Energy storage adjusting method and system based on aqueous sodium-ion battery cluster

Abstract

The invention discloses an energy storage adjusting method and system based on an aqueous sodium-ion battery cluster, and relates to the technical field of aqueous sodium-ion battery cluster adjustment, and the method comprises the steps: obtaining the operation state parameters of each single battery in a battery cluster, constructing a side reaction risk characterization index, and determining a dynamic voltage safety window of each single battery; based on the dynamic voltage safety windows of the single batteries and the similarity of the electrochemical behavior characteristics of the single batteries, logically grouping the single batteries in the battery cluster to form sub-clusters; in each sub-cluster, the equivalent output voltage of the sub-cluster is adjusted according to the dynamic voltage safety window of each single battery, and a quasi-equivalent voltage platform of the sub-cluster is constructed; based on the operation state of each sub-cluster quasi-equivalent voltage platform, determining a target operation voltage interval of the battery cluster, and performing power regulation on the battery cluster in the target operation voltage interval; according to the method, the problems that the discreteness of a voltage platform is aggravated and the side reaction risk is uncontrollable in long-term operation of the water-based sodium-ion battery cluster are solved.

Description

Energy storage regulation method and system based on aqueous sodium-ion battery clusters Technical Field

[0001] This invention relates to the field of aqueous sodium-ion battery cluster regulation technology, and more specifically, to an energy storage regulation method and system based on aqueous sodium-ion battery clusters. Background Technology

[0002] With the continuous expansion of new energy power generation capacity, the demand for energy storage systems in power peak shaving, frequency regulation, and power smoothing is increasing. Aqueous sodium-ion batteries, due to their high safety, abundant raw material resources, and environmental friendliness, are considered a potential technology solution suitable for large-scale energy storage regulation.

[0003] In practical applications, to meet system voltage and power requirements, aqueous sodium-ion batteries typically operate by connecting multiple individual cells in series to form a battery cluster. However, due to the relatively narrow electrochemical stability window of aqueous sodium-ion batteries, small differences in polarization, side reaction rates, and electrochemical states between individual cells are easily amplified during cluster operation. This leads to uneven voltage distribution among the individual cells within the cluster, resulting in voltage plateau dispersion.

[0004] Meanwhile, side reactions such as hydrogen evolution and oxygen evolution exist in aqueous electrolyte systems. When the voltage of a single cell approaches the corresponding electrochemical threshold, these side reactions may occur in a short period of time and cause irreversible performance degradation. Since aqueous sodium-ion batteries are difficult to form a stable interface protection structure, once a side reaction occurs in a single cell, the increased internal resistance and voltage shift will further exacerbate the voltage inconsistency within the battery cluster, thus causing the voltage plateau dispersion to exhibit an uncontrollable evolution trend during long-term operation.

[0005] Current battery management methods commonly used in energy storage systems are mostly based on fixed upper and lower limits of single-cell voltage and voltage consistency balancing strategies. These methods are mainly designed for lithium-ion battery systems and are difficult to adapt to the characteristics of aqueous sodium-ion batteries, which are highly sensitive to voltage deviations and prone to side reaction threshold drift. Under long-term operating conditions such as energy storage regulation, existing technologies are unable to effectively suppress the continuous expansion of voltage plateau dispersion in battery clusters without accelerating individual cell degradation.

[0006] Therefore, there is an urgent need for an energy storage regulation method that addresses the operating characteristics of aqueous sodium-ion battery clusters, in order to meet system regulation requirements while suppressing the voltage plateau dispersion and uncontrollable evolution of the battery clusters. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an energy storage regulation method and system based on aqueous sodium-ion battery clusters. By constructing a dynamic voltage safety window for individual cells, forming a quasi-equivalent voltage platform for sub-clusters, and performing cluster-level power regulation based on a time-sharing interval rotation mechanism, the method effectively solves the problems of increased voltage platform dispersion and uncontrollable side reaction risks in aqueous sodium-ion battery clusters during long-term operation.

[0008] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, this application provides an energy storage regulation method based on an aqueous sodium-ion battery cluster. The method includes: acquiring the operating state parameters of each individual cell in the battery cluster, constructing a side reaction risk characterization index corresponding to each individual cell, and determining the dynamic voltage safety window of each individual cell; logically grouping the individual cells in the battery cluster into sub-clusters based on the similarity of their dynamic voltage safety windows and electrochemical behavior characteristics; within each sub-cluster, adjusting the equivalent output voltage of the sub-cluster according to the dynamic voltage safety window of each individual cell, and constructing a quasi-equivalent voltage platform for the sub-cluster; determining the target operating voltage range of the battery cluster based on the operating state of the quasi-equivalent voltage platform of each sub-cluster, and regulating the power of the battery cluster within the target operating voltage range.

[0009] In one embodiment, constructing a side reaction risk characterization index includes: acquiring terminal voltage and current data of each individual cell in the battery cluster and forming a time series; extracting operating state parameters characterizing the electrochemical behavior of the individual cells based on the time series; calculating state offsets for the operating state parameters and combining each state offset into an intermediate characterization value for the side reaction risk of the individual cells; weighting the intermediate characterization values ​​for the side reaction risk to construct a side reaction risk characterization index for the individual cells; and determining the maximum and minimum allowable operating voltages for each individual cell based on the side reaction risk characterization index to form a dynamic voltage safety window.

