Method for constructing equivalent circuit model of aqueous sodium-ion battery and early warning method thereof

Abstract

This invention relates to the field of new energy battery technology, and discloses a method for constructing an equivalent circuit model of an aqueous sodium-ion battery and its early warning method. The construction method includes: obtaining the initial electrical parameters of the aqueous sodium-ion battery at different temperatures, and establishing the correspondence between the initial electrical parameters and temperature, and / or state of charge; conducting cycle aging tests on the aqueous sodium-ion battery to obtain the degradation coefficient that changes with the number of cycles; constructing a state of charge (SOC) estimation model, which is used to estimate the SOC of the aqueous sodium-ion battery based on external operating parameters, initial electrical parameters, and degradation coefficient, and to calculate the open-circuit voltage; and constructing a core dynamic model, which is used to calculate the predicted parameters of the aqueous sodium-ion battery under normal operating conditions based on various parameters. This invention effectively solves the problem that existing models cannot accurately simulate the internal side reactions and aging characteristics of aqueous sodium-ion batteries, and significantly improves the accuracy of SOC estimation.

Description

Technical Field

[0001] This invention relates to the field of new energy battery technology, specifically to a method for constructing an equivalent circuit model of an aqueous sodium-ion battery and a corresponding early warning method. Background Technology

[0002] Aqueous sodium-ion batteries have broad application prospects in the energy storage field due to their high safety and low cost. However, their cycle life is limited by issues such as electrolyte gas evolution and electrode dissolution, posing special requirements for modeling and early warning technologies. Currently, equivalent circuit models (such as the Thevenin model) combined with open circuit voltage-state of charge (OCV-SOC) lookup tables are often used to describe the battery's dynamic response. This type of model has a simple structure and is suitable for scenarios with moderate accuracy requirements. Early warning mainly relies on voltage / temperature anomaly detection, internal resistance growth analysis, and capacity decay threshold setting.

[0003] However, existing technologies have the following shortcomings: 1. The equivalent circuit model mostly uses the lithium-ion battery system, which does not reflect the characteristics of electrolyte decomposition and electrode dissolution in aqueous batteries, resulting in large simulation errors.

[0004] 2. The model parameters are fixed, which cannot simulate the rapid aging characteristics of water-based batteries, and the reliability of the early warning decreases with the increase of the number of cycles.

[0005] 3. Leakage resistance, as an important parameter reflecting side reactions, has not been included in the early warning indicators, making it difficult to identify early failures. Summary of the Invention

[0006] In view of this, the present invention provides a method for constructing an equivalent circuit model of an aqueous sodium-ion battery and a warning method thereof, in order to solve the problem that the prior art cannot accurately simulate the internal side reactions and cycle aging characteristics of aqueous sodium-ion batteries, resulting in poor warning reliability.

[0007] In a first aspect, the present invention provides a method for constructing an equivalent circuit model of an aqueous sodium-ion battery, comprising: acquiring the initial electrical parameters of the aqueous sodium-ion battery at different temperatures, and establishing a correspondence between the initial electrical parameters and temperature, and / or state of charge; the initial electrical parameters include initial discharge capacity, initial open-circuit voltage, initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance; performing a cycle aging test on the aqueous sodium-ion battery to obtain a degradation coefficient that varies with the number of cycles; constructing a SOC estimation model, the SOC estimation model being used to estimate the state of charge of the aqueous sodium-ion battery based on external operating condition parameters, initial electrical parameters, and degradation coefficient, and calculating the open-circuit voltage; constructing a core dynamic model, the core dynamic model being used to calculate predicted parameters of the aqueous sodium-ion battery under normal operating conditions based on external operating condition parameters, initial electrical parameters, degradation coefficient, and open-circuit voltage; the predicted parameters being fed back to the SOC estimation model to correct the state of charge.

[0008] The present invention provides a method for constructing an equivalent circuit model for an aqueous sodium-ion battery. This method obtains the initial electrical parameters of the aqueous sodium-ion battery at different temperatures and establishes their correspondence with temperature and state of charge (SOC), enabling the model to accurately reflect the intrinsic characteristics of the battery under different ambient temperatures and operating conditions. By conducting cyclic aging tests on the aqueous sodium-ion battery to obtain the degradation coefficient that varies with the number of cycles, the model can dynamically track the performance degradation process throughout the battery's entire lifespan, solving the technical problem that traditional equivalent circuit models cannot simulate battery aging behavior due to fixed parameters. By constructing a SOC estimation model and using it to estimate the SOC and calculate the open-circuit voltage based on external operating parameters, initial electrical parameters, and degradation coefficient, high-precision online estimation of the battery's state of charge is achieved. By constructing a core dynamic model and using it to calculate predicted parameters based on external operating parameters, initial electrical parameters, degradation coefficient, and open-circuit voltage, while simultaneously feeding the predicted parameters back to the SOC estimation model to correct the SOC, a closed-loop correction mechanism is formed. This effectively suppresses the cumulative effect of estimation errors from a single model and significantly improves the convergence speed and steady-state accuracy of SOC estimation.

[0009] The equivalent circuit model constructed in this invention can realistically reproduce the dynamic response characteristics of aqueous sodium-ion batteries under different temperatures, states of charge, and aging levels, providing a high-fidelity digital mapping for battery management systems, thus laying a solid model foundation for the refined management and safe and stable operation of batteries.

[0010] In one optional implementation, the core dynamic model includes: a controlled voltage source, a leakage resistor, an ohmic internal resistance, a polarization resistor, and a polarization capacitor, wherein the polarization resistor and the polarization capacitor are connected in parallel to form an RC network; the controlled voltage source, the ohmic internal resistance, and the RC network are connected in series; the leakage resistor is connected in parallel with the controlled voltage source, and the leakage resistor is used to characterize the energy loss caused by the internal side reactions of the aqueous sodium-ion battery; the controlled voltage source is used to characterize the open-circuit voltage of the aqueous sodium-ion battery.

[0011] The present invention provides a method for constructing an equivalent circuit model of an aqueous sodium-ion battery. This method utilizes the leakage resistance connected in parallel with a controlled voltage source to specifically characterize the energy loss caused by internal side reactions such as electrolyte gas evolution and electrode dissolution in aqueous sodium-ion batteries. By introducing side reaction factors into the equivalent circuit model, the limitation of traditional models being applicable only to lithium-ion battery systems is overcome. This method can more realistically reflect the unique self-discharge behavior and slow degradation mechanism of aqueous sodium-ion batteries, significantly improving the simulation accuracy of the model for the complex electrochemical behavior of the battery, and providing a more accurate model foundation for subsequent state estimation and performance evaluation.

[0012] In one alternative implementation, the formula for the relationship between the parameters of the core dynamic model is as follows:

[0013] in, U OC This is the open-circuit voltage of an aqueous sodium-ion battery; R leak To predict leakage resistance; I leak This is the current across the leakage resistor; U p Polarization voltage; R p Polarization resistor; C p Polarizing capacitor; I This is the operating current; I s For output current; U d For predicting terminal voltage; R 0 represents the internal resistance in ohms.

