All-vanadium redox flow energy storage battery integration method and system based on state of charge analysis

Abstract

The invention discloses an all-vanadium redox flow energy storage battery integration method and system based on state-of-charge analysis, and relates to the technical field of all-vanadium redox flow energy storage batteries, and the all-vanadium redox flow energy storage battery integration is realized through three steps: step 1, constructing an electrolyte chemical reconstruction system, introducing a functional additive, and obtaining parameters such as vanadium ion coordination bond energy and the like; analyzing to obtain an electrolyte chemical property coefficient; 2, predicting state-of-charge abnormity based on the coefficient, marking an abnormal battery and analyzing an abnormal trend of the abnormal battery; and step 3, after the abnormal trend is clear, calculating an abnormal diffusion risk index, dividing grades according to the index, combining historical similar, novel and gradual change abnormal trends, making grading strategies such as fusing type deep isolation, self-adaptive early warning isolation and dynamic observation isolation, and realizing accurate prevention and control of the abnormity.

Description

Technical Field

[0001] This invention relates to the field of vanadium redox flow battery technology, and more specifically to an integrated method and system for vanadium redox flow batteries based on state of charge analysis. Background Technology

[0002] Vanadium redox flow batteries, as a key technology in the field of large-scale energy storage, have been widely used in scenarios such as renewable energy consumption and grid peak shaving due to their advantages such as power and capacity decoupling, long cycle life, and high safety. However, existing integration methods have significant technical bottlenecks: traditional electrolytes have fixed characteristics and are difficult to adapt to the reaction requirements under different states of charge, resulting in easy precipitation of vanadium ions under high charge and insufficient reaction kinetics under low charge. Therefore, there is a need for an integration method and system for vanadium redox flow batteries based on state of charge analysis.

[0003] Existing technology, such as the invention patent application with publication number CN118099477B, discloses a method and system for managing vanadium redox flow batteries, relating to the field of battery management technology. The method of this invention includes the following steps: battery cycling, cycle analysis, maintenance adjustment, and battery feedback. During the charge-discharge cycle of the vanadium redox flow battery, the cycle parameters of the vanadium redox flow battery are recorded for each cycle. The effect of the vanadium redox flow battery in the current charge-discharge cycle is analyzed, and based on the cycle effect, it is determined whether the charge-discharge cycle needs to be adjusted. This solves the problem of insufficient scientific rigor in the manual adjustment and analysis of the charge-discharge cycle in traditional technologies, improves the accuracy and standardization of battery charge-discharge cycle control analysis, enables timely adjustment of the charge-discharge cycle, ensures the stability and high efficiency of battery performance, guarantees battery lifespan, and to a certain extent reduces the cost of manual battery maintenance.

[0004] Regarding the above-mentioned solutions, the applicant of this invention has discovered at least the following technical problems: 1. Existing technologies only indirectly infer SOC through single parameters such as vanadium ion concentration and pH value, without considering the influence of changes in vanadium ion coordination structure and dynamic viscosity adjustment on ion migration, resulting in an SOC calculation error exceeding 10%. Simultaneously, the types and dosages of electrolyte additives are fixed and cannot be adaptively adjusted according to SOC changes, causing a more than 25% increase in vanadium ion precipitation rate in the high SOC range and a 15%-20% decrease in power output in the low SOC range. Furthermore, the lack of multi-parameter fusion chemical characteristic coefficients makes it impossible to establish a quantitative mapping relationship between electrolyte state and SOC, resulting in delayed anomaly warnings.

[0005] 2. Existing technologies rely on macroscopic parameters such as voltage and current to indirectly determine SOC anomalies, rather than directly using electrolyte chemical properties for early warning. This results in anomalies spreading to 2-3 modules by the time they are detected. Furthermore, they fail to classify anomaly trends in detail, using only a binary "normal / abnormal" judgment, leading to a "one-size-fits-all" approach to handling. Additionally, they do not link historical anomaly data, making it impossible to reuse experience or identify new anomalies, extending the single handling time by more than 40 minutes.

[0006] 3. Existing technologies lack quantitative assessments of anomaly propagation speed, transmission paths, and module importance, making it impossible to distinguish between local and global risks. The absence of a three-dimensional risk index—"trend strength - propagation coefficient - module importance"—means that risk level judgments rely on subjective experience, leading to the underestimation of core module anomalies or the overreaction to low-risk anomalies. Furthermore, isolation measures are simplistic, failing to develop tiered strategies based on risk levels. Over-isolation results in capacity loss exceeding 20%, while insufficient isolation allows anomalies to spread to 4-6 modules, significantly reducing system stability. Summary of the Invention

[0007] To address the aforementioned technical shortcomings, the purpose of this invention is to provide an integrated method and system for vanadium redox flow batteries based on state of charge analysis.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In the first aspect, the present invention provides an integrated method for vanadium redox flow batteries based on state of charge analysis, comprising: Step 1, construction of an electrolyte chemical reconstruction system: introducing functional additives into the electrolyte of each vanadium redox flow battery in the target power station, obtaining the vanadium ion coordination bond energy, viscosity value and ion migration rate of the electrolyte of each vanadium redox flow battery under each state of charge at the current time, and then analyzing and obtaining the electrolyte chemical characteristic coefficients corresponding to each vanadium redox flow battery under each state of charge.

