Sodium-ion battery energy intelligent management method and system

Abstract

The invention relates to a sodium ion battery energy intelligent management method and system, and the method comprises the steps: providing a sodium ion battery cell with a layered oxide as a positive electrode and a hard carbon material as a negative electrode, and integrating a multi-band radio frequency antenna array and a radio frequency-DC conversion circuit connected with the multi-band radio frequency antenna array in a battery system, the radio frequency energy capturing module is used for capturing radio frequency signals in the environment and converting the radio frequency signals into direct-current electric energy; a radio frequency communication signal in an environment is received through a radio frequency energy capture module, and after matching, rectification and voltage stabilization, a direct-current supplementary current is output and is converged into an auxiliary charging channel of a battery management system; voltage, temperature and internal resistance parameters of the sodium ion battery cell are collected in real time through the battery management system, and signal intensity information from the radio frequency energy capture module and load demand information identified through the equipment interface are collected at the same time.

Description

A method and system for intelligent energy management of sodium-ion batteries Technical Field

[0001] This application relates to the field of digital data technology, specifically to a method and system for intelligent energy management of sodium-ion batteries. Background Technology

[0002] Sodium-ion batteries are considered a potential alternative to lithium-ion batteries due to their abundant raw materials and low cost. However, existing technologies still have bottlenecks such as relatively low energy density, need to improve cycle life and high and low temperature performance, and lack of intelligent collaboration with the environment.

[0003] Especially when facing ubiquitous smart terminal applications such as 6G communication and the Internet of Things, the devices have higher requirements for power supply stability, environmental adaptability and intelligent management. At present, most battery systems are passive energy storage units, and their energy sources are limited to wired charging or specific wireless charging transmitters, which cannot effectively capture and utilize the communication radio frequency energy that is widely present in the environment.

[0004] Meanwhile, traditional battery management systems (BMS) typically rely on fixed thresholds and simple algorithms for state estimation and protection, lacking accurate prediction of battery health status (SOH) and state of charge (SOC), as well as the ability to dynamically perceive and coordinate the ever-changing load demands and external energy supply. This results in insufficient overall energy efficiency and makes it difficult to meet the all-weather, self-sustaining energy needs of future smart terminals. Summary of the Invention

[0005] In order to solve the problems existing in the prior art, the purpose of this application is to provide a method and system for intelligent energy management of sodium-ion batteries.

[0006] The intelligent energy management method for sodium-ion batteries described in this application includes:

[0007] S101 provides a sodium-ion battery cell with layered oxide as the positive electrode and hard carbon material as the negative electrode, and integrates a multi-band radio frequency antenna array and its connected radio frequency-DC conversion circuit in the battery system to form a radio frequency energy harvesting module for capturing radio frequency signals in the environment and converting them into DC power.

[0008] S102. The radio frequency energy capture module receives radio frequency communication signals from the environment, and after matching, rectification and voltage regulation, outputs DC supplementary current, which is fed into the auxiliary charging channel of the battery management system.

[0009] S103. The voltage, temperature and internal resistance parameters of the sodium-ion battery cell are collected in real time through the battery management system. At the same time, the signal strength information from the radio frequency energy harvesting module and the load demand information identified through the device interface are also collected.

[0010] S104. Input the multi-source state information collected in S103 into the preset neural network prediction model, perform fusion analysis and time-series prediction, obtain the current accurate state of charge and health status assessment results of the battery cell, and obtain an adaptive energy management strategy based on the assessment results, signal strength and load requirements.

[0011] S105. Based on the energy management strategy generated in S104, perform at least one of the following control actions:

[0012] Dynamically adjust the antenna gain of the RF energy harvesting module to optimize energy harvesting efficiency, regulate the supply voltage allocated to the load by the system for dynamic voltage scaling, or control the application of a specific pattern of pulse supplementary current to the battery cell;

[0013] S106. The sodium-ion battery cell, radio frequency energy capture module, multi-source information acquisition unit, artificial intelligence decision-making unit and dynamic scheduling execution unit are connected and logically integrated through the battery management system to obtain a closed-loop intelligent management system that can automatically optimize energy capture, storage and supply according to internal state and external environment.

[0014] Preferably, in S101, the ambient radio frequency signal is collected by a multi-band antenna array, input to a Schottky diode rectifier network through a radio frequency-to-DC conversion circuit to obtain a pulsating DC voltage, and then filtered and regulated to obtain stable DC power. If the voltage reaches the charging threshold of the sodium ion cell, the charging switch is activated to connect the power to the hard carbon or layered oxide sodium ion cell for constant current charging.

[0015] Preferably, in S102, the environmental signal is collected and screened by the radio frequency energy capture module, and a matching signal stream is obtained by signal matching technology. The matching signal stream is converted into pulsating DC by the rectifier circuit and a stable DC current is output by voltage regulation. If the current meets the preset threshold, the DC current is connected to the auxiliary charging channel of the battery management system. The system monitors the current distribution in real time to determine the charging efficiency and dynamically adjusts the radio frequency acquisition parameters to continuously optimize the signal capture effect.

[0016] Preferably, in S103, the battery management system collects the voltage, temperature and internal resistance parameters of the sodium-ion battery cell in real time, calculates the current state of charge, and determines the discharge rate in combination with the load current. At the same time, the radio frequency energy capture module acquires the ambient signal strength, and combines the discharge rate and signal strength parameters to determine whether the conditions for supplementary current intervention are met. If the conditions are met, the DC supplementary current provided by the radio frequency energy capture module is connected to the auxiliary charging channel of the battery management system.