[0014] In one optional implementation, the process of establishing the correspondence between the initial discharge capacity and temperature includes: performing charge-discharge cycles on an aqueous sodium-ion battery at a preset reference temperature until it reaches a stable state; performing multiple capacity calibration tests on the aqueous sodium-ion battery, and taking the average of the discharge capacities obtained from the multiple tests as the initial discharge capacity at that temperature; and establishing the correspondence between the initial discharge capacity and each temperature by fitting the Arrhenius formula based on the initial discharge capacity obtained at that temperature.

[0015] In one optional embodiment, the process of establishing the correspondence between the initial open-circuit voltage and temperature includes: charging the aqueous sodium-ion battery to the charging cut-off voltage at each temperature and then letting it stand for a first preset time; controlling the aqueous sodium-ion battery to discharge to the discharge cut-off voltage according to the discharge current of a preset rate, and recording the terminal voltage during the discharge process as the initial open-circuit voltage at that temperature.

[0016] In one optional implementation, the process of establishing the correspondence between the initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance and temperature, and / or state of charge includes: performing electrochemical tests on an aqueous sodium-ion battery at a preset reference temperature to obtain test data under different states of charge; using a parameter identification algorithm to identify the test data to obtain the initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance corresponding to different states of charge at that temperature; using the Arrhenius formula to perform temperature correction, and establishing the correspondence between the corrected ohmic internal resistance, corrected polarization resistance, and corrected leakage resistance and each temperature and state of charge, as well as establishing the correspondence between the initial polarization capacitance and the state of charge.

[0017] In one alternative implementation, the electrochemical test includes: mixed power pulse characteristic test or electrochemical impedance spectroscopy test.

[0018] In one alternative implementation, the parameter identification algorithm includes: recursive least squares, neural network algorithm, or particle swarm optimization algorithm.

[0019] In one optional implementation, the degradation coefficient includes a capacity degradation coefficient, an ohmic internal resistance degradation coefficient, a polarization resistance degradation coefficient, and a leakage resistance degradation coefficient. The process of obtaining the degradation coefficient includes: performing a cycle aging test on an aqueous sodium-ion battery, and performing an electrochemical test every preset number of cycles during the aging test until the battery capacity decays to a preset capacity threshold; recording the number of cycles and the corresponding battery capacity, ohmic internal resistance, polarization resistance, and leakage resistance for each electrochemical test; and performing curve fitting on the changes in cycle number and battery capacity, cycle number and ohmic internal resistance, cycle number and polarization resistance, and cycle number and leakage resistance, respectively, to obtain the corresponding degradation coefficient.

[0020] The present invention provides a method for constructing an equivalent circuit model for aqueous sodium-ion batteries. This method involves electrochemical testing of the aqueous sodium-ion battery and recording changes in key parameters throughout its entire lifespan. Then, through curve fitting, degradation coefficients corresponding to capacity, ohmic internal resistance, polarization resistance, and leakage resistance are obtained, thereby establishing a quantitative model of battery performance degradation with cycle count. By introducing cycle aging factors into the equivalent circuit modeling system in the form of degradation coefficients, the model can dynamically reflect the aging characteristics of aqueous sodium-ion batteries during long-term use, such as capacity decay, internal resistance increase, and intensified side reactions. Based on the degradation coefficients obtained by this method, precise corrections can be made to the electrochemical characteristics of the battery under different aging states, significantly improving the model's adaptability and estimation accuracy throughout its entire lifespan, and providing reliable data support for battery health status assessment and remaining lifespan prediction.

[0021] In one optional implementation, the method further includes: establishing a two-dimensional comparison table of maximum discharge capacity with temperature and cycle number based on initial electrical parameters and degradation coefficients, and establishing a three-dimensional comparison table of ohmic internal resistance, polarization resistance, polarization capacitance and leakage resistance with state of charge, temperature and cycle number; and obtaining the equivalent parameters of the aqueous sodium-ion battery under different states by means of a lookup table method based on the two-dimensional and three-dimensional comparison tables. The equivalent parameters include maximum discharge capacity, ohmic internal resistance, polarization resistance, polarization capacitance and leakage resistance.

[0022] Secondly, the present invention provides an early warning method for an aqueous sodium-ion battery, comprising: acquiring measured parameters of the aqueous sodium-ion battery during actual operation; inputting the current external operating condition parameters into the equivalent circuit model constructed by the construction method of the first aspect or any corresponding embodiment, and obtaining the predicted parameters output by the equivalent circuit model corresponding to the current operating condition; comparing the measured parameters with the predicted parameters; and if the deviation between the measured parameters and the predicted parameters exceeds a preset value, and the duration of the deviation exceeds a preset time threshold, then triggering an abnormal warning.

[0023] The early warning method for aqueous sodium-ion batteries provided by this invention dynamically compares the measured parameters acquired in real time with the predicted parameters output by the equivalent circuit model, and introduces a dual judgment mechanism of deviation exceeding limits and duration, thereby achieving accurate monitoring of the operating status of aqueous sodium-ion batteries. It fully utilizes the predictive capability of the high-precision equivalent circuit model, using the normal state benchmark value output by the model as a reference to keenly capture minute abnormal fluctuations that occur during battery operation, and effectively filters out noise interference through duration judgment to avoid false alarms. Compared with traditional early warning methods based on a single threshold or simple voltage detection, this invention incorporates leakage resistance parameters, which reflect the characteristics of internal side reactions, into the early warning indicator system. This enables earlier identification of early fault hazards caused by slow degradation such as electrolyte gas evolution and electrode dissolution, significantly improving the accuracy, timeliness, and reliability of the early warning, and providing strong support for the safe and stable operation of aqueous sodium-ion batteries in large-scale energy storage applications.

[0024] In one optional implementation, the measured parameters include the measured terminal voltage and the measured leakage resistance, and the predicted parameters include the predicted terminal voltage and the predicted leakage resistance. The process of triggering an anomaly warning includes: if the difference between the measured terminal voltage and the predicted terminal voltage exceeds a first preset value, and the duration of the difference exceeds a preset time threshold, then a voltage anomaly warning is triggered; if the difference between the measured leakage resistance and the predicted leakage resistance exceeds a second preset value, and the duration of the difference exceeds a preset time threshold, then a leakage resistance anomaly warning is triggered. Attached Figure Description

[0025] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating the method for constructing an equivalent circuit model of an aqueous sodium-ion battery according to an embodiment of the present invention. Figure 2 This is a structural diagram of the core dynamic model according to an embodiment of the present invention; Figure 3 This is a schematic flowchart of an early warning method for an aqueous sodium-ion battery according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the early warning method for aqueous sodium-ion batteries according to an embodiment of the present invention. Figure 5 This is a diagram showing the composition of an equivalent circuit model construction device for an aqueous sodium-ion battery according to an embodiment of the present invention. Figure 6This is a composition diagram of an early warning device for an aqueous sodium-ion battery according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0029] Aqueous sodium-ion batteries possess significant advantages such as high safety, low cost, environmental friendliness, easy recyclability, and support for fast charging. However, limited by issues like electrolyte gas evolution and electrode dissolution, they also suffer from drawbacks such as low energy density, limited cycle life, poor low-temperature performance, and a limited selection of negative electrode materials. These characteristics profoundly influence the development path of their modeling and early warning technologies. Existing equivalent circuit models for aqueous sodium-ion batteries have the following shortcomings: (1) Currently, most of the equivalent circuit models used for simulating aqueous sodium-ion batteries are based on lithium-ion batteries and do not reflect the characteristics of electrolyte decomposition and electrode dissolution in aqueous sodium-ion batteries. Using existing equivalent circuit models for simulation will produce large errors, resulting in poor early warning effect.

