A high-energy all-vanadium redox flow battery performance monitoring and evaluation method

Abstract

This invention discloses a method for performance monitoring and evaluation of high-energy vanadium redox flow batteries, relating to the field of battery technology. The method includes: Step S1: collecting physicochemical data of the electrolyte and spectral data of the vanadium redox flow battery; Step S2: calculating the electrolyte characterization index and separating the vanadium ion absorption peak from the vanadium oxide absorption peak; Step S3: constructing a stability evaluation model and generating an electrolytic stability index; Step S4: setting a stability threshold. When the electrolytic stability index is lower than the stability threshold, it is determined that the target battery system has performance degradation defects. This invention, by decoupling the spectral signals, can clearly identify the spectral characteristics of vanadium ions and vanadium oxides, separating the vanadium ion absorption peak from the vanadium oxide absorption peak, thereby avoiding the influence of vanadium oxides in the electrolyte. It has the advantages of enabling the system to accurately extract effective signals and remove interference, and significantly improves the accuracy of battery performance evaluation.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and specifically to a method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery. Background Technology

[0002] With the large-scale grid connection of renewable energy, vanadium redox flow batteries have become a key technology in the field of large-scale energy storage due to their advantages such as long cycle life, high safety, and independently designable capacity. To improve their energy density and reduce system costs, the industry is committed to developing and applying new high-energy-density systems such as high-concentration vanadium electrolytes, novel additives, and highly active electrodes. However, accurate monitoring and condition assessment of battery performance, especially early warning of potential faults, has become a technical bottleneck restricting their long-term reliable operation and commercialization.

[0003] In existing technologies, monitoring the performance of vanadium redox flow batteries typically relies on the measurement and analysis of key physicochemical parameters of the electrolyte. Compared to conventional vanadium redox flow batteries, high-energy vanadium redox flow batteries, due to their high-energy-density electrolytes, exhibit excessively strong absorption peaks and broadened bands in the UV-Vis spectrum. This can easily mask characteristic spectral signals generated by trace amounts of by-reaction products or impurity ion agglomerates, making it difficult for traditional methods to effectively identify the signal components contributed by particulate matter generation and accumulation in high-energy vanadium redox flow batteries. Furthermore, high-energy electrolytes are more prone to generating insoluble particles at the nanometer to micrometer scale. These particles continuously grow and aggregate during battery cycling, gradually depositing inside porous electrodes, in narrow passages, or on the surface of ion exchange membranes. Ultimately, this leads to irreversible performance degradation, such as channel blockage, reduced active area, and a sharp increase in internal resistance. These two factors interact in the high-energy electrolyte environment, which makes it easy for the UV-Vis spectroscopy and viscosity estimation methods, which are implemented independently in conventional monitoring and evaluation techniques, to misjudge the true health status and potential performance and lifespan risks of high-energy vanadium redox flow batteries, either too high or too low. Summary of the Invention

[0004] This invention provides a method for monitoring and evaluating the performance of high-energy vanadium redox flow batteries, which solves the problem of poor performance evaluation accuracy caused by electrolyte particulate matter when evaluating the performance status of high-energy vanadium redox flow batteries in the prior art.

[0005] This invention is achieved through the following technical solution:

[0006] A method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery, the method comprising:

[0007] Step S1: Arrange a sensor array at the loop node of the electrolyte in the target battery system, set several monitoring cycles equally, and sample in each monitoring cycle to collect the physicochemical data of the electrolyte and the spectral data of the full vanadium solution of the target battery system.

[0008] Step S2: Calculate the electrolyte characterization index to measure the degree of electrolyte degradation based on the electrolyte physicochemical data, and use the spectral decoupling algorithm to separate the vanadium ion absorption peak and the vanadium oxide absorption peak for the full vanadium liquid spectral data.

[0009] Step S3: Construct a stability evaluation model using the electrolyte characterization index, vanadium ion absorption peak and vanadium oxide absorption peak, and use the stability evaluation model to calculate and generate an electrolyte stability index that measures the full-load performance of the target battery system.

[0010] Step S4: Set a stability threshold for the electrolytic stability index, which represents the critical point of the target battery system's health. When the electrolytic stability index is lower than the stability threshold, it is determined that the target battery system has a performance degradation defect.

[0011] Furthermore, the electrolyte physicochemical data includes voltage drop, conductivity, and tetravalent vanadium concentration values ​​collected from the target battery system in each monitoring cycle;

[0012] The voltage drop value represents the additional voltage loss of the target battery system during the current monitoring period, the conductivity value represents the average conductivity of the target battery system during the current monitoring period, and the tetravalent vanadium concentration value represents the average concentration of vanadium ions in the positive tetravalent state of the target battery system during the current monitoring period.

[0013] Furthermore, the calculation process for the electrolyte characterization index includes:

[0014] Let voltage drop be denoted as V, conductivity as σ, and tetravalent vanadium concentration as Cv; set a reference value for vanadium concentration for the target battery system and denote it as Cre; and let the electrolyte characterization index be denoted as Ie.

[0015] The formula for calculating the electrolyte characterization index is as follows: ,

[0016] In the formula, k1 represents the conductivity adjustment parameter and k2 represents the concentration smoothing parameter.

[0017] Furthermore, the process of setting the conductivity adjustment parameter k1 includes:

[0018] Collect and obtain the operating current density of the target battery system during the current monitoring period and express it as J. Set a reference current density under rated operating conditions for the target battery system and express it as Jre. Set the scaling factor ω.

[0019] Then the calculation formula of the conductance adjustment parameter k1 is expressed as: .

[0020] Further, the setting process of the concentration smoothing parameter k2 includes:

[0021] Set the state of charge of the battery in each monitoring period of the target battery system as soc, and let 0 < soc < 1; the value of the concentration smoothing parameter k2 is set as: k2 = Cv ∙ soc.

[0022] Further, the all-vanadium liquid spectral data includes the initial spectral signal collected in the target battery system, and performs smoothing and normalization processing on the initial spectral signal; uses the principal component analysis method to decouple and calculate the initial spectral signal, and decouples the initial spectral signal into a linear combination of the reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak;

[0023] Verify the signal correlation between the initial spectral signal and the reconstructed signal. When the signal correlation meets the verification, use the reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak to construct a stable evaluation model; when the signal correlation does not meet the verification, re-perform the decoupling calculation on the initial spectral signal.

[0024] Further, the process of using the principal component analysis method for decoupling calculation includes:

[0025] Set several wavelength nodes representing signal acquisition points for vanadium ions and vanadium oxides, collect spectral signals at each wavelength node and label them as decoupled spectral signals; represent the initial spectral signal as a to-be-decoupled spectral vector composed of multiple decoupled spectral signals, obtain the covariance matrix of the to-be-decoupled spectral vector, use the eigenvalue decomposition method to obtain the eigenvalues and eigenvectors of the covariance matrix, and project the initial spectral signal onto the vector space of the eigenvectors;

[0026] Sort the eigenvalues from largest to smallest in the vector space from front to back, select several eigenvalues at the front of the sorting and label them as principal component eigenvalues, and label the eigenvectors corresponding to the principal component eigenvalues as principal component eigenvectors; use the principal component eigenvectors to construct the reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak; use the correlation coefficient method to verify the correlation between the reconstructed signal and the initial spectral signal. When the correlation meets the verification, the vanadium ion absorption peak and the vanadium oxide absorption peak participate in constructing the stable evaluation model in the form of reconstructed signals; when the correlation does not meet the verification, use the eigenvalue decomposition method to re-obtain the eigenvalues and eigenvectors and re-construct the reconstructed signal.