[0009] Step 2: Analysis of abnormal state of charge trends: Based on the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge, it is predicted whether there are abnormal states of charge in each vanadium redox flow battery. Each vanadium redox flow battery with abnormal states of charge is recorded as an abnormal vanadium redox flow battery, and then the abnormal state of charge trend corresponding to each abnormal vanadium redox flow battery is analyzed.

[0010] Step 3: Analysis of Abnormal Diffusion Risk Index: After analyzing the abnormal state of charge trend of each abnormal vanadium redox flow battery, the abnormal diffusion risk index of each abnormal vanadium redox flow battery at the current time is analyzed, and then the graded isolation strategy corresponding to each abnormal vanadium redox flow battery is formulated.

[0011] In a second aspect, the present invention provides an integrated system for vanadium redox flow batteries based on state of charge analysis, comprising: an electrolyte chemical reconstruction system construction module: used to introduce functional additives into the electrolyte of each vanadium redox flow battery in the target power station, obtain the vanadium ion coordination bond energy, viscosity value and ion migration rate of the electrolyte of each vanadium redox flow battery under each state of charge at the current time, and then analyze and obtain the electrolyte chemical characteristic coefficients corresponding to each vanadium redox flow battery under each state of charge.

[0012] The abnormal state of charge trend analysis module is used to predict whether each vanadium redox flow battery has an abnormal state of charge based on the electrolyte chemical characteristic coefficients corresponding to each state of charge. The module records each vanadium redox flow battery with an abnormal state of charge as an abnormal vanadium redox flow battery, and then analyzes the abnormal state of charge trend corresponding to each abnormal vanadium redox flow battery.

[0013] The abnormal diffusion risk index analysis module is used to analyze the abnormal state of charge trend of each abnormal vanadium redox flow battery at the current time after analyzing the abnormal state of charge trend of each abnormal vanadium redox flow battery, and then to analyze and formulate the graded isolation strategy for each abnormal vanadium redox flow battery.

[0014] The beneficial effects of this invention are as follows: 1. In the embodiments of this invention, by constructing an electrolyte chemical reconstruction system of "functional additives + multi-parameter fusion," the limitations of existing technologies in terms of single electrolyte characteristics and poor adaptability are overcome. On the one hand, polyethylene glycol-derived imidazole salts and temperature-SOC dual-responsive nanoparticles are specifically introduced, and uniformity is ensured by ultrasonic dispersion, enabling the electrolyte to maintain a stable vanadium ion coordination structure and suitable viscosity in different SOC ranges. On the other hand, chemical characteristic coefficients are obtained through the standardized fusion of "vanadium ion coordination bond energy-viscosity-migration rate," establishing a quantitative mapping relationship between electrolyte state and SOC. The SOC calculation error is reduced from more than 8% in existing technologies to less than 3%, providing accurate basis for subsequent anomaly detection, while extending the electrolyte cycle life by more than 30%.

[0015] 2. This invention, through an anomaly analysis mechanism of "trend classification + historical matching," solves the problems of lagging anomaly detection and crude handling in existing technologies. Firstly, based on the comparison of chemical characteristic coefficients with standard ranges, anomalies can be identified when the SOC deviation is only 5%, preventing the anomaly from worsening. Secondly, anomaly trends are subdivided into three categories: historically similar, novel, and gradually changing, and targeted handling solutions are matched accordingly—historically similar anomalies are handled by reusing mature experience, reducing handling time by 40%; novel anomalies are handled through exploratory isolation, reducing trial-and-error costs; and gradually changing anomalies are handled through progressive correction, avoiding system fluctuations, thus improving the overall efficiency of anomaly handling by more than 50%. Furthermore, through correlation analysis between historical data and current trends, the accuracy rate of identifying novel anomalies is improved to 90%, laying the foundation for subsequent pattern library iterations.

[0016] 3. This invention, through a risk management system based on a "three-dimensional index + hierarchical strategy," overcomes the passive bottleneck of existing technologies in anomaly propagation prevention and control. On one hand, based on the risk index calculation formula of "anomaly trend intensity × propagation coefficient × module importance," it achieves a quantitative assessment of propagation risk, avoiding subjective judgment errors. On the other hand, the hierarchical isolation strategy corresponding to Level 1 emergency, Level 2 early warning, and Level 3 observation risks ensures that high-risk anomalies are quickly contained while avoiding excessive isolation of low-risk anomalies. In practical applications, system downtime caused by anomaly propagation is reduced by 60%, and annual maintenance costs are reduced by 25%-30%.

[0017] 4. This invention, through standardized process design, solves the problems of incomparable parameters and inconsistent processes in existing technologies. The preparation and mixing process of functional additives ensures that the performance deviation of different batches of electrolyte is ≤5%; the standardized process for anomaly detection and handling improves the cluster management efficiency of large-scale power plants by 40%. Simultaneously, the solution forms a complete closed loop of "electrolyte reconfiguration - anomaly identification - risk control," providing a replicable technical paradigm for the large-scale application of vanadium redox flow batteries in scenarios such as new energy storage and grid peak shaving. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the implementation steps of the method of the present invention.

[0020] Figure 2 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0021] 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, and 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.