[0030] (2) Compared with other types of batteries, aqueous sodium-ion batteries age faster. Traditional equivalent circuit models fail to simulate the cyclic aging phenomenon of batteries well (i.e., battery parameters remain fixed over a long time scale). When using traditional equivalent circuit models for simulation, the parameter error gradually increases with the increase of battery charge and discharge cycles, resulting in a gradual decrease in the early warning effect. It is necessary to calibrate the model parameters regularly.

[0031] Therefore, this embodiment provides a method for constructing an equivalent circuit model of an aqueous sodium-ion battery, such as... Figure 1 As shown, it includes: Step S101: Obtain the initial electrical parameters of the aqueous sodium-ion battery at different temperatures, and establish the correspondence between the initial electrical parameters and temperature, and / or state of charge; the initial electrical parameters include initial discharge capacity, initial open-circuit voltage, initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance and initial leakage resistance; Specifically, by conducting systematic tests on aqueous sodium-ion batteries at a preset reference temperature or under different temperature conditions, the initial electrical parameters are obtained and their correspondence with temperature and state of charge is established. Since the electrochemical characteristics of aqueous sodium-ion batteries are significantly affected by ambient temperature, parameters obtained at a single temperature cannot accurately describe the battery's behavior under actual operating conditions. The core of this step lies in experimentally obtaining the distribution patterns of initial discharge capacity, initial open-circuit voltage, initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance at different temperatures and states of charge, and establishing corresponding relationships in the form of functions or data tables to provide fundamental data support for subsequent model construction.

[0032] For example, the initial open-circuit voltage is obtained by directly testing the aqueous sodium-ion battery at different temperatures; the initial discharge capacity, initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance are tested at a preset reference temperature (e.g., 25°C) and extrapolated to other temperatures using the Arrhenius formula, thereby constructing a complete parameter-temperature relationship system. Since the electrochemical characteristics of the aqueous sodium-ion battery are significantly affected by ambient temperature, the distribution patterns of each initial electrical parameter at different temperatures or states of charge are obtained through a combination of reference testing and temperature extrapolation, and corresponding relationships are established in the form of functional relationships or data tables.

[0033] Step S102: Perform a cycle aging test on the aqueous sodium-ion battery to obtain the degradation coefficient as a function of the number of cycles.

[0034] Specifically, aqueous sodium-ion batteries were subjected to cycle aging tests to obtain degradation coefficients that vary with the number of cycles, which are used to describe the decline in battery performance over time. Aqueous sodium-ion batteries age faster than other types of batteries, and their aging characteristics, such as capacity decay, internal resistance increase, and intensified side reactions, significantly affect the accuracy of the model. Through long-term aging experiments, the trajectory of battery parameters changing with the number of cycles was recorded, and this change was quantified in the form of degradation coefficients.

[0035] Step S103: Construct a SOC estimation model. The SOC estimation model is used to estimate the state of charge of an aqueous sodium-ion battery based on external operating parameters, initial electrical parameters and degradation coefficients, and to calculate the open-circuit voltage.

[0036] Specifically, by constructing a SOC estimation model, the system integrates external operating parameters, initial electrical parameters, and degradation coefficients to calculate the state of charge (SOC) of the aqueous sodium-ion battery in real time and calculate the open-circuit voltage. SOC estimation is one of the core functions of the battery management system. Its principle is based on the ampere-hour integral method, which combines the initial discharge capacity at the current temperature with the maximum discharge capacity under the current cycle state after degradation coefficient correction to calculate the battery's SOC. Then, based on the correspondence between the initial open-circuit voltage and the SOC established in step S101, the open-circuit voltage under the current state is calculated by looking up a table or interpolation from the current SOC, and used as the input to the core dynamic model.

[0037] It should be noted that advanced algorithms may or may not be used when estimating battery SOC. This embodiment uses the ampere-hour integration method to calculate SOC without introducing advanced algorithms. Advanced algorithms can include various methods such as the extended Kalman filter, neural networks, and least squares, and are not limited here.

[0038] Step S104: Construct a core dynamic model. The core dynamic model is used to calculate the predicted parameters of the aqueous sodium-ion battery under normal operating conditions based on external operating parameters, initial electrical parameters, degradation coefficient and open circuit voltage. The predicted parameters are fed back to the SOC estimation model to correct the state of charge.

[0039] Specifically, based on external operating parameters, initial electrical parameters, degradation coefficient, and open-circuit voltage, predicted parameters for aqueous sodium-ion batteries under normal operating conditions are calculated, and these predicted parameters are fed back to the SOC estimation model to correct the state of charge. The core dynamic model simulates the electrochemical reaction process inside the battery in the form of a circuit network, and describes the mathematical relationships between various electrical parameters through circuit equations, thereby enabling the prediction of externally measurable or calculable quantities such as battery terminal voltage and leakage resistance.

[0040] For example, external operating parameters include temperature T, operating current I, and battery cycle count N. Built-in parameters include two categories: initial parameters and degradation parameters. Initial parameters are those for a fresh battery, including the relationship between initial discharge capacity and temperature. C max,initial ( T Relationship between open-circuit voltage and SOC and temperature U OC ( SOC,T The relationship between initial ohmic resistance and SOC and temperature. R 0,initial ( SOC,T The relationship between initial polarization resistance and SOC and temperature. R p,initial (SOC,T The relationship between initial polarization capacitance and state of charge (SOC) and temperature. C p,initial ( SOC,T The relationship between initial leakage resistance and SOC and temperature. R leak,initial ( SOC,T The degradation parameters are the decay coefficients of various parameters of the battery as it cycles, including: capacity degradation coefficient. k capacity Ohmic resistance degradation coefficient k 0. Polarization resistance degradation coefficient k p Leakage resistance degradation coefficient k leak These parameters were all obtained in the experiment.

[0041] When constructing the SOC estimation model, the battery SOC is first calculated using the ampere-hour integration method: (1) in, SOC This represents the battery's current state of charge. SOC 0 represents the initial state of charge of the battery. η For battery charging and discharging efficiency, C max (T,N) This represents the maximum discharge capacity in the current cycle state. I This is the operating current.

