Method and system for evaluating health state of whole cluster of super-capacitor energy storage system

Abstract

The invention provides a super capacitor energy storage system whole cluster health state assessment method and system, and relates to the technical field of battery management. The method comprises the following steps: acquiring electrical parameters and temperature parameters of each monomer unit in an energy storage cluster in real time; generating a dynamic weight coefficient based on the performance difference between the monomers; performing individual health status assessment based on a plurality of physical degradation mechanisms; fusing the health states of the monomers into a module health state based on the dynamic weight coefficient; generating the health state of the whole cluster through a decision rule based on the state dispersion characteristics between the modules; and establishing a bidirectional feedback mechanism of the health state and the charge state. According to the design, from data acquisition, difference quantification to hierarchical fusion, a local-intermediate-overall multi-scale evaluation logic is formed, the credibility of whole cluster SOH evaluation is remarkably improved, and more reliable technical support is provided for reliable operation, residual life prediction and economical efficiency optimization of an energy storage system.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method and system for assessing the overall health status of a supercapacitor energy storage system. Background Technology

[0002] With the accelerated global energy transition, the large-scale grid connection of renewable energy has placed higher demands on the flexibility and stability of the power grid. Supercapacitor energy storage systems, with their high power density, rapid response, and long cycle life, have become a core supporting technology for grid frequency regulation, renewable energy consumption, and emergency power supply. As a complex system composed of multiple cells connected in series or parallel, the accurate assessment of its State of Health (SOH) directly affects the reliability, safety, and economy of system operation. However, differences in manufacturing processes, non-uniform operating conditions, and inconsistent long-term aging among supercapacitor cells lead to gradual performance differentiation over time, thus affecting the overall capacity, power output balance, and lifespan of the entire cluster.

[0003] Against this backdrop, scientifically assessing the overall health status of a cluster and revealing its correlation with the status of individual cells has become a key requirement for improving system efficiency. Traditional cell-level SOH assessment methods often focus on modeling the correlation between electrical parameters and time-series data of individual cells. While these methods can reflect local degradation states, they are difficult to directly generalize to the cluster level. They cannot quantify the impact of cell differences on the overall status, leading to cluster-level assessment results deviating from the true level. Existing cluster-level assessment methods mainly rely on simple averaging or minimum methods. Averaging methods assume uniform cell degradation, ignoring the actual performance dispersion. Especially when cell performance differentiation is significant, the assessment results will deviate severely from the true cluster capacity. Minimum methods rely excessively on extreme cells (such as the state of the worst or best performing cells) for inference, are susceptible to noise interference, and cannot accurately characterize the overall health level of the cluster. These shortcomings make the assessment reliability of traditional methods insufficient in complex operating scenarios, making it difficult to meet the needs of precise operation and maintenance and safety management of energy storage systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for assessing the overall health status of a supercapacitor energy storage system, in order to solve the technical problem mentioned in the background art: the traditional methods for assessing the overall health status of the system lack reliability in complex operating scenarios, making it difficult to meet the needs of precise operation and maintenance and safety management of energy storage systems.

[0005] To achieve the above objectives, according to one aspect of the present invention, a method for assessing the overall health status of a supercapacitor energy storage system is provided, the method comprising:

[0006] Real-time acquisition of electrical and temperature parameters of each individual unit within the energy storage cluster;

[0007] Dynamic weighting coefficients are generated based on performance differences between individual units;

[0008] Perform individual health status assessments based on multiple physical degradation mechanisms;

[0009] Based on the dynamic weighting coefficients, the individual health status is merged into the module health status;

[0010] Based on the state dispersion characteristics between modules, clustered health states are generated through decision rules.

[0011] Establish a two-way feedback mechanism between health status and state of charge.

[0012] In one possible implementation, the step of generating dynamic weighting coefficients based on inter-unit performance differences includes:

[0013] Calculate the first difference index that reflects the characteristics of capacity distribution;

[0014] Calculate the second difference index that reflects the internal resistance distribution characteristics;

[0015] Calculate the third difference index that reflects the temperature uniformity characteristics;

[0016] The first difference index, the second difference index, and the third difference index are combined by weighting to generate the dynamic weight coefficient of each individual unit.

[0017] In one possible implementation, the first difference index is quantified by the ratio of the standard deviation to the mean of the volume data;

[0018] The second difference index is quantified by the ratio of the standard deviation to the mean of the internal resistance data;

[0019] The third difference index is quantified by dividing the temperature deviation between the unit temperature and the preset temperature by a dynamic temperature threshold, which is the smaller value between the rated temperature margin and the ambient temperature statistical threshold.

[0020] In one possible implementation, the step of performing a monomer health status assessment based on multiple physical degradation mechanisms includes:

[0021] Assess capacity-related health status associated with capacity decay;

[0022] Assess internal resistance-related health conditions associated with increased internal resistance.

