated by the transmit coil.

[0033] As an advanced feature of this embodiment, the multi-mode communication coordination unit also integrates a link quality prediction module. This module runs as a software algorithm within the decision core of the dual-core communication processor. The link quality prediction module continuously collects and stores historical communication quality data for each communication link, including signal reception strength sequences, link delay sequences, data throughput sequences, and channel interference level sequences from the past few seconds. The module uses time series analysis methods, such as autoregressive integral moving average models or exponential smoothing, to analyze this historical data and predict the communication quality change trend of each communication link within a 500-millisecond time window. The prediction results include predicted values ​​for future signal reception strength and link delay.

[0034] The link quality prediction module recalculates the overall quality score of each link at future time points based on the predicted data. The system has a preset third threshold, typically slightly higher than the second preset threshold that triggers the pre-establishment of parallel links. If predictive analysis indicates that the overall quality score of the currently used primary communication link will drop below this third preset threshold within the next 200 milliseconds, and simultaneously another backup link exists whose predicted overall quality score not only exceeds the third preset threshold but also significantly surpasses the predicted score of the current primary link, then the communication processor will no longer passively wait for quality deterioration. Instead, it will proactively initiate the pre-establishment and seamless switching process for the high-scoring backup link. This predictive, smooth switching mechanism further minimizes the possibility of communication interruptions and greatly enhances the system's adaptability in complex electromagnetic environments.

[0035] For further details, please refer to the appendix. Figure 5 The energy emission unit's emission coil array employs a multi-coil topology to achieve dynamic energy focusing. This array comprises one main emission coil and four auxiliary emission coils. The main emission coil, typically located at the center of the array, is larger and responsible for basic energy coverage. The four auxiliary emission coils are arranged around the main emission coil, for example, directly above, below, to the left, and to the right. Each auxiliary emission coil can be independently energized or de-energized.

[0036] The dynamic power control unit performs dynamic focusing based on information provided by the multimode communication coordination unit. The multimode communication coordination unit estimates the relative position of the receiving device on the charging platform by analyzing the signal reflected from the receiver or using data from sensors built into the receiver. This position information is transmitted to the dynamic power control unit in real time. The dynamic power control unit pre-stores optimal auxiliary coil activation combinations for different location areas. For example, when the receiving device is detected to be located in the upper right area of ​​the charging platform, the dynamic power control unit generates a control signal to selectively activate the auxiliary transmitting coil located in the upper right area via a relay array or semiconductor switch array, while possibly maintaining the activation of the main transmitting coil and the right auxiliary coil. By precisely controlling the excitation phase and amplitude of multiple coils, the system can construct a superimposed electromagnetic field, dynamically focusing the energy transfer hotspot onto the specific area where the receiving device is located. This dynamic focusing mechanism effectively reduces spatial energy loss and significantly improves the coupling efficiency between the transmitting and receiving coils, thus maintaining stable high-power charging even when the device's position shifts slightly.

[0037] This embodiment provides another specific implementation of the above-mentioned wireless charging system based on multimode communication. The core difference lies in the specific implementation technology of the adaptive power mapping algorithm in the dynamic power control unit.

[0038] In this embodiment, the adaptive power mapping algorithm no longer relies on a preset lookup table and interpolation method. Instead, it employs an artificial neural network model trained on massive amounts of charging scenario data to achieve a nonlinear mapping from multidimensional states to optimal transmit power. This neural network model uses a multilayer perceptron architecture. A multilayer perceptron is a typical feedforward neural network, comprising an input layer, at least one hidden layer, and an output layer.

[0039] In this implementation, the input layer of the multilayer perceptron is designed to contain four neurons, which correspond to the four real-time input variables received by the dynamic power control unit: battery voltage, battery current, battery temperature, and coupling coefficient. The input data needs to be standardized before being fed into the network, for example, using Z-score standardization to make its mean 0 and standard deviation 1, in order to accelerate the convergence of network training and improve model stability.