[0042] Then use open-circuit voltage U OC Relationship with SOC function U OC ( SOC ),get U OC And input it into the core dynamic model.

[0043] The equivalent circuit model construction method for aqueous sodium-ion batteries provided in this embodiment obtains the initial electrical parameters of the aqueous sodium-ion battery at different temperatures and establishes their correspondence with temperature and state of charge, enabling the model to accurately reflect the intrinsic characteristics of the battery under different ambient temperatures and operating conditions. By conducting cycle aging tests on the aqueous sodium-ion battery to obtain the degradation coefficient that changes with the number of cycles, the model can dynamically track the performance degradation process throughout the battery's entire life cycle, solving the technical problem that traditional equivalent circuit models cannot simulate battery aging behavior due to fixed parameters. By constructing a SOC estimation model and making it estimate the state of charge and calculate the open-circuit voltage based on external operating condition parameters, initial electrical parameters, and degradation coefficient, high-precision online estimation of the battery state is achieved. By constructing a core dynamic model and making it calculate the predicted parameters based on external operating condition parameters, initial electrical parameters, degradation coefficient, and open-circuit voltage, and simultaneously feeding the predicted parameters back to the SOC estimation model to correct the state of charge, a closed-loop correction mechanism is formed, which effectively suppresses the cumulative effect of single model estimation error and significantly improves the convergence speed and steady-state accuracy of SOC estimation.

[0044] The equivalent circuit model constructed in this embodiment can realistically reproduce the dynamic response characteristics of aqueous sodium-ion batteries under different temperatures, different states of charge, and different degrees of aging. It provides a high-fidelity digital mapping for the battery management system, thus laying a solid model foundation for the refined management and safe and stable operation of the battery.

[0045] In some alternative implementations, such as Figure 2 As shown, the core dynamic model includes: a controlled voltage source (whose open-circuit voltage is...) Uoc Leakage resistance R leak Ohmic internal resistance R 0. Polarization resistance R p and polarization capacitor C p Among them, polarization resistance R p With polarization capacitor C p When connected in parallel, they form an RC network; controlled voltage source, ohmic internal resistance R 0 and RC network series connection; leakage resistance R leak Connected in parallel with a controlled voltage source, leakage resistance R leak Used to characterize the energy loss caused by internal side reactions in aqueous sodium-ion batteries; a controlled voltage source is used to characterize the open-circuit voltage of aqueous sodium-ion batteries.

[0046] Specifically, Figure 2 In the middle, the controlled voltage source simulates the actual open-circuit voltage of the battery. Uoc It is the core of the model's voltage reference; ohmic internal resistance R 0 reflects the ionic and electronic conduction impedance of the battery electrolyte, electrodes and separator, and conforms to the basic internal conductivity characteristics of the battery; R p and C p The constructed RC network accurately simulates the polarization effect during battery charging and discharging, reproducing the dynamic change law of polarization voltage; while the leakage resistance connected in parallel with the controlled voltage source... R leak Designed specifically for aqueous sodium-ion batteries, this model can characterize the self-discharge phenomenon and energy loss caused by unique internal side reactions such as electrolyte gas evolution and electrode dissolution. This allows the model to better reflect the actual electrochemical behavior of aqueous sodium-ion batteries and achieve accurate simulation of battery operation.

[0047] Specifically, the formulas relating the parameters of the core dynamic model are as follows: (2) in, U OC This is the open-circuit voltage of an aqueous sodium-ion battery; R leak To predict leakage resistance; I leak This is the current across the leakage resistor; U p Polarization voltage; R p Polarization resistor; C p Polarizing capacitor; I This is the operating current; I s For output current; U d For predicting terminal voltage; R 0 represents the internal resistance in ohms.

[0048] The equivalent circuit model construction method for aqueous sodium-ion batteries provided in this embodiment utilizes the leakage resistance connected in parallel with the controlled voltage source to specifically characterize the energy loss caused by internal side reactions such as electrolyte gas evolution and electrode dissolution in aqueous sodium-ion batteries. By introducing side reaction factors into the equivalent circuit model, the limitation of traditional models being applicable only to lithium-ion battery systems is overcome. This method can more realistically reflect the unique self-discharge behavior and slow degradation mechanism of aqueous sodium-ion batteries, significantly improving the simulation accuracy of the model for the complex electrochemical behavior of batteries and providing a more accurate model foundation for subsequent state estimation and performance evaluation.

[0049] In some optional implementations, the process of establishing the correspondence between the initial open-circuit voltage and temperature includes: (1) At each temperature, the aqueous sodium-ion battery is charged to the charging cutoff voltage and then left to stand for a first preset time.

[0050] (2) Control the aqueous sodium-ion battery to discharge to the discharge cutoff voltage according to the preset discharge current, and record the terminal voltage during the discharge process as the initial open circuit voltage at that temperature.

[0051] Specifically, firstly, the aqueous sodium-ion battery is charged to the charging cutoff voltage at a constant current and constant voltage at various set temperatures. Then, it is allowed to stand for a first preset time. This is to eliminate the hysteresis effect of polarization, allowing the internal electrochemical system of the battery to reach equilibrium and ensuring that the measured voltage value is the true open-circuit potential. Next, the battery is discharged at a preset discharge rate using a constant current until the voltage drops to the discharge cutoff voltage. During this complete discharge process, the battery's terminal voltage data is collected and recorded in real time. Based on the above experimental data, a complete characteristic curve of the initial open-circuit voltage at that temperature can be constructed.

[0052] For example, aqueous sodium ions were tested at ambient temperatures of 5, 25, 35, and 45°C as follows: Three 1C charge-discharge cycles were performed to bring the battery to a stable state. The battery was then charged at a 1C rate until the voltage reached 1.3V, followed by constant-voltage charging at 1.3V until the current was less than 0.02C, and then left to stand for 1 hour. At this point, the battery was fully charged (SOC=100%). It was then discharged at a 0.1C current to the lower cutoff voltage (0.6V), and the terminal voltage under discharge conditions was monitored, which was considered the initial open-circuit voltage of the battery. U OC .

[0053] In some optional implementations, the process of establishing the relationship between initial discharge capacity and temperature includes: (1) At a preset reference temperature, the aqueous sodium-ion battery is charged and discharged until it reaches a stable state.

[0054] (2) Perform multiple capacity calibration tests on the aqueous sodium-ion battery and take the average discharge capacity obtained from the multiple tests as the initial discharge capacity at that temperature.

[0055] (3) Based on the initial discharge capacity obtained at this temperature, the Arrhenius formula is used to fit and establish the correspondence between the initial discharge capacity and each temperature.