[0010] In one embodiment, based on the side reaction risk characterization index, the maximum and minimum allowable operating voltages of each individual battery cell are determined to form a dynamic voltage safety window. This includes: dividing the operating state of each individual battery cell into multiple risk level intervals based on the side reaction risk characterization index; setting a corresponding voltage regulation strategy for each risk level interval to limit the allowable expansion or contraction of the individual battery cell's operating voltage relative to a reference operating voltage interval; adjusting the reference operating voltage interval of the individual battery cell based on the voltage regulation strategy to obtain the maximum and minimum allowable operating voltages of the individual battery cell; and using the maximum and minimum operating voltages as the dynamic voltage safety window of the individual battery cell in the current operating cycle.

[0011] In one embodiment, based on the similarity of the dynamic voltage safety window and electrochemical behavior characteristics of each individual cell, the individual cells in the battery cluster are logically grouped to form subclusters. This includes: calculating the similarity index between the dynamic voltage safety windows of each individual cell, screening out individual cells with similarity, and forming a candidate group set; obtaining electrochemical behavior characteristic parameters for each candidate group set, and performing consistency verification, and determining the individual cells that meet the consistency requirements as the same logical group to form a subcluster.

[0012] In one embodiment, a consistency check is performed, and individual cells that meet the consistency requirements are identified as the same logical group to form a sub-cluster. This includes: calculating the electrochemical behavior difference metric between any two individual cells in the candidate group set; performing statistical processing on the electrochemical behavior difference metric to obtain a set difference index; if the set difference index falls within the range defined by a preset consistency judgment condition, it is determined that the individual cells in the candidate group set are consistent at the electrochemical behavior level, otherwise they are inconsistent.

[0013] In one embodiment, within each sub-cluster, the equivalent output voltage of the sub-cluster is adjusted according to the dynamic voltage safety window of each individual cell to construct a quasi-equivalent voltage platform for the sub-cluster. This includes: calculating the initial equivalent output voltage of the sub-cluster and determining the voltage regulation margin of each individual cell within the sub-cluster; calculating the target output voltage of each individual cell in the current operating cycle based on the voltage regulation margin and the initial equivalent output voltage; and adjusting the output voltage of the sub-cluster through robust control based on the target output voltage to construct a quasi-equivalent voltage platform for the sub-cluster.

[0014] In one embodiment, based on the target output voltage, the output voltage of the sub-cluster is adjusted through robust control, and a quasi-equivalent voltage platform of the sub-cluster is constructed. This includes: establishing a robust control model for each individual cell, wherein the model uses the individual cell output voltage or current as a state variable, the sub-cluster output voltage as the output, and considers a disturbance vector; calculating the control input for each individual cell based on the robust control model, which is used to adjust the individual cell output voltage or current; and adjusting the individual cells within the sub-cluster in real time according to the control input, so that the output of each individual cell converges towards the target output voltage, forming a stable quasi-equivalent voltage platform.

[0015] In one embodiment, based on the operating state of the quasi-equivalent voltage platform of each sub-cluster, a target operating voltage range for the battery cluster is determined, and power regulation of the battery cluster is performed within the target operating voltage range. This includes: based on the quasi-equivalent voltage platform of each sub-cluster, summarizing the dynamic voltage safety window of individual cells within each sub-cluster to determine the operable voltage range of each sub-cluster; based on the operable voltage range and the quasi-equivalent voltage platform of each sub-cluster, calculating the voltage regulation capability parameters of each sub-cluster; based on a time-sharing interval rotation mechanism, combined with the operable voltage range of each sub-cluster, determining the target operating voltage range of the battery cluster in the current operating cycle; within the target operating voltage range, combined with external load requirements and the voltage regulation capability parameters of each sub-cluster, calculating the achievable power regulation range of the battery cluster, and coordinating control of the quasi-equivalent voltage platform of the sub-cluster according to the power regulation range.

[0016] In one embodiment, based on a time-division interval rotation mechanism and combined with the operable voltage ranges of each sub-cluster, the target operating voltage range of the battery cluster in the current operating cycle is determined. This includes: performing a consistency analysis on the operable voltage ranges of each sub-cluster; when it is detected that the operable voltage ranges of multiple sub-clusters do not have a common intersection or the intersection is less than a preset safety margin threshold in the same time period, it is determined that there is a conflicting sub-cluster set; in the case of determining that there is a conflicting sub-cluster set, the operating cycle of the battery cluster is divided into several consecutive time periods; in each time period, some sub-clusters are allowed to operate preferentially in their optimal operable voltage range, while other sub-clusters operate in the suboptimal voltage range under the premise of ensuring safety constraints; according to the voltage regulation capability and side reaction risk of each sub-cluster, a rotation priority is assigned to each sub-cluster in the conflicting sub-cluster set, so that the sub-clusters with higher priority operate preferentially in the optimal operable voltage range, and the sub-clusters with lower priority operate in the suboptimal voltage range; in each time period, the optimal operable voltage range of the preferentially operating sub-cluster is combined with the suboptimal ranges of other sub-clusters to calculate the target operating voltage range of the battery cluster in the current operating cycle.

[0017] Secondly, this application provides an energy storage regulation system based on an aqueous sodium-ion battery cluster. This system includes: a voltage safety window construction module, used to acquire the terminal voltage and current time series of each individual cell in the battery cluster, extract electrochemical behavior operating state parameters, calculate state offsets and weighted construct side reaction risk characterization indicators, divide operating intervals according to risk levels and adjust the reference voltage interval, and determine the dynamic voltage safety window of each individual cell; a sub-cluster construction module, used to logically group the individual cells in the battery cluster based on the similarity of their dynamic voltage safety windows and electrochemical behavior characteristics, forming sub-clusters; a sub-cluster adjustment module, used to adjust the equivalent output voltage of each sub-cluster according to the dynamic voltage safety window of each individual cell, constructing a quasi-equivalent voltage platform for the sub-cluster; and a cluster-level control module, used to determine the target operating voltage range of the battery cluster based on the operating state of the quasi-equivalent voltage platform of each sub-cluster, and to perform power regulation of the battery cluster within the target operating voltage range.