[0023] Assess the health status of thermodynamic models related to changes in thermal properties;

[0024] The capacity-type health state, the internal resistance-type health state, and the thermodynamic model health state are weighted and fused.

[0025] In one possible implementation, the capacity-based health status has the highest weight in the weighted allocation fusion.

[0026] In one possible implementation, the step of fusing the individual health states into a module health state using the dynamic weights includes:

[0027] Calculate the weighted average of the individual health status assessments;

[0028] Introduce a bottleneck compensation coefficient that is positively correlated with the dispersion of health status;

[0029] The module health status is determined by combining the weighted average value and the worst individual evaluation value.

[0030] In one possible implementation, the decision rule is implemented through a system comprising the following layers:

[0031] The input layer receives a multidimensional feature vector representing the discreteness of the state;

[0032] The output layer generates cluster health status correction factors and balancing strategy parameters;

[0033] The decision-making layer applies a preset rule base to perform state correction reasoning.

[0034] In one possible implementation, the method for assessing the overall health status of the supercapacitor energy storage system further includes:

[0035] The health status of individual cells is dynamically compensated based on ambient temperature and operating current.

[0036] In one possible implementation, the step of establishing the bidirectional feedback mechanism between the health state and the state of charge includes:

[0037] Based on the parameters obtained from the health status assessment, the open-circuit voltage mapping relationship and polarization voltage calculation parameters in the state of charge estimation are corrected.

[0038] The health status assessment parameters are updated based on the voltage change characteristics and current time series data obtained during the state of charge estimation process.

[0039] According to another aspect of the present disclosure, a cluster health status assessment system for a supercapacitor energy storage system is provided, the system comprising:

[0040] The first execution unit is configured to collect electrical and temperature parameters of each individual unit within the energy storage cluster in real time.

[0041] The second execution unit is configured to generate dynamic weight coefficients based on the performance differences between individual units;

[0042] The third execution unit is configured to perform individual health status assessments based on multiple physical degradation mechanisms;

[0043] The fourth execution unit is configured to merge the individual health status into the module health status based on the dynamic weighting coefficient;

[0044] The fifth execution unit is configured to generate cluster health status based on the state dispersion characteristics between modules through decision rules;

[0045] The sixth execution unit is configured to establish a two-way feedback mechanism between the health state and the charged state.

[0046] The above-described one or more technical solutions in the embodiments of this application have at least one or more of the following technical effects:

[0047] This invention provides a method for assessing the overall health status of a supercapacitor energy storage system. By real-time acquisition of individual cell electrical and temperature parameters, a multi-parameter fusion data foundation is constructed, overcoming the limitations of single-parameter modeling. Furthermore, this invention innovatively introduces dynamic weighting coefficients, dynamically adjusting weights based on real-time performance differences between cells, solving the problem of traditional weight allocation being disconnected from actual conditions, and accurately characterizing the contribution of individual cell discreteness to the overall cluster. Further, it constructs a cell SOH assessment model based on multiple physical degradation mechanisms, deepening the characterization of individual cell degradation states from a mechanistic perspective. Through dynamic weighting, the individual cell health status is fused into the module health status, achieving collaborative feature extraction at the individual and module levels, avoiding the accumulation of errors from jump-based assessments. The overall cluster SOH is generated by combining the state discreteness between modules with decision rules, accurately capturing the overall degradation pattern of the cluster. Finally, a bidirectional feedback mechanism between health status and state of charge (SOC) is established to simultaneously optimize the coupling relationship between state variables. This design, from data acquisition and differential quantification to hierarchical fusion, forms a multi-scale evaluation logic of "local-intermediate-overall", which significantly improves the credibility of the whole cluster SOH evaluation and provides more reliable technical support for the reliable operation, remaining lifetime prediction and economic optimization of energy storage systems.

[0048] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for assessing the overall health status of a supercapacitor energy storage system according to an exemplary embodiment.

[0050] Figure 2This is a schematic diagram of the composition structure of a supercapacitor energy storage system cluster health status assessment system module according to an exemplary embodiment.

[0051] Explanation of reference numerals in the attached figures: 100, first execution unit; 200, second execution unit; 300, third execution unit; 400, fourth execution unit; 500, fifth execution unit; 600, sixth execution unit. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in further detail below with reference to the accompanying drawings.