[0056] Specifically, the aqueous sodium-ion battery is first subjected to charge-discharge cycles at a preset reference temperature (e.g., 25°C) to achieve a stable electrochemical state and eliminate the influence of early cycle fluctuations on the test results. Then, multiple capacity calibration tests are performed on the battery at this reference temperature. The average discharge capacity obtained from these multiple tests is used as the initial discharge capacity at that temperature to reduce random errors in a single test. Finally, based on the initial discharge capacity obtained at the reference temperature, the Arrhenius equation is used to describe the physical law of battery capacity change with temperature. This equation is then fitted and extrapolated to other temperatures, thus establishing a complete correspondence between the initial discharge capacity and each temperature. This method, based on reference temperature testing and combined with a temperature extrapolation model, avoids the experimental costs associated with conducting numerous tests at all temperatures, while ensuring the integrity and accuracy of the parameter system.

[0057] For example, the sodium-ion battery under test was placed in a 25°C incubator and cycled three times with a 1C current to achieve a stable state. Subsequently, a capacity calibration test was performed three times (resting – 1C constant current / constant voltage charging – resting – 1C constant current discharging), and the average of the three discharge capacities was taken. C max,initial This represents the initial discharge capacity of the battery.

[0058] Using the Arrhenius formula C max,initial Temperature correction is performed to obtain C max,initial ( T ): (3) in, T ref In this embodiment, the temperature is tested. T ref The temperature is 25℃, where T is the actual temperature. C max,initial The initial discharge capacity is at 25°C. C max,initial ( T The initial discharge capacity at the current temperature is 0. R The molar gas constant, R =8.314 J / (mol·K); E x is the activation energy, and is a constant.

[0059] In some optional implementations, the process of establishing the correspondence between the initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance and temperature and state of charge includes: (1) Electrochemical tests were conducted on the aqueous sodium-ion battery at a preset reference temperature to obtain test data under different states of charge.

[0060] (2) The parameter identification algorithm is used to identify the test data and obtain the initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance and initial leakage resistance corresponding to different states of charge at the temperature.

[0061] (3) Use the Arrhenius formula to perform temperature correction, and establish the correspondence between the corrected ohmic internal resistance, the corrected polarization resistance and the corrected leakage resistance and each temperature and state of charge, as well as the correspondence between the initial polarization capacitance and the state of charge.

[0062] Specifically, firstly, electrochemical tests are conducted on an aqueous sodium-ion battery at a preset reference temperature to obtain voltage and current response data under different states of charge. Then, a parameter identification algorithm is used to analyze the test data, extracting the initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance corresponding to different states of charge at the reference temperature from the response curves, thus mapping experimental data to electrical parameters. Based on this, the Arrhenius equation is used to perform temperature correction on the initial ohmic internal resistance, initial polarization resistance, and initial leakage resistance. According to the physical laws governing the change of resistance parameters with temperature, these parameters are extrapolated to other temperatures, obtaining the complete distribution of each corrected resistance parameter at various temperatures and states of charge. Simultaneously, since the polarization capacitance is less affected by temperature, a direct correspondence between the initial polarization capacitance and the state of charge is established. This method, based on reference temperature testing and combined with parameter identification algorithms and temperature correction models, effectively reduces the workload of comprehensive testing at multiple temperatures while ensuring parameter accuracy.

[0063] For example, a fresh aqueous sodium-ion battery was placed in a 25°C incubator and cycled three times with a 1C current to allow the battery to reach a stable state. The battery was then fully charged to 100% SOC, and a Hybrid Pulse Power Characterization (HPPC) test was performed. The test steps included: (1) After pulse discharge of the battery using 1C current, let it stand still.

[0064] (2) Adjust the battery SOC to 95% and repeat the HPPC test to finally obtain the test data of the full SOC range [0% SOC, 100% SOC], with an SOC sampling interval of 5%.

[0065] (3) Based on the recursive least squares method, the voltage-time relationship of the static step in the HPPC test curve is identified by the formula of the relationship between the parameters of the core dynamic model (i.e., formula (2)). The battery parameters in the full SOC range of 25℃, [0% SOC, 100% SOC] are obtained, including the relationship between the initial ohmic internal resistance and SOC. R0,initial ( SOC Relationship between initial polarization resistance and SOC R p,initial ( SOC Relationship between initial polarization capacitance and SOC C p,initial ( SOC Relationship between initial leakage resistance and SOC R leak,initial ( SOC ).

[0066] (4) The relationship between the above and SOC is corrected for temperature using the Arrhenius formula, resulting in the following: R 0,initial ( SOC, T ), R p,initial ( SOC,T ), R leak,initial ( SOC,T )relation: (4) in, T ref In this embodiment, the HPPC test temperature is... T ref The temperature is 25℃, where T is the actual temperature. R x,initial ( SOC The initial resistance at 25°C includes... R 0,initial ( SOC ), R p,initial ( SOC ), R leak,initial ( SOC ); R x,initial ( SOC,T ) represents the initial resistance at the current temperature, including R 0,initial ( SOC,T ), R p,initial ( SOC,T ), R leak,initial ( SOC,T ); R The molar gas constant, R =8.314 J / (mol·K); E x is the activation energy, and is a constant.

[0067] Since the effect of temperature on polarization capacitance is negligible, therefore: (5) Optionally, electrochemical testing may include: mixed power pulse characteristic testing or electrochemical impedance spectroscopy testing.

[0068] Optionally, the parameter identification algorithm includes: recursive least squares method, neural network algorithm or particle swarm optimization algorithm.

[0069] In some optional implementations, the degradation coefficients include capacitance degradation coefficients, ohmic internal resistance degradation coefficients, polarization resistance degradation coefficients, and leakage resistance degradation coefficients. The process for obtaining the degradation coefficients includes: (1) Perform cycle aging tests on aqueous sodium-ion batteries and perform an electrochemical test every preset number of cycles during the aging test until the battery capacity decays to the preset capacity threshold.

[0070] (2) Record the number of cycles and the corresponding battery capacity, internal resistance, polarization resistance and leakage resistance for each electrochemical test.

[0071] (3) Curve fitting was performed on the relationship between cycle number and battery capacity, cycle number and ohmic internal resistance, cycle number and polarization resistance, and cycle number and leakage resistance to obtain the corresponding degradation coefficients.

[0072] Specifically, in order to accurately obtain the degradation coefficient that reflects the parameter decay law of aqueous sodium-ion batteries with cycle aging, and this coefficient covers the degradation coefficients of capacity, ohmic internal resistance, polarization resistance and leakage resistance, the aqueous sodium-ion batteries are first subjected to full life cycle cycle aging test to simulate the charge and discharge cycle conditions in actual use of the battery. During the aging test, electrochemical tests are carried out periodically according to the preset number of cycles to continuously track the parameter changes in the battery aging process until the battery capacity decays to the preset capacity threshold and the test is terminated to ensure that the collected aging data covers the main aging stages of the battery.