[0017] Preferably, in S104, the battery management system collects the voltage, temperature and internal resistance parameters of the sodium-ion battery cell in real time, and calculates its current state of charge accordingly. Combined with the load current parameters, the system determines the discharge rate of the battery cell. At the same time, the radio frequency energy capture module acquires the ambient signal strength. The system combines the discharge rate and signal strength to determine whether the conditions for supplementary current intervention are met. If the conditions are met, the DC supplementary current output by the radio frequency energy capture module is connected to the auxiliary charging channel of the battery management system.

[0018] Preferably, in S105, the received power is obtained through the radio frequency energy capture module, and the real-time difference is calculated in combination with the load consumption. Based on this, the access ratio of supplementary energy is determined, and the antenna direction and gain are dynamically adjusted according to the access ratio to optimize the output of DC supplementary current. The DC supplementary current is matched with the cell output current. If the matching state exceeds the preset range, an intermittent pulse supplementary current is applied to the sodium-ion cell, and the power supply voltage amplitude is adjusted accordingly to obtain stable output energy at the load end.

[0019] Preferably, in S106, the voltage and current of the sodium-ion battery cell and the real-time received power of the radio frequency module are obtained by the multi-source information acquisition unit to calculate the available supplementary power. The artificial intelligence decision-making unit determines the power access ratio based on the available supplementary power and environmental data, and the dynamic scheduling unit adjusts the antenna attitude and optimizes the DC current output based on the power access ratio. If the difference between the DC current and the battery cell current exceeds the threshold, a fixed frequency intermittent pulse current is applied to the battery cell, and the power supply adjustment amplitude is determined based on the change in the battery cell terminal voltage.

[0020] The sodium-ion battery energy intelligent management system described in this application includes:

[0021] The sodium-ion battery module is composed of layered oxide as the positive electrode material and hard carbon as the negative electrode material. The positive electrode uses a P2 or O3 dual-phase coexisting layered oxide, and the negative electrode uses a three-dimensional conductive network constructed by nitrogen and sulfur co-doped hard carbon and graphene. The battery module has the characteristics of high energy density, wide temperature range operation capability and long cycle life.

[0022] A radio frequency energy harvesting module, integrated within the battery system, includes:

[0023] Multi-band radio frequency antenna array, covering Sub-6GHz, millimeter wave and terahertz frequency bands, is used to receive radio frequency communication signals in the environment;

[0024] The radio frequency to DC conversion circuit, connected to a multi-band radio frequency antenna array, includes a Schottky diode rectifier network, a matching network, and a filtering and voltage regulation circuit, used to convert the received radio frequency signal into stable DC power;

[0025] The auxiliary charging interface outputs the converted DC supplemental current to the auxiliary charging channel of the battery management system;

[0026] The battery management main control module is connected to the sodium-ion battery cell module and the radio frequency energy harvesting module. It is used to collect information on battery cell status, radio frequency signal strength and load demand. Through the built-in AI prediction model, it performs multi-source data fusion analysis and strategy generation, and dynamically controls radio frequency energy harvesting, load power supply and pulse power replenishment operations.

[0027] The system communication connection module connects to the battery management main control module and supports multiple standard power interfaces and communication protocols, including USB-C, PoE, and PCIe.

[0028] The sodium-ion battery energy intelligent management method and system described in this application have the advantage that, through the built-in multi-band radio frequency energy capture and conversion module, the communication radio frequency signals that are widely present in the environment are converted into DC power to replenish the battery, significantly extending the standby and working time of the terminal device and reducing the dependence on external wired charging.

[0029] By integrating a dedicated AI processing unit and predictive models, the system can analyze the battery's internal state, external signal environment, and load demand in real time, and dynamically formulate and execute the optimal energy dispatch strategy, including adjusting the energy capture intensity, load voltage, or triggering pulse charging, so that the battery can be transformed from a passive component into an active intelligent energy node.

[0030] By employing intrinsically stable electrode materials and electrolyte systems, combined with an AI management system for precise monitoring and predictive maintenance of battery status, the battery's high safety and operational stability are ensured in a wide temperature range, long cycle time, and potentially complex electromagnetic environments.

[0031] The modular design supports flexible size customization and is compatible with a variety of mainstream power interfaces and communication protocols, making it easy to integrate into various smart terminal devices. It provides an efficient, intelligent, and adaptive core energy solution for ubiquitous interconnected devices in the 6G era. Attached Figure Description

[0032] Figure 1 is a flowchart of a sodium-ion battery intelligent energy management method according to this application;

[0033] Figure 2 is a flowchart of a sodium-ion battery energy intelligent management method described in this application. Detailed Implementation

[0034] As shown in Figures 1 and 2, the intelligent energy management method for sodium-ion batteries described in this application includes:

[0035] As shown in Figures 1-2, S101 uses a sodium-ion battery cell with layered oxide as the positive electrode and hard carbon material as the negative electrode, and integrates a multi-band radio frequency antenna array and its connected radio frequency-DC conversion circuit in the battery system to form a radio frequency energy capture module for capturing radio frequency signals in the environment and converting them into DC power.

[0036] Further, in step S101, the intensity distribution of multi-band radio frequency signals in the environment is obtained;

[0037] Receive radio frequency signals through a multi-band radio frequency antenna array;

[0038] The radio frequency signals acquired by the antenna array are input into the radio frequency to DC conversion circuit;

[0039] A Schottky diode rectifier network is used to rectify the radio frequency signal to obtain a pulsating DC voltage;

[0040] Stable DC power is obtained by filtering and stabilizing pulsating DC voltage.

[0041] Determine whether the voltage value of the stable DC power reaches the charging threshold of the sodium-ion battery cell; if it does, activate the charging control switch.

[0042] DC power that meets the requirements is connected to a sodium-ion battery cell composed of a hard carbon negative electrode and a layered oxide positive electrode for constant current charging.

[0043] Specifically, in step S101, a sodium-ion battery cell with layered oxide as the positive electrode and hard carbon material as the negative electrode is constructed, and a multi-band radio frequency antenna array and radio frequency-DC conversion circuit are integrated to achieve radio frequency energy capture.