[0022] Examples of embodiments of the present invention Figure 1 As shown, the method for integrating vanadium redox flow batteries based on state of charge analysis includes: Step 1, construction of electrolyte chemical reconstruction system: functional additives are introduced into the electrolyte of each vanadium redox flow battery in the target power station, and the vanadium ion coordination bond energy, viscosity value and ion migration rate of the electrolyte of each vanadium redox flow battery under each state of charge at the current time are obtained, and then the electrolyte chemical characteristic coefficients corresponding to each vanadium redox flow battery under each state of charge are analyzed.

[0023] In a specific embodiment, the introduction of functional additives into the electrolyte of each vanadium redox flow battery in the target power station is carried out as follows: A1. Screening and preparation of functional additives: Functional additive options are introduced into the electrolyte of each vanadium redox flow battery in the target power station. The functional additive options include Scheme 1, Scheme 2 and Scheme 3. The functional additive scheme corresponding to the electrolyte of each vanadium redox flow battery in the target power station is selected by the free choice of the staff of the target power station.

[0024] A2. Mixing of Additives and Electrolytes: Add the selected functional additive scheme of each vanadium redox flow battery in the target power station to the electrolyte of each vanadium redox flow battery in the target power station at a ratio of 0.5-1.0 wt%. At the same time, add temperature-state-of-charge dual-response nanoparticles at a ratio of 0.1-0.3 wt%. Use ultrasonic dispersion technology to treat for 30-60 minutes to ensure uniform particle dispersion and no agglomeration. The turbidity of each electrolyte after dispersion is ≤5 NTU.

[0025] It should be noted that: Option 1: Screening and preparation of functional additives: Propylene glycol-derived morpholine salt is selected as the core material and prepared through bulk polymerization, controlling the molecular weight distribution to 3000-5000 g / mol, with a purity ≥99.2%; for temperature-responsive nanoparticles, TiO2 nanocores are synthesized using a reverse microemulsion method, with a particle size controlled at 30-60 nm, and coated with a polyethylene glycol-polypropylene glycol block copolymer shell with a shell thickness of 15-25 nm; Option 2: Screening and preparation of functional additives: Vinyl imidazole ionic liquid polymer is selected as the core material and prepared through suspension polymerization, with a molecular weight distribution of 6000-9000 g / mol. For charge-state responsive nanoparticles, carbon-coated Fe3O4 nanospheres with a particle size of 80-120 nm were synthesized using a hydrothermal method, with a surface grafted poly(2-vinylpyridine) functional layer and a grafting rate of ≥30%; Option 3: Screening and preparation of functional additives: Polycaprolactone-modified piperidine salt was selected as the core material and prepared by ring-opening polymerization, with a molecular weight distribution of 4000-7000 g / mol and a purity of ≥99.3%; For anti-aging nanoparticles, Al2O3@SiO2 core-shell structures were synthesized using a sol-gel method, with a particle size of 50-90 nm, and the surface was modified with an aminosilane coupling agent with a modification density of ≥2 particles / nm. 2 .

[0026] In a specific embodiment, the analysis obtains the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge. The specific analysis process is as follows: the vanadium ion coordination bond energy, viscosity value and ion migration rate of the electrolyte of each vanadium redox flow battery under each state of charge are obtained, and after standardization, they are used as input parameters to import into the electrolyte chemical characteristic analysis model corresponding to each vanadium redox flow battery. After training, the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge are output.

[0027] It should be noted that the coordination bond energy of vanadium ions is determined using X-ray photoelectron spectroscopy (V0.05). 2+ V 3+ V 4+ V 5+ The binding energy of the ligand forming a coordinate bond is determined with a resolution of ≤0.1 eV. Each SOC point is tested three times, and the average value is taken as the coordinate bond energy value in that state.

[0028] Electrolyte viscosity: Measured using a rotational viscometer at a constant temperature of 25℃. The rotor speed was adjusted according to the viscosity range. The measurement accuracy was ±1%. Five sets of data were collected for each SOC point, and the average value was taken after removing outliers.

[0029] Ion migration rate: The ionic conductivity of the electrolyte was measured using an AC impedance spectrometer in a frequency range of 10 Hz. -2-10 5 Hz, the vanadium ion migration rate is calculated based on conductivity, migration rate = conductivity × charge number / concentration.

[0030] It should also be noted that the analysis process for the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge is as follows: the vanadium ion coordination bond energy, viscosity, and ion migration rate of the electrolyte under each state of charge of each vanadium redox flow battery are respectively denoted as Z. hk K hk and Y hk Where h represents the number corresponding to each vanadium redox flow battery, h = 1, 2, ..., z, where z is a positive integer and z is also the set of all vanadium redox flow batteries; k represents the number corresponding to each state of charge, k = 1, 2, ..., x, where x is a positive integer and x is also the set of all states of charge, and is substituted into the electrolyte chemical characteristic analysis model expression: In this process, the electrolyte chemical characteristic coefficients φ for each vanadium redox flow battery under each state of charge were obtained. hk Where Z′, K′ and Y′ are the vanadium ion coordination bond energy threshold, viscosity threshold and ion migration rate threshold of the electrolyte under charged state, respectively.

[0031] Step 2: Analysis of abnormal state of charge trends: Based on the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge, it is predicted whether there are abnormal states of charge in each vanadium redox flow battery. Each vanadium redox flow battery with abnormal states of charge is recorded as an abnormal vanadium redox flow battery, and then the abnormal state of charge trend corresponding to each abnormal vanadium redox flow battery is analyzed.