[0018] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: Addressing the voltage plateau dispersion and side reaction risks that easily occur in aqueous sodium-ion battery clusters during long-term operation, this application achieves precise adjustment of the sub-cluster output voltage by performing real-time quantitative analysis of the dynamic voltage safety window and electrochemical behavior of individual cells within the sub-cluster. Furthermore, it coordinates the priority and suboptimal operation of conflicting sub-clusters through a time-sharing interval rotation mechanism, ensuring that the target operating voltage range for the entire cluster is both safe and achievable. This effectively suppresses the accumulation of individual cell voltage deviations, reduces the probability of side reactions such as hydrogen and oxygen evolution, and simultaneously considers the system's power regulation capability. Compared to existing methods with fixed voltage upper and lower limits and balancing strategies, this application significantly improves the stability, safety, and performance optimization capabilities of aqueous sodium-ion battery clusters during long-term large-scale energy storage operation. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 is a schematic flowchart of the energy storage regulation method based on aqueous sodium-ion battery clusters provided in an embodiment of this application.

[0021] Figure 2 is a schematic diagram of the energy storage regulation system based on an aqueous sodium-ion battery cluster provided in an embodiment of this application.

[0022] Figure 3 is a line graph comparing the similarity and threshold of the dynamic voltage safety window of a single battery cell provided in the embodiments of this application.

[0023] Figure 4 is a line graph comparing the electrochemical behavior difference measurement value and the consistency judgment condition provided in the embodiments of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "a plurality of" or "several" means two or more, unless otherwise explicitly specified.

[0026] It should also be noted that, in this document, terms such as “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes the aforementioned element.

[0027] Referring to Figure 1, the flowchart of the energy storage regulation method based on an aqueous sodium-ion battery cluster provided by the present invention includes the following steps: S1, obtaining the operating state parameters of each individual cell in the battery cluster, constructing a side reaction risk characterization index corresponding to each individual cell, and determining the dynamic voltage safety window of each individual cell, including: during the operation of the battery cluster, using the battery management system to collect the terminal voltage data of each individual cell in the battery cluster and the current data flowing through each individual cell in real time, and forming a corresponding voltage-current time series; based on the formed voltage-current time series, extracting operating state parameters characterizing the electrochemical behavior of each individual cell, the operating state parameters including the transient response characteristics of the individual cell voltage to the current change, the voltage recovery time of the individual cell after the current change ends, the equivalent internal resistance or internal resistance change trend of the individual cell, and the fluctuation amplitude of the individual cell voltage within a predetermined time window; preprocessing the operating state parameters. The preprocessing includes amplitude normalization and noise filtering to eliminate the influence of different parameter dimensions and sampling scales on subsequent analysis. Based on the preprocessed operating state parameters, the state offset corresponding to each operating state parameter is calculated. The state offset is used to characterize the degree of deviation of the current operating state parameter from its historical stable state or preset reference state. Based on the state offset, the state offset is combined according to preset association rules to obtain an intermediate characterization of the side reaction risk of a single cell. The association rules are used to reflect the relative influence of different operating state parameters on the probability of hydrogen evolution reaction or oxygen evolution reaction. The preset association rules refer to: based on the electrochemical mechanism of hydrogen evolution reaction and oxygen evolution reaction in aqueous sodium-ion batteries, the correlation between the state offset of different operating state parameters and the probability of side reaction occurrence is predetermined to reflect the relative sensitivity of each operating state parameter to the risk of side reaction. Specifically, the association rule includes at least the following: First, determining the positive or negative correlation between different state offsets and the risk of side reactions. For example, when the voltage transient overshoot, internal resistance increase, or voltage recovery time extension of a single cell exceeds its steady state, the corresponding state offset is determined to have a positive promoting effect on the risk of side reactions. Second, assigning different participation levels to different state offsets based on the strength of the influence of different operating state parameters on the electrode interface reaction, so that the state offsets that are more sensitive to side reactions occupy a higher influence weight in the combined processing. On this basis, the state offsets corresponding to each operating state parameter are combined according to the association rule. The combined processing includes accumulating multiple state offsets in the same direction, suppressing and canceling, or determining consistency, thereby forming an intermediate characterization of the risk of side reactions that can comprehensively reflect the current activity level of the interface reaction of a single cell. This intermediate characterization is used to characterize the superposition effect of multiple operating state anomalies at the same time, rather than relying on any single parameter.

[0028] The intermediate characterization quantities of side reaction risks are weighted according to a pre-set weighting rule to construct a side reaction risk characterization index for each individual battery cell. This index quantifies the degree to which the current operating state of the individual battery cell approaches the electrochemical threshold of hydrogen evolution reaction (HER) or oxygen evolution reaction (OER). The weighting rule reflects the relative contribution of different intermediate characterization quantities of side reaction risks to the probability of HER or OER occurrence, and the weights can be set based on historical operating data, experimental calibration results, or battery design parameters. Through weighting, multi-source, multi-dimensional risk intermediate information is compressed into a single, continuously changing risk characterization index. This ensures that the index changes monotonically with the deterioration of the individual battery cell's operating state, thereby providing a quantitative description of the degree to which the current operating state of the individual battery cell approaches the electrochemical threshold of side reaction and offering a direct basis for subsequent adjustments to the dynamic voltage safety window.