[0044] First, information technology was used to simulate and optimize the material ratio of sodium-ion batteries, selecting layered oxides such as NaNi. 0.5 Mn 0.502 As the positive electrode material, its theoretical specific capacity is about 120 mAh / g. The negative electrode uses hard carbon material with a porosity controlled at 60% and a specific surface area of ​​500 m² / g. The cycle stability is calculated by electrochemical simulation software, predicting that the capacity retention rate will reach 85% after 1000 cycles at 1C rate. The optimal electrolyte formulation is generated, such as a solution of 1 mol / L NaPF6 dissolved in EC:DEC with a volume ratio of 1:1.

[0045] A multi-band radio frequency antenna array was designed using electromagnetic simulation tools, covering the 900MHz, 2.4GHz and 5.8GHz frequency bands, with antenna gains of 5dBi, 8dBi and 10dBi respectively. The array layout was optimized through finite element analysis to ensure that the signal receiving efficiency was improved to 75%, and the matching impedance between the antenna array and the ambient radio frequency signal was calculated to be 50 ohms.

[0046] The pre-designed RF-DC conversion circuit uses Schottky diode rectification with a target conversion efficiency of 60%. Circuit simulation software calculates that the output voltage is 3.3V when the input power is -20dBm, which meets the charging requirements of the battery cell. At the same time, it integrates a power management algorithm to dynamically adjust the load distribution, so that the power loss of the energy capture module is less than 10% in different frequency bands.

[0047] By modeling the data interaction between the battery cell and the radio frequency module through system integration simulation, it is predicted that when the ambient radio frequency power density is 0.1μW / cm², about 0.5mWh of energy can be captured per hour, which is sufficient to support the operation of low-power IoT devices. Logically, this forms a complete chain from material design to energy conversion to system application.

[0048] In one embodiment, S101 uses a Schottky diode rectifier network to rectify the radio frequency signal to obtain a pulsating DC voltage.

[0049] By filtering and stabilizing the pulsating DC voltage, stable DC power is obtained.

[0050] Determine the output voltage value V of stable DC power out Has the charging voltage threshold V of the sodium-ion battery cell been reached? th ;

[0051] The conditions for judgment are:

[0052] Among them, V out This represents the stable DC voltage output of the RF-DC converter circuit, V. th This is the minimum effective charging voltage threshold preset according to the sodium-ion battery cell chemical system;

[0053] If V is satisfied out ≥V th Then, the charging control switch is activated, and DC power is connected to the sodium-ion battery cell for constant current charging.

[0054] As shown in Figures 1-2, S102 receives radio frequency communication signals from the environment through the radio frequency energy capture module. After matching, rectification and voltage regulation, it outputs DC supplementary current, which flows into the auxiliary charging channel of the battery management system.

[0055] Furthermore, in step S102, the radio frequency signals in the environment are collected in real time by the radio frequency energy capture module, and the collected signals are initially screened to obtain usable communication signal data;

[0056] Signal matching technology is used to process the filtered communication signal data, adjust the frequency characteristics of the signal, and output the matched signal stream;

[0057] The matched signal stream is converted by a rectifier-conversion circuit to convert the AC signal into a pulsating DC signal and determine the initial current data after conversion.

[0058] Based on the initial current data, a voltage regulation circuit is applied to smooth the voltage and output a stable DC current.

[0059] If a stable DC current meets the preset current threshold, it is used as a supplementary current to access the auxiliary charging channel of the battery management system to determine whether the access is stable.

[0060] The battery management system monitors the incoming supplementary current in real time, obtains the current distribution status, and determines the final charging efficiency data.

[0061] Based on the final charging efficiency data, adjust the acquisition parameters of the radio frequency energy harvesting module.

[0062] Specifically, in step S102, an RF energy harvesting module is integrated into a sodium-ion battery system constructed with a layered oxide positive electrode and a hard carbon negative electrode for the purpose of supplementing charging with environmental RF signals.

[0063] First, density functional theory combined with molecular dynamics simulations were used to optimize the crystal structure of the layered oxide cathode, and Na was selected. 0.67 Ni 0.33 Mn 0.6702 The composition was determined by calculating the sodium ion diffusion barrier to be 0.28 eV, and the optimal sodium content range was identified as 0.65 to 0.70 eV. Simultaneously, multi-scale pore modeling was performed on the hard carbon anode, controlling the average pore size within the range of 1.2 nm to 1.8 nm, and setting the specific surface area to 420 m² / g. The sodium ion intercalation process was simulated using the Monte Carlo method, predicting that the initial coulombic efficiency at 0.5 C rate could reach 92.5%.

[0064] A broadband radio frequency antenna array was designed using the finite-difference time-domain method. Multi-resonance matching was performed for the 700MHz, 1.8GHz, and 3.5GHz communication bands. The antenna elements adopted a fractal structure to achieve gains of 4.8dBi, 7.2dBi, and 9.5dBi for each band, respectively. After iterative optimization of the array spacing and feed network using a genetic algorithm, the overall radiation efficiency was improved to 78%. Impedance matching calculation results showed that the return loss at each frequency point was better than -18dB.

[0065] A radio frequency to DC conversion circuit based on CMOS technology was constructed. Low-threshold MOSFETs were selected to replace some Schottky diodes to reduce the turn-on voltage to 0.15V. A multi-stage voltage multiplier rectifier topology was adopted. Through SPICE simulation, the output DC voltage of 3.6V was obtained under the condition of input power of -18dBm, and the conversion efficiency reached 58%. At the same time, a power point tracking algorithm based on reinforcement learning was embedded to dynamically adjust the number of rectifier branches in operation according to the real-time received power density, so that the overall energy loss of the system was controlled within 8%.