[0073] Subsequently, the results of each electrochemical test were accurately recorded, and the specific values ​​of battery capacity, ohmic internal resistance, polarization resistance, and leakage resistance at the corresponding number of cycles were simultaneously retained to form a complete battery aging parameter dataset. Finally, based on this dataset, professional curve fitting was performed on the relationship between cycle number and battery capacity, and between cycle number and each resistance parameter. By fitting, the decay rate and change law of each parameter with the increase of cycle number were quantified, and the corresponding capacity, ohmic internal resistance, polarization resistance, and leakage resistance degradation coefficients were finally obtained.

[0074] For example, an aqueous sodium-ion battery was placed in a 25°C chamber and cycled three times with a 1C current to allow the battery to reach a stable state. Afterward, a cyclic aging test was performed at a 1C current, with an HPPC test conducted every 50 cycles, using the same method as the HPPC test described above. This continued until the battery capacity decreased to 80% of its maximum capacity.

[0075] Then, the aging curve is fitted according to the following formula to obtain the capacity degradation coefficient. k capacity Ohmic resistance degradation coefficient k 0. Polarization resistance degradation coefficient k p Leakage resistance degradation coefficient k leak The formula is: (6) Where N is the number of iterations; C max ( T,N This represents the relationship between the battery's maximum discharge capacity and temperature after N cycles. R 0( SOC,T,N ), R p ( SOC,T,N ), C p ( SOC,T,N ), R leak ( SOC,T,N The figures represent the relationships between ohmic internal resistance and temperature, polarization resistance and temperature, polarization capacitance and temperature, and leakage resistance and temperature at different SOCs after N cycles.

[0076] The equivalent circuit model construction method for aqueous sodium-ion batteries provided in this embodiment involves electrochemical testing of the aqueous sodium-ion battery and recording the changes in key parameters throughout its entire life cycle. Then, through curve fitting, the degradation coefficients corresponding to capacity, ohmic internal resistance, polarization resistance, and leakage resistance are obtained, thereby establishing a quantitative model of battery performance degradation with cycle count. By introducing cycle aging factors into the equivalent circuit modeling system in the form of degradation coefficients, the model can dynamically reflect the aging characteristics of aqueous sodium-ion batteries during long-term use, such as capacity decay, internal resistance increase, and intensified side reactions. Based on the degradation coefficients obtained by this method, the electrochemical characteristics of the battery under different aging states can be accurately corrected, significantly improving the model's adaptability and estimation accuracy throughout its entire life cycle, and providing reliable data support for battery health status assessment and remaining life prediction.

[0077] In some alternative implementations, it also includes: (1) Based on the initial electrical parameters and degradation coefficient, a two-dimensional comparison table of maximum discharge capacity with temperature and number of cycles is established, and a three-dimensional comparison table of ohmic internal resistance, polarization resistance, polarization capacitance and leakage resistance with state of charge, temperature and number of cycles is established.

[0078] (2) Based on two-dimensional and three-dimensional comparison tables, the equivalent parameters of aqueous sodium-ion batteries under different conditions are obtained by table lookup method. The equivalent parameters include maximum discharge capacity, ohmic internal resistance, polarization resistance, polarization capacitance and leakage resistance.

[0079] Specifically, to enable the equivalent circuit model to quickly and accurately adapt to parameter changes in aqueous sodium-ion batteries under different operating conditions, a standardized parameter lookup table system will be built based on the initial electrical parameters and degradation coefficients obtained in the early stages. On the one hand, a two-dimensional lookup table is established to correlate the maximum discharge capacity with temperature and cycle count. On the other hand, a three-dimensional lookup table is constructed to link the ohmic internal resistance, polarization resistance, polarization capacitance, and leakage resistance with the state of charge, temperature, and cycle count. This transforms the battery parameter change patterns under multi-factor coupling into intuitive quantitative data tables. Subsequently, during the actual operation of the model, the corresponding equivalent parameters such as maximum discharge capacity, ohmic internal resistance, polarization resistance, polarization capacitance, and leakage resistance can be quickly retrieved directly by looking up the table, based on the battery's current temperature, cycle count, state of charge, and other actual operating conditions. This eliminates the need for complex formula calculations in real time, improving the efficiency of parameter retrieval and ensuring that the parameter values ​​closely match the battery's current actual operating state.

[0080] For example, establish C max ( T,N A two-dimensional table is used to obtain the battery's state information under different conditions using a lookup table method. C max ;Establish R 0( SOC,T,N ), R p ( SOC,T,N ), C p ( SOC,T,N ), R leak ( SOC,T,N A three-dimensional table is used to obtain battery information under different states through a lookup table method. R 0、 R p , C p , R leak .

[0081] This embodiment provides an early warning method for aqueous sodium-ion batteries, such as... Figure 3 As shown, it includes: Step S201: Obtain the measured parameters of the aqueous sodium-ion battery during actual operation.

[0082] Specifically, by acquiring real-time measured parameters of aqueous sodium-ion batteries during actual operation, a raw data foundation is provided for subsequent early warning analysis. The selected measured parameters cover key electrical quantities that reflect the battery's operating status, including but not limited to directly measurable physical quantities such as terminal voltage, charging / discharging current, and surface temperature, as well as characteristic parameters such as leakage resistance obtained indirectly through specific testing methods. The core of this step lies in establishing a high-precision, high-frequency data acquisition channel to ensure the accuracy and real-time nature of the measured data, providing a reliable benchmark for subsequent comparison with model predictions. The measured leakage resistance is typically obtained by applying a current pulse excitation of preset amplitude and duration when the battery is in a static state, simultaneously acquiring the voltage and current responses, and then calculating it based on Ohm's law.

[0083] Step S202: After inputting the current external operating condition parameters into the equivalent circuit model constructed by the construction method of the first aspect or any of its corresponding embodiments, the predicted parameters output by the equivalent circuit model corresponding to the current operating condition are obtained.

[0084] Specifically, the current external operating condition parameters are input into a pre-built equivalent circuit model, driving the model to run and output predicted parameters corresponding to the current operating conditions. External operating condition parameters include the battery's current operating temperature, operating current, and accumulated cycle count—boundary conditions that directly affect battery behavior. Based on the initial electrical parameters established during the initial construction phase, their correspondence with temperature and state of charge, the degradation coefficient varying with cycle count, and the circuit structure of the core dynamic model, the equivalent circuit model calculates in real-time the predicted terminal voltage and predicted leakage resistance of the battery under normal operating conditions by solving a system of circuit differential equations. The core of this step is to fully leverage the simulation and prediction capabilities of the equivalent circuit model, using the "normal state baseline value" output by the model as a reference standard for subsequent early warning judgments.

[0085] Step S203: Compare the measured parameters with the predicted parameters.