[0029] Based on the constructed side reaction risk characterization index, the maximum and minimum allowable operating voltages of each individual cell are determined, forming the dynamic voltage safety window for each individual cell.

[0030] Further, based on the constructed side reaction risk characterization index, the maximum and minimum allowable operating voltages of each individual battery cell are determined, forming a dynamic voltage safety window for each individual battery cell. This includes: dividing the operating state of each individual battery cell into multiple risk level intervals, including low-risk, medium-risk, and high-risk states, according to the side reaction risk characterization index; exemplarily, recording the historical change sequence of the side reaction risk characterization index within a pre-set time window; and then, based on the historical change sequence, extracting the change trend of the side reaction risk characterization index within that time window, where the change trend includes the average increase / decrease rate of the index over time, the duration of continuous unidirectional change, or the magnitude of change; then... The changing trend is compared with a pre-set reference threshold, which is a numerical range that can be determined by historical operating data, experimental calibration results, or design parameters. When the changing trend shows that the indicator is continuously rising and the average increase or decrease rate exceeds the high-risk threshold, it is determined to be a high-risk trend. When the changing trend shows that the indicator fluctuates within the low-risk threshold and does not change continuously in one direction, it is determined to be a low-risk trend. All other cases are determined to be medium-risk trends. Finally, based on the determination results of the changing trend, the operating status of a single battery cell is divided into the corresponding low-risk, medium-risk, or high-risk level range, thereby completing the risk level range division based on the changing trend of the side reaction risk characterization indicator.

[0031] A corresponding voltage regulation strategy is set for each risk level range. The voltage regulation strategy is used to limit the allowable expansion or contraction of the working voltage of a single cell relative to its reference working voltage range. Specifically, at the low risk level, the voltage regulation strategy allows the single cell to operate within a wider voltage range; at the medium risk level, the voltage regulation strategy restricts the further expansion of the working voltage to the high or low voltage end; at the high risk level, the voltage regulation strategy forces the single cell to retreat to a more conservative voltage operating range by simultaneously tightening the highest and lowest working voltages, thereby reducing the possibility of side reactions.

[0032] Based on the voltage regulation strategy, within the current risk level range, the reference operating voltage range of a single battery cell is adjusted to obtain the maximum and minimum allowable operating voltages of the single battery cell. Specifically, after determining the risk level range of a single battery cell and obtaining the corresponding voltage regulation strategy, the reference operating voltage range of that single battery cell is used as a reference range. The upper and lower boundaries of the reference operating voltage range are adjusted according to the voltage regulation strategy. The adjustment includes, but is not limited to, lowering the upper limit or raising the lower limit of the reference operating voltage range according to a preset adjustment ratio, adjustment step size, or adjustment rate.

[0033] The highest and lowest operating voltages are used as the dynamic voltage safety window for a single cell within the current operating cycle, thus constraining the operating voltage of the single cell within the dynamic voltage safety window.

[0034] S2, based on the similarity of the dynamic voltage safety window and electrochemical behavior characteristics of each individual cell, the individual cells in the battery cluster are logically grouped to form sub-clusters. This includes: calculating the similarity index (Euclidean distance) between the dynamic voltage safety windows of each individual cell, based on the dynamic voltage safety window of each individual cell. The similarity index is used to characterize the degree of consistency of different individual cells in terms of the width of the allowable operating voltage range, the upper limit position, or the lower limit position. As shown in Figure 3, through the similarity index, a set of individual cells with similarity at the dynamic voltage safety window level is initially screened to form several candidate group sets. Similarity is defined as the corresponding similarity index exceeding a preset similarity threshold. For each candidate group set, consistency verification is performed in combination with the corresponding electrochemical behavior characteristics. According to the consistency verification results, the individual cells in the candidate group sets that meet the consistency requirements are determined as the same logical group, forming a sub-cluster.

[0035] Furthermore, for each candidate group set, consistency verification is performed based on the corresponding electrochemical behavior characteristics, including: for each candidate group set, obtaining the electrochemical behavior characteristic parameters corresponding to each individual cell within that candidate group set; wherein, the obtained electrochemical behavior characteristic parameters include side reaction risk characterization indicators, internal resistance characteristic parameters, and voltage response characteristic parameters. The internal resistance characteristic parameters are used to characterize the ability of an individual cell to impede current changes under given operating conditions and its changing trend, reflecting the influence of the battery's internal conductivity and interface transport state on energy loss and heat generation characteristics; the voltage response characteristic parameters are used to characterize the transient change behavior of the individual cell's terminal voltage over time when the current changes, reflecting the speed of the battery's electrochemical reaction kinetics and polarization process.

[0036] Electrochemical behavior characteristic parameters are normalized to eliminate the influence of different parameter dimensions and numerical scales on consistency judgment. Based on the normalized electrochemical behavior characteristic parameters, a corresponding electrochemical behavior characteristic vector is constructed for each individual cell in the candidate group set. Each electrochemical behavior characteristic vector is composed of the side reaction risk characterization index, internal resistance characteristic parameter, and voltage response characteristic parameter in a predetermined order. Based on the electrochemical behavior characteristic vector, an electrochemical behavior difference metric is calculated between any two individual cells in the candidate group set. The difference metric is obtained by calculating the distance or similarity between the corresponding electrochemical behavior characteristic vectors. The electrochemical behavior difference metric is then statistically analyzed. The data is processed to obtain a set difference index characterizing the overall electrochemical consistency of the candidate group set. The set difference index includes at least one of the maximum value, average value, or variance of the difference measure within the candidate group set. As shown in Figure 4, the set difference index is compared with a preset consistency judgment condition. If the set difference index falls within the range defined by the consistency judgment condition, the individual cells in the candidate group set are determined to be consistent in terms of electrochemical behavior. If the consistency judgment condition is not met, the candidate group set is split or the individual cells whose electrochemical behavior differences exceed the preset range are removed, and the remaining individual cells are used as candidate group sets again for consistency verification.

