A method and device for evaluating the service life of a water-based sodium-ion battery energy storage system and a product

Abstract

The present application relates to the technical field of water-based sodium-ion battery energy storage system life evaluation, and discloses a water-based sodium-ion battery energy storage system life evaluation method, device and product, comprising: obtaining real-time electrolyte state parameter set in the operation process of the water-based sodium-ion battery energy storage system, and real-time electrochemical impedance spectrum and real-time voltage relaxation curve under the current multi-physical field coupling detection working condition; extracting features from the real-time electrochemical impedance spectrum and the real-time voltage relaxation curve, and determining a real-time feature parameter set; based on the real-time electrolyte state parameter set and the real-time feature parameter set, processing through a target life prediction model based on material attenuation mechanism to obtain a health state evaluation value and a remaining service life prediction value of the water-based sodium-ion battery energy storage system. Therefore, by implementing the present application, the electrolyte state and the multi-physical field electrochemical characteristics are fused into the mechanism type model for the first time, and accurate, interpretable and real-time life evaluation of the water-based sodium-ion battery energy storage system is realized.

Description

Technical Field

[0001] This invention relates to the field of energy storage system technology, specifically to a method, apparatus, and product for assessing the lifespan of an aqueous sodium-ion battery energy storage system. Background Technology

[0002] With the large-scale development of new energy power systems, energy storage technology has become crucial for addressing the volatility and intermittency of renewable energy. Aqueous sodium-ion batteries, due to their inherent safety, environmental friendliness, and low cost, show great promise for large-scale energy storage applications.

[0003] The lifespan of an energy storage system is a core indicator affecting its overall lifecycle economics. Current energy storage system lifespan assessment methods mainly fall into two categories: one is based on cell electrochemical models, assessing the relationship between internal battery parameters and capacity degradation; the other is a data-driven approach, utilizing machine learning algorithms to fit the relationship between operational data and lifespan. However, both methods have significant limitations when applied to aqueous sodium-ion batteries.

[0004] Related Technology 1 proposes a lifespan assessment method for energy storage systems that considers system topology. It uses a general generating function method to transfer the uncertainty of individual cell health status to the system level and combines cell rating and system reconfiguration strategies for lifespan simulation. This method focuses on optimizing system architecture and reconfiguration strategies but does not deeply consider changes in the electrochemical characteristics of the battery itself, especially the unique degradation mechanism of aqueous sodium-ion batteries. Related Technology 2 proposes a lifespan protection method for dynamically reconfigurable energy storage systems. It assesses system health by calculating health coefficients and load coefficients and adjusts the operating strategy based on the health status. This method focuses on real-time control strategies, but the input parameters are still based on traditional state variables such as SOC and SOH, failing to capture the side reaction information unique to aqueous electrolytes.

[0005] Aqueous sodium-ion batteries exhibit unique degradation mechanisms: cathode materials, such as Prussian blue analogues, experience crystal structure collapse and transition metal ion dissolution during cycling, forming a CEI film on the cathode surface; while anode materials like sodium titanium phosphate do not form an SEI film, they may be accompanied by hydrogen evolution side reactions; and aqueous electrolytes are prone to oxygen evolution reactions at high voltages, leading to water loss and changes in electrolyte concentration. These material-level degradation characteristics have not yet been effectively incorporated into the lifespan assessment system for energy storage systems.

[0006] Therefore, how to provide a life assessment method that can integrate the degradation mechanism of aqueous sodium-ion battery materials and is also engineering-feasible has become an urgent problem to be solved in this field. Summary of the Invention

[0007] This invention provides a method, apparatus, and product for assessing the lifespan of an aqueous sodium-ion battery energy storage system, in order to address the problem that the degradation characteristics of aqueous sodium-ion battery materials are not fully considered in the prior art.

[0008] In a first aspect, the present invention provides a method for assessing the lifespan of an aqueous sodium-ion battery energy storage system, the method comprising: The system acquires real-time electrolyte state parameters, real-time electrochemical impedance spectroscopy (EIS) and voltage relaxation curves under current multi-physics coupling detection conditions during the operation of an aqueous sodium-ion battery energy storage system. Features are extracted from the real-time EIS and voltage relaxation curves, and a real-time feature parameter set is determined. Based on the real-time electrolyte state parameter set and the real-time feature parameter set, a target lifetime prediction model based on material degradation mechanisms is used to obtain the health status assessment value and remaining lifetime prediction value of the aqueous sodium-ion battery energy storage system.

[0009] The present invention provides a lifespan assessment method for aqueous sodium-ion battery energy storage systems. By simultaneously acquiring electrolyte state and electrochemical multiphysics field signals, it can comprehensively cover the three-layer degradation information of aqueous sodium-ion battery materials, interfaces, and electrolytes, providing full-dimensional raw data for lifespan assessment. Furthermore, by extracting features from real-time electrochemical impedance spectroscopy and real-time voltage relaxation curves, it can extract interfacial side reactions and ion transport kinetics indicators from the raw signals, eliminating redundant data and thus helping to make the assessment signals more focused and sensitive. Furthermore, by inputting the real-time electrolyte state parameter set and real-time feature parameter set into a target lifespan prediction model based on material degradation mechanisms, it can output a health status assessment value and a remaining lifespan prediction value for the aqueous sodium-ion battery energy storage system. This integrates the material degradation mechanism, avoiding the black-box problem of purely data-driven approaches, making the output results physically interpretable, and significantly improving the assessment accuracy and reliability. Therefore, by implementing this invention, for the first time, the electrolyte state and multiphysics field electrochemical features are integrated and input into a mechanistic model, achieving accurate, interpretable, and real-time lifespan assessment of aqueous sodium-ion battery energy storage systems, solving the deficiency of traditional methods that do not cover the battery-specific degradation mechanism.

[0010] In one optional implementation, acquiring a set of real-time electrolyte state parameters during the operation of an aqueous sodium-ion battery energy storage system includes: Data sets of internal gas pressure changes and real-time operating temperature values ​​during the operation of an aqueous sodium-ion battery energy storage system are obtained. Based on the internal gas pressure change data set, hydrogen evolution rate and oxygen evolution rate values ​​are obtained through ideal gas law processing. Based on the hydrogen evolution rate and oxygen evolution rate values, the cumulative water consumption of the electrolyte in the aqueous sodium-ion battery energy storage system is calculated using Faraday's law and electrolyte volume parameters. The electrolyte concentration change rate is calculated based on the cumulative water consumption, the initial electrolyte volume, and the initial salt concentration of the aqueous sodium-ion battery energy storage system. The real-time electrolyte state parameter set is determined based on the real-time operating temperature value, hydrogen evolution rate value, and electrolyte concentration change rate.

[0011] The present invention provides a lifespan assessment method for aqueous sodium-ion battery energy storage systems. By acquiring the internal gas pressure and operating temperature of the battery and calculating the hydrogen evolution rate and oxygen evolution rate using the ideal gas law, the intensity of the core side reactions in aqueous batteries is quantified, thus directly reflecting the degree of electrolyte decomposition and negative electrode side reactions. Furthermore, by combining Faraday's law and electrolyte volume parameters to calculate the cumulative water consumption of the electrolyte in the aqueous sodium-ion battery energy storage system, the degradation of the core components of the aqueous electrolyte is accurately tracked, providing a quantitative basis for electrolyte degradation. Furthermore, by calculating the electrolyte concentration change rate, the method reflects the electrolyte concentration degradation caused by water loss, capturing long-term accumulated hidden degradation. Furthermore, by combining real-time operating temperature, hydrogen evolution rate, and electrolyte concentration change rate, a three-in-one real-time electrolyte state parameter set of temperature, gas production, and concentration is formed, comprehensively characterizing the entire life cycle state of the aqueous electrolyte. Therefore, by implementing this invention, online quantitative monitoring of electrolyte state is achieved through indirect calculation, without the need for direct sampling and testing, exhibiting strong engineering feasibility and accurately capturing the unique hydrogen evolution, water consumption, and concentration drift degradation characteristics of aqueous batteries.