[0053] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of systems and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0054] Figure 1 The flowchart below illustrates a method for assessing the overall health status of a supercapacitor energy storage system according to an exemplary embodiment. The method includes the following steps:

[0055] In step S100, the electrical and temperature parameters of each individual unit within the energy storage cluster are collected in real time. This step requires the synchronous collection of electrical and temperature parameters to reflect the current state of the individual unit. For example, the real-time electrical parameters include voltage and current, which can be collected in real time by individual unit voltage sensors and Hall sensors to collect the terminal voltage and charging / discharging current of each individual unit within the energy storage cluster. The temperature parameter is collected for the surface temperature of each individual unit. If a temperature sensor is integrated inside the unit, the internal core temperature is collected synchronously. The temperature can be collected in real time through a distributed temperature sensor network. The temperature sensors need to be evenly distributed in key locations of the unit, such as the tabs and electrode active areas, to ensure that the temperature data can reflect the uniformity of the internal heat distribution of the unit.

[0056] Furthermore, after completing the multi-source data acquisition in step S100, the acquired data can be quality checked and outliers removed to ensure the accuracy and reliability of subsequent evaluations.

[0057] In step S200, dynamic weighting coefficients are generated based on the performance differences between individual units. Step S200 addresses the bias in cluster state assessment caused by traditional evaluation methods neglecting individual unit performance differences. Its core objective is to dynamically adjust the contribution weight of each unit to cluster health by quantifying the real-time performance differences between units. This makes highly differentiated units more sensitive to the representation of cluster state, while reasonably reducing the weight of low-differentiated units. This provides a difference-sensitive weighting basis for subsequent fusion of individual and module health states, improving the accuracy of cluster health state assessment.

[0058] In the implementation process, firstly, by analyzing the performance of individual cells in key performance dimensions such as capacity, internal resistance, and temperature, the differential characteristics reflecting their deviation from the average level of the entire cluster are extracted. These differential characteristics reflect the particularity of the individual cells in the entire cluster and are the core basis for weight allocation. Secondly, based on the above differential characteristics, adjustable parameters reflecting the importance of different performance dimensions can be combined to generate dynamic contribution weights of each individual cell to the health of the entire cluster. These weights can be adjusted according to the changes in the real-time performance differences of the individual cells, avoiding the underestimation of highly differential cells or the overestimation of low-differentiation cells in traditional fixed weights, such as the simple averaging method, where all individual cells have equal weights. This ensures that the weight allocation is more in line with the true contribution of the individual cells in the actual operating scenario.

[0059] In step S300, a single-cell health status assessment based on multiple physical degradation mechanisms is performed. Step S300 comprehensively characterizes the health status of the single cell through collaborative analysis of multiple physical mechanisms, solving the assessment bias problem caused by the information limitations of single-parameter assessment. Its core objective is to comprehensively assess the aging degree of the single cell from different physical levels such as material loss, impedance change, and heat accumulation by integrating degradation characteristics of multiple dimensions such as capacity, internal resistance, and temperature, providing highly robust single-cell-level data support for the subsequent fusion of the health status of single cells and modules.

[0060] In step S400, the individual health status is fused into the module health status based on the dynamic weighting coefficient. The core objective of step S400 is to use a dynamic weighting mechanism to fuse the individual health status assessment results into the module-level health status, thus solving the module status deviation problem caused by the traditional simple averaging method ignoring individual differences. For example, firstly, based on the performance differences between individual units, such as capacity decay rate, internal resistance growth characteristics, and temperature response deviation, the contribution weight of each individual unit to the module health is dynamically generated. This weight reflects the particularity of the individual unit in the module. The weight of highly differentiated units is adjusted specifically because they have a greater impact on the overall performance of the module, while the weight of low-differentiation units is reasonably reduced to avoid overestimation of discrete units by the averaging method. Secondly, the individual health status is weighted and fused through dynamic weighting to obtain the module health status assessment value. This process not only considers the current health level of the individual units but also incorporates their impact on the overall performance of the module, ensuring that the fusion result is more consistent with the actual operating state of the module.

[0061] In step S500, the overall health status of the cluster is generated based on the state dispersion characteristics between modules using decision rules. The state dispersion between modules is quantified, representing the degree of difference in the health status of each module. This dispersion reflects the aging consistency of different modules within the cluster; high dispersion indicates significant performance differentiation within the cluster, while low dispersion indicates a uniform cluster state. Secondly, based on preset decision rules, the module dispersion characteristics are intelligently matched. The rule design can be based on engineering experience and historical data, covering the cluster state judgment logic under typical operating conditions. For example, when the module dispersion exceeds a critical threshold, the cluster is judged to have a risk of performance differentiation; when the dispersion continues to rise and is accompanied by a rapid decline in the state of health (SOH) of individual modules, the cluster is judged to have entered a local degradation stage; when the dispersion is low and the SOH of all modules is stable, the cluster is judged to be in a healthy and stable state. Finally, the final evaluation result of the overall health status of the cluster is output through the decision rules. The results take into account both the dispersion characteristics between modules and the actual health level of each module. This avoids misjudgment of the overall cluster status due to anomalies in a single module and captures the overall aging trend of the cluster, providing a reliable basis for operation and maintenance decisions and remaining life prediction of energy storage systems.