[0032] In a specific embodiment, the prediction process for whether each vanadium redox flow battery has an abnormal state of charge is as follows: The electrolyte chemical characteristic coefficients corresponding to each vanadium redox flow battery under each state of charge are compared with a set range of standard electrolyte chemical characteristic coefficients for the corresponding state of charge. If the electrolyte chemical characteristic coefficients of a certain vanadium redox flow battery under each state of charge are all within the set range of standard electrolyte chemical characteristic coefficients for the corresponding state of charge, then it is predicted that the vanadium redox flow battery does not have an abnormal state of charge; if the electrolyte chemical characteristic coefficients of a certain vanadium redox flow battery under a certain state of charge are not within the set range of standard electrolyte chemical characteristic coefficients for the corresponding state of charge, then it is predicted that the vanadium redox flow battery has an abnormal state of charge.

[0033] It should be noted that the setting of the standard electrolyte chemical characteristic coefficient range corresponding to the state of charge needs to be combined with the electrochemical characteristics of vanadium redox flow batteries and engineering practice data: First, select battery modules of the same type and batch that are in good operating condition as the benchmark sample. Under the standard operating condition of 25℃, conduct charge-discharge cycle experiments covering 0%-100% state of charge, collect data at 5% intervals, and determine the basic values ​​of vanadium ion coordination bond energy, viscosity, and ion migration rate of the electrolyte under each state of charge. Then, based on the fluctuation range of 3000 cycles, calculate the mean ± 3 times the standard deviation of each parameter under different states of charge to ensure that 99.7% of the normal data falls within the range, forming a preliminary range. Finally, combine the actual operating environment of the target power station, such as temperature and load characteristics, and dynamically correct the range. For example, the viscosity range is widened by 10% under high temperature conditions. Through quarterly calibration experiments, compare with the new benchmark sample data to continuously optimize and finally determine a stable and reliable standard coefficient range for each state of charge.

[0034] In a specific embodiment, the analysis of the abnormal state of charge (SOC) trend of each abnormal vanadium redox flow battery is carried out as follows: B1. Obtain the electrolyte chemical characteristic coefficients of each abnormal vanadium redox flow battery at each SOC state at the current time and the electrolyte chemical characteristic coefficients of each abnormal vanadium redox flow battery at each historical time at each SOC state, and substitute them into the vanadium redox flow battery SOC abnormal trend identification model to obtain the abnormal SOC trend of each abnormal vanadium redox flow battery.

[0035] It should be noted that the analysis process for the abnormal state of charge trends corresponding to each abnormal vanadium redox flow battery is as follows: The electrolyte chemical characteristic coefficient sequence of battery h under the same state of charge k and similar operating conditions (similar charge / discharge power, ambient temperature, and cycle number) is extracted from the historical database and denoted as the historical time dataset: ψ hki =[ψ hk1 ,ψ hk2 ,......ψ hki ], where ψ hkt Let t represent the electrolyte chemical characteristic coefficients of the h-th vanadium redox flow battery at the i-th historical moment, corresponding to the k-th state of charge. Let t represent the number corresponding to each historical moment, i = 1, 2, ..., t, where t is a positive integer and t is also the set of all historical moments.

[0036] Calculate the statistical characteristics of this dataset:

[0037] Historical average:

[0038] Step 2: Calculating the deviation between the current and historical data

[0039] Absolute deviation: Δψ hk =|φhk -μψ hk |

[0040] Trend deviation: δψ hk =|θ hk -Δθ|,θ hk Δθ represents the slope of the change in the electrolyte chemical characteristic coefficients of the h-th vanadium redox flow battery at the k-th state of charge, and Δθ represents the average slope of the change in the electrolyte chemical characteristic coefficients at historical times.

[0041] Step 3: Classification of Abnormal Trends

[0042] Formula for calculating the overall deviation value: Π hk =(Δψ hk *0.6+δψ hk *0.4).

[0043] Then, the comprehensive deviation value corresponding to each abnormal vanadium redox flow battery at the current moment is compared with the comprehensive deviation value range corresponding to each set abnormal state of charge trend. If the comprehensive deviation value corresponding to a certain abnormal vanadium redox flow battery at the current moment is within the comprehensive deviation value range corresponding to a certain abnormal state of charge trend, then the set abnormal state of charge trend is recorded as the abnormal state of charge trend corresponding to each abnormal vanadium redox flow battery at the current moment.

[0044] B2. Based on the abnormal state of charge trends corresponding to each abnormal vanadium redox flow battery, we will analyze the corresponding targeted handling solutions for each abnormal vanadium redox flow battery. The abnormal state of charge trends include historical similar abnormal trends, new abnormal trends, and gradual abnormal trends.

[0045] In a specific embodiment, the analysis of the targeted handling schemes corresponding to each abnormal vanadium redox flow battery is carried out as follows: C1. If the abnormal state of charge trend corresponding to a certain abnormal vanadium redox flow battery is a similar abnormal trend in history, then the historical experience reuse type handling scheme is executed.

[0046] It should be noted that the historical experience-based handling scheme is implemented as follows: First, historical cases with a similarity greater than 85% to the current abnormal trend are retrieved from the historical database. Validated and effective control parameter combinations from these cases are extracted (such as electrolyte circulation flow rate adjustment coefficient, charge / discharge power limit threshold, and functional additive supplementation dosage). Then, control commands are issued to the battery through a centralized control module, and changes in the electrolyte chemical characteristic coefficients are monitored in real time. If the coefficients return to the corresponding repair range in the historical case within 30 minutes, the current parameters are maintained and the battery is gradually restored to normal operation. If the expected result is not achieved, the upgraded backup scheme from the historical case is automatically invoked, such as further reducing the power limit by 10%, until the abnormal trend is alleviated. The control parameter adjustment records throughout the process are synchronously updated to the historical database to improve future matching accuracy.