[0012] In one optional implementation, features are extracted from the real-time electrochemical impedance spectroscopy and real-time voltage relaxation curves, and a set of real-time feature parameters is determined, including: The real-time electrochemical impedance spectroscopy is separated, and the semi-circular arc in the mid-frequency region is determined. A pre-defined equivalent circuit fitting model is used to fit the semi-circular arc in the mid-frequency region, and the impedance value of the positive electrode CEI film under the current multi-physics field coupling detection condition is determined. Based on the impedance values ​​of the positive electrode CEI film under two consecutive multi-physics field coupling detection conditions, the rate of increase of the positive electrode CEI film impedance is determined. The real-time voltage relaxation curve is mathematically fitted, and the ion diffusion time constant is determined. The ion diffusion time constant is used to characterize the decay of the apparent diffusion coefficient of sodium ions in the bulk phase and interface layer of the electrode material. Based on the rate of increase of the positive electrode CEI film impedance and the ion diffusion time constant, the real-time characteristic parameter set is determined.

[0013] The present invention provides a lifespan assessment method for aqueous sodium-ion battery energy storage systems. By separating and processing real-time electrochemical impedance spectroscopy and determining the semi-circular arc in the mid-frequency region, it can accurately locate the positive electrode CEI film and the charge transfer impedance signal region, eliminating interference from other impedances. Furthermore, by fitting an equivalent circuit to obtain the impedance value of the positive electrode CEI film, the degree of side reactions at the positive electrode / electrolyte interface can be quantitatively characterized. Furthermore, by calculating the rate of increase of the positive electrode CEI film impedance, the accumulation rate of interfacial side reactions can be reflected, which helps to provide early warning of battery aging and is more sensitive than capacity decay. Furthermore, by mathematically fitting the real-time voltage relaxation curve and determining the ion diffusion time constant, the decay of sodium ion transport kinetics can be characterized, directly reflecting the degree of positive electrode crystal structure collapse and pore blockage. Furthermore, by combining the rate of increase of the positive electrode CEI film impedance and the ion diffusion time constant to form a real-time characteristic parameter set that includes both interfacial side reactions and kinetic decay characteristics, it can cover the core aging mechanisms of electrode materials and interfaces. Therefore, by implementing this invention, by extracting water-based battery-specific features from multi-physics field signals, it is possible to sensitively capture early decay signals such as positive electrode CEI film growth and ion diffusion obstruction, thereby helping to achieve early warning and accurate evaluation.

[0014] In one alternative implementation, the method further includes: This study acquires historical electrolyte state parameters of an aqueous sodium-ion battery energy storage system during its historical operation, as well as historical electrochemical impedance spectroscopy (EIS) and voltage relaxation curves of multiple aqueous sodium-ion battery cells produced in the same batch as the system at corresponding aging stages. Features are extracted from the historical EIS and voltage relaxation curves to determine multiple historical feature parameter sets. The electrodes of multiple aqueous sodium-ion battery cells are characterized to obtain multiple material characterization datasets. Correlation analysis is performed on the historical electrolyte state parameter sets, the multiple historical feature parameter sets, and the multiple material characterization datasets to determine multiple key feature parameter sets related to battery capacity degradation. Based on these key feature parameter sets, a target lifetime prediction model based on the material degradation mechanism is constructed through training with a pre-defined neural network algorithm.

[0015] The present invention provides a lifespan assessment method for aqueous sodium-ion battery energy storage systems. By acquiring historical electrolyte state parameter sets and historical electrochemical impedance spectroscopy and voltage relaxation curves of aqueous sodium-ion battery cells from the same batch, it ensures that the model training data is consistent with the target battery materials and processes, thus improving model adaptability. Furthermore, by extracting historical feature parameter sets, it unifies the feature caliber of online assessment and offline modeling, ensuring data homogeneity and consistent mechanisms. Furthermore, by characterizing the electrodes of multiple aqueous sodium-ion battery cells, it establishes a mapping between external electrical signals and changes in internal material structure, thereby endowing the model with physical meaning. Furthermore, correlation analysis can screen out indicators strongly correlated with capacity decay, reducing model redundancy and improving prediction efficiency and accuracy. Furthermore, based on multiple key feature parameter sets, a target lifespan prediction model based on material decay mechanisms is trained and constructed using a pre-set neural network algorithm, integrating the advantages of material mechanisms and data-driven approaches, and balancing interpretability and predictive performance. Therefore, by implementing this invention, a mechanism and data fusion model is established through offline calibration and material characterization, which solves the problems of the inability to interpret pure data models and the difficulty in implementing pure mechanism models in engineering, and can be adapted to large-scale energy storage system applications.

[0016] In one alternative implementation, the method further includes: The temperature adaptability correction factor is determined based on the type of aqueous electrolyte in the aqueous sodium-ion battery energy storage system; the target lifetime prediction model based on the material degradation mechanism is then corrected using the temperature adaptability correction factor.

[0017] The present invention provides a lifespan assessment method for aqueous sodium-ion battery energy storage systems. By determining a temperature adaptability correction factor based on the type of aqueous electrolyte in the system, the method can provide differentiated corrections for the temperature characteristics of different aqueous electrolytes, thereby improving the model's environmental adaptability. Furthermore, the temperature adaptability correction factor is used to modify the target lifespan prediction model based on material degradation mechanisms. This corrects signal deviations caused by increased viscosity and decreased conductivity at low temperatures, ensuring assessment accuracy across the entire temperature range. Therefore, by implementing this invention, the interference of temperature on ion transport and impedance signals can be eliminated, ensuring that the lifespan assessment results are unaffected by operating temperature fluctuations and remain stable and reliable under all operating conditions.

[0018] In one alternative implementation, the method further includes: When the health status assessment value is less than the preset threshold, an early warning message is generated and the set of allowable working intervals for the aqueous sodium-ion battery energy storage system is adjusted. The set of allowable working intervals is used to suppress the growth of the positive electrode CEI film and the decomposition side reaction of the electrolyte in the aqueous sodium-ion battery energy storage system.

[0019] The present invention provides a lifespan assessment method for aqueous sodium-ion battery energy storage systems. By generating an early warning message when the health status assessment value falls below a preset threshold, it achieves early alarm triggering, allowing sufficient time for maintenance and preventing sudden battery failure. Simultaneously, by adjusting the allowable operating range set of the aqueous sodium-ion battery energy storage system, it can actively inhibit CEI film growth and electrolyte decomposition, delaying battery aging. Therefore, by implementing this invention, a closed loop of assessment, early warning, and control is achieved, shifting from passive monitoring to proactive health management, extending the overall lifespan of the energy storage system, and reducing maintenance costs.

[0020] In a second aspect, the present invention provides a life assessment device for an aqueous sodium-ion battery energy storage system, the device comprising: The acquisition module is used to acquire the real-time electrolyte state parameter set, real-time electrochemical impedance spectroscopy, and real-time voltage relaxation curve under the current multi-physics field coupling detection condition during the operation of the aqueous sodium-ion battery energy storage system; the extraction module is used to extract features from the real-time electrochemical impedance spectroscopy and real-time voltage relaxation curve and determine the real-time feature parameter set; the processing module is used to obtain the health status assessment value and remaining service life prediction value of the aqueous sodium-ion battery energy storage system based on the real-time electrolyte state parameter set and real-time feature parameter set, through the target lifetime prediction model based on the material degradation mechanism.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-described method for evaluating the lifespan of an aqueous sodium-ion battery energy storage system or any corresponding embodiment thereof.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the life assessment method for an aqueous sodium-ion battery energy storage system described in the first aspect or any corresponding embodiment thereof.

[0023] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the life assessment method for an aqueous sodium-ion battery energy storage system described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0024] 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.

[0025] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the lifespan assessment method for an aqueous sodium-ion battery energy storage system according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the life assessment method for an aqueous sodium-ion battery energy storage system based on multi-scale feature detection according to an embodiment of the present invention. Figure 4 This is a schematic diagram of a multi-physics coupling detection operation according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the principle of calculating the rate of change of electrolyte concentration based on gas production according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the offline material characterization and model construction process according to an embodiment of the present invention; Figure 7 This is a structural block diagram of a water-based sodium-ion battery energy storage system life assessment device according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] 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.

[0027] 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.

[0028] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the execution of the lifetime assessment method for aqueous sodium-ion battery energy storage systems depends is described herein. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0030] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0031] This invention provides a method for assessing the lifespan of an aqueous sodium-ion battery energy storage system. By integrating the electrolyte state with multi-physical field electrochemical characteristics into a mechanistic model, it achieves accurate, interpretable, and real-time lifespan assessment of the aqueous sodium-ion battery energy storage system, thus overcoming the shortcomings of traditional methods that do not cover the battery's unique degradation mechanism.