[0062] In step S600, a two-way feedback mechanism between health status and state of charge (SOC) is established. In traditional methods, health status reflects the long-term aging degree of a single cell, such as capacity decay and internal resistance growth, while SOC reflects the real-time charge level, such as the current charge as a percentage of the rated capacity. Although independent, the two are strongly coupled: the calculation of SOC depends on capacity parameters (affected by health status), while the assessment of health status needs to be indirectly verified through the trend of SOC change (such as the correlation between cycle number and capacity decay). Independent estimation is prone to error propagation, and health status deviation will affect the accuracy of SOC. In turn, SOC error will interfere with health status determination. Step S600 breaks down this barrier by establishing a two-way feedback mechanism, achieving synchronous optimization and coordinated correction of health status and SOC.

[0063] By collecting real-time electrical and temperature parameters of individual cells, a multi-parameter fusion data foundation is constructed, overcoming the limitations of single-parameter modeling. This invention also innovatively introduces dynamic weighting coefficients, dynamically adjusting weights based on real-time performance differences between cells, solving the problem of traditional weight allocation being disconnected from actual conditions, and accurately characterizing the contribution of individual cell discreteness to the entire cluster. Furthermore, a cell SOH assessment model is constructed based on multiple physical degradation mechanisms, deepening the characterization of individual cell degradation states from a mechanistic perspective. Through dynamic weighting, the health status of individual cells is fused into the health status of the module, achieving collaborative feature extraction at the cell and module levels, avoiding the accumulation of errors from jump-based assessments. The cluster SOH is generated by combining the state discreteness between modules with decision rules, accurately capturing the overall degradation pattern of the cluster. Finally, a bidirectional feedback mechanism between health status and state of charge is established, simultaneously optimizing the coupling relationship between state variables. This design, from data acquisition and differential quantification to hierarchical fusion, forms a multi-scale assessment logic of "local-intermediate-overall," significantly improving the credibility of the cluster health status assessment and providing more reliable technical support for the reliable operation, remaining lifetime prediction, and economic optimization of energy storage systems.

[0064] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] In an exemplary embodiment, the step of generating dynamic weighting coefficients based on inter-unit performance differences includes:

[0066] The first difference index reflecting the capacity distribution characteristics is calculated. The capacity distribution characteristics reflect the dispersion and distribution of capacity values ​​among individual supercapacitor cells. Capacity is an important parameter for measuring the energy storage capacity of a supercapacitor, representing the amount of charge it can store. In practical applications, due to differences in manufacturing processes, aging levels, and operating conditions, the capacity of individual cells will gradually differ. The capacity distribution characteristics quantify these differences and reflect the consistency and uniformity of the energy storage system in terms of energy storage capacity.

[0067] Specifically, the first difference index is used to quantify the dispersion of capacity distribution among individual supercapacitor cells. For example, the first difference index can be the capacity variation coefficient, which is quantified by the ratio of the standard deviation of the capacity data to the mean. The specific calculation formula is as follows:

[0068]

[0069] in, Indicates the first The current capacity of each individual unit can be obtained through... The values ​​are obtained by measurement, where I is the current, T is the time, and V is the voltage; This represents the average capacity of all individual units within the module. This average is the arithmetic mean of the capacity values ​​of all individual units, reflecting the overall energy storage capacity of the energy storage system. Indicates the number of monomers; The standard deviation of capacity reflects the dispersion of capacity distribution, that is, the degree of deviation between the capacity value of a single unit and the average capacity value. The larger the standard deviation of capacity, the greater the capacity difference between single units. For example, if the capacity values ​​of most single units are close to the average value, the standard deviation is small. Conversely, if the capacity values ​​of some single units deviate greatly from the average value, the standard deviation is large. The capacity variation coefficient (CV) represents the ratio of the standard deviation of capacity to the mean. By normalizing the standard deviation, it facilitates comparisons between different energy storage systems. A larger CV indicates a more uneven capacity distribution and greater capacity differences between individual units. For example, if... , but This indicates that the capacity distribution is relatively uniform; if, but This indicates that the capacity distribution is uneven.

[0070] The second difference index reflecting the internal resistance distribution characteristics is calculated. The internal resistance distribution characteristics reflect the dispersion and distribution of the internal resistance values ​​between individual supercapacitor cells. Internal resistance is a key parameter affecting the charging and discharging performance of supercapacitors, representing its internal resistance to current. The larger the internal resistance, the greater the energy loss during charging and discharging, and the lower the charging and discharging efficiency. The internal resistance distribution characteristics reflect the consistency and uniformity of the energy storage system in terms of charging and discharging efficiency by quantifying the differences in internal resistance between individual cells.