[0086] Specifically, the measured parameters obtained in step S201 are compared item by item with the predicted parameters obtained in step S202, and the deviation value between the two is calculated. The principle of the comparison is based on the fundamental assumption that "a deviation of the measured value from the benchmark value indicates an abnormal battery state," and the magnitude of the deviation is obtained through mathematical calculations. The core of this step is to establish a unified comparison benchmark and deviation calculation method to ensure that the deviations of different types of parameters (such as voltage and resistance) are comparable and interpretable. For terminal voltage, the difference between the measured terminal voltage and the predicted terminal voltage is calculated; for leakage resistance, the difference between the measured leakage resistance and the predicted leakage resistance is calculated. The deviation calculation results will serve as the basis for subsequent early warning triggering.

[0087] Step S204: If the deviation between the measured parameter and the predicted parameter exceeds the preset value, and the duration of the deviation exceeds the preset time threshold, an abnormal warning is triggered.

[0088] Specifically, the measured parameters include the measured terminal voltage and the measured leakage resistance, and the predicted parameters include the predicted terminal voltage and the predicted leakage resistance; the process of triggering an anomaly warning includes: (1) If the difference between the measured terminal voltage and the predicted terminal voltage exceeds the first preset value, and the duration of the difference exceeds the preset time threshold, then a voltage abnormality warning is triggered.

[0089] (2) If the difference between the measured leakage resistance and the predicted leakage resistance exceeds the second preset value, and the duration of the difference exceeds the preset time threshold, then an abnormal leakage resistance warning will be triggered.

[0090] Specifically, based on the deviation value calculated in step S203, a comprehensive judgment is made in conjunction with a preset threshold and time window to determine whether to trigger an anomaly warning. This step employs a dual judgment mechanism: first, it judges whether the deviation amplitude exceeds a preset value to identify abnormal states that exceed the normal fluctuation range; second, it judges whether the over-limit state continues for more than a preset time threshold to filter out false alarms caused by instantaneous noise or measurement interference. Only when both the deviation amplitude and duration conditions are met simultaneously is it determined to be a real anomaly and a warning signal is triggered. The core of this mechanism lies in balancing the sensitivity and accuracy of the warning, enabling timely detection of early fault hazards while effectively avoiding interference from false alarms to system operation. The warning signal can be distinguished into voltage anomaly warnings and leakage resistance anomaly warnings based on the source of the deviation, providing maintenance personnel with more accurate fault location information.

[0091] For example, refer to Figure 4 The specific steps for issuing an early warning are as follows: (1) Use a voltage measuring device to synchronously obtain the actual battery terminal voltage U t .

[0092] (2) When the system is in a static state, a current pulse with known amplitude and duration is applied to the battery. The voltage and current are sampled synchronously with high precision. The measured leakage resistance R is calculated based on the core dynamic model (formula (2)). leak '.

[0093] (3) An alert will be triggered if any of the following conditions are met: (a) Actual terminal voltage U t and predicted terminal voltage U d In comparison, if the difference between the two is greater than a certain set voltage threshold (|U) t -U d ∣≧U th Meanwhile, the duration is greater than a set threshold (t≧t). th ).

[0094] (b) Actual leakage resistance R leak 'and predicted leakage resistance R leak In comparison, if the difference between the two is greater than a certain set resistance threshold (|R) leak -R leak '∣≧R th Meanwhile, the duration is greater than a set threshold (t≧t). th ).

[0095] It should be noted that the voltage threshold U th Time threshold t th and leakage resistance threshold R th All settings are based on actual needs and are not limited here.

[0096] The early warning method for aqueous sodium-ion batteries provided in this embodiment dynamically compares the measured parameters acquired in real time with the predicted parameters output by the equivalent circuit model, and introduces a dual judgment mechanism based on deviation exceeding limits and duration, thereby achieving accurate monitoring of the operating status of aqueous sodium-ion batteries. It fully utilizes the predictive capability of the high-precision equivalent circuit model, using the normal state benchmark value output by the model as a reference to keenly capture minute abnormal fluctuations that occur during battery operation, and effectively filters out noise interference through duration judgment to avoid false alarms. Compared with traditional early warning methods based on a single threshold or simple voltage detection, this embodiment incorporates characteristic parameters reflecting internal side reactions, such as leakage resistance, into the early warning indicator system, enabling earlier identification of early fault hazards caused by slow degradation such as electrolyte gas evolution and electrode dissolution. This significantly improves the accuracy, timeliness, and reliability of the early warning, providing strong support for the safe and stable operation of aqueous sodium-ion batteries in large-scale energy storage applications.

[0097] This embodiment also provides an equivalent circuit model construction device for an aqueous sodium-ion battery. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0098] This embodiment provides a device for constructing an equivalent circuit model of an aqueous sodium-ion battery, such as... Figure 5 As shown, it includes: The parameter calibration module 501 is used to acquire the initial electrical parameters of the aqueous sodium-ion battery at different temperatures and establish the correspondence between the initial electrical parameters and temperature, and / or state of charge. The initial electrical parameters include the initial discharge capacity, initial open-circuit voltage, initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance.

[0099] The aging characterization module 502 is used to perform cycle aging tests on aqueous sodium-ion batteries and obtain the degradation coefficient that changes with the number of cycles.

[0100] The State of Charge (SOC) estimation module 503 is used to construct an SOC estimation model. The SOC estimation model is used to estimate the state of charge of an aqueous sodium-ion battery based on external operating parameters, initial electrical parameters, and degradation coefficients, and to calculate the open-circuit voltage.

[0101] The dynamic response module 504 is used to construct the core dynamic model. The core dynamic model is used to calculate the predicted parameters of the aqueous sodium-ion battery under normal operating conditions based on external operating parameters, initial electrical parameters, degradation coefficient and open circuit voltage. The predicted parameters are used to feed back to the SOC estimation model to correct the state of charge.

[0102] The equivalent circuit model construction apparatus for aqueous sodium-ion batteries provided in this embodiment of the invention can execute the method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0103] This embodiment also provides a warning device for an aqueous sodium-ion battery, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0104] This embodiment provides an early warning device for aqueous sodium-ion batteries, such as... Figure 6As shown, it includes: The data acquisition module 601 is used to acquire measured parameters of the aqueous sodium-ion battery during actual operation.

[0105] The model prediction module 602 is used to input the current external operating condition parameters into the equivalent circuit model constructed by the construction method of the first aspect or any of its corresponding embodiments, and then obtain the prediction parameters output by the equivalent circuit model corresponding to the current operating condition.

[0106] The early warning judgment module 603 is used to compare the measured parameters with the predicted parameters; if the deviation between the measured parameters and the predicted parameters exceeds the preset value and the duration of the deviation exceeds the preset time threshold, an abnormal warning is triggered.

[0107] The early warning device for aqueous sodium-ion batteries provided in this embodiment of the invention can execute the method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0108] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0109] The following is a detailed reference. Figure 7 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 001, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 002 or a program loaded from memory 008 into random access memory (RAM) 003. The RAM 003 also stores various programs and data required for the operation of the electronic device. The processor 001, ROM 002, and RAM 003 are interconnected via bus 004. An input / output (I / O) interface 005 is also connected to bus 004.