[0032] According to an embodiment of the present invention, an embodiment of a life assessment method for an aqueous sodium-ion battery energy storage system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] This embodiment provides a method for assessing the lifespan of an aqueous sodium-ion battery energy storage system, which can be used in the aforementioned mobile terminals, such as mobile phones and tablets. Figure 2 This is a flowchart of a lifespan assessment method for an aqueous sodium-ion battery energy storage system according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the real-time electrolyte state parameter set during the operation of the aqueous sodium-ion battery energy storage system, as well as the real-time electrochemical impedance spectrum and real-time voltage relaxation curve under the current multi-physics field coupling detection condition.

[0034] In one optional embodiment, the aqueous sodium-ion battery energy storage system refers to a large-scale energy storage device composed of sodium-ion batteries with Prussian blue analogues as positive electrodes, sodium titanium phosphate as negative electrodes, and sodium salt aqueous solution as electrolyte. It has the characteristics of high safety, low cost, and environmental friendliness, and can be used in scenarios such as new energy grid connection and grid peak shaving.

[0035] In one optional embodiment, the real-time electrolyte state parameter set represents a set of parameters that reflect the deterioration state of the aqueous electrolyte, collected in real time during the operation of the aqueous sodium-ion battery energy storage system. These parameters may include parameters such as hydrogen evolution rate, electrolyte concentration change rate, and temperature.

[0036] In an optional embodiment, the multi-physics coupling detection condition refers to a detection condition that is periodically triggered during battery operation, which may include: applying a preset low-frequency AC disturbance signal to simultaneously excite the ion polarization of the electrode body, the positive electrode CEI film, and the electrolyte, and acquiring an integrated condition of electrochemical impedance spectroscopy and voltage relaxation curves before and after the disturbance.

[0037] For example, the low-frequency AC disturbance signal has a frequency range of 0.01 Hz to 10 Hz and is used to differentially excite the ion polarization process in the electrode material body, the positive electrode CEI film, and the electrolyte in an aqueous sodium-ion battery.

[0038] In an optional embodiment, real-time electrochemical impedance spectroscopy (EIS) refers to the impedance spectrum data measured after applying a low-frequency AC perturbation under multi-physics field coupling detection conditions. It is used to separate the impedance of the positive electrode CEI film and the charge transfer impedance, reflecting the degree of interfacial side reactions.

[0039] In one optional embodiment, the real-time voltage relaxation curve represents the curve of the battery voltage naturally relaxing and recovering over time after the disturbance signal stops, and is used to extract the ion diffusion time constant to reflect the decay of sodium ion transport kinetics.

[0040] For example, when the battery in an aqueous sodium-ion battery energy storage system is in a quiescent or low current (<0.05C) state, the BMS (Battery Management System) triggers a detection at a fixed cycle (e.g., 24 hours).

[0041] Furthermore, a low-frequency AC disturbance signal of 0.01Hz-10Hz is applied, with a small amplitude and lasting for two complete cycles.

[0042] Furthermore, a reference electrochemical impedance spectroscopy and voltage baseline were acquired before the perturbation; after the perturbation, a real-time electrochemical impedance spectroscopy was acquired immediately; then, the system was kept still, and a 30-minute voltage relaxation process was acquired to obtain the real-time voltage relaxation curve.

[0043] Step S202: Extract features from the real-time electrochemical impedance spectroscopy and real-time voltage relaxation curve, and determine the real-time feature parameter set.

[0044] In an optional embodiment, the real-time feature parameter set is used to quantitatively characterize the internal decay state of the aqueous sodium-ion battery, and may include the positive electrode CEI film impedance growth rate and the ion diffusion time constant.

[0045] In one optional embodiment, signal analysis and quantification calculations are performed on the real-time electrochemical impedance spectroscopy and real-time voltage relaxation curves collected under the current operating conditions. Key indicators reflecting the internal interface side reactions and ion dynamics decay of the battery can be separated and extracted from them, and finally a set of real-time feature parameters that can be directly input into the lifetime prediction model are formed.

[0046] Step S203: Based on the real-time electrolyte state parameter set and the real-time characteristic parameter set, the target lifetime prediction model based on the material degradation mechanism is used to obtain the health status assessment value and the remaining lifetime prediction value of the aqueous sodium-ion battery energy storage system.

[0047] In one optional embodiment, the target lifetime prediction model based on material degradation mechanism represents an interpretable lifetime prediction model that takes the physical mechanism of material degradation in aqueous sodium-ion batteries as the underlying basis, establishes a quantitative mapping relationship between external measurable electrical signals and internal material degradation through offline material characterization (XRD / SEM / XPS), and integrates data-driven algorithms.

[0048] Furthermore, this target lifetime prediction model based on material degradation mechanism can correlate characteristic parameters with actual degradation processes such as active sodium loss, electrode crystal structure changes, CEI film growth, and electrolyte decomposition.

[0049] In an optional embodiment, the health status assessment value is used to quantitatively represent the overall aging degree of the aqueous sodium-ion battery energy storage system at the current moment, and directly reflects the ratio of the battery's current usable capacity to its initial capacity.

[0050] In one optional embodiment, the remaining useful life prediction value represents the equivalent number of cycles / usable time that the battery can still operate safely and stably under the current health status and operating conditions, as output by the model.

[0051] In one optional embodiment, the real-time electrolyte state parameter set and the real-time feature parameter set are used as a unified input, which can fully cover the multi-scale decay information of aqueous sodium-ion battery materials, interfaces, and electrolytes.

[0052] Furthermore, the pre-trained target lifetime prediction model based on material degradation mechanism has already established a quantitative correlation between characteristic parameters, material structure changes, and capacity decay. Therefore, the input parameters can directly correspond to real physical degradation processes such as positive electrode CEI film growth, ion diffusion obstruction, electrolyte moisture consumption, and concentration drift.

[0053] Furthermore, by using material decay kinetics as a constraint and combining algorithms to extrapolate the current decay rate and trend, the current health status can be calculated, and the future usable life can be predicted, ensuring that the results are physically reasonable and engineering reliable.

[0054] The lifetime assessment method for aqueous sodium-ion battery energy storage systems provided in this embodiment, by simultaneously acquiring electrolyte state and electrochemical multiphysics field signals, can comprehensively cover the three-layer degradation information of aqueous sodium-ion battery materials, interfaces, and electrolytes, providing full-dimensional raw data for lifetime assessment. Furthermore, by extracting features from real-time electrochemical impedance spectroscopy and real-time voltage relaxation curves, interfacial side reactions and ion transport kinetics indicators can be extracted from the raw signals, eliminating redundant data and thus helping to make the assessment signals more focused and sensitive. Furthermore, by inputting the real-time electrolyte state parameter set and real-time feature parameter set into a target lifetime prediction model based on material degradation mechanisms, the system can output a health status assessment value and a predicted remaining lifetime value for the aqueous sodium-ion battery energy storage system. This integrates the material degradation mechanism, avoiding the black-box problem of purely data-driven approaches, making the output results physically interpretable, and significantly improving the assessment accuracy and reliability. Therefore, by implementing this invention, for the first time, the electrolyte state and multiphysics field electrochemical features are integrated and input into a mechanistic model, achieving accurate, interpretable, and real-time lifetime assessment of aqueous sodium-ion battery energy storage systems, solving the deficiency of traditional methods that do not cover the battery-specific degradation mechanism.

[0055] In some optional embodiments, obtaining the real-time electrolyte state parameter set during the operation of the aqueous sodium-ion battery energy storage system in step S201 includes: Step a1: Obtain the dataset of internal gas pressure changes and real-time operating temperature values ​​during the operation of the aqueous sodium-ion battery energy storage system.

[0056] In one optional embodiment, the battery internal gas pressure change dataset represents the continuously changing internal pressure sequence data collected by MEMS gas pressure sensors in the gas chamber of a single cell / module during battery operation, which is used to reflect the gas accumulation of hydrogen evolution and oxygen evolution side reactions in aqueous electrolytes.

[0057] In one optional embodiment, a gas cavity can be reserved in the battery cell / module, and a MEMS gas pressure sensor can be installed there. Then, samples are continuously taken at fixed time intervals to monitor the changes in internal gas pressure of the battery in real time, and the corresponding dataset of internal gas pressure changes of the battery is obtained.