[0071] Specifically, the second difference index is used to quantify the dispersion of the internal resistance distribution among individual supercapacitor cells. For example, the second difference index can be the internal resistance variation coefficient, which is quantified by the ratio of the standard deviation to the mean of the internal resistance data. The specific calculation formula is as follows:

[0072]

[0073] in, Indicates the first The DC internal resistance of each individual cell; This represents the average internal resistance of the module, which is the arithmetic mean of the internal resistance values ​​of all individual units, reflecting the overall internal resistance level of the energy storage system. Indicates the number of monomers; This represents the standard deviation of internal resistance. This standard deviation measures the dispersion of the internal resistance value of a single unit, that is, the degree of deviation of the internal resistance value of a single unit from the average internal resistance value. The larger the value of the standard deviation of internal resistance, the greater the difference in internal resistance between single units. This represents the coefficient of variation of internal resistance, which is the ratio of the standard deviation to the mean of the internal resistance. Normalizing the standard deviation facilitates comparisons between different energy storage systems. A larger coefficient of variation indicates a more uneven distribution of internal resistance and greater differences in internal resistance between individual units. For example, if... This indicates that the internal resistance distribution is relatively uniform; if This indicates that the internal resistance distribution is uneven.

[0074] The third difference index, which reflects the temperature uniformity characteristics, is calculated. Temperature uniformity characteristics reflect the degree of temperature distribution uniformity among individual supercapacitor cells. Temperature is one of the key factors affecting the performance of supercapacitors. Excessive temperature will accelerate electrolyte decomposition and electrode material aging, shortening the life of supercapacitors. Temperature uniformity characteristics reflect the uniformity and stability of the energy storage system in terms of temperature control by quantifying the temperature differences between individual cells.

[0075] Specifically, the third difference index is used to quantify the degree of temperature distribution uniformity among individual supercapacitor cells. For example, the third difference index can be quantified by dividing the temperature deviation between the cell temperature and the preset temperature by a dynamic temperature threshold. This dynamic temperature threshold is the smaller of the rated temperature margin and the ambient temperature statistical threshold, representing the maximum allowable temperature deviation threshold. The ratio of the temperature deviation to the dynamic temperature threshold measures the degree of deviation between the cell temperature and the preset temperature. A larger ratio indicates that the current cell temperature is closer to the maximum allowable value, and the current cell temperature is uneven. For example, if the ratio of the temperature deviation to the dynamic temperature threshold is close to 1, it means that the temperature deviation is close to the maximum allowable value. Generally, the smaller the ratio of the temperature deviation to the dynamic temperature threshold, the better, to ensure the uniformity of temperature distribution and the safety of the system. The specific calculation formula for the third difference index is as follows:

[0076]

[0077] in, Temperature deviation can be the difference between the current temperature of a single unit and the preset temperature. The preset temperature can be selected according to actual needs and is usually the upper limit of the normal operating temperature. The dynamic temperature threshold is calculated using the following formula:

[0078]

[0079] in, This refers to the rated maximum operating temperature of a single unit. For ambient temperature, The temperature standard deviation reflects the dispersion of the temperature distribution. By introducing the temperature standard deviation... This allows for the quantification of temperature fluctuations, meaning that the dynamic temperature threshold will be dynamically adjusted based on temperature fluctuations, rather than remaining fixed. The average temperature reflects the central trend of the temperature distribution, which helps to identify changes in the overall temperature level, rather than just local temperature differences. By calculating the standard deviation and average temperature, the characteristics of the temperature distribution can be quantified. Indicates the rated temperature margin; This indicates the statistical threshold for ambient temperature.

[0080] For example, suppose :

[0081]

[0082] Then, calculate :

[0083]

[0084] Finally, calculate :

[0085]

[0086] In this example, the dynamic temperature threshold was determined to be 37°C, rather than simply... Because the system's tolerance to temperature fluctuations is reduced after taking into account the temperature standard deviation and average temperature, the accuracy of the judgment is improved.

[0087] The above formula not only considers the dispersion and central tendency of temperature distribution, but also improves the accuracy and reliability of temperature management by selecting the smaller value between the rated temperature margin and the statistical threshold of ambient temperature, thus providing an important guarantee for the safe operation of energy storage systems.

[0088] The first, second, and third difference indices are weighted and combined to generate a dynamic weight coefficient for each individual unit. The dynamic weight coefficient refers to the weight calculated in real-time and assigned to each individual unit based on the performance differences between individual units during the overall health status assessment of the supercapacitor energy storage system. These weights reflect the relative importance of the individual unit in its current operating state and its influence on the overall health status of the cluster; a higher weight indicates a greater impact of that individual unit on the overall health status.

[0089] For example, dynamic weighting coefficients can be expressed as: ,in, Represents a single unit Dynamic weights, The unit number and dynamic weighting coefficient are used to define the individual unit. The calculation formula is as follows:

[0090]

[0091] in, , , These are weighting coefficients, representing the weights of capacity uniformity, internal resistance uniformity, and temperature uniformity, respectively.