[0110] Typically, the following devices can be connected to I / O interface 005: input devices 006 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 007 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 008 including, for example, magnetic tapes, hard disks, etc.; and communication devices 009. Communication device 009 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0111] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 009, or installed from memory 008, or installed from ROM 002. When the computer program is executed by processor 001, it performs the functions defined in the methods of the embodiments of the present invention.

[0112] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0113] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0114] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0115] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing an equivalent circuit model of an aqueous sodium-ion battery, characterized in that, include: The initial electrical parameters of an aqueous sodium-ion battery at different temperatures are obtained, and the correspondence between the initial electrical parameters and temperature and / or state of charge is established. The initial electrical parameters include initial discharge capacity, initial open-circuit voltage, initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance; Cyclic aging tests were conducted on aqueous sodium-ion batteries to obtain the degradation coefficient as a function of the number of cycles. A State of Charge (SOC) estimation model is constructed, which is used to estimate the state of charge of an aqueous sodium-ion battery based on external operating parameters, the initial electrical parameters, and the degradation coefficient, and to calculate the open-circuit voltage. A core dynamic model is constructed, which is used to calculate the predicted parameters of the aqueous sodium-ion battery under normal operating conditions based on the external operating parameters, the initial electrical parameters, the degradation coefficient and the open circuit voltage; the predicted parameters are used to feed back to the SOC estimation model to correct the state of charge.

2. The construction method according to claim 1, characterized in that, The core dynamic model includes: a controlled voltage source, leakage resistance, ohmic internal resistance, polarization resistance, and polarization capacitor, wherein... The polarization resistor and the polarization capacitor are connected in parallel to form an RC network; The controlled voltage source, the ohmic internal resistance, and the RC network are connected in series. The leakage resistor is connected in parallel with the controlled voltage source, and the leakage resistor is used to characterize the energy loss caused by the internal side reaction of the aqueous sodium-ion battery. The controlled voltage source is used to characterize the open-circuit voltage of an aqueous sodium-ion battery.

3. The construction method according to claim 2, characterized in that, The formulas relating the parameters of the core dynamic model are as follows: in, U OC This is the open-circuit voltage of an aqueous sodium-ion battery; R leak To predict leakage resistance; I leak This is the current across the leakage resistor; U p Polarization voltage; R p Polarization resistor; C p Polarizing capacitor; I This is the operating current; I s For output current; U d For predicting terminal voltage; R 0 represents the internal resistance in ohms.

4. The construction method according to claim 1, characterized in that, The process of establishing the relationship between the initial discharge capacity and temperature includes: At a preset reference temperature, the aqueous sodium-ion battery was charged and discharged until it reached a stable state. Multiple capacity calibration tests were conducted on the aqueous sodium-ion battery, and the average discharge capacity obtained from the multiple tests was taken as the initial discharge capacity at that temperature. Based on the initial discharge capacity obtained at this temperature, the relationship between the initial discharge capacity and each temperature is established by fitting the Arrhenius formula.

5. The construction method according to claim 1, characterized in that, The process of establishing the correspondence between the initial open-circuit voltage and temperature includes: At each temperature, the aqueous sodium-ion battery was charged to the charging cutoff voltage and then left to stand for a first preset time. The aqueous sodium-ion battery is controlled to discharge to the discharge cutoff voltage according to the preset discharge current, and the terminal voltage during the discharge process is recorded as the initial open circuit voltage at that temperature.

6. The construction method according to claim 1, characterized in that, The process of establishing the correspondence between the initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance, and initial leakage resistance and temperature, and / or state of charge includes: Electrochemical tests were conducted on an aqueous sodium-ion battery at a preset reference temperature to obtain test data under different states of charge. The test data is identified using a parameter identification algorithm to obtain the initial ohmic internal resistance, initial polarization resistance, initial polarization capacitance and initial leakage resistance corresponding to different states of charge at the temperature. Temperature correction is performed using the Arrhenius formula, and the correspondence between the corrected ohmic internal resistance, the corrected polarization resistance, and the corrected leakage resistance and each temperature and state of charge is established, as well as the correspondence between the initial polarization capacitance and the state of charge is established.

7. The construction method according to claim 6, characterized in that, The electrochemical tests include: mixed power pulse characteristic test or electrochemical impedance spectroscopy test.

8. The construction method according to claim 6, characterized in that, The parameter identification algorithm includes: recursive least squares method, neural network algorithm or particle swarm optimization algorithm.

9. The construction method according to claim 1, characterized in that, The degradation coefficients include capacitance degradation coefficients, ohmic internal resistance degradation coefficients, polarization resistance degradation coefficients, and leakage resistance degradation coefficients. The process of obtaining the degradation coefficients includes: A cycle aging test was conducted on the aqueous sodium-ion battery, and an electrochemical test was performed every preset number of cycles during the aging test until the battery capacity decayed to a preset capacity threshold. Record the number of cycles and the corresponding battery capacity, internal resistance in ohms, polarization resistance and leakage resistance for each electrochemical test; Curve fitting was performed on the relationships between cycle number and battery capacity, cycle number and ohmic internal resistance, cycle number and polarization resistance, and cycle number and leakage resistance to obtain the corresponding degradation coefficients.

10. The construction method according to claim 9, characterized in that, Also includes: Based on the initial electrical parameters and the degradation coefficient, a two-dimensional comparison table of maximum discharge capacity with temperature and cycle number is established, and a three-dimensional comparison table of ohmic internal resistance, polarization resistance, polarization capacitance and leakage resistance with state of charge, temperature and cycle number is established. Based on the two-dimensional and three-dimensional lookup tables, the equivalent parameters of aqueous sodium-ion batteries under different conditions are obtained by the lookup table method. The equivalent parameters include maximum discharge capacity, ohmic internal resistance, polarization resistance, polarization capacitance, and leakage resistance.

11. A method for early warning of aqueous sodium-ion batteries, characterized in that, include: Obtain the measured parameters of the aqueous sodium-ion battery during actual operation; After inputting the current external operating condition parameters into the equivalent circuit model constructed by the construction method described in any one of claims 1 to 10, the predicted parameters output by the equivalent circuit model corresponding to the current operating condition are obtained. The measured parameters are compared with the predicted parameters; If the deviation between the measured parameter and the predicted parameter exceeds a preset value, and the duration of the deviation exceeds a preset time threshold, an anomaly warning is triggered.

12. The early warning method according to claim 11, characterized in that, The measured parameters include the measured terminal voltage and the measured leakage resistance, and the predicted parameters include the predicted terminal voltage and the predicted leakage resistance. The process of triggering the abnormal warning includes: If the difference between the measured terminal voltage and the predicted terminal voltage exceeds a first preset value, and the duration of the difference exceeds a preset time threshold, a voltage anomaly warning is triggered. If the difference between the measured leakage resistance and the predicted leakage resistance exceeds a second preset value, and the duration of the difference exceeds a preset time threshold, an abnormal leakage resistance warning is triggered.

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