[0058] At the same time, the real-time operating temperature value can be obtained synchronously through a temperature sensor.

[0059] Step a2: Based on the dataset of gas pressure changes inside the battery, the hydrogen evolution rate and oxygen evolution rate are obtained by processing with the ideal gas law.

[0060] In an optional embodiment, the ideal gas law is a fundamental equation in physicochemical science, used to convert measurable macroscopic quantities such as pressure, volume, and temperature into the amount of gaseous substance, and then to calculate the hydrogen evolution / oxygen evolution rate, expressed as: .in, Indicates pressure (Pa); Represents volume (m) 3 ); The amount of substance (mol) represents the quantity of gas particles. This represents the molar gas constant, with a common value of 8.314 J / (mol·K); It represents the thermodynamic temperature (K).

[0061] In an optional embodiment, the hydrogen evolution rate value represents the amount or volume of hydrogen gas generated by the hydrogen evolution side reaction at the battery negative electrode per unit time, and is used to reflect the intensity of the side reaction at the negative electrode of the aqueous sodium-ion battery.

[0062] In an optional embodiment, the oxygen evolution rate value represents the amount or volume of oxygen generated by the oxygen evolution side reaction at the positive electrode of the battery per unit time, and is used to reflect the severity of the oxidative decomposition of the aqueous electrolyte.

[0063] In an alternative embodiment, the pressure change can be converted into the amount of gas generated, and then the generation rates of hydrogen and oxygen can be obtained.

[0064] For example, first determine the time interval. Internal pressure changes Then, according to the ideal gas law, it can be seen that in the battery gas chamber volume... Unchanged, temperature Under basically stable conditions, the change in gas pressure inside the battery Changes in the amount of gaseous substances Proportional.

[0065] Furthermore, through the determined pressure change The change in the total amount of gas produced can be directly calculated. , then according to the fixed ratio of H2:O2 = 2:1 for the water electrolysis side reaction of the aqueous battery, the amounts of hydrogen evolution and oxygen evolution can be obtained respectively, and then the corresponding rates, that is, the hydrogen evolution rate value and the oxygen evolution rate value, can be calculated.

[0066] Step a3, based on the hydrogen evolution rate value and the oxygen evolution rate value, use Faraday's law and the electrolyte volume parameter to inversely calculate the cumulative consumption amount of the electrolyte water in the aqueous sodium-ion battery energy storage system.

[0067] In an optional embodiment, Faraday's law represents the law of electrochemical equivalent, which is used to establish a quantitative conversion relationship between the amount of gas-producing substances and the amount of water consumed in the electrochemical reaction.

[0068] Exemplarily, 2H2O→2H2↑+O2↑, consuming 2 mol of 2H2O produces 2 mol of H2 + 1 mol of O2.

[0069] In an optional embodiment, the electrolyte volume parameter may include the initial volume of the electrolyte when the battery leaves the factory and the volume of the gas chamber配套with the sensor .

[0070] In an optional embodiment, the cumulative consumption amount of the electrolyte water represents the total volume / mass of the water in the electrolyte decomposed and consumed due to the hydrogen evolution / oxygen evolution side reaction since the battery started running, which can directly reflect the degree of electrolyte deterioration.

[0071] Exemplarily, the amount of water consumed per unit time is: . Then, integrating with respect to time, the cumulative consumption amount of water (amount of substance) can be calculated: . Finally, convert it to volume, that is, the cumulative consumption amount of the electrolyte water .

[0072] Where ; .

[0073] Step a4, calculate the electrolyte concentration change rate according to the cumulative consumption amount of the electrolyte water, the initial volume of the electrolyte in the aqueous sodium-ion battery energy storage system, and the initial salt concentration of the electrolyte.

[0074] In an optional embodiment, assuming that the sodium salt does not participate in the side reaction and the total amount of solute remains unchanged, the concentration increases as the solvent decreases, and then the corresponding electrolyte concentration change rate is calculated.

[0075] Exemplarily, the electrolyte concentration change rate can be calculated using the following relational expression (1): (1) In the formula: represents the electrolyte concentration change rate; Indicates the initial volume of the electrolyte; This indicates the initial salt concentration of the electrolyte.

[0076] Step a5: Determine the real-time electrolyte state parameter set based on the real-time operating temperature value, hydrogen evolution rate value, and electrolyte concentration change rate.

[0077] In an optional embodiment, the real-time operating temperature value, hydrogen evolution rate value, and electrolyte concentration change rate are integrated to form a real-time electrolyte state parameter set.

[0078] In some optional implementations, step S202 above includes: Step S2021: Separate the real-time electrochemical impedance spectroscopy and determine the semi-circular arc in the mid-frequency region.

[0079] In an optional embodiment, the mid-frequency semicircular arc represents a characteristic pattern from real-time electrochemical impedance spectroscopy (EIS), corresponding to the impedance response of the CEI film and charge transfer process in the positive electrode of an aqueous sodium-ion battery. It is a dedicated frequency band region for separating and calculating the impedance of the CEI film.

[0080] In one optional embodiment, different electrochemical processes correspond to different frequency bands in the electrochemical impedance spectroscopy: high frequency → ohmic impedance; mid frequency → CEI film and charge transfer impedance; low frequency → diffusion impedance.

[0081] Furthermore, real-time electrochemical impedance spectroscopy data are read, and the impedance point set corresponding to the mid-frequency band is extracted from the Nyquist plot. Then, the semi-circular arc presented in this interval is determined.

[0082] Step S2022: Using a preset equivalent circuit fitting model, the semi-circular arc in the mid-frequency region is fitted, and the impedance value of the positive electrode CEI film under the current multi-physics field coupling detection condition is determined.

[0083] In an optional embodiment, a preset equivalent circuit fitting model represents the equivalent circuit model used to analyze the electrochemical impedance spectrum, such as the R(QR(QR)) equivalent circuit fitting model.

[0084] In an optional embodiment, the positive electrode CEI film impedance value represents the positive electrode interface film impedance obtained by fitting an equivalent circuit, and is used to quantitatively characterize the degree of side reactions at the positive electrode / electrolyte interface.

[0085] In an optional embodiment, the semi-circular arc in the mid-frequency region is composed of the positive electrode CEI film impedance and the charge transfer impedance. Therefore, the mid-frequency region semi-circular arc data can be fitted using the R(QR(QR)) equivalent circuit fitting model, and the positive electrode CEI film impedance value can be output. and charge transfer impedance value .

[0086] Step S2023: Determine the positive electrode CEI film impedance growth rate based on the positive electrode CEI film impedance values ​​under two consecutive multi-physics field coupling detection conditions.

[0087] In an optional embodiment, the positive electrode CEI film impedance growth rate represents the relative rate of change of the positive electrode CEI film impedance value of an aqueous sodium-ion battery within two consecutive multi-physics coupling detection cycles, and is used to quantitatively characterize the accumulation rate and severity of side reactions at the positive electrode / electrolyte interface.

[0088] Among them, the positive electrode / electrolyte interface side reactions may include the oxidative decomposition of aqueous electrolytes at high potentials, the dissolution of metal ions from Prussian blue analogues, and the deposition of by-products.

[0089] In an optional embodiment, the impedance value of the positive electrode CEI film can be determined based on two consecutive multiphysics coupling detection conditions. and Calculate the growth rate of the impedance of the positive electrode CEI film. The following relation (2) is shown: (2) in, This indicates the time interval between two consecutive multiphysics coupling detection conditions.

[0090] Furthermore, the greater the increase in the impedance growth rate of the positive electrode CEI film, the more severe the side reactions at the positive electrode / electrolyte interface (such as electrolyte oxidation and decomposition, metal ion dissolution, and by-product deposition).

[0091] Step S2024: Perform mathematical fitting on the real-time voltage relaxation curve and determine the ion diffusion time constant.

[0092] In an optional embodiment, the ion diffusion time constant is used to characterize the decay of the apparent diffusion coefficient of sodium ions in the bulk phase and interface layer of the electrode material, and to reflect the degradation of kinetic performance caused by the collapse of the Prussian blue analog crystal structure or the blockage of pores.