[0027] Furthermore, the correlation coefficient method is used to verify the correlation between the initial spectral signal and the reconstructed signal. The correlation coefficient between the initial spectral signal and the reconstructed signal is calculated. A correlation threshold is set for the correlation coefficient. When the correlation coefficient is less than the correlation threshold, the correlation is deemed not to meet the verification. When the correlation coefficient is greater than or equal to the correlation threshold, the correlation is deemed to meet the verification.

[0028] Furthermore, the process of the correlation coefficient method includes:

[0029] Let the correlation coefficient be denoted as R, the total number of wavelength nodes be denoted as n and their ordinal numbers be denoted as i; let the decoupled reconstructed signal be denoted as Sre, the initial spectral signal be denoted as St; and let the wavelength nodes be denoted as ω.

[0030] The formula for calculating the correlation coefficient is: ,

[0031] Where, ω i St(ω) represents the i-th wavelength node ω; i Sre(ω) represents the spectral signal intensity of the initial spectral signal at the i-th wavelength node ω. i ) represents the spectral signal intensity of the decoupled and reconstructed signal at the i-th wavelength node ω; This represents the average intensity of the initial spectral signal across all wavelength nodes. This represents the average spectral signal intensity of the decoupled and reconstructed signal with respect to all wavelength nodes.

[0032] Furthermore, the calculation forms of the initial spectral signal and the decoupled reconstructed signal are set as follows:

[0033] Let m be the total number of selected principal component eigenvalues ​​and j be their ordinal numbers; let PC represent the eigenvectors, and let PC be the eigenvector of the j-th principal component. j Let the decoupled reconstruction signal include a first decoupled signal and a second decoupled signal. Let the first decoupled signal represent the spectral signal intensity of the vanadium ion absorption peak and be denoted as Sre1, and let the second decoupled signal represent the spectral signal intensity of the vanadium oxide absorption peak and be denoted as Sre2. Set the initial signal weight, the first decoupled weight, and the second decoupled weight and denot them as a, b, and c, respectively.

[0034] Then at the wavelength node ω i The initial spectral signal St is represented as: ,

[0035] Wavelength node ω i The first decoupling signal Sre1 is represented as: ,

[0036] Wavelength node ω iThe second decoupling signal Sre2 is represented as: ,

[0037] Wherein, the initial signal weight, the first decoupling weight, and the second decoupling weight corresponding to the j-th principal component eigenvector are respectively represented as a j b j and c j .

[0038] Furthermore, the construction process of the stability evaluation model is set as follows:

[0039] Let the electrolytic stability index be denoted as Is, and let vanadium ion absorption coefficient γ1 and vanadium oxide absorption coefficient γ2 be defined; let the spectral signal intensity of the vanadium ion absorption peak be denoted as Sre1, and let the spectral signal intensity of the vanadium oxide absorption peak be denoted as Sre2.

[0040] The calculation formula for the stability evaluation model is then expressed as: .

[0041] Furthermore, when the target battery system's charge increases by more than 80%, the vanadium ion absorption coefficient γ1 is set to be greater than the vanadium oxide absorption coefficient γ2; when the target battery system's charge decreases by less than 20%, the vanadium ion absorption coefficient γ1 is set to be less than the vanadium oxide absorption coefficient γ2.

[0042] Furthermore, when the wavelength length of the wavelength node is between 300-400nm, a first decoupling weight b is set. j Greater than the second decoupling weight c j When the wavelength length of the wavelength node is above 400nm, set the first decoupling weight b. j Less than the second decoupling weight c j .

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] 1. By decoupling the spectral signals, the spectral characteristics of vanadium ions and vanadium oxides can be clearly identified, and the absorption peaks of vanadium ions and vanadium oxides can be separated, thereby avoiding the influence of vanadium oxides in the electrolyte. This allows the system to accurately extract the effective signal and remove interference, significantly improving the accuracy of performance evaluation.

[0045] 2. The electrolyte characterization index, calculated based on the physicochemical data of the electrolyte, serves as a precursor indicator of battery performance degradation. It can reflect the health status of the electrolyte in real time. Combined with the stability assessment model, it provides a multi-level assessment mechanism that directly reflects the degree of electrolyte deterioration and can comprehensively assess the performance stability of the battery.

[0046] 3. By introducing principal component analysis for signal decoupling, dynamically adjusting weights, and real-time monitoring and optimization of signal changes caused by particulate matter, the accuracy of performance evaluation is improved. This enables better handling of the potential impact of particulate matter on battery health and provides strong support for the reliable operation of high-energy vanadium redox flow batteries. Attached Figure Description

[0047] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0050] Example 1, such as Figure 1 As shown in the figure, this embodiment is a method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery. The method includes:

[0051] Step S1: Arrange a sensor array at the loop node of the electrolyte in the target battery system, set several monitoring cycles equally, and sample in each monitoring cycle to collect the physicochemical data of the electrolyte and the spectral data of the full vanadium solution of the target battery system.

[0052] Step S2: Calculate the electrolyte characterization index to measure the degree of electrolyte degradation based on the electrolyte physicochemical data, and use the spectral decoupling algorithm to separate the vanadium ion absorption peak and the vanadium oxide absorption peak for the full vanadium liquid spectral data.

[0053] Step S3: Construct a stability evaluation model using the electrolyte characterization index, vanadium ion absorption peak and vanadium oxide absorption peak, and use the stability evaluation model to calculate and generate an electrolyte stability index that measures the full-load performance of the target battery system.

[0054] Step S4: Set a stability threshold for the electrolytic stability index, which represents the critical point of the target battery system's health. When the electrolytic stability index is lower than the stability threshold, it is determined that the target battery system has a performance degradation defect.

[0055] High-energy vanadium redox flow batteries typically use high-concentration vanadium electrolytes, with vanadium concentrations generally higher than those in conventional flow batteries. This means that high-energy flow batteries can store more energy in the same volume of electrolyte, thus increasing the battery's energy density. Conventional vanadium redox flow batteries use lower-concentration vanadium electrolytes. Although their energy density is lower, this design makes the battery cost more controllable and performs well in low-energy-density applications. There are some key differences between high-energy and conventional vanadium redox flow batteries in monitoring and evaluating battery performance. High-energy electrolytes are more prone to generating insoluble particles during operation. These particles typically grow and aggregate as the battery cycles. Over time, these particles can deposit on porous electrodes, tubing, and ion exchange membranes, leading to channel blockage, reduced active area, and increased internal resistance, making it easy to mask or ignore signals from trace byproducts or impurity ions. Because conventional vanadium redox flow batteries use lower-concentration vanadium electrolytes, the rate of particle formation and byproduct accumulation is slower. The performance degradation of conventional batteries is relatively less pronounced. When using conventional evaluation methods for high-energy vanadium redox flow batteries, the rate or magnitude of battery degradation may be underestimated, especially in the case of particle deposition and electrolyte degradation, which may fail to accurately predict the actual health status of the battery.