[0037] It should be noted that by combining the dynamic voltage safety window of each individual cell in the battery cluster with its electrochemical behavior characteristics for consistency verification, a refined assessment and grouping of individual cells in terms of voltage operating range and reaction risk is achieved, thus forming logically consistent sub-clusters with similar performance. The advantages are twofold: firstly, it effectively reduces local risks caused by individual cell voltage deviations or differences in side reaction activity, improving the operational consistency and safety margin within the sub-cluster; secondly, through normalization based on quantitative indicators, multi-dimensional difference measurement, and statistical judgment, it avoids the uncertainty brought about by human experience judgment, making the grouping results repeatable and interpretable, and providing a reliable data foundation for subsequent sub-cluster-level dynamic voltage regulation and energy allocation, thereby improving the overall energy utilization efficiency and power performance of the battery cluster while ensuring safety.

[0038] S3, within each sub-cluster, the equivalent output voltage of the sub-cluster is adjusted according to the dynamic voltage safety window of each individual cell to construct a stable quasi-equivalent voltage platform for the sub-cluster to the outside world. This includes: calculating the initial equivalent output voltage of the sub-cluster based on the dynamic voltage safety window of each individual cell, wherein the initial equivalent output voltage is obtained by weighted averaging of the allowable operating voltages of the individual cells within the sub-cluster; determining the voltage regulation margin of each individual cell within the sub-cluster based on the initial equivalent output voltage, wherein the voltage regulation margin includes the upward adjustment margin of the individual cell obtained based on the difference between the highest allowable operating voltage of the individual cell and the initial equivalent output voltage, and the downward adjustment margin of the individual cell obtained based on the difference between the initial equivalent output voltage and the lowest operating voltage; calculating the target output voltage of each individual cell in the current operating cycle based on the voltage regulation margin and the initial equivalent output voltage; the specific calculation formula for the target output voltage is as follows:

[0039] In the formula, For the first sub-cluster The target output voltage of each individual cell. This is the initial equivalent output voltage. For the first The margin for upward adjustment of each individual cell, For the first The margin for downward adjustment of each individual cell, , These are the weighting coefficients.

[0040] Based on the target output voltage, the output voltage of the sub-cluster is adjusted through robust control, and a stable quasi-equivalent voltage platform is constructed for the sub-cluster to present to the outside world.

[0041] Furthermore, based on the target output voltage, robust control is used to adjust the output voltage of individual cells within the sub-cluster, and a stable quasi-equivalent voltage platform is constructed for the sub-cluster to the outside world. This includes: establishing a robust control model for each individual cell, where the individual cell output voltage or current is used as a state variable, the sub-cluster output voltage is used as the system output, and the model considers disturbance vectors including changes in individual cell parameters (internal resistance change, capacity decay, temperature effect), measurement error or sensor noise, differences in electrochemical behavior (side reaction activity, voltage response difference), and the influence of external load changes (total current demand fluctuation), and uses the individual cell dynamic voltage safety window as a control constraint. The specific calculation formula for the robust control model is as follows:

[0042]

[0043] In the formula, For the first The state vector of each individual cell includes the output voltage and output current of that cell. For the first The time derivative of the state vector of a single cell represents the dynamics of the cell state over time. For the first The state transition matrix of a single cell describes the influence of the cell's own state on the rate of state change. For the first The control input matrix of a single cell describes the control input. The effects of state changes include control inputs such as output current regulation, series-parallel combined switch state adjustment, and voltage correction applied by the fine-tuning equalizer. C is the output matrix, used to combine all individual unit states into a sub-cluster equivalent output voltage. The linear combination of these values ​​reflects the contribution ratio of each individual cell to the overall output voltage. D is the direct transfer matrix, describing the control input. Direct impact on sub-cluster output voltage (such as rapid voltage correction introduced by equalizers or current distribution devices). Let be the perturbation vector of the i-th individual cell, representing the impact of external or internal uncertainties on the state.

[0044] Based on the robust control model, the control input for each individual cell is calculated to adjust the output voltage or current of the individual cell. According to the control input, the individual cells in the sub-cluster are adjusted in real time through a current distribution device, a voltage equalizer, or a series-parallel combination mechanism of switches, so that the output of each individual cell approaches the target output voltage, and the overall output voltage of the sub-cluster converges to the target equivalent output voltage, forming a stable quasi-equivalent voltage platform. The quasi-equivalent voltage platform refers to the stable and approximately equivalent overall output voltage level that the sub-cluster presents to the outside world by adjusting the output voltage of each individual cell.

[0045] The target equivalent output voltage refers to the stable voltage value that the sub-cluster wants to provide to the outside world during the current operating cycle, which is used to guide the output adjustment of individual cells to form a quasi-equivalent voltage platform.

[0046] It should be noted that by quantitatively analyzing the dynamic voltage safety window of individual cells within the sub-cluster and calculating the target output voltage of each individual cell, the sub-cluster output voltage can be precisely adjusted, thereby enabling the sub-cluster to present a stable quasi-equivalent voltage platform to the outside world, effectively balancing the differences between individual cells and the risk of side reactions. By introducing robust control, disturbances such as changes in individual cell parameters, measurement errors, differences in electrochemical behavior, and external load fluctuations can be taken into account, thus maintaining the stability of the sub-cluster output voltage when facing these uncertainties and disturbances. This solves the problem that traditional control methods are prone to output voltage fluctuations and instability under dynamic loads or individual cell differences.