[0093] In an optional embodiment, the real-time voltage relaxation curve can be mathematically fitted using the exponential decay function shown in the following equation (3), and the ion diffusion time constant can be determined. : (3) In the formula: express The real-time battery voltage at time t, i.e., the voltage relaxation curve over time. Measured value at the location; The time variable representing the relaxation process starts timing from the end of the disturbance; This represents the steady-state final value of voltage relaxation, that is, the stable open-circuit voltage reached after the battery has fully relaxed. It represents the amplitude of voltage relaxation, the total magnitude of voltage change during relaxation, and reflects the degree of polarization; Indicates the ion diffusion time constant; The exponent term of an exponential function describes how quickly voltage decays over time.

[0094] Step S2025: Determine the real-time characteristic parameter set based on the positive electrode CEI film impedance growth rate and ion diffusion time constant.

[0095] In an alternative embodiment, the positive electrode CEI film impedance growth rate and ion diffusion time constant are combined to form a corresponding set of real-time feature parameters for input to the lifetime prediction model.

[0096] In some optional implementations, the target lifetime prediction model based on the material degradation mechanism in step S203 above is obtained through the following steps: Step b1: Obtain the historical electrolyte state parameter set of the aqueous sodium-ion battery energy storage system during its historical operation, as well as multiple historical electrochemical impedance spectra and multiple historical voltage relaxation curves of multiple aqueous sodium-ion battery cells produced in the same batch as the aqueous sodium-ion battery energy storage system at the corresponding aging stages.

[0097] In one optional embodiment, the aqueous sodium-ion battery cell refers to the smallest battery cell produced in the same batch, with the same materials and process as the energy storage system to be evaluated. It uses a Prussian blue analog cathode, sodium titanium phosphate anode, and aqueous sodium salt electrolyte for offline calibration and model training.

[0098] In one alternative embodiment, the aging stage refers to artificially set cycle nodes that represent different degrees of battery aging, such as 0, 100, 500, 1000, and 1500 cycles, which can cover the entire life cycle from brand new to significant degradation.

[0099] In one optional embodiment, multiple aqueous sodium-ion battery cells produced in the same batch as the aqueous sodium-ion battery energy storage system are selected and tested at aging stages such as 0 / 100 / 500 / 1000 / 1500 cycles. Furthermore, historical electrochemical impedance spectroscopy and historical voltage relaxation curves are collected at each aging stage.

[0100] Furthermore, the specific acquisition process can be referred to the relevant description in step S201 above, and will not be repeated here.

[0101] Step b2 involves extracting features from multiple historical electrochemical impedance spectra and multiple historical voltage relaxation curves, and determining multiple sets of historical feature parameters.

[0102] The specific acquisition process can be found in the relevant description in step S202 above, and will not be repeated here.

[0103] Step b3 involves performing material characterization on the electrodes of multiple aqueous sodium-ion battery cells to obtain multiple material characterization datasets.

[0104] In one optional embodiment, the material characterization dataset represents a collection of electrode microstructure data obtained through material analysis methods such as XRD, SEM, and XPS, which is used to reflect material attenuation information such as crystal structure, morphology, elemental valence state, and CEI film composition.

[0105] In one alternative embodiment, by disassembling the aged battery under an inert atmosphere and performing microscopic tests on the positive and negative electrodes, the corresponding material degradation data can be obtained.

[0106] For example, disassembly is performed at different aging stages, and multiple aqueous sodium-ion battery cells are disassembled and corresponding electrode samples are obtained under inert atmosphere protection.

[0107] Furthermore, XRD analysis was performed on the Prussian blue analogue material of the positive electrode samples to analyze crystal structure changes, SEM analysis to observe morphological evolution, and XPS analysis to analyze elemental valence states and CEI film composition. Simultaneously, XRD and SEM analysis were performed on the sodium titanium phosphate material of the negative electrode to analyze structural stability. Finally, the above analytical results were integrated to form multiple corresponding material characterization datasets.

[0108] Step b4 involves performing correlation analysis on the historical electrolyte state parameter set, multiple historical characteristic parameter sets, and multiple material characterization datasets, and identifying multiple key characteristic parameter sets related to battery capacity decay.

[0109] In one optional embodiment, by performing statistical correlation analysis on three types of data—historical electrolyte state parameter set, historical characteristic parameter set, and multiple material characterization datasets—it is possible to screen out features that are highly correlated with battery capacity decay and have a significant impact on lifespan, and form a set of key characteristic parameters for each aging stage.

[0110] For example, the battery capacity decay rate / capacity retention rate (SOH) is first determined as the dependent variable, and historical characteristic parameters, historical electrolyte state parameters, and material characterization data are determined as independent variables.

[0111] Secondly, by aligning the electrochemical characteristics, electrolyte state, material characterization results, and capacity data of monomers from the same batch at the same aging stage, a one-to-one corresponding sample matrix was formed, and the Pearson correlation coefficient between each independent variable and the capacity decay rate was calculated. .

[0112] in, The closer the correlation is to 1 or -1, the stronger the correlation. The closer to 0, the weaker the correlation.

[0113] Finally, features that meet preset threshold rules can be selected as key features strongly correlated with capacity decay, forming multiple sets of corresponding key feature parameters. For example, retaining... The characteristics of this feature are key features that are strongly correlated with capacity decay.

[0114] Step b5: Based on multiple key feature parameter sets, and trained by a preset neural network algorithm, a target lifetime prediction model based on the material degradation mechanism is constructed.

[0115] In one optional embodiment, a preset neural network algorithm is used to establish a nonlinear mapping relationship between key features and capacity decay and lifetime, which can be particle filtering, correlation vector machine or neural network algorithm, etc.

[0116] In one optional embodiment, a lifetime prediction model that integrates the material degradation mechanism can be constructed by using multiple key feature parameter sets as model inputs and health status (SOH) and remaining useful life as output labels, and training with a preset neural network / particle filter / correlation vector machine.

[0117] For example, the training sample set is first constructed using the key feature parameter set corresponding to each aging stage as the input sample and the SOH (health status), remaining cycle count, and capacity decay amount corresponding to the aging stage as the output labels.

[0118] Furthermore, the key features in the training sample set are input into the corresponding algorithm model, and supervised learning is performed with SOH and remaining lifetime as labels. Then, the model weights / parameters are continuously optimized through iteration to make the model output value as consistent as possible with the measured capacity and lifetime, and a well-trained target lifetime prediction model is obtained.

[0119] In some optional implementations, the above method further includes: Step c1: Determine the temperature adaptability correction factor based on the type of aqueous electrolyte in the aqueous sodium-ion battery energy storage system.

[0120] In one optional embodiment, the aqueous electrolyte type refers to the specific formulation category of the aqueous sodium salt electrolyte used in the aqueous sodium-ion battery. Different formulations exhibit significant differences in viscosity, conductivity, and temperature sensitivity. For example, they can be distinguished based on the type of sodium salt, solvent system, and additive composition.

[0121] In one optional embodiment, the temperature adaptability correction factor is used to compensate for the interference of increased electrolyte viscosity and decreased conductivity on ion transport dynamics and impedance characteristics under low temperature conditions, so that the lifetime prediction model remains stable and accurate across the entire temperature range.

[0122] In an optional embodiment, the temperature can be established based on the specific formulation type of the aqueous electrolyte used in the current aqueous sodium-ion battery energy storage system. With correction factor The correspondence between them was determined, and the temperature adaptability correction factor applicable to this electrolyte system was identified. .

[0123] For example, first determine the type of aqueous electrolyte used in the aqueous sodium-ion battery energy storage system, such as a high-concentration sodium salt system, a neutral buffer system, or a system with additives for stabilization.

[0124] Secondly, the conductivity, viscosity, and impedance temperature characteristics of the electrolyte can be tested within the full temperature range of 0℃ to 60℃.

[0125] Then, temperature can be established. With correction factor Mapping relationship or piecewise function: (1) Normal temperature range (25℃±5℃): No correction or only minor correction; (2) Low temperature range (<20℃): This is used to reduce the weighting of the ion diffusion time constant and impedance; (3) High temperature range (>40℃): Or make appropriate adjustments.

[0126] Finally, the temperature adaptation correction factor Solidified as built-in parameters of the model that are tied to the electrolyte type.

[0127] Step c2 involves using a temperature adaptability correction factor to correct the target lifetime prediction model based on the material degradation mechanism.