[0040] The hidden layer is set to one layer, containing 128 neurons. This number represents a balance between model expressive power and computational complexity. Each neuron in the hidden layer receives a weighted input from all neurons in the input layer, adds a bias term, and then passes it through a non-linear activation function to produce the output. In this embodiment, the activation function used in the hidden layer is the rectified linear unit (RCU). The mathematical expression for the rectified linear unit is: Compared to traditional Sigmoid or Tanh functions, it effectively alleviates the gradient vanishing problem and makes model computation more efficient. Its calculation process can be represented by the following mathematical form: ; in, Representing the One input variable, Represents the input layer The first neuron is connected to the hidden layer. The connection weights of each neuron Represents the hidden layer Bias of each neuron Represents the hidden layer The output of each neuron.

[0041] The output layer contains one neuron, responsible for outputting the calculated optimal transmit power value. Since the transmit power value is a positive real number, the output layer typically does not have an activation function, or uses a linear activation function, i.e., it directly outputs the weighted sum.

[0042] The training of this multilayer perceptron model is an offline process. The massive amount of charging scenario data required for training comes from a large amount of measured data in a laboratory environment, covering different types of receiving devices, different battery states, different placement locations, and different ambient temperatures. Each training sample contains a set of input vectors, namely a specific combination of battery voltage, current, temperature, and coupling coefficient, and a label value, namely the optimal transmit power value determined by expert rules or experimental optimization under that operating condition.

[0043] The training process uses mean squared error (MSE) as the loss function to measure the difference between the optimal transmit power value predicted by the neural network and the true label value. The MSE loss function motivates the model to learn to minimize the prediction error. Model weight optimization employs gradient descent and its variants, such as the Adam optimizer. The optimization process uses backpropagation to calculate the gradient of the loss function with respect to each weight and bias parameter in the network, and then updates these parameters in the opposite direction of the gradient. This process is iterated multiple times until the model's prediction accuracy on the validation set reaches a preset requirement, and the loss function converges to a stable low value.

[0044] The trained neural network model and its final weights and bias parameters are permanently stored in the non-volatile memory of the dynamic power control unit, such as flash memory. During system operation, the dynamic power control unit performs a forward propagation calculation of the neural network each time it receives a new set of real-time data. It feeds the standardized input data into the network, performs weighted summation and corrected linear unit activation in the hidden layers, and then performs linear weighting in the output layer, ultimately directly outputting a precise optimal transmit power value. This neural network-based adaptive power mapping algorithm is particularly adept at handling complex, nonlinear system relationships, and can discover fine-grained mapping rules that are difficult to design manually. Therefore, under certain edge conditions, it may provide more accurate and robust power control than lookup table interpolation methods.

[0045] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wireless charging system based on multi-mode communication, characterized by, Comprise: An energy transmitting unit, including a high-frequency inverter circuit and an array of transmitting coils, for generating an alternating electromagnetic field; An energy receiving unit, integrated inside a powered device, including a receiving coil, a rectification filter circuit and a load management module, for capturing electromagnetic energy and converting it into direct current to charge a battery; A multi-mode communication coordination unit, composed of dual-core communication processors deployed at the transmitting end and the receiving end, which embeds a near-field communication module, a Bluetooth low energy module and a Wi-Fi direct module; A dynamic power control unit, integrated into the main controller of the transmitting end, for receiving real-time state data from the multi-mode communication coordination unit and outputting power adjustment instructions to the high-frequency inverter circuit.