[0092] This represents the impact of capacity consistency on weights. Capacity variation coefficient. The smaller, The larger the value, the higher the capacity consistency between individual units, and the greater the positive impact on the weight.

[0093] This indicates the impact of internal resistance consistency on weights, specifically the internal resistance variation coefficient. The smaller, The larger the value, the higher the consistency of internal resistance between individual units, and the greater the positive impact on the weight.

[0094] This represents the impact of temperature uniformity on the weights, and is the ratio of temperature deviation to the dynamic temperature threshold. The smaller, The larger the value, the higher the temperature uniformity among individual units, and the greater the positive impact on the weight.

[0095] This formula calculates the dynamic weighting coefficient for each individual cell by comprehensively considering the differences in capacity, internal resistance, and temperature. Each weight coefficient represents the impact of a performance index on the weight, while the corresponding difference index quantifies the performance differences between individual units. By dynamically adjusting the weights, the overall health status of the supercapacitor energy storage system can be assessed more accurately, thereby achieving more reliable operation and more effective maintenance.

[0096] In an exemplary embodiment, the weighting coefficients , , The specific value range is as follows:

[0097]

[0098] in, The weight reflects the capacity consistency between individual units. The higher the capacity consistency, the larger α is, and the greater the impact on the overall health status. The weight reflects the consistency of internal resistance between individual units. The higher the consistency of internal resistance, the larger β is, and the greater the impact on the overall health status. The weighting reflects the temperature uniformity between individual units. The higher the temperature uniformity, the larger γ, and the greater the impact on the overall health status. α≥0.4β indicates that the lower limit of the capacity uniformity weight should be greater than 0.4 times the internal resistance uniformity weight to ensure that capacity uniformity accounts for a sufficient proportion of the weighting; β≤(α+γ) / 2 indicates that the upper limit of the internal resistance uniformity weight should be less than half of the sum of the capacity uniformity and temperature uniformity weights to prevent the internal resistance uniformity weight from being too high. The lower limit of the weight of temperature uniformity should be greater than 0.1 times (1-α) to ensure that temperature uniformity accounts for a certain proportion in the weight.

[0099] Furthermore, the weighting coefficients need to meet the following constraints: + That is, the sum of all weight coefficients must be equal to 1 to ensure the integrity of the weight allocation.

[0100] In an exemplary embodiment, the initial weight coefficients can be calculated using a Support Vector Regression (SVR) model, as shown in the following formula:

[0101]

[0102] Among them, its input parameters are , , and actual health status Its output is the optimal weight coefficient. , , .

[0103] To adapt to changes in individual unit performance, the weighting coefficients need to be updated in real time. The update formula is as follows:

[0104]

[0105] Among them, 0.9 and 0.1 are weight update coefficients, used to balance the impact of historical weights and new estimation errors. 0.9 indicates that the impact of historical weights accounts for 90%, which is a large proportion; 0.1 indicates that the impact of the current estimation error accounts for 10%, which is a small proportion. This indicates the capacity estimation error; This indicates the error in internal resistance estimation; , This represents the temperature estimation error. The coefficient weights can be continuously updated using real-time collected data and the estimation error. , , To adapt to changes in monomer properties.

[0106] In an exemplary embodiment, the step of performing a monomer health status assessment based on multiple physical degradation mechanisms includes:

[0107] Assessing capacity-dependent health status (CDS) is crucial for evaluating the health status related to capacity degradation. Capacity is a key indicator of a supercapacitor's energy storage capability, and its degradation directly impacts the overall performance and lifespan of the energy storage system. Therefore, accurate assessment of CDS is essential for reliable system operation and lifespan prediction. The core idea of ​​CDS assessment is to quantify the degree of capacity degradation by comparing the current capacity of a single cell with its initial capacity. The current capacity is the actual capacity of the single cell during its current operational phase, which can be obtained through constant current discharge testing. The specific calculation formula is as follows:

[0108]

[0109] in, Indicates the current capacity value of a single cell; Indicates the discharge current; This represents the time difference between reaching full discharge voltage; This indicates the time point at which discharge begins, corresponding to the time required to charge to the rated voltage. This indicates the time point at which the discharge ends, corresponding to the time point when the discharge reaches the cutoff voltage. Indicates the rated voltage. The cutoff voltage is represented by the above formula. The capacity of a single supercapacitor cell can be accurately calculated using this formula. This method is based on a constant current discharge process and quantifies the actual energy storage capacity of a single cell by measuring the changes in discharge current, time, and voltage.