[0128] In one alternative embodiment, temperature does not directly represent battery aging, but it can alter the amplitude of electrochemical signals, leading to model misjudgments. Therefore, during the model inference or training phase, a temperature-adaptive correction factor is used. By introducing a target lifetime prediction model based on material degradation mechanism, temperature-weighted corrections can be made to characteristics such as the cathode CEI film impedance growth rate and ion diffusion time constant, thereby eliminating the interference of temperature on lifetime assessment results.

[0129] For example, real-time operating temperature can be introduced in the model input layer or feature fusion layer. And query the corresponding Then, temperature corrections were applied to the key characteristic parameters, and the corrected positive electrode CEI film impedance growth rate and ion diffusion time constant were obtained.

[0130] Furthermore, by inputting the modified parameters and electrolyte state parameters into the model for calculation, the model can output a temperature-corrected health status assessment value and a predicted remaining service life value, ensuring that the assessment results are free from temperature bias under different seasons, regions, and heat dissipation conditions.

[0131] In some optional implementations, the above method further includes: Step d1: When the health status assessment value is less than the preset threshold, generate an early warning message and adjust the set of allowable operating ranges for the aqueous sodium-ion battery energy storage system.

[0132] In an alternative embodiment, the set of operating intervals may be used to suppress the growth of the positive electrode CEI film and the decomposition side reactions of the electrolyte in an aqueous sodium-ion battery energy storage system, and may include limiting its maximum depth of charge and discharge, charge and discharge power, or adjusting its charge and discharge cutoff voltage.

[0133] In one optional embodiment, when the health status assessment value (SOH) output by the model is lower than the preset safety threshold, it indicates that the positive electrode CEI film has thickened rapidly, ion diffusion is hindered, and the hydrogen / oxygen evolution of the electrolyte is aggravated. If the full power operation continues, it will fail rapidly. At this time, the system automatically generates a warning message, actively reduces / limits the set of allowable working ranges of the battery, and suppresses the excessive growth of the positive electrode CEI film and the side reaction of electrolyte water decomposition by reducing the operating intensity, thereby delaying battery aging and protecting the safety of the energy storage system.

[0134] For example, the health status assessment value (SOH) output by the model is compared with a first preset threshold (such as 80% SOH). When the health status assessment value is less than the preset threshold, the battery management system (BMS) generates a graded warning signal, which may include the warning level, the location of the abnormal battery / module, the current SOH and remaining life, and the recommended adjustment strategy.

[0135] Furthermore, the generated tiered early warning signals can be sent to the corresponding operation and maintenance terminal or energy storage system monitoring platform.

[0136] Furthermore, the BMS automatically applies one or more of the following restrictions to the module containing the battery, which may include: (1) Limit the maximum depth of charge and discharge (DOD): for example, reduce it from 90% to 70%; (2) Limit the maximum charge and discharge power: for example, reduce it from 1C to 0.5C; (3) Adjust the charge and discharge cutoff voltage: appropriately reduce the charging cutoff potential to avoid electrolyte decomposition caused by high potential.

[0137] Furthermore, by adjusting the operating window as described above, the polarization of the positive electrode interface can be reduced, and the CEI film can be inhibited from thickening further; the electrode potential can be reduced, and the side reactions of hydrogen evolution / oxygen evolution in the electrolyte can be decreased; the electrolyte concentration can be stabilized, and the overall aging of the battery can be delayed.

[0138] Furthermore, the health status can be reassessed periodically. If the SOH rises or stabilizes, the operating range can be gradually restored; if it continues to decline, it will enter deep protection or prompt replacement.

[0139] In one example, a method for lifetime assessment of an aqueous sodium-ion battery energy storage system based on multi-scale feature detection is provided, including the following steps: Step S11: During the operation of the aqueous sodium-ion battery energy storage system, a multi-physics coupling detection condition is periodically triggered and executed once; the detection condition includes: applying a preset low-frequency AC disturbance signal, and collecting the battery's electrochemical impedance spectroscopy data and voltage relaxation curve before and after the disturbance.

[0140] Among them, the frequency range of the low-frequency AC disturbance signal is 0.01 Hz to 10 Hz, which is used to differentiate the ion polarization process in the electrode material body, the positive electrode CEI film and the electrolyte in the aqueous sodium-ion battery.

[0141] Step S21: Extract at least one set of characteristic parameters from the electrochemical impedance spectroscopy data and voltage relaxation curves to characterize the degree of interfacial side reactions and the decay of ion transport kinetics inside the aqueous sodium-ion battery; the set of characteristic parameters includes: the rate of increase of impedance of the cathode CEI film and the ion diffusion time constant.

[0142] The calculation process for the impedance growth rate of the positive electrode CEI film includes: separating the semi-circular arc in the mid-frequency region from the electrochemical impedance spectrum, and obtaining the charge transfer impedance by fitting an equivalent circuit. and the impedance of the positive electrode CEI film Calculate the relative change rate of the cathode CEI film impedance RCEI during two consecutive detection cycles. As a quantitative indicator characterizing the degree of accumulation of side reactions at the cathode / electrolyte interface, the side reactions at the cathode / electrolyte interface include the oxidative decomposition of aqueous electrolytes at high potentials, the dissolution of metal ions from Prussian blue analogues, and the deposition of byproducts on the cathode surface.

[0143] Furthermore, the calculation process of the sub-diffusion time constant includes: mathematically fitting the voltage relaxation curve and extracting the time constant of the relaxation process. Time constant Used to characterize the decay of the apparent diffusion coefficient of sodium ions in the bulk phase and interface layer of electrode materials, reflecting the degradation of kinetic performance caused by the collapse of the crystal structure or blockage of pores of Prussian blue analogues.

[0144] Step S31: Obtain the electrolyte state parameters of the aqueous sodium-ion battery energy storage system during operation; the electrolyte state parameters include hydrogen evolution rate, electrolyte concentration change rate, and temperature.

[0145] The methods for obtaining electrolyte state parameters include: real-time monitoring of internal gas pressure changes using gas pressure sensors installed in individual battery cells or modules; calculation of hydrogen evolution rate and / or oxygen evolution rate using the ideal gas law; and, based on the hydrogen evolution rate and oxygen evolution rate, inversely calculating the cumulative water consumption in the electrolyte using Faraday's law and electrolyte volume parameters. According to water consumption and the initial volume of electrolyte Initial salt concentration Calculate the rate of change of electrolyte concentration. It is assumed that the salt components do not participate in side reactions and remain in the electrolyte in ionic form; the operating temperature of the battery is collected in real time by a temperature sensor.

[0146] Step S41: Input the extracted feature parameter set and electrolyte state parameters into a lifetime prediction model based on the material degradation mechanism; the model was pre-calibrated through offline experiments to establish a quantitative mapping relationship between the feature parameter set, electrolyte state parameters and the loss of battery active sodium inventory and the change of electrode material crystal structure.

[0147] The method for constructing a lifetime prediction model based on material degradation mechanisms includes the following offline calibration steps: Aqueous sodium-ion battery cells produced in the same batch as the energy storage system were selected, disassembled at different aging stages, and electrode samples were obtained under inert atmosphere protection. The obtained electrode samples were characterized by the following methods: XRD analysis of the crystal structure changes of the positive electrode Prussian blue analog material, SEM observation of the morphological evolution, and XPS analysis of the elemental valence state and CEI film composition; XRD and SEM analysis of the structural stability of the negative electrode sodium titanium phosphate material. Before disassembly, the electrochemical impedance spectroscopy data and voltage relaxation curves of these battery cells at the corresponding aging stages were recorded simultaneously, and a set of characteristic parameters was extracted, including the positive electrode CEI film impedance growth rate and ion diffusion time constant, as well as the electrolyte state parameters monitored simultaneously. Correlation analysis was performed on material characterization data with feature parameter sets and electrolyte state parameters to screen out key features that are strongly correlated with battery capacity decay. Based on the key features selected, a lifetime prediction model that integrates the material degradation mechanism is constructed using particle filtering, correlation vector machine or neural network algorithms, and the model is then embedded into the evaluation module of the battery management system.

[0148] Furthermore, a temperature adaptation correction factor is introduced into the lifetime prediction model. Differential weighting of characteristic parameters within different temperature ranges; temperature adaptability correction factor. Based on the type of aqueous electrolyte, it is used to correct the effects of increased electrolyte viscosity and decreased conductivity on ion transport dynamics under low-temperature conditions.

[0149] Step S51: The lifetime prediction model outputs the current health status assessment results and remaining lifetime prediction values ​​of the aqueous sodium-ion battery energy storage system.