[0056] The target battery system is a high-performance vanadium redox flow battery used for monitoring and evaluating its performance status. Deploying a sensor array means deploying a set of sensor devices at key loop nodes in the electrolyte circuit of the vanadium redox flow battery. This sensor array can monitor the physicochemical properties of the electrolyte in real time and collect electrolyte physicochemical data. This sensor array includes, but is not limited to, multiple sensors for measuring electrolyte conductivity, vanadium ion concentration, temperature, pH value, and other related parameters. By rationally arranging the sensor array, comprehensive and accurate data collection of the electrolyte's operation within the battery system can be ensured, providing the necessary foundational data for subsequent analysis. The entire monitoring process is divided into several monitoring cycles, with real-time sampling operations during each cycle to collect various electrolyte data indicators. The monitoring cycle settings can be dynamically adjusted based on factors such as changes in battery system load and electrolyte performance. The electrolyte physicochemical data refers to physicochemical parameters that affect electrolyte performance and can be used to measure changes in physical properties caused by normal concentration variations and abnormal changes caused by particle agglomeration and blockage. In specific applications, this may include data such as pH value, ion concentration, electrolyte viscosity, and temperature. The spectral data of the vanadium redox flow battery can be obtained through spectral analysis techniques, such as ultraviolet-visible spectroscopy and near-infrared spectroscopy, to acquire the absorption peaks and band data of different vanadium substances at different wavelengths, including the absorption peaks of vanadium ions and vanadium oxides. During the operation of a high-energy vanadium redox flow battery, changes in electrolyte performance directly affect the battery's efficiency and lifespan. Electrolyte degradation is typically caused by factors such as changes in conductivity, concentration, and particulate matter formation, which are difficult to accurately reflect using traditional methods. The electrolyte characterization index is used to measure the degree of electrolyte degradation and provides a quantitative basis for real-time monitoring of battery performance. As a common implementation method, in practical applications, the electrolyte characterization index can be obtained by weighted summation of several electrolyte physicochemical data.

[0057] Since performance monitoring of vanadium redox flow batteries often relies on the analysis of electrolyte spectral data, particularly the absorption peaks of vanadium ions and vanadium oxides, and the presence of trace by-reaction products and impurity ion aggregates in the electrolyte can cause spectral signal overlap and band broadening, thus interfering with the identification of vanadium ion and vanadium oxide absorption peaks, this embodiment processes the spectral signals acquired from the vanadium redox flow battery to separate the absorption peaks containing vanadium ions and vanadium oxides. This decouples the originally mixed spectral signals, thereby avoiding interference from particulate matter and other impurities. During battery operation, the proportion of vanadium ions and vanadium oxides affects the conductivity and reactivity of the electrolyte, thus impacting the battery's charge-discharge performance. Spectral decoupling algorithms are commonly used to separate absorption peaks or characteristic signals of different components from complex spectral signals, especially in cases of signal overlap or interference. The purpose of spectral decoupling is to separate mixed spectral signals into their individual components, thereby accurately analyzing the concentration or properties of each component.

[0058] The stability assessment model is used to comprehensively evaluate the health status and performance of high-energy vanadium redox flow batteries. In practice, various physicochemical data and historical operating data of the battery can be used, combined with weight allocation, to construct the assessment model. The model can learn from input battery operating data such as voltage, current, electrolyte concentration, and temperature, and thus predict the stability or health status of the battery. The electrolytic stability index is a comprehensive indicator for measuring the stability of the electrolyte in a vanadium redox flow battery system. Its physical meaning is that by quantifying the health status of the electrolyte, it reflects the performance changes of the electrolyte and the reliability of the battery performance under different operating conditions. The signal intensity of the vanadium ion absorption peak and vanadium oxide absorption peak involved in the electrolytic stability index is directly related to the concentration of vanadium ions and vanadium oxides in the battery electrolyte. The stability threshold can be set empirically based on historical data in practice. For example, a test can be performed on a brand-new, undamaged high-energy vanadium redox flow battery to obtain the electrolytic stability index, which can be used as a full-performance reference value. The stability threshold can then be set proportionally based on this full-performance reference value. Considering that the degradation of high-energy vanadium redox flow batteries is not necessarily linear, the electrolytic stability index of multiple high-energy vanadium redox flow batteries with different service lives can be collected as data samples for estimation.

[0059] Furthermore, as a feasible implementation, the electrolyte physicochemical data includes voltage drop, conductivity, and tetravalent vanadium concentration values ​​collected from the target battery system in each monitoring cycle;

[0060] The voltage drop value represents the additional voltage loss of the target battery system during the current monitoring period, the conductivity value represents the average conductivity of the target battery system during the current monitoring period, and the tetravalent vanadium concentration value represents the average concentration of vanadium ions in the positive tetravalent state of the target battery system during the current monitoring period.

[0061] The voltage drop value represents the additional voltage loss of the target battery system during the current monitoring period, reflecting the energy loss caused by factors such as electrolyte degradation and particle accumulation within the battery. The voltage drop value directly reflects changes in the battery's internal resistance, thus revealing performance degradation caused by electrolyte degradation or electrode contamination. An increase in battery internal resistance is usually accompanied by a decrease in performance. If the voltage drop value gradually increases, it indicates potential signs of battery performance degradation, decreased internal fluidity, and increased voltage loss, thus enabling early diagnosis. Specifically: the voltage drop value reflects the impact of battery internal resistance; increased internal resistance leads to an increased voltage drop value, resulting in decreased battery performance; decreased battery operating efficiency also increases the voltage drop value, and an increased voltage drop value indicates a deterioration in the battery's health.

[0062] The conductivity value represents the average conductivity of the target battery system during the current monitoring period, reflecting the ion conduction capacity of the electrolyte and indirectly measuring the health status of the electrolyte. As the electrolyte deteriorates, the conductivity typically decreases, reflecting a decline in the efficiency of ion conduction within the battery. The accumulation of particulate matter, which is prone to occur in high-energy electrolytes, can hinder the free flow of ions, thereby reducing conductivity. Regular monitoring of conductivity values ​​helps to detect the presence of particulate matter accumulation inside the battery in a timely manner.

[0063] In this embodiment, conductivity and voltage drop are used together as key indicators reflecting the ionic conductivity of the electrolyte. There is a close relationship between conductivity and voltage drop: voltage drop reflects the additional voltage loss caused by internal resistance during battery use; increased internal resistance leads to increased voltage drop, and this increase is usually related to a decrease in electrolyte conductivity. Higher conductivity results in smoother ion migration within the battery and lower internal resistance; conversely, low conductivity hinders ion migration, increasing internal resistance and thus increasing voltage drop. When battery performance degrades, the trends in conductivity and voltage drop directly reflect the risk of increased internal resistance. Therefore, they are commonly used battery monitoring parameters in the field. Although conductivity alone has accuracy issues when used in high-energy vanadium redox flow batteries, in this embodiment, conductivity can still be selected as the main parameter for calculation, used in conjunction with voltage drop.

[0064] The tetravalent vanadium concentration value represents the average concentration of vanadium ions in the tetravalent state in the target battery system during the current monitoring period, directly reflecting the concentration change of tetravalent vanadium ions in the electrolyte. Tetravalent vanadium is an important active material in vanadium redox flow batteries; its concentration change reflects the redox process and directly reflects the battery's charge / discharge state. Monitoring the tetravalent vanadium concentration effectively allows for understanding the changes in the active material in the battery electrolyte and timely monitoring of the battery's energy storage and release status. In practice, the tetravalent vanadium concentration can be measured using electrochemical methods, such as conductivity or potentiometry.

[0065] Furthermore, as a feasible implementation method, the calculation process of the electrolyte characterization index includes:

[0066] Let voltage drop be denoted as V, conductivity as σ, and tetravalent vanadium concentration as Cv; set a reference value for vanadium concentration for the target battery system and denote it as Cre; and let the electrolyte characterization index be denoted as Ie.