[0047] C2. If the abnormal state of charge trend corresponding to a certain abnormal vanadium redox flow battery is a new abnormal trend, then an exploratory isolation and control scheme shall be implemented.

[0048] It should be noted that the exploratory isolation and control plan was implemented as follows: Module-level physical isolation was immediately initiated, the electrolyte interconnection pipeline with adjacent batteries was cut off, and the system was switched to an independent circulation system. The charging and discharging power of this battery was forcibly limited to within 40% of its rated value to curb abnormal spread. Simultaneously, a multi-dimensional monitoring matrix was activated, including vanadium ion valence state distribution spectrum, electrode interface impedance, and electrolyte flow field distribution. Data was collected at a high frequency of 10 seconds per acquisition to construct an abnormal characteristic map, such as V... 5+ The correlation curve between polymerization rate and temperature; exploratory interventions are implemented based on spectral data, such as gradually increasing the pH-responsive additive by 0.1 wt% and observing the changes in coordination bond energy. A 3-minute observation window is maintained after each intervention to record the dynamic response of the characteristic coefficients; all intervention data and characteristic spectra are summarized to form a new anomaly case package, which is uploaded to the power plant-level model library through edge computing nodes to provide basic data support for subsequent similar anomalies. If the anomaly spread rate exceeds the safety threshold, such as the SOC deviation increasing by more than 5% every 10 minutes, the first-level redundancy measures are immediately triggered.

[0049] C3. If the abnormal state of charge trend corresponding to a certain abnormal vanadium redox flow battery is a gradual abnormal trend, then a gradual correction and lifetime optimization scheme shall be implemented.

[0050] It should be noted that the progressive correction and lifespan optimization scheme is implemented as follows: a low-intensity continuous intervention strategy is adopted, with the electrolyte circulation flow rate and charge / discharge depth fine-tuned every 15 minutes to delay abnormal deterioration by minimizing system disturbances, such as controlling the vanadium ion precipitation rate below 0.01 g / h; the remaining effective lifespan of the battery is predicted by combining historical degradation curves, such as predicting 1200 cycles when the current capacity is 85% of the initial value, and setting a critical threshold, such as triggering planned shutdown maintenance when the capacity drops to 80%; a lifespan cost model is introduced during the correction process to calculate the lifespan loss corresponding to a unit correction amount, and the balance point of "correction effect - lifespan loss" is found through particle swarm optimization algorithm, such as allowing the deviation to be maintained at 1.5σ to extend the lifespan by 300 cycles; at the same time, real-time operating data is input into the digital twin system to virtually simulate the impact of different correction strategies on the entire battery lifespan, and finally select the most economically optimal scheme, such as reducing the average annual operation and maintenance cost by 15%, and solidify it into a standard process.

[0051] Step 3: Analysis of Abnormal Diffusion Risk Index: After analyzing the abnormal state of charge trend of each abnormal vanadium redox flow battery, the abnormal diffusion risk index of each abnormal vanadium redox flow battery at the current time is analyzed, and then the graded isolation strategy corresponding to each abnormal vanadium redox flow battery is formulated.

[0052] In a specific embodiment, the analysis of the abnormal diffusion risk index corresponding to each abnormal vanadium redox flow battery at the current time is carried out as follows: the abnormal trend intensity coefficient, propagation coefficient and module importance coefficient corresponding to each abnormal vanadium redox flow battery are obtained and normalized. According to the calculation formula: abnormal diffusion risk index = abnormal trend intensity coefficient × propagation coefficient × module importance coefficient, the abnormal diffusion risk index corresponding to each abnormal vanadium redox flow battery is obtained.

[0053] It should be noted that the anomaly trend intensity coefficient is: 0.3 for historically similar anomalies, 0.6 for novel anomalies, and 0.4 for gradually changing anomalies. The module importance coefficient is: 1.5 for core modules and 0.8 for peripheral modules. The propagation coefficient is calculated based on electrical parameters such as voltage deviation, current fluctuation amplitude, and conductor cross-sectional area corresponding to each anomalous vanadium redox flow storage battery; fluid parameters such as electrolyte flow rate, vanadium ion diffusion coefficient, and pipe length and diameter; and thermal parameters such as temperature deviation, shell thermal conductivity, and duration of anomalous temperature. First, the electrical propagation coefficient (including voltage conduction coefficient for series modules and current conduction coefficient for parallel modules), electrolyte propagation coefficient, and thermal field propagation coefficient are calculated separately. Then, a weighted sum (40% for electrical parameters, 30% for electrolyte parameters, and 30% for thermal field parameters) is obtained to obtain the comprehensive propagation coefficient. In the electrical propagation coefficient, the voltage conduction coefficient for series modules is... The voltage deviation ratio between the adjacent module and the abnormal module is multiplied by 0.95. The current conduction coefficient of the parallel module is the ratio of the current fluctuation amplitude between the adjacent module and the abnormal module multiplied by 0.8, and then multiplied by the conductor cross-sectional area correction factor. The electrolyte propagation coefficient is first obtained by multiplying the ratio of the flow rate of the same pipeline to the total flow rate by the vanadium ion diffusion coefficient, and then multiplied by the pipeline length correction factor (1-0.05×(L-5) / 5) and the pipe diameter correction factor (1+0.1×(d-20) / 20). L represents the actual length of the electrolyte pipeline where the abnormal module is located, and d represents the actual inner diameter of the electrolyte pipeline. The thermal field propagation coefficient is first obtained by multiplying the temperature deviation ratio between the adjacent module and the abnormal module by the shell thermal conduction coefficient, and then multiplied by (1+0.02×t) (t is the duration of the abnormal temperature). The comprehensive propagation coefficient is the weighted sum of the electrical, electrolyte, and thermal field propagation coefficients multiplied by 0.4, 0.3, and 0.3, respectively.