2. The wireless charging system based on multi-mode communication according to claim 1, wherein, The workflow of the multi-mode communication coordination unit includes: In the system initialization phase, the near-field communication module is used as the default link to establish a connection first; the communication processor continuously monitors the signal strength and error rate of the near-field communication link, and when the signal strength is lower than the first preset threshold or the error rate is higher than the second preset threshold, the parallel link pre-establishment mechanism is started; In the parallel link pre-establishment mechanism, the communication processor synchronously activates the scanning and handshake processes of the Bluetooth low energy module and the Wi-Fi direct module, while maintaining the existing connection of the near-field communication link; The communication processor performs millisecond-level polling and quantitative evaluation of the real-time communication quality indicators of the three parallel links, including signal reception strength, link delay, data throughput and channel interference level; The quantitative evaluation uses a weighted scoring model to assign specific weights to each indicator and calculate the comprehensive quality score of each link; the communication processor selects the link with the highest comprehensive quality score as the main communication link and performs seamless switching operation; During the seamless switching process, the communication processor first synchronizes the current session state and the power control parameters to be adjusted to the target link through the original link, and then completes the link switching within a 2-millisecond time window.

3. The multi-mode communication based wireless charging system of claim 1, wherein, The dynamic power control unit performs power regulation based on real-time data received from the main communication link, which includes the current voltage, current, temperature of the receiving end battery and the coupling coefficient estimate of the receiving coil; The dynamic power control unit embeds an adaptive power mapping algorithm that dynamically calculates the optimal transmission power value based on real-time data; the adaptive power mapping algorithm first constructs a multi-dimensional state space with battery voltage, current, temperature and coupling coefficient as input variables, and maps the corresponding optimal transmission power value in this state space through table lookup interpolation method or pre-set neural network model; The dynamic power control unit converts the calculated optimal transmission power value into a pulse width modulation signal to directly drive the power switch devices in the high-frequency inverter circuit.

4. The multi-mode communication based wireless charging system of claim 1, wherein, The multi-mode communication coordination unit also integrates a link quality prediction module; The link quality prediction module predicts the quality change trend of each communication link within the next 500 milliseconds based on historical communication data using time series analysis method; If the prediction shows that the integrated quality score of the current primary communication link will drop below the third preset threshold within the next 200 milliseconds, and the predicted score of another standby link will significantly exceed that of the current primary link, the communication processor will start the parallel link pre-establishment and seamless handover process for the standby link in advance.

5. The multi-mode communication based wireless charging system of claim 1, wherein, The transmitting coil array of the energy transmitting unit adopts a multi-coil topology, including one main transmitting coil and four auxiliary transmitting coils; The dynamic power control unit selectively activates a specific auxiliary transmitting coil by controlling the relay array to achieve dynamic focusing of the electromagnetic energy transmission area according to the receiving device position information provided by the multi-modal communication coordination unit.

6. The multi-mode communication based wireless charging system of claim 1, wherein, In the adaptive power mapping algorithm of the dynamic power control unit, the neural network model is a multilayer perceptron trained by massive charging scene data. The input layer of the multilayer perceptron includes four neurons corresponding to four real-time input variables, the hidden layer includes 128 neurons and uses a rectified linear unit as the activation function, and the output layer is one neuron outputting the optimal transmitting power value.

7. The multi-mode communication based wireless charging system of claim 6, wherein, The training process of the multilayer perceptron uses mean square error as the loss function and iteratively optimizes the network weight and bias parameters by gradient descent method.

8. The multi-mode communication based wireless charging system of claim 1, wherein, The dual-core communication processor in the multi-modal communication coordination unit adopts a heterogeneous computing architecture, one core of which is dedicated to processing and scheduling of the communication protocol stack, and the other core is responsible for real-time analysis and decision-making of communication quality data.

9. The multi-mode communication based wireless charging system of claim 1, wherein, The load management module of the energy receiving unit is internally integrated with a voltage conversion circuit, a current sampling resistor, a temperature sensor, and a battery management chip; the voltage conversion circuit adjusts the output voltage and current curve according to the charging characteristics of the battery; The current sampling resistor monitors the current value in the charging loop in real time; the temperature sensor is installed close to the surface of the battery; and the battery management chip is responsible for integrating voltage, current, and temperature data. 10.A wireless charging method based on multi-mode communication, characterized in that, Wireless charging is performed by a multi-modal communication-based wireless charging system according to any one of claims 1 to 9.

Images