[0067] The formula for calculating the electrolyte characterization index is as follows: ,

[0068] In the formula, k1 represents the conductivity adjustment parameter and k2 represents the concentration smoothing parameter.

[0069] The electrolyte characterization index (Ie) is a basic indicator of the electrolyte's health or stability. It's a purely numerical calculation model, requiring no dimensionless processing, and is used to reflect the concentration of vanadium ions and other components, chemical stability, conductivity, and other characteristics of the electrolyte. A higher Ie indicates a better electrolyte health and higher battery system stability and performance. Using the electrolyte characterization index to describe the electrolyte's health represents its overall performance.

[0070] Voltage drop (V) represents the additional voltage loss caused by the battery's internal resistance. The higher the battery's internal resistance, the greater the voltage drop, the lower the battery's efficiency, and the worse its health. As V increases, the denominator increases, thus decreasing Ie. This means that an increase in voltage drop indicates a worse battery health. When the conductivity σ is high, the electrolyte ions are more conductive, current transmission in the battery is smoother, internal resistance is lower, and the voltage drop V is smaller; conversely, when the conductivity is low, the battery's internal resistance increases, and the voltage drop V increases.

[0071] The conductivity value σ reflects the ionic conductivity of an electrolyte, representing the ease with which ions migrate within the electrolyte. In high-energy-density systems, suspended particulate matter increases resistance to ion migration, leading to a decrease in σ. A decrease in conductivity σ is usually due to electrolyte degradation or reduced fluidity caused by particulate matter deposition; a decrease in conductivity σ increases resistance to current flow within the battery. Conductivity reflects the ability of ions in the electrolyte to conduct electricity; the stronger the conductivity of the electrolyte, the higher the battery's reaction efficiency and energy conversion efficiency, and the better its overall health. Since conductivity is located at the molecular level, an increase in conductivity leads to an increase in Ie, indicating a better battery health.

[0072] The tetravalent vanadium concentration value Cv reflects the concentration of tetravalent vanadium ions in the battery. Changes in the tetravalent vanadium concentration in the electrolyte directly affect battery performance. The vanadium concentration reference value Cre is a preset ideal tetravalent vanadium concentration, used to compare with the actual concentration, reflecting the availability and health of vanadium in the battery. The exponential term in the formula... The penalty factor, representing the imbalance of chemical valence states, is used to measure the degree to which the tetravalent vanadium concentration deviates from the reference value. In the electrolyte characterization index calculation formula of this embodiment, since a higher Ie indicates a better electrolyte health, the larger the deviation between the tetravalent vanadium concentration value Cv and the vanadium concentration reference value Cre, the better the electrolyte health. It will also increase. The larger the part, The smaller the fractional term, the more severe the side reactions. The greater the deviation of the tetravalent vanadium concentration value Cv from the vanadium concentration reference value Cre, the more severe the side reactions. For example, when the tetravalent vanadium concentration value Cv deviates significantly from the vanadium concentration reference value Cre and becomes increasingly higher, the fractional term... A larger value usually indicates a valence imbalance dominated by hydrogen evolution at the negative electrode or electrolyte contamination, which will lead to a decline in the battery's health. As the tetravalent vanadium concentration Cv deviates from the vanadium concentration reference value Cre and becomes smaller, the fractional term... The larger and higher the concentration of tetravalent vanadium ions, the more pronounced the decline in battery health. A significant reduction in tetravalent vanadium ions is usually due to the instability of pentavalent vanadium ions, which undergo a precipitation reaction to form solid vanadium pentoxide. This process consumes a large amount of active material on the positive electrode side. To maintain charge balance, the system adjusts through transmembrane migration or side reactions, directly resulting in a continuous decrease in the concentration of tetravalent vanadium ions. In practical applications, both excessively high and low tetravalent vanadium concentrations (Cv) indicate that high-energy vanadium redox flow batteries may have experienced significant or even irreversible degradation. An excessively high tetravalent vanadium concentration (Cv) may lead to severe hydrogen evolution at the negative electrode, valence imbalance, and increased risk of precipitation on the positive electrode side. Conversely, an excessively low tetravalent vanadium concentration (Cv) is not a gradual, mild degradation, but a more pronounced and serious fault signal, usually accompanied by irreversible loss of active material. This indicates that the active material in the high-energy vanadium redox flow battery may have left the electrolyte system, either as precipitate blocking the electrodes or membranes, or through leakage. This directly corresponds to the potential performance risks of permanent capacity loss and physical blockage of the flow channels in the high-energy vanadium redox flow battery.

[0073] The conductivity adjustment parameter k1 and concentration smoothing parameter k2 are used to adjust the influence of conductivity and vanadium concentration on the electrolyte characterization index, helping to balance the contributions of different parameters to battery health assessment and avoid excessive influence of any one parameter on the results. The conductivity adjustment parameter k1 adjusts the influence of conductivity on the characterization index, ensuring that the contribution of conductivity to the health status assessment is not overly sensitive. The concentration smoothing parameter k2 adjusts the influence of concentration differences, preventing excessive fluctuations in the characterization index when the concentration difference is small. The selection of adjustment parameters k1 and k2 can be optimized according to the characteristics of different battery systems, so that the characterization index more accurately reflects the battery's health status. In specific implementations, to ensure a positive correlation between conductivity and concentration difference and battery health, the conductivity adjustment parameter k1 and concentration smoothing parameter k2 are usually taken as values ​​greater than zero, and can be set according to empirical rules.

[0074] As a feasible implementation method, the process of setting the conductivity adjustment parameter k1 includes:

[0075] Collect and obtain the operating current density of the target battery system during the current monitoring period and express it as J. Set a reference current density under rated operating conditions for the target battery system and express it as Jre. Set the scaling factor ω.

[0076] The formula for calculating the conductance adjustment parameter k1 is as follows: ;

[0077] The process of setting the concentration smoothing parameter k2 includes:

[0078] Set the state of charge of the target battery system in each monitoring period as soc, and let 0 < soc < 1; the value of the concentration smoothing parameter k2 is set as: k2 = Cv ∙ soc.

[0079] The operating current density J refers to the magnitude of the current passing through the unit effective reaction area when the target battery system is in the actual charging or discharging operation state, and is used to characterize the electrochemical reaction load intensity of the battery under the current working condition. The reference current density Jre represents the reference current density under the rated working condition. J / Jre represents the reference index of the working intensity of the current battery. The proportionality coefficient ω takes a value greater than 0 and is used to calibrate the adjustment amplitude.

[0080] The state of charge soc of the battery is a state parameter used to characterize the proportion of the electric energy currently stored in the target battery system relative to its rated available capacity, and is used to reflect the energy margin level of the battery during the charging and discharging process. When soc approaches 1, it indicates that the target battery system is in a high charge storage state, the proportion of high-valence active ions in the electrolyte increases, and the overall oxidation state level of the system is relatively high. When soc approaches 0, it indicates that the target battery system is in a low charge storage state, the proportion of low-valence active ions in the electrolyte increases, and the overall reduction state level of the system is relatively high.

[0081] The conductance adjustment parameter k1 is used to measure the conductance adjustment of the electrolyte under the current working state. The conductance of the battery is closely related to the current density, and especially under different loads, the conductance will change. During the actual operation process, the current density J of the battery may fluctuate, especially during the charging and discharging cycles. Therefore, the conductance adjustment parameter k1 reflects the change of conductance through the proportional relationship with the reference current density Jre. Based on the ratio between the current density and the set rated reference current density, the conductance of the electrolyte can be adjusted in real time. This dynamic adjustment can ensure that under the actual working conditions, the influence of conductance is effectively controlled, thereby improving the accuracy of the performance evaluation model.