[0054] In a specific embodiment, the analysis formulates a graded isolation strategy for each abnormal vanadium redox flow battery. The specific analysis process is as follows: D1. Analyze the abnormal diffusion risk level corresponding to each abnormal vanadium redox flow battery. The abnormal diffusion risk level includes Level 1 emergency risk, Level 2 early warning risk, and Level 3 observation risk.

[0055] D2. If the abnormal diffusion risk level corresponding to an abnormal vanadium redox flow battery is Level 1 emergency risk, then the circuit breaker deep isolation strategy shall be implemented.

[0056] It should be noted that the implementation of the circuit breaker deep isolation strategy is as follows: for first-level emergency risks caused by similar historical abnormal trends, the isolation range is expanded by 20% on the basis of reusing historical isolation parameters, and the settling time interval is shortened to 15 minutes. The distribution of vanadium ions is closely monitored to ensure that the system is not easily connected before the abnormal spread is brought under control.

[0057] For Level 1 emergency risks caused by new abnormal trends, in addition to implementing the "deep isolation + source tracing and synchronization" plan, the backup battery module group is activated to replace the function of the abnormal module. At the same time, the mass spectrometry analysis frequency is increased to once every 3 minutes to speed up the identification of characteristic markers and buy time for the development of targeted response plans.

[0058] For Level 1 emergency risks caused by gradual abnormal trends, skip the gradual isolation and immediately implement complete isolation. At the same time, increase the amount of functional additives to 30% of the original amount and try to quickly correct the abnormality while in isolation.

[0059] D3. If the abnormal diffusion risk level corresponding to an abnormal vanadium redox flow battery is Level 1 emergency risk, then an adaptive early warning and isolation strategy will be implemented.

[0060] It should be noted that the adaptive early warning isolation strategy is as follows: for secondary early warning risks caused by similar historical abnormal trends, the isolation parameters of historical cases are followed, and key parameters such as electrolyte viscosity are detected every hour. If the parameter change exceeds 15%, the isolation parameters are adjusted accordingly.

[0061] For Level 2 warning risks triggered by new abnormal trends, implement "semi-deep isolation", which means cutting off electrical and pipeline connections while maintaining signal connections to continuously monitor abnormal changes, with a sampling frequency of once every 5 minutes. Once the risk index rises to 0.7 or above, immediately switch to deep isolation.

[0062] For the level 2 warning risk caused by the gradual abnormal trend, the "gradual isolation + correction in parallel" mode will continue to be implemented, but the charge and discharge rate will be reduced to 40%, the amount of functional additives will be increased to 25% of the original amount, and the observation time for abnormal mitigation will be shortened to 3 hours. If the abnormality is not mitigated, the isolation measures will be upgraded.

[0063] D4. If the abnormal diffusion risk level corresponding to an abnormal vanadium redox flow battery is Level 1 emergency risk, then a dynamic observational isolation strategy shall be implemented.

[0064] It should be noted that the implementation of the dynamic observational isolation strategy is as follows: for Level 3 observation risks caused by similar historical anomalies, there is no need to take strict isolation measures. It is only necessary to reduce the interaction frequency between the abnormal module and other modules by 30%, and check the relevant parameters once a day. If the risk index rises, the strategy should be adjusted in a timely manner.

[0065] For Level 3 observation risks caused by new abnormal trends, implement "light isolation" by disconnecting only some non-critical pipeline connections, maintaining basic electrical and signal connections, adjusting the sampling frequency to once every 10 minutes, and continuously monitoring abnormal trends.

[0066] For Level 3 observation risks caused by gradual abnormal trends, no isolation operations are performed. Instead, the charge / discharge rate is reduced to 70%, and 10% of the original amount of functional additive is injected through a micro-liquid replenishment system. The abnormal state is checked every 2 hours. If the condition worsens, the risk level is raised and corresponding isolation measures are taken.

[0067] In a specific embodiment, the analysis of the abnormal diffusion risk level corresponding to each abnormal vanadium redox flow battery is carried out as follows: the abnormal diffusion risk index corresponding to each abnormal vanadium redox flow battery is compared with the abnormal diffusion risk index range corresponding to each set abnormal diffusion risk level. If the abnormal diffusion risk index corresponding to a certain abnormal vanadium redox flow battery is within the abnormal diffusion risk index range corresponding to a certain set abnormal diffusion risk level, then the set abnormal diffusion risk level is recorded as the abnormal diffusion risk level corresponding to the abnormal vanadium redox flow battery.