[0066] The output of the RF capture module is connected to the auxiliary charging port of the battery management system. A dynamic response model of the cell's state of charge and capture current is established through multi-physics coupling simulation. In a typical urban scenario with an environmental RF power density of 0.08 μW / cm², the average replenishment power per hour is calculated to be about 0.42 mWh, which effectively extends the standby time of the low-power sensor node under the condition of no external power supply. A complete closed-loop logic chain from material optimization, antenna design, circuit conversion to system energy management is obtained.

[0067] As shown in Figures 1-2, S103 collects the voltage, temperature and internal resistance parameters of the sodium-ion battery cell in real time through the battery management system, and simultaneously collects the signal strength information from the radio frequency energy harvesting module, as well as the load demand information identified through the device interface.

[0068] Furthermore, in step S103, the voltage parameters, temperature parameters, and internal resistance parameters of the sodium-ion battery cell are collected in real time through the battery management system;

[0069] Calculate the current state of charge of the sodium-ion battery cell based on voltage, temperature, and internal resistance parameters.

[0070] Based on the current state of charge value and the load current parameters obtained through the device interface, the discharge rate value of the sodium-ion battery cell is determined, and the signal strength parameters are obtained through the radio frequency energy capture module.

[0071] Based on the signal strength parameters and discharge rate value, determine whether the conditions for supplementary current intervention are met.

[0072] If the conditions for supplementary current intervention are met, the DC supplementary current value is obtained from the radio frequency energy harvesting module;

[0073] Connect the DC supplemental current value to the auxiliary charging channel of the battery management system.

[0074] Specifically, in step S103, the high-precision data acquisition unit integrated in the battery management system monitors the terminal voltage, internal resistance, and surface temperature of the sodium-ion battery cell in real time at a sampling frequency of 1Hz. The voltage resolution reaches 0.1mV, the temperature measurement accuracy is ±0.2℃, and the internal resistance is calculated using the 1kHz AC excitation method with an error of less than 2%. At the same time, the radio frequency signal strength indication value RSSI output by the radio frequency energy harvesting module is acquired, covering a range of -110dBm to -30dBm, and converted into a received power density estimate.

[0075] Periodically read the load current demand curve through the device's I2C or SPI interface and calculate the average power demand P for the next 5 minutes. load When the system detects that the cell voltage is below 3.35V or the temperature exceeds 45℃, the protection logic is triggered. If the RSSI is higher than -65dBm and P load If the power output is less than 1.2 times the instantaneous output power of the capture module, the auxiliary charging mode is activated. The management system uses a state estimation algorithm based on Kalman filtering to fuse voltage, temperature, internal resistance, and capture current data, and updates the estimated state of charge (SOC) value in real time. The filter process noise covariance is set to 1e. -4 The measurement noise covariance is set to 1e. -3 After 50 iterations, the SOC convergence error was controlled within 0.8%.

[0076] Meanwhile, a fuzzy logic controller is introduced to dynamically limit the charging current. The input variables are SOC deviation, temperature deviation and power margin, and the output is the current ratio limit coefficient. When the temperature deviation is greater than 5℃, the charging current is limited to below 0.05C. When the power margin is less than 10%, it is automatically reduced to 0.02C to avoid the risk of overcharging.

[0077] Through the aforementioned closed-loop control mechanism, under the typical urban radio frequency environment power density of 0.07μW / cm², the system can reduce standby power consumption from the initial 15μW to the net charging state, thereby increasing the cell SOC by approximately 1.8% within 24 hours, thus significantly improving the autonomous operation capability of self-powered IoT nodes.

[0078] In one embodiment, S103 collects the voltage parameter U, temperature parameter T, and internal resistance parameter R of the sodium-ion battery cell in real time through the battery management system. i Simultaneously, it collects signal strength information from the radio frequency energy harvesting module, as well as load demand information identified through the device interface;

[0079] Based on the collected voltage U, temperature T, and internal resistance R i and load current I load Calculate the current state of charge (SOC) of the sodium-ion battery cell;

[0080] The State of Occurrence (SOC) is estimated using the Extended Kalman Filter (EKF) algorithm. The calculation formula includes the state prediction equation and the observation equation.

[0081]

[0082] Among them, SOC k and SOC k-1 Let k and k represent time k and k respectively. -1 The estimated state of charge at time t;

[0083] η is the Coulomb efficiency;

[0084] I bat,k-1 and I bat,k k -1 The cell current at time k (positive when charging, negative when discharging).

[0085] Δt is the sampling time interval;

[0086] C nom This refers to the nominal capacity of the battery cell;

[0087] w k This is process noise;

[0088] U k The cell terminal voltage measured at time k;

[0089] OCV(SOC k (SOC) is the state of charge. k The corresponding open-circuit voltage function;

[0090] v k For measuring noise;

[0091] Based on the current SOC and load current I load Determine the discharge rate C of the battery cell. rate :

[0092]

[0093] Convert signal strength information into an estimated radio frequency received power value P. rx ;

[0094] Overall SOC, discharge rate C rate and RF receiving power P rx Determine whether the conditions for supplementary current intervention are met;

[0095] The conditions for judgment are:

[0096]

[0097] Among them, SOC low The preset low battery alarm threshold;

[0098] P min This is the preset effective threshold for RF capture power;

[0099] C th The preset discharge rate threshold;

[0100] If the above conditions are met, then the DC supplementary current I is obtained from the radio frequency energy harvesting module. supp It is then connected to the auxiliary charging channel of the battery management system.

[0101] As shown in Figures 1-2, in step S104, the multi-source state information collected in step S103 is input into a preset neural network prediction model for fusion analysis and time-series prediction to obtain the current accurate state of charge and health status assessment results of the battery cell. Based on the assessment results, signal strength, and load requirements, an adaptive energy management strategy is obtained.