[0082] The concentration smoothing parameter k2 is used to adjust the smoothing process of the vanadium ion concentration in the electrolyte. Especially during the charging and discharging cycles of the battery, the concentration of vanadium ions will fluctuate with the change of the state of charge soc of the battery. The change of the concentration is adjusted by combining the concentration of tetravalent vanadium Cv and the state of charge soc of the battery. During the charging and discharging process of the battery, the change of the state of charge will directly affect the concentration of tetravalent vanadium ions in the electrolyte. The concentration smoothing parameter k2 actually smooths the change of the tetravalent vanadium concentration through the state of charge of the battery, thereby reducing the error caused by the fluctuation of the electrolyte concentration during the performance evaluation process. During the charging and discharging process of the battery, the concentration of tetravalent vanadium may fluctuate greatly, especially during rapid charging and discharging. By introducing the state of charge soc of the battery for adjustment, these fluctuations can be effectively smoothed, thereby avoiding misjudgment of performance caused by sharp changes in concentration.

[0083] Furthermore, as a feasible implementation method, the full vanadium liquid spectral data includes the initial spectral signal collected in the target battery system, which is then smoothed and normalized; principal component analysis is used to decouple the initial spectral signal into a linear combination of the reconstructed signals of vanadium ion absorption peaks and vanadium oxide absorption peaks.

[0084] The signal correlation between the initial spectral signal and the reconstructed signal is verified. When the signal correlation meets the verification, the reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak are used to construct a stable evaluation model. When the signal correlation does not meet the verification, the decoupling calculation is re-performed on the initial spectral signal.

[0085] Smoothing and normalization of spectral signals help remove noise and adjust signal intensity to better suit analytical needs. Smoothing reduces short-term fluctuations caused by equipment noise, environmental variations, etc., while normalization standardizes the signal for easier subsequent analysis and comparison. Principal component analysis (PCA) maps the original spectral signal to a new orthogonal space through linear transformation, where the variance of the data is maximized, allowing for the identification of the most significant components. PCA can effectively decouple spectral signals in battery systems, extracting characteristic signals from vanadium ion absorption peaks and vanadium oxide absorption peaks.

[0086] Furthermore, as a feasible implementation method, the process of decoupling calculation using principal component analysis includes:

[0087] Several wavelength nodes representing signal acquisition points are set for vanadium ions and vanadium oxides. Spectral signals are acquired at each wavelength node and labeled as decoupled spectral signals. The initial spectral signal is represented as a spectral vector to be decoupled, which is composed of multiple decoupled spectral signals. The covariance matrix of the spectral vector to be decoupled is obtained. The eigenvalue decomposition method is used to obtain the eigenvalues ​​and eigenvectors of the covariance matrix. The initial spectral signal is projected onto the vector space of the eigenvectors.

[0088] In the vector space, the eigenvalues ​​are sorted from largest to smallest, and the top few eigenvalues ​​are selected and labeled as principal component eigenvalues. The eigenvectors corresponding to the principal component eigenvalues ​​are labeled as principal component eigenvectors. The reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak are constructed using the principal component eigenvectors. The correlation coefficient method is used to verify the correlation between the reconstructed signal and the initial spectral signal. When the correlation meets the verification, the vanadium ion absorption peak and the vanadium oxide absorption peak are used as reconstructed signals to participate in the construction of a stable evaluation model. When the correlation does not meet the verification, the eigenvalue decomposition method is used to obtain the eigenvalues ​​and eigenvectors again and reconstruct the reconstructed signal.

[0089] The signal correlation coefficient is used to measure the similarity between the initial spectral signal and the reconstructed signal after decoupling. By calculating the correlation coefficient between the two, it can be determined whether the decoupling process effectively preserves the key information in the initial signal. In specific implementations, Pearson correlation coefficient or cosine similarity can be used for calculation. To achieve effective decoupling of the vanadium ion absorption peak and the vanadium oxide absorption peak in a vanadium redox flow battery, several wavelength nodes representing signal acquisition points are set. Each wavelength node corresponds to the spectral region of interest in the battery system, and spectral signals are acquired at these nodes. The acquired spectral signals are labeled as decoupled spectral signals for subsequent principal component analysis decoupling processing. By selecting appropriate wavelength nodes, signals are acquired in the characteristic absorption peak regions of vanadium ions and vanadium oxides. These acquired spectral signals represent the absorption characteristics of different substances in the target battery system. The wavelength nodes of the acquisition points can be optimized according to the absorption characteristics of vanadium ions and vanadium oxides and the wavelength response range of the spectral analysis instrument used. As a specific example, the trivalent vanadium ion absorption peak is usually located in the region of 450 nm to 750 nm, especially around 550 nm (yellow-green region). This absorption peak is related to the electronic transitions of ions; other weaker absorption peaks may appear in the ultraviolet region, typically between 300 nm and 400 nm. The absorption peak of tetravalent vanadium ions is usually located in the 400 nm to 550 nm region, especially near 450 nm; the main absorption peak of vanadium oxide is located in the 450 nm to 550 nm region, which is related to the electronic transitions and redox reactions of vanadium oxide.

[0090] The initial spectral signal is represented as a spectral vector to be decoupled, composed of multiple decoupled spectral signals. This vector contains spectral data from different wavelength nodes collected from the target battery system. Based on this, the covariance matrix of the spectral vector to be decoupled is obtained. The covariance matrix quantifies the correlation between spectral signals, thus providing a mathematical basis for subsequent signal decoupling and analysis. The covariance matrix is ​​decomposed using eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors. Eigenvalues ​​reflect the variability of the signal in various directions, while eigenvectors determine the specific coordinate systems of these directions. The initial spectral signal is projected onto the vector space formed by the eigenvectors, thereby achieving dimensionality reduction and decoupling of the signal. The core purpose of this step is to transform the signal to the principal component space, removing redundant components, making subsequent signal reconstruction and analysis simpler and more accurate, ultimately achieving effective separation of the vanadium ion absorption peak and the vanadium oxide absorption peak.

[0091] In vector space, eigenvalue decomposition is first performed on the eigenvalues ​​of the covariance matrix. The obtained eigenvalues ​​are then sorted in order of magnitude, starting with the largest eigenvalue and proceeding to the smallest. This sorting method clearly defines the magnitude of signal variability corresponding to each eigenvalue. Several eigenvalues ​​at the beginning of the sort are selected; these represent the most important principal components of the signal. These selected eigenvalues ​​are labeled "principal component eigenvalues" and correspond to "principal component eigenvectors." The selected principal component eigenvectors are used to construct the reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak. These principal component eigenvectors are the core components for effectively decomposing the initial spectral signal, preserving the main information of the signal to the greatest extent. Using the principal component eigenvectors, the signals of the vanadium ion and vanadium oxide absorption peaks can be reconstructed, recovering their respective independent signals. Principal component eigenvalues ​​are scalars associated with each principal component, representing the contribution of that principal component to the data variance. Simply put, the larger the principal component eigenvalue, the more information that principal component contains in the data. Principal component eigenvalues ​​measure the explanatory power of the corresponding principal component in the original dataset, that is, the importance of the principal component in explaining the variance of the data. In principal component analysis, the variance of the data is measured by projecting the data onto the principal components. Each principal component has an eigenvalue, which characterizes the contribution of the principal component to the total variance of the data. A larger eigenvalue means that the principal component has a stronger descriptive power for the data. In practice, the principal component with the largest eigenvalue is selected for further analysis because it contains the most information about the data. The principal component eigenvector is the direction of the projected data, determining the "main pattern of change" of the dataset in that direction. The eigenvector describes the most important "pattern" or direction in the data, indicating along which the dataset has the greatest variability; each eigenvector corresponds to an eigenvalue. The eigenvector indicates the main direction of the data distribution, while the eigenvalue represents the degree of variation in that direction. In principal component analysis, the eigenvector indicates how the data is rearranged in a new space. The eigenvector of each principal component defines the direction of that principal component in high-dimensional space. Using these feature vectors, we can construct a new coordinate system in which the projection of the data onto the coordinate system can best represent the variability of the original data.