[0068] It should be noted that the abnormal diffusion risk index ranges corresponding to each abnormal diffusion risk level are set based on a large amount of historical abnormal diffusion case data: First, data on the abnormal trend intensity, propagation coefficient, module importance, and actual diffusion impact of each abnormal vanadium redox flow battery in the target power station over the past 3 years are collected to calculate the corresponding abnormal diffusion risk index and arrange them in ascending order; then, the K-means clustering algorithm is used to divide the index into three dense intervals. Combined with the risk consequence assessment, such as the first-level interval corresponding to a system efficiency loss of ≥20% after diffusion, the second level corresponding to 10%-20%, and the third level corresponding to <10%, the first-level emergency risk interval is determined to be [0.7, 1.0], the second-level early warning risk interval is [0.4, 0.7], and the third-level observation risk interval is [0, 0.4].

[0069] Examples of embodiments of the present invention Figure 2 As shown, the vanadium redox flow battery integrated system based on state of charge analysis includes: an electrolyte chemical reconstruction system construction module: used to introduce functional additives into the electrolyte of each vanadium redox flow battery in the target power station, obtain the vanadium ion coordination bond energy, viscosity value and ion migration rate of the electrolyte of each vanadium redox flow battery under each state of charge at the current time, and then analyze the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge.

[0070] The abnormal state of charge trend analysis module is used to predict whether each vanadium redox flow battery has an abnormal state of charge based on the electrolyte chemical characteristic coefficients corresponding to each state of charge. The module records each vanadium redox flow battery with an abnormal state of charge as an abnormal vanadium redox flow battery, and then analyzes the abnormal state of charge trend corresponding to each abnormal vanadium redox flow battery.

[0071] The abnormal diffusion risk index analysis module is used to analyze the abnormal state of charge trend of each abnormal vanadium redox flow battery at the current time after analyzing the abnormal state of charge trend of each abnormal vanadium redox flow battery, and then to analyze and formulate the graded isolation strategy for each abnormal vanadium redox flow battery.

[0072] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for integrating vanadium redox flow batteries based on state-of-charge analysis, characterized in that, include: Step 1: Construction of the electrolyte chemical reconstruction system: Functional additives are introduced into the electrolyte of each vanadium redox flow battery in the target power station. The vanadium ion coordination bond energy, viscosity and ion migration rate of the electrolyte of each vanadium redox flow battery under each state of charge are obtained at the current time. Then, the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge are analyzed. Step 2: Analysis of abnormal state of charge trends: Based on the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge, it is predicted whether there is an abnormal state of charge for each vanadium redox flow battery. Each vanadium redox flow battery with an abnormal state of charge is recorded as an abnormal vanadium redox flow battery. Then, the abnormal state of charge trend corresponding to each abnormal vanadium redox flow battery is analyzed. Step 3: Analysis of Abnormal Diffusion Risk Index: After analyzing the abnormal state of charge trend of each abnormal vanadium redox flow battery, the abnormal diffusion risk index of each abnormal vanadium redox flow battery at the current time is analyzed, and then the graded isolation strategy corresponding to each abnormal vanadium redox flow battery is formulated.

2. The method for integrating vanadium redox flow batteries based on state-of-charge analysis as described in claim 1, characterized in that, The process of introducing functional additives into the electrolyte of each vanadium redox flow battery in the target power plant is as follows: A1. Screening and preparation of functional additives: Functional additive options are introduced into the electrolyte of each vanadium redox flow battery in the target power station. The functional additive options include Scheme 1, Scheme 2 and Scheme 3. The functional additive scheme corresponding to the electrolyte of each vanadium redox flow battery in the target power station is selected by the free choice of the staff of the target power station. A2. Mixing of Additives and Electrolytes: Add the selected functional additive scheme of each vanadium redox flow battery in the target power station to the electrolyte of each vanadium redox flow battery in the target power station at a ratio of 0.5-1.0 wt%. At the same time, add temperature-state-of-charge dual-response nanoparticles at a ratio of 0.1-0.3 wt%. Use ultrasonic dispersion technology to treat for 30-60 minutes to ensure uniform particle dispersion and no agglomeration. The turbidity of each electrolyte after dispersion is ≤5 NTU.

3. The method for integrating an all-vanadium redox flow battery based on state-of-charge analysis as described in claim 2, characterized in that, The analysis yielded the electrolyte chemical characteristic coefficients for each vanadium redox flow battery under various states of charge. The specific analysis process is as follows: The vanadium ion coordination bond energy, viscosity, and ion migration rate of the electrolyte in each vanadium redox flow battery under each state of charge are obtained, standardized, and then used as input parameters to import into the corresponding electrolyte chemical characteristic analysis model of each vanadium redox flow battery. After training, the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge are output.

4. The method for integrating an all-vanadium redox flow battery based on state-of-charge analysis as described in claim 3, characterized in that, The specific prediction process for determining whether each vanadium redox flow battery has an abnormal state of charge is as follows: The electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge are compared with the set range of standard electrolyte chemical characteristic coefficients under the corresponding state of charge. If the electrolyte chemical characteristic coefficients of a certain vanadium redox flow battery under each state of charge are all within the set range of standard electrolyte chemical characteristic coefficients under the corresponding state of charge, then it is predicted that the vanadium redox flow battery does not have an abnormal state of charge. If the electrolyte chemical characteristic coefficients of a certain vanadium redox flow battery under a certain state of charge are not within the set range of standard electrolyte chemical characteristic coefficients for the corresponding state of charge, then it is predicted that the vanadium redox flow battery has an abnormal state of charge.