[0150] Furthermore, the above step S51 is followed by: Step S6: When the health status assessment result is lower than the first preset threshold, generate an early warning message and adjust the operating window of the module where the battery is located, including limiting its maximum charge and discharge depth, charge and discharge power, or adjusting its charge and discharge cutoff voltage, in order to suppress the further growth of the positive electrode CEI film and the decomposition side reaction of the electrolyte.

[0151] The lifetime assessment method for aqueous sodium-ion battery energy storage systems based on multi-scale feature detection provided in this example has the following beneficial effects: 1. Multi-scale feature fusion for more accurate assessment: This example is the first to integrate macroscopic electrochemical characteristics (CEI membrane impedance, diffusion time constant) with electrolyte state parameters (hydrogen evolution rate, concentration change), constructing a lifetime assessment system covering multiple scales of electrode materials, interfaces, and electrolytes. Compared to traditional methods that rely solely on conventional state quantities such as voltage, current, and temperature, this example can capture the internal degradation signals of aqueous sodium-ion batteries earlier and more accurately.

[0152] 2. Closely aligned with the characteristics of aqueous batteries, the technical solution exhibits significant differentiation: Addressing the unique degradation mechanisms of aqueous sodium-ion batteries, such as the absence of an SEI film but the presence of a CEI film, susceptibility to hydrogen and oxygen evolution side reactions, and volatile electrolyte concentration, this example demonstrates the design of specialized detection parameters and evaluation methods. The increase in the impedance of the positive electrode CEI film directly characterizes the degree of accumulation of side reactions at the positive electrode / electrolyte interface, while the electrolyte concentration change rate, estimated based on gas production, reflects electrolyte degradation caused by water decomposition. These parameters are unique to aqueous batteries, clearly distinguishing them from existing general lithium battery life assessment methods.

[0153] 3. Deep integration of material mechanism and data-driven approach, resulting in a highly interpretable model: This example establishes a mapping relationship between material degradation characteristics and externally measurable parameters through offline material characterization (XRD, SEM, XPS), giving the lifetime prediction model clear physical meaning and materials science basis, avoiding the uninterpretability of the black-box operation of purely data-driven models. Furthermore, after the model is embedded in the BMS, only real-time measurable parameters need to be input to output prediction results, balancing mechanistic depth with engineering practicality.

[0154] 4. The project is highly feasible and easy to deploy in existing energy storage systems: The detection condition (low-frequency AC disturbance) proposed in this example can be implemented based on existing BMS hardware. The gas pressure sensor can be installed by reserving a gas cavity during battery packaging or by connecting to a gas bag. Electrolyte concentration changes are indirectly calculated through gas production, avoiding the difficulties of direct testing. The entire solution does not change the basic architecture of the existing energy storage system and has good compatibility and promotional value.

[0155] 5. Provide accurate decision-making basis for system operation and maintenance: When the evaluation result is lower than the preset threshold, this example can generate early warning information and guide the adjustment of operation strategy (such as limiting the depth of charge and discharge, power, etc.), so as to achieve targeted protection of aging batteries, delay the overall degradation of the system, and reduce operation and maintenance costs.

[0156] In a specific example, taking an energy storage system using a Prussian blue analogue as the positive electrode material, sodium titanium phosphate as the negative electrode material, and an aqueous electrolyte containing sodium salts as the electrolyte, the life assessment method for an aqueous sodium-ion battery energy storage system based on multi-scale feature detection provided in the above example is illustrated. Figure 3 As shown, it includes the following steps: Step S1: Periodically trigger the multiphysics coupling detection condition.

[0157] Specifically, during normal operation of the energy storage system, the battery management system triggers a detection condition every 24 hours. The detection condition is as follows: when the battery is in a quiescent state or operating at low current (current < 0.05 C), the BMS controls the application of a constant current sinusoidal perturbation signal with a frequency of 0.1 Hz and an amplitude of 0.05 C to the battery, lasting for two complete cycles. Before and after applying the perturbation signal, the battery's electrochemical impedance spectroscopy data and voltage relaxation curve (relaxation time 30 minutes) are collected, respectively.

[0158] Furthermore, such as Figure 4 As shown, low-frequency AC disturbance signals can differentially excite the ion polarization process in electrode materials, CEI films, and electrolytes, making the subsequent extraction of characteristic parameters have clear physical meaning.

[0159] Step S2: Extract the feature parameter set.

[0160] Specifically, from the collected electrochemical impedance spectroscopy data, an equivalent circuit model R(QR(QR)) is fitted using ZView software to separate the semi-circular arc in the mid-frequency region, thereby obtaining the impedance of the positive electrode CEI film. and charge transfer impedance Calculate the difference between this test and the previous test. Relative rate of change: This is the growth rate of the cathode CEI film impedance. The larger this growth rate, the more severe the side reactions at the cathode / electrolyte interface (such as electrolyte oxidation and decomposition, metal ion dissolution, and by-product deposition).

[0161] Furthermore, an exponential decay function is used from the voltage relaxation curve. Perform fitting and extract the time constant of the relaxation process. The time constant The apparent diffusion coefficient of sodium ions in the bulk phase of the electrode material and the CEI film. An increase indicates a decline in ion diffusion kinetics, which may be related to the collapse of the Prussian blue analog crystal structure or blockage of the channels.

[0162] Step S3: Obtain electrolyte state parameters.

[0163] Specifically, such as Figure 5 As shown, a MEMS gas pressure sensor is installed in the air cavity reserved at the top of each battery cell to monitor changes in internal gas pressure in real time.

[0164] Furthermore, taking a single battery cell as an example, during a one-month operating cycle, the gas pressure was monitored to gradually increase from the initial 101.3 kPa to 108.5 kPa, and the gas chamber volume... ,temperature According to the ideal gas law The cumulative gas production was calculated. Gas chromatography analysis determined the generated gas to be H2 and O2 (volume ratio 2:1), then the hydrogen evolution amount... , oxygen evolution amount .

[0165] Furthermore, according to Faraday's law, the amount of water consumed in the hydrogen evolution and oxygen evolution reaction... The corresponding mass of water Δ Initial volume of electrolyte Density ≈ 1 g / mL, water content 90%, initial water mass The proportion of water consumed .

[0166] Furthermore, assuming that the sodium salt in the electrolyte does not participate in the side reactions and remains in the solution, the initial salt concentration... The current concentration is: .

[0167] Furthermore, the rate of change in concentration Although the changes are small, they can accumulate to a significant level over a long period of time.

[0168] Meanwhile, the operating temperature is collected in real time by temperature sensors placed inside the battery module, which is used for temperature correction of the subsequent model.

[0169] Step S4: Input the lifetime prediction model.

[0170] Specifically, the feature parameter set (positive electrode CEI membrane impedance growth rate, ion diffusion time constant) extracted in step S2 and the electrolyte state parameters (hydrogen evolution rate, electrolyte concentration change rate, temperature) obtained in step S3 are input into the pre-trained lifetime prediction model.

[0171] The construction process of this lifetime prediction model is as follows: Figure 6 As shown, the offline calibration steps include the following: (1) Select 50 aqueous sodium-ion battery cells produced in the same batch as the demonstration project and test them for different number of cycles (0, 100, 500, 1000, 1500) under laboratory conditions.

[0172] (2) At each aging node, the battery’s EIS data and voltage relaxation curve are recorded first, and the characteristic parameter set is extracted.

[0173] (3) Disassemble the battery in an inert atmosphere glove box and remove the positive and negative electrodes. Perform XRD analysis on the Prussian blue analogue material of the positive electrode to analyze crystal structure changes, SEM analysis to observe morphological evolution, and XPS analysis to analyze elemental valence states and CEI film composition. Perform XRD and SEM analysis on the sodium titanium phosphate negative electrode.

[0174] (4) Pearson correlation analysis was performed on the material characterization data (such as XRD peak shift Δ2θ, CEI component content in XPS, and crack density observed by SEM) with the simultaneously measured set of characteristic parameters. The results showed that the increase rate of impedance of the cathode CEI film was strongly positively correlated with the CEI film thickness and the amount of transition metal dissolution. ); Ion diffusion time constant It is strongly positively correlated with the shrinkage rate of the Prussian blue unit cell parameter ( The hydrogen evolution rate is strongly positively correlated with the amount of by-products deposited on the surface of the negative electrode material. ).