[0092] Furthermore, the correlation coefficient method is used to verify the correlation between the initial spectral signal and the reconstructed signal. The correlation coefficient between the initial spectral signal and the reconstructed signal is calculated. A correlation threshold is set for the correlation coefficient. When the correlation coefficient is less than the correlation threshold, the correlation is deemed not to meet the verification. When the correlation coefficient is greater than or equal to the correlation threshold, the correlation is deemed to meet the verification.

[0093] The correlation coefficient between the initial spectral signal and the reconstructed signal is calculated to reflect their linear relationship. The correlation coefficient is a standard for measuring signal similarity, typically ranging from -1 to 1. A value closer to 1 indicates a stronger relationship and better meets the expectations of decoupling. The correlation coefficient method is an intuitive verification method; by calculating the linear correlation between two signals, the quality of signal decoupling can be quickly assessed. A threshold is set based on the calculated correlation coefficient to determine whether the signal meets the verification requirements. The threshold setting directly affects the rigor of the verification; if the correlation coefficient is greater than or equal to the set threshold, the signal decoupling is effective; otherwise, the decoupling calculation needs to be repeated.

[0094] Specifically, the process of the correlation coefficient method includes:

[0095] Let the correlation coefficient be denoted as R, the total number of wavelength nodes be denoted as n and their ordinal numbers be denoted as i; let the decoupled reconstructed signal be denoted as Sre, the initial spectral signal be denoted as St; and let the wavelength nodes be denoted as ω.

[0096] The formula for calculating the correlation coefficient is: ,

[0097] Where, ω i St(ω) represents the i-th wavelength node ω; i Sre(ω) represents the spectral signal intensity of the initial spectral signal at the i-th wavelength node ω. i ) represents the spectral signal intensity of the decoupled and reconstructed signal at the i-th wavelength node ω; This represents the average intensity of the initial spectral signal across all wavelength nodes. This represents the average spectral signal intensity of the decoupled and reconstructed signal with respect to all wavelength nodes.

[0098] Wavelength nodes ωi are sampling points of the spectrometer, each corresponding to a specific wavelength position; St(ωi) is the measured absorbance at that wavelength node, while Sre(ωi) is the absorbance calculated using the principal component analysis reconstruction algorithm. Arranging the intensity values ​​of the n wavelength nodes forms a point in an n-dimensional space, with each spectrum being a point in this high-dimensional space. The correlation coefficient measures the linear relationship between two sets of signals, with a value range of [-1, 1], where 1 represents perfect positive correlation, -1 represents perfect negative correlation, and 0 represents no correlation. The closer the correlation coefficient is to 1, the stronger the similarity between the decoupled reconstructed signal and the initial spectral signal, indicating a more successful decoupling process. Each wavelength node corresponds to a sampling point of a spectral signal, reflecting different frequency components of the spectral signal. With n wavelength nodes, each node has a corresponding spectral signal intensity. These nodes allow for detailed analysis of signal variations at different frequencies. During calculation, the mean value of each signal is subtracted from its wavelength node value; this is called mean removal. The purpose is to eliminate the influence of constant terms on the correlation, ensuring that the correlation coefficient calculation only reflects the relative changes in the signals. The correlation coefficient method, by calculating the linear correlation between signals, intuitively reflects the degree of matching between the initial signal and the decoupled reconstructed signal.

[0099] In the formula and The numerator represents the mean, or centering process, used to remove the overall vertical shift caused by light intensity fluctuations, window contamination, or changes in electrolyte background absorption. It allows verification to focus on the shape of the spectrum—peak shape, peak position, and relative peak height—rather than absolute intensity. After centering, the data reflects the fluctuation value of each wavelength point relative to the average level, which is precisely the region where characteristic peaks are located. The numerator in the fraction represents the dot product of the two centering vectors, used to measure the degree to which the two spectra fluctuate in the same direction at each wavelength point. If at a certain wavelength point ωi, both St and Sre are above their respective means, i.e., both have peaks, then the product and contribution are positive. If both are below the mean, i.e., both have troughs, then the product is also positive. If one is above the mean and the other is below the mean, the product is negative, and the contribution is negative. The larger the numerator, the more consistent the fluctuation trends (peak and trough positions) of the two spectra at all wavelength points. The denominator in the fraction represents the product of the magnitudes of the two centering vectors, serving a normalization function. Partially represents the overall energy or fluctuation amplitude of the initial spectral signal. The partial representation indicates the overall energy of the reconstructed spectral signal. Dividing the molecule by the product of these two moduli is equivalent to scaling both vectors to unit length and then calculating the cosine of their angle. This makes the value of R unaffected by the absolute intensity of the spectrum (such as light intensity or electrolyte dilution), focusing only on the similarity of shapes.

[0100] Furthermore, as a feasible implementation, the calculation forms of the initial spectral signal and the decoupled reconstructed signal are set as follows:

[0101] Let m be the total number of selected principal component eigenvalues ​​and j be their ordinal numbers; let PC represent the eigenvectors, and let PC be the eigenvector of the j-th principal component. j Let the decoupled reconstruction signal include a first decoupled signal and a second decoupled signal. Let the first decoupled signal represent the spectral signal intensity of the vanadium ion absorption peak and be denoted as Sre1, and let the second decoupled signal represent the spectral signal intensity of the vanadium oxide absorption peak and be denoted as Sre2. Set the initial signal weight, the first decoupled weight, and the second decoupled weight and denot them as a, b, and c, respectively.

[0102] Then at the wavelength node ω i The initial spectral signal St is represented as: ,

[0103] Wavelength node ω i The first decoupling signal Sre1 is represented as: ,

[0104] Wavelength node ω i The second decoupling signal Sre2 is represented as: ,

[0105] Wherein, the initial signal weight, the first decoupling weight, and the second decoupling weight corresponding to the j-th principal component eigenvector are respectively represented as a j b j and c j .

[0106] Spectral signal intensity is a physical quantity used to characterize the amount of radiant energy per unit wavelength interval. It is a core parameter in spectral analysis, quantitatively reflecting the energy level transition process of matter through the number of photons. It is typically described by physical quantities such as absorbance, transmittance, or reflectance, and is a value related to the change in light intensity at different wavelengths in the spectrum. In this embodiment, the initial spectral signal, the first decoupling signal, and the second decoupling signal are all vectors, composed of several spectral signal intensities, and these values ​​correspond to spectral data at different wavelengths.

[0107] The initial spectral signal St(ωi) represents the signal at the i-th wavelength node ω. i The initial spectral signal intensity measured above; the first decoupling signal Sre1 represents the signal at the i-th wavelength node ω. i The spectral signal intensity of the vanadium ion absorption peak measured above; the second decoupling signal Sre2 represents the intensity of the vanadium ion absorption peak at the i-th wavelength node ω. i The spectral signal intensity of the vanadium oxide absorption peak was measured.