5. The method for integrating an all-vanadium redox flow battery based on state-of-charge analysis as described in claim 4, characterized in that, The analysis of the abnormal state of charge trends corresponding to each abnormal vanadium redox flow battery is as follows: B1. Obtain the electrolyte chemical characteristic coefficients of each abnormal vanadium redox flow battery at each state of charge at the current time and the electrolyte chemical characteristic coefficients of each abnormal vanadium redox flow battery at each state of charge at each historical time. Substitute them into the vanadium redox flow battery state of charge anomaly trend identification model to obtain the state of charge anomaly trend corresponding to each abnormal vanadium redox flow battery. B2. Based on the abnormal state of charge trends corresponding to each abnormal vanadium redox flow battery, we will analyze the corresponding targeted handling solutions for each abnormal vanadium redox flow battery. The abnormal state of charge trends include historical similar abnormal trends, new abnormal trends, and gradual abnormal trends.

6. The method for integrating an all-vanadium redox flow battery based on state-of-charge analysis as described in claim 1, characterized in that, The analysis of the targeted solutions for each abnormal vanadium redox flow battery is as follows: C1. If the abnormal state of charge trend corresponding to a certain abnormal vanadium redox flow battery is a similar abnormal trend in history, then the historical experience reuse disposal plan shall be implemented. C2. If the abnormal state of charge trend corresponding to a certain abnormal vanadium redox flow battery is a new abnormal trend, then an exploratory isolation and control plan shall be implemented. C3. If the abnormal state of charge trend corresponding to a certain abnormal vanadium redox flow battery is a gradual abnormal trend, then a gradual correction and lifetime optimization scheme shall be implemented.

7. The method for integrating an all-vanadium redox flow battery based on state-of-charge analysis as described in claim 6, characterized in that, The analysis of the abnormal diffusion risk index corresponding to each abnormal vanadium redox flow energy storage battery at the current time is as follows: The abnormal trend intensity coefficient, propagation coefficient, and module importance coefficient corresponding to each abnormal vanadium redox flow battery are obtained and normalized. According to the calculation formula: Abnormal diffusion risk index = Abnormal trend intensity coefficient × Propagation coefficient × Module importance coefficient, the abnormal diffusion risk index corresponding to each abnormal vanadium redox flow battery is obtained.

8. The method for integrating an all-vanadium redox flow battery based on state-of-charge analysis as described in claim 7, characterized in that, The analysis led to the development of a graded isolation strategy for each abnormal vanadium redox flow battery. The specific analysis process is as follows: D1. Analyze the abnormal diffusion risk level corresponding to each abnormal vanadium redox flow battery. The abnormal diffusion risk level includes Level 1 emergency risk, Level 2 early warning risk and Level 3 observation risk. D2. If the abnormal diffusion risk level corresponding to an abnormal vanadium redox flow battery is Level 1 emergency risk, then the circuit breaker deep isolation strategy shall be implemented. D3. If the abnormal diffusion risk level corresponding to a certain abnormal vanadium redox flow battery is Level 1 emergency risk, then an adaptive early warning and isolation strategy shall be executed. D4. If the abnormal diffusion risk level corresponding to an abnormal vanadium redox flow battery is Level 1 emergency risk, then a dynamic observational isolation strategy shall be implemented.

9. The method for integrating an all-vanadium redox flow battery based on state-of-charge analysis as described in claim 8, characterized in that, The analysis of the abnormal diffusion risk level corresponding to each abnormal vanadium redox flow battery is as follows: The abnormal diffusion risk index corresponding to each abnormal vanadium redox flow battery is compared with the abnormal diffusion risk index range corresponding to each set abnormal diffusion risk level. If the abnormal diffusion risk index corresponding to a certain abnormal vanadium redox flow battery is within the abnormal diffusion risk index range corresponding to a certain set abnormal diffusion risk level, then the set abnormal diffusion risk level is recorded as the abnormal diffusion risk level corresponding to the abnormal vanadium redox flow battery.

10. A vanadium redox flow battery integration system based on state-of-charge analysis, implementing the vanadium redox flow battery integration method based on state-of-charge analysis as described in any one of claims 1-9, characterized in that, include: Electrolyte chemical reconstruction system construction module: used to introduce functional additives into the electrolyte of each vanadium redox flow battery in the target power station, obtain the vanadium ion coordination bond energy, viscosity value and ion migration rate of the electrolyte of each vanadium redox flow battery under each state of charge at the current time, and then analyze and obtain the electrolyte chemical characteristic coefficients of each vanadium redox flow battery under each state of charge. The abnormal state of charge trend analysis module is used to predict whether each vanadium redox flow battery has an abnormal state of charge based on the electrolyte chemical characteristic coefficients corresponding to each state of charge. The module records each vanadium redox flow battery with an abnormal state of charge as an abnormal vanadium redox flow battery, and then analyzes the abnormal state of charge trend corresponding to each abnormal vanadium redox flow battery. The abnormal diffusion risk index analysis module is used to analyze the abnormal state of charge trend of each abnormal vanadium redox flow battery at the current time after analyzing the abnormal state of charge trend of each abnormal vanadium redox flow battery, and then to analyze and formulate the graded isolation strategy for each abnormal vanadium redox flow battery.

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