[0102] Further, in step S104, the terminal voltage, core temperature, and AC impedance spectrum data of the sodium-ion battery cell are collected through the battery management system;

[0103] The estimated current state of charge of the sodium-ion battery cell is calculated based on the terminal voltage, core temperature, and AC impedance spectrum data.

[0104] The reference discharge rate of the sodium-ion battery cell is determined based on the current estimated state of charge and the power request value at the load end.

[0105] The received power level is obtained through the radio frequency energy capture module;

[0106] Determine whether the conditions for accessing radio frequency supplemental energy are met based on the received power level and the reference discharge rate. If the conditions for accessing radio frequency supplemental energy are met, proceed to the next step. If not, maintain the sodium-ion battery cell as the sole power source.

[0107] If the conditions for accessing radio frequency supplemental energy are met, DC supplemental current is extracted from the radio frequency energy capture module.

[0108] The extracted DC supplemental current is connected to the parallel supplemental channel of the battery management system, and together with the sodium-ion battery cell, it outputs energy to the load.

[0109] Specifically, in step S104, the collected multi-source state information is input into a preset neural network prediction model for fusion analysis and time series prediction. The specific implementation method is as follows:

[0110] The voltage data of the sodium-ion battery cell was set to a typical value of 3.2V, the temperature data to a measured value of 40.5℃, and the internal resistance data to a state parameter of 0.05 ohms. The RF signal strength data was set to -70dBm and the load demand data to an average power demand of 0.5W. The data were standardized and the mean-variance normalization method was used to ensure that the values ​​of each parameter were between 0 and 1, so as to avoid the influence of dimensional differences on model training.

[0111] The processed data was input into the constructed multilayer perceptron neural network model, which contains three hidden layers with 64, 32, and 16 neurons respectively. The ReLU activation function was used, and the Adam optimizer was used during training with a learning rate of 0.001 and a batch size of 32. After 1000 iterations of optimization, the loss function of the multilayer perceptron neural network model converged to below 0.002.

[0112] The multilayer perceptron neural network model performs time-series prediction on the input data. Combined with a long short-term memory (LSTM) network module, and with a time step of 10 minutes, it predicts the trend of cell state of charge (SOC) change within the next 30 minutes, outputting a predicted SOC value of 78.5%. It also assesses the state of health (SOH) by analyzing the internal resistance growth rate and capacity decay rate, calculating an SOH value of 92.3%.

[0113] Based on the above evaluation results, the system further analyzes the signal strength and load requirements. If the signal strength is higher than -75dBm and the load power is lower than 0.6W, an adaptive energy management strategy is generated to prioritize the allocation of radio frequency captured energy to charge the battery cell, with the charging current limited to 0.1C. At the same time, by comparing historical data (SOC change rate of 0.2% in the previous hour), the energy allocation ratio is dynamically adjusted to ensure that the battery cell maintains stable output under high load scenarios.

[0114] If the load demand suddenly increases to 0.8W, the system automatically calls the backup energy allocation algorithm, and combined with the predicted SOC decline trend, temporarily increases the output power to 0.7W to ensure the continuity of equipment operation.

[0115] In one embodiment, the multi-source state information collected in S103 is input into a preset neural network prediction model for fusion analysis and time-series prediction to obtain the current accurate state of charge (SOC) and state of health (SOH) assessment of the battery cell, and an adaptive energy management strategy is generated based on the assessment results, signal strength and load requirements.

[0116] The neural network prediction model is a time-series prediction model that includes a Long Short-Term Memory (LSTM) network. Before model training and inference, the multi-source state information of the input is standardized. The standardization formula is as follows:

[0117]

[0118] Where, x norm Here, x represents the standardized feature value, μ represents the original feature value, σ represents the mean of the feature on the training dataset, and σ represents the standard deviation.

[0119] The assessment of state of health (SOH) is based on capacity decay, and the calculation formula is as follows:

[0120]

[0121] Among them, C current C represents the currently estimated actual capacity of the battery cells. initial This is the initial rated capacity of the battery cell.

[0122] As shown in Figures 1-2, in step S105, based on the energy management strategy generated in step S104, at least one of the following control actions is performed:

[0123] Dynamically adjust the antenna gain of the RF energy harvesting module to optimize energy harvesting efficiency, regulate the supply voltage allocated to the load by the system for dynamic voltage scaling, or control the application of a specific pattern of pulse supplemental current to the battery cell.

[0124] Further, in step S105, the current received power level is obtained through the radio frequency energy capture module;

[0125] Calculate the real-time difference based on the received power level and the load power consumption;

[0126] The supplementary energy access ratio is determined based on the real-time difference.

[0127] The antenna directional and gain parameters are adjusted by using the supplementary energy access ratio.

[0128] The optimized DC supplementary current value is obtained by adjusting the antenna directional parameters and antenna gain parameters;

[0129] The matching state is obtained based on the optimized DC supplementary current value and the cell output current;

[0130] If the matching status exceeds the preset range, an intermittent pulse supplementary current is applied to the sodium-ion battery cell.

[0131] The power supply voltage amplitude is adjusted according to the intermittent pulse supplement current, and energy is output to the load through the adjusted power supply voltage amplitude.

[0132] Specifically, in step S105, according to the generated energy management strategy, the system automatically performs multi-dimensional control actions to achieve efficient energy utilization. First, dynamic antenna gain adjustment is performed on the radio frequency energy capture module. The current strategy determines that the signal strength is -68dBm and is at a medium-high level. The controller calculates the optimal gain value through an embedded algorithm and adopts a real-time search method based on gradient descent to iteratively adjust the antenna gain from the initial 5dBi to 7.8dBi in a step size of 0.2dBi. After each iteration, the captured power increment is collected, and the adjustment stops when the power increase is less than 0.01mW.