[0110] In practical applications, it is necessary to strictly control test conditions and consider the influence of environmental factors to improve the accuracy and reliability of capacity measurement. For example, during the charging phase, the individual cell is charged to its rated voltage (e.g., 2.7V) using constant current charging. After charging, it is left to stand for a period of time (e.g., 30 minutes) to eliminate polarization effects and ensure voltage stability. The charging current I ≤ 0.5C, where C is the rated capacity of the individual cell, to ensure the charging process is safe and repeatable. For example, for a single cell with a rated capacity of 1000F, the charging current should be less than or equal to 500A. During the discharging phase, constant current discharge is used to discharge to the cutoff voltage (e.g., 1.5V). The recommended discharge current range is 0.2C-1C. For example, for a single cell with a rated capacity of 1000F, the discharge current should be between 200A and 1000A. Additionally, the ambient temperature can be controlled within the range of 25±2℃ to reduce the impact of temperature on capacity measurement. During the data acquisition phase, the voltage sampling frequency must be greater than or equal to 10Hz to ensure accurate capture of voltage changes; current fluctuations must be ≤ ±1% to ensure the stability of the discharge current.

[0111] Alternatively, a more accurate capacity value can be obtained by integrating the change in current over time and then dividing by the voltage difference. This method is more accurate but requires more computation.

[0112] Based on the above capacity calculation results, the current capacity is obtained. Then, calculate the volume-based health status using the following formula:

[0113]

[0114] in, Indicates a volume-based health status; Indicates the current capacity; This indicates the initial capacity of a single unit, which is usually calibrated at the factory or measured during initial use; This represents the ratio of the current capacity to the initial capacity, reflecting the relative degree of capacity decay; for example, when... When it reaches 100%, it indicates that there is no capacity decay and the health status is optimal. A value of 0% indicates that the capacity has completely decayed, representing the worst state of health; assuming an initial capacity... Current capacity ,calculate This indicates that the current capacity is 95% of the initial capacity, meaning the capacity has decreased by 5%, and the capacity health status is 95%.

[0115] Assessing the internal resistance-related health status (IRS) is crucial for quantifying the impact of individual cell internal resistance growth on performance. Internal resistance is a critical factor affecting the power density and efficiency of supercapacitors; its increase leads to reduced charging and discharging efficiency and increased temperature rise, ultimately impacting the performance and lifespan of the entire energy storage system. Therefore, accurate assessment of IRS is essential for reliable system operation. The core idea of ​​IRS assessment is to quantify the relative degree of internal resistance growth by comparing the current internal resistance of an individual cell with its initial internal resistance. The current internal resistance represents the DC internal resistance of the individual cell during its current operating phase, which can be obtained through a short-time current loading test. The specific steps include: first, stopping charging and discharging and allowing the cell to stand for a period of time, such as 5 minutes, to allow the polarization voltage to decay; then, recording the open-circuit voltage. and temperature T; then, apply a short-time operating current. Prioritize using the existing equalization current in the BMS to avoid triggering protection. Measure the applied voltage at the instant the current is applied (usually within 100ms). DC internal resistance The specific calculation formula is as follows:

[0116]

[0117] For example, applying a short-time operating current Open circuit voltage Loading voltage Then the DC internal resistance is That is, the current DC internal resistance of the supercapacitor is .

[0118] Based on the above calculation results of DC internal resistance, the current DC internal resistance is obtained. Then, the internal resistance type health status is calculated using the following formula, the specific calculation formula is as follows:

[0119]

[0120] in, This indicates an internal resistance-type health condition; This indicates the initial DC internal resistance of a single unit, such as the factory-calibrated value. It serves as a reference value for the increase in internal resistance and reflects the internal resistance characteristics of the supercapacitor in its initial state. This indicates the current DC internal resistance of a single unit, reflecting the actual value of the current internal resistance and demonstrating the change in internal resistance of the supercapacitor during use. This represents the DC internal resistance of a single cell at the end of its lifespan. It is typically 1.5 to 2 times the initial internal resistance and serves as the limit for internal resistance growth, indicating the level of internal resistance when the supercapacitor reaches the end of its lifespan. This represents the difference between the current internal resistance and the initial internal resistance, reflecting the actual increase in internal resistance. This represents the difference between the end-of-life internal resistance and the initial internal resistance, signifying the maximum permissible range of internal resistance growth. The ratio of the current internal resistance difference to the initial internal resistance relative to the maximum permissible range of internal resistance growth quantifies the relative degree of internal resistance growth. For example, when... When the current internal resistance equals the initial internal resistance, the internal resistance has not changed, and the internal resistance has not increased, the health state is optimal; when The time indicates that the internal resistance has increased to the end of its lifespan, representing the worst health condition; assuming the initial DC internal resistance... Current DC internal resistance DC internal resistance at the end of life Calculate the internal resistance growth ratio Calculate the remaining proportion ,calculate This means that the current internal resistance is 1.4 times the initial internal resistance, the internal resistance has increased by 40%, and the healthy state is 60%.