[0047] S4, based on the operating state of the quasi-equivalent voltage platform of each sub-cluster, determine the target operating voltage range of the battery cluster, and perform power regulation on the battery cluster within the target operating voltage range, including: based on the quasi-equivalent voltage platform of each sub-cluster, for each sub-cluster, summarizing the dynamic voltage safety windows of each individual cell in the sub-cluster, thereby determining the operating voltage range corresponding to each sub-cluster, wherein the operating voltage range is obtained by the intersection of the dynamic voltage safety windows of the individual cells in the sub-cluster at the sub-cluster level; based on the operating voltage range of the sub-cluster, calculate the voltage regulation capability parameter of each sub-cluster relative to its quasi-equivalent voltage platform, wherein the voltage regulation capability parameter... The parameters include the sub-cluster upward voltage regulation margin and the sub-cluster downward voltage regulation margin. The sub-cluster upward or downward voltage regulation margin is defined as the maximum voltage regulation range that the sub-cluster as a whole can achieve without violating the dynamic voltage safety window of any single cell in the sub-cluster. It should be noted that the reference voltage of the sub-cluster quasi-equivalent voltage platform is used as the central reference. The voltage regulation capability parameters are defined by the difference between the upper and lower limits of the operating voltage range and the reference voltage. The difference between the upper limit and the reference voltage is the sub-cluster upward voltage regulation margin, and the difference between the lower limit and the reference voltage is the sub-cluster downward voltage regulation margin.

[0048] Based on a time-sharing interval rotation mechanism, the target operating voltage range of the battery cluster in the current operating cycle is determined according to the operating voltage range of each sub-cluster. Within the target operating voltage range, the power regulation range that the battery cluster can achieve within the target operating voltage range is calculated by combining the external load demand and the voltage regulation capability parameters of each sub-cluster. For example, within the determined target operating voltage range, the real-time current demand or power demand curve corresponding to the current external load demand is first obtained, and combined with the voltage regulation capability parameters corresponding to each sub-cluster, the effective voltage range and the upper limit of the current that each sub-cluster can carry within the target operating voltage range are determined. On this basis, the upper and lower limits of the target operating voltage range are used as the voltage constraints of the entire cluster, and the available output current ranges of each sub-cluster within the voltage range are superimposed and summarized to obtain the maximum output power and minimum stable output power of the battery cluster within the target operating voltage range, thereby forming the power regulation range that the battery cluster can achieve.

[0049] Based on the power regulation range, the quasi-equivalent voltage platform and its output power of the sub-clusters are coordinated and controlled. Specifically, using the target operating voltage range as a unified voltage constraint boundary, the required output power of the battery cluster is allocated among the sub-clusters. Combined with the upward and downward voltage regulation margins of each sub-cluster, the power regulation share it can undertake is dynamically determined. For sub-clusters with strong voltage regulation capabilities (this indicator is calculated based on the distance of the sub-cluster's quasi-equivalent voltage platform relative to its upper and lower safe voltage limits, and can be expressed as the sum of the maximum upward and downward voltage amplitudes that the sub-cluster can adjust; the larger the margin, the stronger the sub-cluster's voltage regulation capability) and in the priority rotation phase, their quasi-equivalent voltage platform setting value is increased or their output current range is expanded, allowing them to undertake more power output or regulation tasks. Conversely, for sub-clusters with small voltage margins or high side reaction risk indicators... For subclusters with a preset threshold, their quasi-equivalent voltage platform is lowered or their output current is limited to keep them operating within a relatively conservative power range. During this process, the voltage of each subcluster is continuously fine-tuned by monitoring whether it approaches the boundary of its dynamic voltage safety window, thus achieving closed-loop control of the subcluster output voltage. At the same time, the power distribution is rolled over according to the external load demand, ensuring that the sum of the output power of each subcluster always meets the target power requirement and does not exceed the overall power adjustment range of the entire cluster. Furthermore, by combining a time-sharing interval rotation mechanism, the control of priority subclusters is switched in different time periods, allowing each subcluster to rotate orderly between its optimal and suboptimal voltage ranges. This ensures that all individual cells are within the dynamic voltage safety window, thereby achieving smooth adjustment of the overall output power of the battery cluster, balanced risk distribution, and optimized operating efficiency.

[0050] Furthermore, the time-division interval rotation mechanism, which determines the target operating voltage range of the battery cluster in the current operating cycle based on the operating voltage range of each sub-cluster, includes: performing interval consistency analysis on the operating voltage range of each sub-cluster; when it is detected that the operating voltage ranges of multiple sub-clusters do not have a common intersection or the common intersection is less than a preset safety margin threshold in the same time period, it is determined that there is a conflicting set of sub-clusters that are mutually exclusive or overlapping; wherein, the safety margin threshold is determined by historical operating data, experimental calibration results or battery design parameters.

[0051] When conflicting subclusters are identified, the operating cycle of the battery cluster is divided into several consecutive time periods. Within each time period, some subclusters are allowed to operate preferentially within their optimal operating voltage range, while other subclusters operate in a suboptimal manner under the constraints of safety and dynamic voltage safety windows. A rotation priority is assigned to each subcluster within the conflicting subcluster set based on its voltage regulation capability and side reaction risk. It should be noted that within the conflicting subcluster set, each subcluster is ranked according to a comprehensive priority value calculated using normalized weighted averages. Normalized weighted averages refer to the calculation that combines voltage regulation capability and side reaction risk... These two indicators with different dimensions are converted to the same scale (e.g., between 0 and 1) and weighted and summed according to preset weights to obtain a comprehensive priority value. Subclusters with higher comprehensive priority values ​​(i.e., ranked higher) are given priority rotation rights, allowing them to operate in the optimal operating voltage range in the time-sharing rotation mechanism. Subclusters with lower comprehensive priority values ​​(i.e., ranked lower) operate in the second-best voltage range in the corresponding time period. This ensures that each subcluster can make full use of the safety margin and reduce the overall risk of side reactions during the rotation process, achieving balanced and safe regulation of the target operating voltage range.