[0133] This process takes approximately 1.2 seconds, during which the capture power increases from 4.5mW to 6.7mW;

[0134] The system dynamically scales the load supply voltage. The current average load demand is 0.48W. Combined with the predicted SOC value of 79.2%, the voltage regulation module searches down from 3.4V in 0.05V steps. When the voltage drops to 3.15V, the load power consumption decreases to 0.41W. At the same time, the current fluctuation is monitored and kept within ±3mA to avoid triggering the low voltage protection threshold of 3.0V, thus obtaining a closed-loop negative feedback regulation of voltage and power consumption.

[0135] When the SOC is below 80% and the captured power is insufficient to maintain balance, the system starts the pulse supplementary charging mode, using a constant current pulse method with adjustable duty cycle. The pulse current amplitude is set to 0.15C, i.e., 45mA, the pulse width is 200ms, the interval is 800ms, and the duty cycle is 25%. Each pulse lasts for 15 seconds and then pauses for 30 seconds to monitor voltage stabilization. By comparing the cumulative injected power with the SOC increment, the pulse parameters are corrected in real time. If the SOC rise rate is less than 0.08% / min, the duty cycle is automatically increased to 35% to ensure that the power can still be slowly restored in low energy input scenarios.

[0136] The three control actions are executed in parallel and prioritized as follows: antenna gain adjustment first, followed by voltage scaling, and finally pulse supplementation, to obtain a complete adaptive energy closed-loop control link.

[0137] In one embodiment, at least one of the following control actions is performed according to the energy management strategy generated in S104:

[0138] The antenna gain is dynamically adjusted by using the gradient descent method to dynamically adjust the antenna gain G of the RF energy harvesting module with the goal of optimizing energy harvesting efficiency.

[0139] The iterative formula for gain adjustment is expressed as:

[0140]

[0141] Among them, Gk and G k+1 These are the antenna gain values ​​at the k-th and (k+1)-th iterations, respectively;

[0142] α is the learning rate or step size coefficient;

[0143] For the k-th iteration, the received power P rx Approximate gradient relative to the change in antenna gain G;

[0144] Adjusting the load supply voltage involves reducing its supply voltage V while ensuring the load circuit functions normally. dd To achieve energy savings, the system iteratively searches for the optimal V based on load performance requirements and predicted SOC values. dd Load dynamic power consumption P dyn The relationship between voltage and frequency f can be approximated as:

[0145]

[0146] Among them, C eff The effective switched capacitor for the load circuit;

[0147] The control pulse supplement current is achieved by applying a specific pattern of pulse current to the cell for supplemental charging when the SOC is low and the RF capture energy is insufficient. The average pulse charging current I... avg The duty cycle D and the peak pulse current I pulse Decide:

[0148]

[0149] The system monitors the rise rate R of SOC in real time. soc :

[0150]

[0151] Wherein, ΔSOC is the change in SOC within the time interval Δt;

[0152] If R soc If the rate falls below the preset target value by 0.08% / min, the system automatically increases the duty cycle D to improve the average charging current I. avg .

[0153] As shown in Figures 1-2, in step S106, the sodium-ion battery cell, radio frequency energy capture module, multi-source information acquisition unit, artificial intelligence decision-making unit, and dynamic scheduling execution unit are connected and logically integrated through the battery management system to obtain a closed-loop intelligent management system that can automatically optimize energy capture, storage, and supply based on internal state and external environment.

[0154] Further, in step S106, the current voltage and current values ​​of the sodium-ion battery cell are obtained;

[0155] The real-time received power of the radio frequency energy capture module is acquired through a multi-source information acquisition unit;

[0156] The available replenishment power is calculated based on the real-time received power and the current voltage and current values ​​of the sodium-ion battery cell.

[0157] An artificial intelligence decision-making unit is used to analyze the current available supplementary power and external environmental data to determine the supplementary power access ratio;

[0158] The antenna attitude parameters of the radio frequency energy capture module are adjusted by the dynamic scheduling execution unit according to the ratio of supplementary power access.

[0159] The adjusted DC current value output by the RF energy harvesting module is obtained;

[0160] If the difference between the DC current value and the current value of the sodium-ion battery cell exceeds a preset threshold, an intermittent pulse current of fixed frequency is applied to the sodium-ion battery cell.

[0161] The adjustment range of the power supply voltage is determined based on the change in the terminal voltage of the sodium-ion battery cell after the application of intermittent pulse current.

[0162] A battery management system is used to output energy to the load according to the adjustment range of the supply voltage.

[0163] Specifically, in step S106, the system connects and logically integrates the sodium-ion battery cell, radio frequency energy capture module, multi-source information acquisition unit, artificial intelligence decision-making unit and dynamic scheduling execution unit through the battery management system to form a closed-loop intelligent management system.

[0164] The multi-source information acquisition unit first samples the radio frequency signal strength, ambient temperature, and battery cell voltage in real time at a sampling frequency of 10Hz. After conversion by a 12-bit ADC, the signal strength is -72dBm, the temperature is 28.4℃, and the battery cell voltage is 3.62V. The data is transmitted to the artificial intelligence decision unit via the SPI bus. This unit adopts a lightweight neural network model. The input layer normalizes the above parameters, and the hidden layer uses the ReLU activation function. Through forward propagation, the energy capture priority weight is calculated to be 0.68, the storage optimization weight is 0.25, and the supply regulation weight is 0.07.

[0165] After receiving the decision weights, the dynamic scheduling execution unit initiates the logic integration process. First, based on the weights, it allocates the rectification efficiency threshold of the RF energy capture module. The controller then runs a PID algorithm with a proportional coefficient Kp=0.35, an integral coefficient Ki=0.012, and a derivative coefficient Kd=0.08, adjusting the capture voltage from 1.8V to 2.3V. The error convergence time is 0.9 seconds, and the efficiency is increased from 62% to 78%.