[0108] PCj (ω i The denoted ω represents the spectral signal associated with the j-th principal component at wavelength node ωi, which can also be understood as the contribution of the j-th principal component obtained from principal component analysis to the spectral signal at wavelength node ωi. Each principal component PC j They are all derived from combinations of the original spectral signals, and they represent the main direction of variation in the data, that is, the pattern or trend in the original data.

[0109] Each principal component eigenvector corresponds to a specific direction after PCA dimensionality reduction, representing the main component of the signal variation. By projecting the initial signal onto these eigenvectors, the main features of the signal can be extracted. The number of principal components, m, is set, typically selecting the first m principal components based on the cumulative variance contribution rate. The selected principal components should be able to cover the main variation information of the signal. The value of the initial spectral signal at each wavelength node ωi is obtained by weighted summation of the eigenvectors of the first m principal components.

[0110] The initial signal weight a j Controlling the contribution of each principal component's eigenvector to the initial spectral signal determines the importance of each principal component in representing the initial signal. The first decoupling weight b j The contribution of the principal components to the decoupling signal of the vanadium ion absorption peak is controlled, determining which principal components contribute more to the vanadium ion absorption peak; the third decoupling weight c j The contribution of principal components to the decoupling signal of the vanadium oxide absorption peak is controlled, determining which principal components contribute more to the vanadium oxide absorption peak. The weights of the eigenvectors of each initial signal weight are determined by a. j This represents the contribution of the eigenvector to the initial signal. The calculation of the two decoupled signals is similar to that of the initial spectral signal, but different weights b are used. j and c j By weighted summation of principal component eigenvectors, each decoupled signal can effectively extract the components related to the vanadium ion absorption peak and vanadium oxide absorption peak from the original signal. By adjusting the initial signal weights, the first decoupling weight, and the second decoupling weight, the influence of different components of the initial and decoupled signals on the final signal can be flexibly controlled. This flexibility allows the model to be optimized according to different application requirements or signal characteristics.

[0111] As a feasible implementation method, when the wavelength length of the wavelength node is between 300-400nm, a first decoupling weight b is set. j Greater than the second decoupling weight c j When the wavelength length of the wavelength node is above 400nm, set the first decoupling weight b. j Less than the second decoupling weight c j .

[0112] Within the 300-400 nm wavelength range, vanadium ion absorption is significant, and the signal from the vanadium ion absorption peak is typically strong in this range. Therefore, a larger first decoupling weight can ensure its influence in the reconstructed signal. Because of the strong vanadium ion absorption in this wavelength range, the vanadium ion absorption peak can be set to dominate the reconstructed signal. Thus, the first decoupling weight bj is greater than the second decoupling weight cj, ensuring that the reconstructed signal primarily reflects the absorption characteristics of vanadium ions in this range, while the influence of vanadium oxides is appropriately suppressed. Regarding vanadium oxides, such as V₂O₅, their absorption peaks in the ultraviolet spectral region typically appear above 400 nm, and their absorption characteristics differ from those of vanadium ions. In the 300-400 nm wavelength range, the absorption of vanadium oxides is weak, resulting in a relatively small signal contribution. Therefore, in this range, the second decoupling weight cj can be set greater than the first decoupling weight bj. In these wavelength ranges, the signal characteristics of vanadium oxides will dominate, better reflecting their influence and thus optimizing the accuracy of the decoupling signal.

[0113] Furthermore, as a feasible implementation method, the construction process of the stability evaluation model is set as follows:

[0114] Let the electrolytic stability index be denoted as Is, and let vanadium ion absorption coefficient γ1 and vanadium oxide absorption coefficient γ2 be defined; let the spectral signal intensity of the vanadium ion absorption peak be denoted as Sre1, and let the spectral signal intensity of the vanadium oxide absorption peak be denoted as Sre2.

[0115] The calculation formula for the stability evaluation model is then expressed as: .

[0116] The vanadium ion absorption peak and vanadium oxide absorption peak represent the presence of vanadium ions and vanadium oxides in the battery, and these two spectral signals reflect changes in these two substances, respectively. In the calculation formula, the electrolyte health Ie serves as the baseline value, determining the battery's stability under conditions without any other disturbances. The spectral signal intensity Sre1 of the vanadium ion absorption peak and Sre2 of the vanadium oxide absorption peak represent the influence of vanadium ions and vanadium oxides, respectively; changes in their concentrations affect the battery's operational stability, and increasing these signals leads to decreased battery stability. By calculating the electrolytic stability index, the effects of electrolyte degradation and changes in the concentrations of vanadium ions and vanadium oxides on battery stability can be comprehensively considered. A higher electrolytic stability index indicates better battery system stability, while a lower index may indicate that the battery is in a state of degradation or is about to fail. The vanadium ion absorption coefficient γ1 and the vanadium oxide absorption coefficient γ2 determine the degree of influence of the vanadium ion absorption peak and the vanadium oxide absorption peak on the electrolytic stability index. In practice, the vanadium ion absorption coefficient γ1 and the vanadium oxide absorption coefficient γ2 can be set using empirical rules; by adjusting γ1 and γ2, the model's sensitivity to these two signals can be controlled. For example, if changes in vanadium ion concentration have a significant impact on battery performance, then γ1 can be set to a larger value, and vice versa. To ensure the physical rationality of the model, in this embodiment, an increase in vanadium ion concentration generally contributes to battery stability, therefore the vanadium ion absorption coefficient γ1 is a positive value; the actual concentration of vanadium oxide also increases, but this usually leads to battery performance degradation, therefore the vanadium oxide absorption coefficient γ2 is also a positive value, and in specific implementations, the vanadium oxide absorption coefficient γ2 should be smaller than the vanadium ion absorption coefficient γ1.

[0117] Furthermore, as a feasible implementation method, when the charge of the target battery system increases by more than 80%, the vanadium ion absorption coefficient γ1 is set to be greater than the vanadium oxide absorption coefficient γ2; when the charge of the target battery system decreases by less than 20%, the vanadium ion absorption coefficient γ1 is set to be less than the vanadium oxide absorption coefficient γ2.

[0118] During charging, when the battery capacity exceeds 80%, the concentration of vanadium ions in the battery typically increases as they participate in the oxidation reaction during charging. At this point, the absorption characteristics of vanadium ions become more pronounced, therefore, the vanadium ion absorption coefficient should be relatively large to better reflect the impact of vanadium ions on battery stability. Simultaneously, as charging progresses, the concentration of vanadium oxides becomes relatively stable; therefore, the absorption characteristics of vanadium oxides have a smaller impact on stability at this stage, and the vanadium oxide absorption coefficient should be relatively small. During discharging, when the battery capacity drops below 20%, the concentration of vanadium ions in the battery is relatively low, while the concentration of vanadium oxides may increase, especially under conditions of over-discharge or battery aging, where vanadium oxides may form and gradually increase. At this point, the vanadium oxide absorption coefficient has a greater impact on battery stability. When the vanadium ion concentration is low, the vanadium ion absorption coefficient needs to be correspondingly reduced to reflect its relatively smaller role in battery stability assessment.