[0052] Within each time period, the optimal operating voltage range of the priority sub-cluster is combined with the sub-cluster's second-best range to calculate the global target operating voltage range that the entire battery cluster can safely achieve in the current operating cycle. The target operating voltage range ensures that the output voltage of all sub-clusters is within their respective dynamic voltage safety windows.

[0053] Specifically, during each time-sharing period, when combining the optimal operating voltage range of the priority sub-cluster with the sub-optimal range of other sub-clusters, a cross-analysis of the allowable voltage range of all sub-clusters can be performed first. The optimal range of the priority sub-cluster is used as a reference benchmark, while the upper and lower limits of the dynamic voltage safety window of other sub-clusters within their sub-optimal ranges are included as constraints in the calculation. By taking the intersection of the allowable voltage ranges of each sub-cluster or by weighting them according to a pre-set weight, a global target operating voltage range is generated that satisfies the output requirements of the priority sub-cluster while ensuring the safe operation of other sub-clusters within their respective dynamic voltage safety windows. This global target operating voltage range ensures that the overall output voltage of the battery cluster remains achievable and safe within the current operating cycle, as well as constrains the dynamic voltage safety windows of the sub-clusters, thereby achieving coordination of voltage conflicts and controllability of overall cluster power or energy regulation.

[0054] It should be noted that by introducing a time-sharing interval rotation mechanism, when the operating voltage ranges of multiple subclusters conflict or cannot completely overlap, the battery cluster operating cycle can be divided into several consecutive time periods. Within different time periods, the priority output of each subcluster can be adjusted sequentially, so that while some subclusters operate in their optimal voltage range, other subclusters can operate safely in their suboptimal range. This effectively coordinates the voltage conflicts between subclusters, ensuring that the overall cluster output voltage remains within a safe and achievable target operating voltage range, thereby achieving safety, balance, and performance optimization of the entire cluster operation.

[0055] Referring to Figure 2, the schematic diagram of the energy storage regulation system based on aqueous sodium-ion battery clusters provided by the present invention includes a voltage safety window construction module, a sub-cluster construction module, a sub-cluster regulation module, and a cluster-level control module. These modules are interconnected: the voltage safety window construction module is used to acquire the operating state parameters of each individual cell in the battery cluster, construct the side reaction risk characterization index corresponding to each individual cell, and determine the dynamic voltage safety window of each individual cell; the sub-cluster construction module is used to logically group the individual cells in the battery cluster to form sub-clusters based on the similarity of their dynamic voltage safety windows and electrochemical behavior characteristics; the sub-cluster regulation module is used to adjust the equivalent output voltage of each sub-cluster according to the dynamic voltage safety window of each individual cell, constructing a quasi-equivalent voltage platform for the sub-cluster; and the cluster-level control module is used to determine the target operating voltage range of the battery cluster based on the operating state of the quasi-equivalent voltage platforms of each sub-cluster, and to regulate the power of the battery cluster within the target operating voltage range.

[0056] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0057] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0058] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0060] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0061] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy storage regulation method based on aqueous sodium-ion battery clusters, characterized in that, The process includes the following steps: obtaining the terminal voltage and current time series of each individual cell in the battery cluster, extracting electrochemical behavior operating state parameters, calculating state offsets and constructing a weighted index for side reaction risk characterization, dividing the operating range according to risk level and adjusting the reference voltage range, and determining the dynamic voltage safety window of each individual cell; based on the similarity of the dynamic voltage safety window and electrochemical behavior characteristics of each individual cell, logically grouping the individual cells in the battery cluster to form sub-clusters; within each sub-cluster, adjusting the equivalent output voltage of the sub-cluster according to the dynamic voltage safety window of each individual cell to construct a quasi-equivalent voltage platform for the sub-cluster; based on the operating state of the quasi-equivalent voltage platform of each sub-cluster, determining the target operating voltage range of the battery cluster, and adjusting the power of the battery cluster within the target operating voltage range.

2. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 1, characterized in that, The construction of the side reaction risk characterization index includes: acquiring the terminal voltage and current data of each individual cell in the battery cluster and forming a time series; extracting the operating state parameters characterizing the electrochemical behavior of the individual cells based on the time series; calculating the state offset of the operating state parameters and combining the state offsets into intermediate characterization quantities of the side reaction risk of the individual cells; weighting the intermediate characterization quantities of the side reaction risk to construct the side reaction risk characterization index of the individual cells; and determining the maximum and minimum allowable operating voltage of each individual cell based on the side reaction risk characterization index to form a dynamic voltage safety window.

3. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 2, characterized in that, The process of determining the maximum and minimum allowable operating voltages of each individual battery cell based on the side reaction risk characterization index to form a dynamic voltage safety window includes: dividing the operating state of each individual battery cell into multiple risk level ranges based on the side reaction risk characterization index; setting corresponding voltage regulation strategies for each risk level range to limit the allowable expansion or contraction of the individual battery cell's operating voltage relative to the reference operating voltage range; adjusting the reference operating voltage range of the individual battery cell based on the voltage regulation strategy to obtain the maximum and minimum allowable operating voltages of the individual battery cell; and using the maximum and minimum operating voltages as the dynamic voltage safety window of the individual battery cell within the current operating cycle.

4. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 1, characterized in that, The method involves logically grouping individual cells in a battery cluster into sub-clusters based on the similarity of their dynamic voltage safety windows and electrochemical behavior characteristics. This includes: calculating the similarity index between the dynamic voltage safety windows of each individual cell, selecting individual cells with similarity to form a candidate group set; obtaining electrochemical behavior characteristic parameters for each candidate group set and performing consistency verification; and determining individual cells that meet the consistency requirements as the same logical group to form a sub-cluster.

5. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 4, characterized in that, The consistency verification process involves identifying individual cells that meet the consistency requirements as the same logical group, forming a sub-cluster. This includes: calculating the electrochemical behavior difference metric between any two individual cells within the candidate group set; statistically processing the electrochemical behavior difference metric to obtain a set difference index; and determining that the individual cells within the candidate group set are consistent in electrochemical behavior if the set difference index falls within the range defined by a preset consistency judgment condition, otherwise they are inconsistent.

6. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 1, characterized in that, Within each sub-cluster, the equivalent output voltage of the sub-cluster is adjusted according to the dynamic voltage safety window of each individual cell to construct a quasi-equivalent voltage platform for the sub-cluster. This includes: calculating the initial equivalent output voltage of the sub-cluster and determining the voltage regulation margin of each individual cell within the sub-cluster; calculating the target output voltage of each individual cell in the current operating cycle based on the voltage regulation margin and the initial equivalent output voltage; and adjusting the output voltage of the sub-cluster through robust control based on the target output voltage to construct a quasi-equivalent voltage platform for the sub-cluster.

7. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 6, characterized in that, The method of adjusting the output voltage of the sub-cluster based on the target output voltage and constructing a quasi-equivalent voltage platform for the sub-cluster includes: establishing a robust control model for each individual cell, wherein the model uses the individual cell output voltage or current as a state variable, the sub-cluster output voltage as the output, and considers the disturbance vector; calculating the control input for each individual cell based on the robust control model, which is used to adjust the individual cell output voltage or current; and adjusting the individual cells within the sub-cluster in real time according to the control input, so that the output of each individual cell converges towards the target output voltage, forming a stable quasi-equivalent voltage platform.

8. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 1, characterized in that, The process of determining the target operating voltage range of the battery cluster based on the operating status of the quasi-equivalent voltage platform of each sub-cluster, and adjusting the power of the battery cluster within the target operating voltage range, includes: based on the quasi-equivalent voltage platform of each sub-cluster, summarizing the dynamic voltage safety window of individual cells within each sub-cluster to determine the operable voltage range of each sub-cluster; based on the operable voltage range and the quasi-equivalent voltage platform of each sub-cluster, calculating the voltage regulation capability parameters of each sub-cluster; based on a time-sharing interval rotation mechanism, combined with the operable voltage range of each sub-cluster, determining the target operating voltage range of the battery cluster in the current operating cycle; within the target operating voltage range, combined with external load requirements and the voltage regulation capability parameters of each sub-cluster, calculating the achievable power regulation range of the battery cluster, and coordinating the control of the quasi-equivalent voltage platform of the sub-cluster according to the power regulation range.

9. The energy storage regulation method based on aqueous sodium-ion battery clusters according to claim 8, characterized in that, The time-division interval rotation mechanism, combined with the operational voltage range of each sub-cluster, determines the target operational voltage range of the battery cluster within the current operating cycle. This includes: performing a consistency analysis on the operational voltage ranges of each sub-cluster; when multiple sub-clusters' operational voltage ranges do not have a common intersection or the intersection is less than a preset safety margin threshold within the same time period, a conflicting sub-cluster set is identified. In the case of a conflicting sub-cluster set, the battery cluster's operating cycle is divided into several consecutive time periods. Within each time period, some sub-clusters are allowed to preferentially operate within their optimal operational voltage range, while other sub-clusters operate within their suboptimal voltage range under safety constraints; based on the voltage regulation capability and side reaction risk of each sub-cluster, a rotation priority is assigned to each sub-cluster within the conflicting sub-cluster set, ensuring that higher-priority sub-clusters operate within their optimal operational voltage range and lower-priority sub-clusters operate within their suboptimal voltage range; within each time period, the optimal operational voltage range of the preferentially operating sub-clusters is combined with the suboptimal ranges of other sub-clusters to calculate the target operational voltage range of the battery cluster within the current operating cycle.

10. A system using the energy storage regulation method based on an aqueous sodium-ion battery cluster as described in any one of claims 1-9, characterized in that, include: The voltage safety window construction module is used to obtain the terminal voltage and current time series of each cell in the battery cluster, extract the electrochemical behavior operating state parameters, calculate the state offset and construct the side reaction risk characterization index by weight, divide the operating range according to the risk level and adjust the reference voltage range, and determine the dynamic voltage safety window of each cell. The sub-cluster construction module is used to logically group the individual cells in the battery cluster based on the similarity of their dynamic voltage safety windows and electrochemical behavior characteristics to form sub-clusters; the sub-cluster adjustment module is used to adjust the equivalent output voltage of the sub-cluster according to the dynamic voltage safety windows of each individual cell within each sub-cluster, thereby constructing a quasi-equivalent voltage platform for the sub-cluster. The cluster-level control module is used to determine the target operating voltage range of the battery cluster based on the operating status of the quasi-equivalent voltage platform of each sub-cluster, and to adjust the power of the battery cluster within the target operating voltage range.

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