[0166] Storage optimization of sodium-ion cells: The artificial intelligence decision unit predicts the SOC to be 65.3% through Kalman filtering. The scheduling unit automatically switches to low leakage mode, shuts down unnecessary bypass circuits, and reduces the leakage current from 120μA to 35μA. At the same time, it monitors the rate of change of internal resistance. If the change exceeds 0.5mΩ / min, it triggers the equalization circuit to compensate for the single-cell voltage difference with a 0.08A equalization current. Finally, the voltage difference is controlled within 8mV.

[0167] For load supply, the scheduling unit dynamically allocates the discharge path of the buffer capacitor according to the weight. It adopts a buck-boost converter, sets the upper limit of output current to 220mA, and automatically adjusts the duty cycle to 68% based on the real-time load demand of 185mA. The output voltage is stabilized at 3.28V, and the ripple is less than 15mV. The entire closed-loop process cycles once every 5 seconds. The status synchronization between units is realized through the CAN bus to ensure automatic optimization of energy capture, storage and supply in complex environments.

[0168] If the technical solution of this application involves the collection, processing, or application of personal information, the relevant products have, before implementing any personal information processing activities, fully and clearly informed individuals of the processing rules in accordance with the "Personal Information Protection Law of the People's Republic of China" and other current laws and regulations, and obtained their voluntary and explicit consent. If sensitive personal information is involved, the product has obtained the individual's separate consent before processing, and such consent is given in an explicit manner. For example, prominent signs are set up in the area where information collection devices such as cameras are located, clearly indicating "Entering is considered as consent to the collection of personal information"; or through pop-ups, checkboxes, user-initiated uploads, etc., under the premise of clearly listing the processor's identity, processing purpose, processing method, and information type, the user actively completes the authorization operation.

[0169] The above mechanism ensures that all personal information processing activities are based on legal authorization and fully comply with national compliance requirements regarding personal information protection.

[0170] This embodiment provides a method for sodium-ion battery systems applicable to multiple scenarios, and its specific implementation is as follows:

[0171] In the battery cell materials, the positive electrode uses a layered oxide material with a dual-phase coexistence of P2 or O3, specifically with the chemical formula Na. 0.72 Ni 0.25Mn 0.65 Ti 0.1 O2;

[0172] Titanium doping effectively suppresses lattice oxygen loss, and manganese is distributed in a gradient to form electron tunnel channels. X-ray diffraction (XRD) refinement analysis shows that the P2 phase accounts for about 68% and the O3 phase accounts for about 32% in the material. The reversible specific capacity of the cathode material can reach 185mAh / g under 0.1C rate and 25℃ conditions.

[0173] The negative electrode uses a nitrogen (N) and sulfur (S) co-doped hard carbon material, and constructs a three-dimensional conductive network with graphene. The specific surface area of ​​the negative electrode material is about 1280 m² / g, the interlayer spacing is 0.392 nm, and the initial charge-discharge coulombic efficiency reaches 93.7%.

[0174] The electrolyte uses 1.8 mol / L NaPF6 as the electrolyte salt, dissolved in a mixed solvent composed of ethylene carbonate (EC), fluoroethylene carbonate (FEC), and 1,3-dioxolane (DOL) in a volume ratio of 3:1:6. To improve safety, 2 wt% triphenyl phosphate (TPP) is added as a flame retardant, and 0.5 wt% fluoroethylene carbonate (FVC) is added as a film-forming additive.

[0175] Differential scanning calorimetry (DSC) testing showed that the thermal runaway initiation temperature of this cell system reached 286°C.

[0176] At a low temperature of -40℃, the capacity retention rate is 81.3% when discharged at a rate of 0.2C.

[0177] After 2000 cycles at 70°C, the capacity retention rate was 91.6%.

[0178] The battery cell has an energy density of 248Wh / kg, a volumetric energy density of 486Wh / L, a nominal voltage of 3.65V, and supports 10C pulse discharge for 2 seconds.

[0179] In the integration of the radio frequency energy harvesting module, a four-band microstrip antenna array is embedded inside the battery casing, covering the Sub-6GHz (3.5GHz / 4.9GHz), millimeter wave (26GHz / 39GHz) and terahertz (140GHz / 220GHz) frequency bands;

[0180] The antenna substrate is made of low-temperature co-fired ceramic (LTCC) with a dielectric constant of 9.2 and a loss tangent of 0.0012.

[0181] The antenna output is connected to an ultra-low power radio frequency to direct current (RF-DC) converter module, the core of which includes:

[0182] Schottky barrier diode array, model SMS7630, with a turn-on voltage of 0.15V and a cutoff frequency of up to 300GHz;

[0183] Multi-stage LC resonant matching network with a quality factor Q value ≥ 120;

[0184] Adaptive impedance transformer with a dynamic adjustment range of 1:25;

[0185] The RF energy harvesting module achieves a rectification efficiency of 63.2% with an input power of -60dBm at a 26GHz signal.

[0186] With an input power of -40dBm at 3.5GHz, the rectification efficiency reaches 81.7%;

[0187] The converted DC power is isolated by a nanocrystalline soft magnetic transformer with a saturation magnetic induction intensity of 1.35T before being connected to the auxiliary charging channel of the battery management system (BMS).

[0188] In AI adaptive energy management, the battery management system (BMS) is equipped with a dedicated neural network coprocessor (NPU) that runs a lightweight long short-term memory network (LSTM) model with fewer than 1.2 million parameters (<1.2M).

[0189] BMS analyzes three types of information in real time:

[0190] The battery state of charge (SOC) and state of health (SOH) are obtained by fusing multi-source data of voltage, temperature, and internal resistance based on the Kalman filter algorithm.