[0175] (5) Based on the selected key features, a particle filter algorithm is used to construct a lifetime prediction model that integrates the material degradation mechanism. The model input is... The output is the current SOH and the remaining number of cycles.

[0176] Step S6: Early warning and strategy adjustment.

[0177] Specifically, when the state of harmonics (SOH) of a single battery cell falls below a first preset threshold, the battery management system (BMS) generates a warning message and automatically adjusts the operating window of the module containing that battery: limiting the maximum depth of charge / discharge from 90% to 70% and reducing the maximum charge / discharge power from 1 C to 0.5 C. This adjustment effectively suppresses further growth of the positive electrode CEI film and electrolyte decomposition, thus delaying battery aging.

[0178] In summary, this example demonstrates a multi-scale, precise assessment of the lifetime of an aqueous sodium-ion battery energy storage system by integrating macroscopic electrochemical characteristic parameters and electrolyte state parameters, combined with a mechanistic model constructed using offline material characterization. This method reflects the material properties of aqueous batteries while also possessing engineering feasibility, providing a powerful tool for health management and operational decision-making in large-scale energy storage systems.

[0179] This embodiment also provides a life assessment device for an aqueous sodium-ion battery energy storage system. 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 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.

[0180] This embodiment provides a lifespan assessment device for an aqueous sodium-ion battery energy storage system, such as... Figure 7 As shown, the device includes: The acquisition module 701 is used to acquire the real-time electrolyte state parameter set during the operation of the aqueous sodium-ion battery energy storage system, as well as the real-time electrochemical impedance spectrum and real-time voltage relaxation curve under the current multi-physics field coupling detection condition.

[0181] The extraction module 702 is used to extract features from real-time electrochemical impedance spectroscopy and real-time voltage relaxation curves, and to determine the real-time feature parameter set.

[0182] The processing module 703 is used to obtain the health status assessment value and the remaining service life prediction value of the aqueous sodium-ion battery energy storage system by processing the target life prediction model based on the material decay mechanism based on the real-time electrolyte state parameter set and the real-time characteristic parameter set.

[0183] The aqueous sodium-ion battery energy storage system life assessment device provided in this embodiment of the invention can execute the aqueous sodium-ion battery energy storage system life assessment 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 above modules are the same as in the corresponding embodiments described above, and will not be repeated here.

[0184] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0185] The following is a detailed reference. Figure 8 This diagram illustrates a suitable structural schematic 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.) 801, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 802 or a program loaded from memory 808 into random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device. The processor 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0186] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 8 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.

[0187] 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 a communication device 809, or installed from a memory 808, or installed from a ROM 802. When the computer program is executed by the processor 801, it performs the functions defined in the aqueous sodium-ion battery energy storage system lifetime assessment method of the embodiments of the present invention.

[0188] Figure 8 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.

[0189] 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. When the software or computer code is accessed and executed by the computer, processor, or hardware, the life assessment method for aqueous sodium-ion battery energy storage systems shown in the above embodiments is implemented.

[0190] 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.

[0191] 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 assessing the lifespan of an aqueous sodium-ion battery energy storage system, characterized in that, The method includes: To obtain the real-time electrolyte state parameter set during the operation of an aqueous sodium-ion battery energy storage system, as well as the real-time electrochemical impedance spectroscopy and real-time voltage relaxation curve under the current multi-physics field coupling detection conditions; Feature extraction is performed on the real-time electrochemical impedance spectroscopy and the real-time voltage relaxation curve, and a set of real-time feature parameters is determined. Based on the real-time electrolyte state parameter set and the real-time characteristic parameter set, and after processing by the target lifetime prediction model based on the material degradation mechanism, the health status assessment value and the remaining lifetime prediction value of the aqueous sodium-ion battery energy storage system are obtained.

2. The method according to claim 1, characterized in that, Obtain the real-time electrolyte state parameter set during the operation of the aqueous sodium-ion battery energy storage system, including: Acquire the dataset of internal gas pressure changes and real-time operating temperature values ​​during the operation of the aqueous sodium-ion battery energy storage system; Based on the data set of gas pressure changes inside the battery, the hydrogen evolution rate and oxygen evolution rate are obtained by processing with the ideal gas law. Based on the hydrogen evolution rate and oxygen evolution rate, the cumulative water consumption of the electrolyte in the aqueous sodium-ion battery energy storage system is calculated in reverse using Faraday's law and electrolyte volume parameters. The electrolyte concentration change rate is calculated based on the cumulative water consumption of the electrolyte, the initial electrolyte volume of the aqueous sodium-ion battery energy storage system, and the initial salt concentration of the electrolyte. The real-time electrolyte state parameter set is determined based on the real-time operating temperature value, the hydrogen evolution rate value, and the electrolyte concentration change rate.

3. The method according to claim 1, characterized in that, Feature extraction is performed on the real-time electrochemical impedance spectroscopy and the real-time voltage relaxation curve, and a set of real-time feature parameters is determined, including: The real-time electrochemical impedance spectroscopy is separated, and the semi-circular arc in the mid-frequency region is determined. Using a preset equivalent circuit fitting model, the semi-circular arc in the mid-frequency region is fitted, and the impedance value of the positive electrode CEI film under the current multi-physics field coupling detection condition is determined. The rate of increase of the positive electrode CEI film impedance is determined based on the impedance values ​​of the positive electrode CEI film under two consecutive multi-physics field coupling detection conditions. The real-time voltage relaxation curve is mathematically fitted, and the ion diffusion time constant is determined. The ion diffusion time constant is used to characterize the decay of the apparent diffusion coefficient of sodium ions in the bulk phase and interface layer of the electrode material. The real-time characteristic parameter set is determined based on the positive electrode CEI film impedance growth rate and the ion diffusion time constant.

4. The method according to claim 1, characterized in that, The method further includes: The system obtains the historical electrolyte state parameter set of the aqueous sodium-ion battery energy storage system during its historical operation, as well as the historical electrochemical impedance spectra and historical voltage relaxation curves of multiple aqueous sodium-ion battery cells produced in the same batch as the aqueous sodium-ion battery energy storage system at the corresponding aging stages. Feature extraction is performed on the multiple historical electrochemical impedance spectra and the multiple historical voltage relaxation curves, and multiple sets of historical feature parameters are determined; Material characterization was performed on the electrodes of the multiple aqueous sodium-ion battery cells to obtain multiple material characterization datasets; Correlation analysis was performed on the historical electrolyte state parameter set, the multiple historical characteristic parameter sets, and the multiple material characterization datasets to determine multiple key characteristic parameter sets related to battery capacity decay; Based on multiple key feature parameter sets and trained by a preset neural network algorithm, the target lifetime prediction model based on the material degradation mechanism is constructed.

5. The method according to claim 4, characterized in that, The method further includes: The temperature adaptability correction factor is determined based on the type of aqueous electrolyte in the aqueous sodium-ion battery energy storage system. The temperature adaptability correction factor is used to correct the target lifetime prediction model based on the material degradation mechanism.

6. The method according to claim 1, characterized in that, The method further includes: When the health status assessment value is less than a preset threshold, an early warning message is generated and the set of allowable operating intervals of the aqueous sodium-ion battery energy storage system is adjusted. The set of allowable operating intervals is used to suppress the growth of the positive electrode CEI film and the decomposition side reaction of the electrolyte in the aqueous sodium-ion battery energy storage system.

7. A life assessment device for an aqueous sodium-ion battery energy storage system, characterized in that, The device includes: The acquisition module is used to acquire the real-time electrolyte state parameter set during the operation of the aqueous sodium-ion battery energy storage system, as well as the real-time electrochemical impedance spectrum and real-time voltage relaxation curve under the current multi-physics field coupling detection conditions. The extraction module is used to extract features from the real-time electrochemical impedance spectroscopy and the real-time voltage relaxation curve, and to determine the real-time feature parameter set; The processing module is used to obtain the health status assessment value and the remaining service life prediction value of the aqueous sodium-ion battery energy storage system by processing the real-time electrolyte state parameter set and the real-time feature parameter set through a target life prediction model based on the material degradation mechanism.

8. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the life assessment method for an aqueous sodium-ion battery energy storage system as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the lifetime assessment method for an aqueous sodium-ion battery energy storage system as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the life assessment method for an aqueous sodium-ion battery energy storage system as described in any one of claims 1 to 6.

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