[0119] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery, characterized in that, The method includes: Step S1: Arrange a sensor array at the loop nodes of the electrolyte in the target battery system, equally set several monitoring periods and sample within each monitoring period to collect the electrolyte physical and chemical data and the all-vanadium solution spectrum data of the target battery system; Step S2: Calculate the electrolyte characterization index for measuring the degree of electrolyte deterioration based on the electrolyte physical and chemical data, and use the spectral decoupling algorithm for the all-vanadium solution spectrum data to separate and generate the vanadium ion absorption peak and the vanadium oxide absorption peak; Step S3: Use the electrolyte characterization index, the vanadium ion absorption peak and the vanadium oxide absorption peak to construct a stability evaluation model, and use the stability evaluation model to calculate and generate the electrolytic stability index for measuring the full-load performance of the target battery system; Step S4: Set a stability threshold representing the health critical point of the target battery system for the electrolytic stability index. When the electrolytic stability index is lower than the stability threshold, it is determined that the target battery system has a performance degradation defect; The electrolyte physical and chemical data includes the voltage stack drop value, the conductivity value and the tetravalent vanadium concentration value collected from the target battery system in each monitoring period; The voltage stack drop value represents the additional voltage loss amount of the target battery system in the current monitoring period, the conductivity value represents the average conductivity value of the target battery system in the current monitoring period, and the tetravalent vanadium concentration value represents the average concentration of vanadium ions in the +4 valence state of the target battery system in the current monitoring period; The calculation process of the electrolyte characterization index includes: Let the voltage stack drop value be represented as V, let the conductivity value be represented as σ, and let the tetravalent vanadium concentration value be represented as Cv; set a vanadium concentration reference value for the target battery system and represent it as Cre, and let the electrolyte characterization index be represented as Ie, The formula for calculating the electrolyte characterization index is as follows: , where k1 in the formula represents the conductivity adjustment parameter and k2 represents the concentration smoothing parameter; The construction process of the stability evaluation model is set as: Let the electrolytic stability index be represented as Is, and set the vanadium ion absorption coefficient γ1 and the vanadium oxide absorption coefficient γ2; let the spectral signal intensity of the vanadium ion absorption peak be represented as Sre1, and let the spectral signal intensity of the vanadium oxide absorption peak be represented as Sre2, The calculation formula for the stability evaluation model is then expressed as: .

2. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 1, characterized in that, The setting process of the conductivity adjustment parameter k1 includes: Collect and obtain the operating current density of the target battery system in the current monitoring period and represent it as J, set the reference current density under the rated working condition of the target battery system and represent it as Jre, and set a proportionality coefficient ω, The formula for calculating the conductance adjustment parameter k1 is as follows: .

3. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 1, characterized in that, The setting process of the concentration smoothing parameter k2 includes: Set the state of charge of the battery in each monitoring period of the target battery system as soc, and let 0 < soc < 1; the value of the concentration smoothing parameter k2 is set as: k2 = Cv ∙ soc.

4. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 1, characterized in that, The all-vanadium solution spectrum data includes the initial spectral signal collected in the target battery system, and performs smoothing and normalization processing on the initial spectral signal; uses the principal component analysis method to perform decoupling calculation on the initial spectral signal, and decouples the initial spectral signal into a linear combination of the reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak; The signal correlation between the initial spectral signal and the reconstructed signal is verified. When the signal correlation meets the verification, the reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak are used to construct a stable evaluation model. When the signal correlation does not meet the verification, the decoupling calculation is re-performed on the initial spectral signal.

5. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 4, characterized in that, The process of decoupling calculations using principal component analysis includes: Several wavelength nodes representing signal acquisition points are set for vanadium ions and vanadium oxides. Spectral signals are acquired at each wavelength node and labeled as decoupled spectral signals. The initial spectral signal is represented as a spectral vector to be decoupled, which is composed of multiple decoupled spectral signals. The covariance matrix of the spectral vector to be decoupled is obtained. The eigenvalue decomposition method is used to obtain the eigenvalues ​​and eigenvectors of the covariance matrix. The initial spectral signal is projected onto the vector space of the eigenvectors. In the vector space, the eigenvalues ​​are sorted from largest to smallest, and the top few eigenvalues ​​are selected and labeled as principal component eigenvalues. The eigenvectors corresponding to the principal component eigenvalues ​​are labeled as principal component eigenvectors. The reconstructed signals of the vanadium ion absorption peak and the vanadium oxide absorption peak are constructed using the principal component eigenvectors. The correlation coefficient method is used to verify the correlation between the reconstructed signal and the initial spectral signal. When the correlation meets the verification, the vanadium ion absorption peak and the vanadium oxide absorption peak are used as reconstructed signals to participate in the construction of a stable evaluation model. When the correlation does not meet the verification, the eigenvalue decomposition method is used to obtain the eigenvalues ​​and eigenvectors again and reconstruct the reconstructed signal.

6. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 5, characterized in that, The correlation coefficient method is used to verify the correlation between the initial spectral signal and the reconstructed signal. The correlation coefficient between the initial spectral signal and the reconstructed signal is calculated. A correlation threshold is set for the correlation coefficient. When the correlation coefficient is less than the correlation threshold, the correlation is deemed not to meet the verification. When the correlation coefficient is greater than or equal to the correlation threshold, the correlation is deemed to meet the verification.

7. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 6, characterized in that, The process of the correlation coefficient method includes: Let the correlation coefficient be denoted as R, the total number of wavelength nodes be denoted as n and their ordinal numbers be denoted as i; let the decoupled reconstructed signal be denoted as Sre, the initial spectral signal be denoted as St; and let the wavelength nodes be denoted as ω. The formula for calculating the correlation coefficient is: , Where, ω i St(ω) represents the i-th wavelength node ω; i Sre(ω) represents the spectral signal intensity of the initial spectral signal at the i-th wavelength node ω. i ) represents the spectral signal intensity of the decoupled and reconstructed signal at the i-th wavelength node ω; This represents the average intensity of the initial spectral signal across all wavelength nodes. This represents the average spectral signal intensity of the decoupled and reconstructed signal with respect to all wavelength nodes.

8. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 7, characterized in that, The calculation forms of the initial spectral signal and the decoupled reconstructed signal are set as follows: Let m be the total number of selected principal component eigenvalues ​​and j be their ordinal numbers; let PC represent the eigenvectors, and let PC be the eigenvector of the j-th principal component. j Let the decoupled reconstruction signal include a first decoupled signal and a second decoupled signal. Let the first decoupled signal represent the spectral signal intensity of the vanadium ion absorption peak and be denoted as Sre1, and let the second decoupled signal represent the spectral signal intensity of the vanadium oxide absorption peak and be denoted as Sre2. Set the initial signal weight, the first decoupled weight, and the second decoupled weight and denot them as a, b, and c, respectively. Then at the wavelength node ω i The initial spectral signal St is represented as: , Wavelength node ω i The first decoupling signal Sre1 is represented as: , Wavelength node ω i The second decoupling signal Sre2 is represented as: , Wherein, the initial signal weight, the first decoupling weight, and the second decoupling weight corresponding to the j-th principal component eigenvector are respectively represented as a j b j and c j .

9. The method for performance monitoring and evaluation of a high-energy vanadium redox flow battery according to claim 1, characterized in that, When the target battery system's charge increases by more than 80%, the vanadium ion absorption coefficient γ1 is set to be greater than the vanadium oxide absorption coefficient γ2; when the target battery system's charge decreases by less than 20%, the vanadium ion absorption coefficient γ1 is set to be less than the vanadium oxide absorption coefficient γ2.

10. The method for monitoring and evaluating the performance of a high-energy vanadium redox flow battery according to claim 8, characterized in that, When the wavelength length of the wavelength node is between 300-400nm, set the first decoupling weight b. j Greater than the second decoupling weight c j When the wavelength length of the wavelength node is above 400nm, set the first decoupling weight b. j Less than the second decoupling weight c j .

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