[0191] The intensity spectrum of local communication signals is obtained through the built-in spectrum sensor;

[0192] Application scenario load characteristics identified through USB-C or PCIe interface protocols;

[0193] When the system determines that the SOC has dropped to the 40% threshold, the NPU initiates a three-level linkage response strategy:

[0194] Level 1: Activate the radio frequency energy harvesting enhancement mode to dynamically increase the antenna array gain by 6dB;

[0195] Level 2: The load balancing algorithm is used to moderately reduce the power supply voltage of non-critical circuits by about 0.15V to reduce overall power consumption.

[0196] Level 3: Trigger pulse-type supplemental charging, applying a pulse current with a peak current of 2.8A with a period of 500ms;

[0197] Using the above strategy, the battery SOC recovered from 40% to 82.3% ± 1.7% within approximately 18 minutes.

[0198] The battery system is designed for a cycle life of over 10 years, with a total cycle count of >4380 based on 1.2 deep cycles per day, and a capacity decay rate of no more than 0.012% per cycle;

[0199] In this application, the battery adopts a modular stacked structure. In this embodiment, the dimensions are customized to 65mm×48mm×3.2mm (thickness), which conforms to the IEC 62133-2:2017 mechanical impact standard.

[0200] The sodium-ion battery energy intelligent management system described in this application includes:

[0201] The sodium-ion battery module is composed of layered oxide as the positive electrode material and hard carbon as the negative electrode material. The positive electrode uses a P2 or O3 dual-phase coexisting layered oxide, and the negative electrode uses a three-dimensional conductive network constructed by nitrogen and sulfur co-doped hard carbon and graphene. The battery module has the characteristics of high energy density, wide temperature range operation capability and long cycle life.

[0202] A radio frequency energy harvesting module, integrated within the battery system, includes:

[0203] Multi-band radio frequency antenna array, covering Sub-6GHz, millimeter wave and terahertz frequency bands, is used to receive radio frequency communication signals in the environment;

[0204] The radio frequency to DC conversion circuit, connected to a multi-band radio frequency antenna array, includes a Schottky diode rectifier network, a matching network, and a filtering and voltage regulation circuit, used to convert the received radio frequency signal into stable DC power;

[0205] The auxiliary charging interface outputs the converted DC supplemental current to the auxiliary charging channel of the battery management system;

[0206] The battery management main control module is connected to the sodium-ion battery cell module and the radio frequency energy harvesting module. It is used to collect information on battery cell status, radio frequency signal strength and load demand. Through the built-in AI prediction model, it performs multi-source data fusion analysis and strategy generation, and dynamically controls radio frequency energy harvesting, load power supply and pulse power replenishment operations.

[0207] The system communication connection module realizes the connection and logical integration of each unit through the battery management main control module, supports multiple standard power interfaces and communication protocols, including USB-C, PoE, and PCIe, and can be integrated into various smart terminal devices.

[0208] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this application.

Claims

1. A method for intelligent energy management of sodium-ion batteries, characterized in that, include: S101. Configure a radio frequency energy harvesting module, wherein the radio frequency energy harvesting module is electrically connected to a multi-band radio frequency antenna array installed in the battery management system; S102. Configure an auxiliary charging channel for inputting DC supplementary current into the battery management system. The DC supplementary current is generated by the radio frequency energy harvesting module receiving radio frequency signals from the environment. S103. Obtain the voltage, temperature, and internal resistance parameters of the sodium-ion battery cell collected in real time by the battery management system, the intermediate frequency signal strength information collected by the radio frequency energy harvesting module, and the load demand information identified by the device interface to obtain multi-source state information. S104. Input the multi-source state information into a preset neural network prediction model to generate an evaluation result reflecting the current accurate state of charge and health of the battery cell. Based on the evaluation result, signal strength, and load demand, obtain an adaptive energy management strategy. S105. Determine a control action according to the adaptive energy management strategy. The control action is selected from at least one of the following: adjusting the antenna gain of the radio frequency energy harvesting module; adjusting the voltage of the load supply; and inputting the DC supplementary current into the battery cell.

2. The intelligent energy management method for sodium-ion batteries according to claim 1, characterized in that, In S101, the battery management system specifically includes: layered oxide as the positive electrode, including P2 or O3 dual-phase coexisting layered oxide, and hard carbon material as the negative electrode, including nitrogen and sulfur co-doped hard carbon material.

3. The intelligent energy management method for sodium-ion batteries according to claim 1, characterized in that, Specifically, S101 also includes a multi-band radio frequency antenna array covering at least two of the Sub-6GHz, millimeter wave, and terahertz frequency bands.

4. The intelligent energy management method for sodium-ion batteries according to claim 1, characterized in that, The neural network prediction model includes a Long Short-Term Memory (LSTM) module.

5. The intelligent energy management method for sodium-ion batteries according to claim 1, characterized in that, In S105, the antenna gain of the RF energy harvesting module is dynamically adjusted by using a gradient descent-based search method for iterative adjustment.

6. The intelligent energy management method for sodium-ion batteries according to claim 1, characterized in that, In S105, inputting the DC supplementary current to the battery cell specifically involves applying an intermittent constant current pulse with an adjustable duty cycle.

7. A system for executing the intelligent energy management method for a sodium-ion battery according to any one of claims 1-6, characterized in that, The sodium-ion battery cell module uses layered oxide material for the positive electrode and hard carbon material for the negative electrode. The radio frequency energy harvesting module, integrated inside the battery system, includes a multi-band radio frequency antenna array and a connected radio frequency to DC conversion circuit, used to capture ambient radio frequency signals and convert them into DC supplementary current. The battery management main control module, connected to the sodium-ion battery cell module and the radio frequency energy harvesting module, is used to collect information on cell status, radio frequency signal strength, and load demand, and dynamically control radio frequency energy harvesting, load power supply, and pulse supplementation operations. The system communication connection module, connected to the battery management main control module, uses multiple standard power interfaces and communication protocols for connection.

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