[0121] The health status of thermodynamic models related to changes in thermal characteristics is assessed. The health status of thermodynamic models quantifies the impact of changes in the thermal characteristics of individual units on their performance. These changes include variations in core-shell thermal resistance, shell-environment thermal resistance, core heat capacity, and shell heat capacity. These changes directly affect the heat dissipation performance, temperature rise characteristics, and overall reliability of supercapacitors. The core idea of ​​thermodynamic model health status assessment is to establish a thermodynamic model to describe the changes in the thermal characteristics of individual units and to quantify their impact on the health status based on changes in model parameters. The thermal characteristics of supercapacitors can be described by the following thermodynamic models:

[0122]

[0123] The following are the parameter definitions:

[0124]

[0125] Among them, the core heat capacity The core's ability to store heat, and the shell's heat capacity. This indicates the shell's ability to store heat. This matrix describes the heat storage characteristics inside a supercapacitor. This vector represents the rate of change of core and shell temperatures over time; core-shell thermal resistance. This represents the thermal resistance between the core and the shell, reflecting the ease with which heat is transferred from the core to the shell; shell-ambient thermal resistance. It represents the thermal resistance between the casing and the environment, reflecting the ease with which heat is transferred from the casing to the environment; This matrix describes the heat transfer characteristics inside and outside the supercapacitor; Q represents the heat generation rate, which indicates the heat generated inside the supercapacitor, usually generated by current passing through the internal resistance; This indicates the effect of ambient temperature on core temperature. This vector describes the heat generation and heat exchange with the environment inside the supercapacitor; the equation describes the heat transfer and temperature change process of the supercapacitor under given parameters. By solving this equation, the temperature changes of the core and shell over time can be obtained. Through the above thermodynamic model governing equation, the heat transfer and temperature change mechanism inside the supercapacitor can be understood in depth. This model provides a theoretical basis for assessing the impact of changes in thermal characteristics on health status.

[0126] In an exemplary embodiment, the input vector of the thermodynamic model is:

[0127]

[0128] in, Indicates time The current input at any given time is measured in amperes. Current is the main source of heat generated inside the supercapacitor. Changes in current affect the heat generation rate Q, which reflects the current load of the supercapacitor under different operating conditions. Indicates time The ambient temperature at any given time is expressed in Kelvin. Ambient temperature is an important factor affecting the heat dissipation and temperature changes of supercapacitors, and it reflects the influence of the external environment on the thermal characteristics of supercapacitors. Indicates time The surface temperature at time t, measured in Kelvin, is used in the model to estimate the core temperature. Key parameters for calculating thermal resistance can typically be obtained through infrared thermography or embedded temperature sensors, reflecting changes in the surface temperature of the supercapacitor. By monitoring current, ambient temperature, and surface temperature in real time, the operating status and environmental conditions of the supercapacitor can be dynamically understood. These parameters serve as inputs to the thermodynamic model for calculating the core temperature. Thermal resistance and heat capacity Key parameters, etc.

[0129] In an exemplary embodiment, the output vector of the thermodynamic model is:

[0130]

[0131] in, This represents the health state of a thermodynamic model based on changes in thermal properties, reflecting the impact of these changes on the health state of the supercapacitor. It can be determined based on the thermal property variation factor. The calculation formula is as follows:

[0132]

[0133] Thermal property change factor This indicates the degree of change in current thermal properties relative to initial thermal properties. The specific calculation formula is as follows:

[0134]

[0135] in, This indicates the initial core-shell thermal resistance. This indicates the initial core heat capacity. This indicates the current core-to-shell thermal resistance. Indicates the current core heat capacity; This is a comprehensive representation of the current thermal characteristics parameters, reflecting the thermal characteristics and heat storage capacity of the core under the current state. Larger values ​​indicate that the core has higher thermal resistance and heat capacity, which may mean lower heat transfer efficiency or stronger heat storage capacity. This comprehensive representation of initial thermal characteristic parameters serves as a benchmark for comparison, reflecting the thermal characteristics and heat storage capacity of supercapacitors in their initial state. This represents the ratio of the current thermal characteristic parameters to the initial thermal characteristic parameters, reflecting the relative change in thermal characteristics; for example, when... When this occurs, it indicates a deterioration in thermal properties, such as an increase in thermal resistance or a decrease in heat capacity; when When, it indicates that the thermal properties have not changed; when When this occurs, it indicates that the thermal properties have improved, such as a decrease in thermal resistance or an increase in heat capacity.

[0136] Will Substitution The calculation formula can be used to obtain the health state of the thermodynamic model; where This is a proportionality coefficient, for example, 0.95, used to adjust the sensitivity of the SOH calculation; The critical failure threshold representing the change in thermal properties, for example 1.8, is used to represent the maximum allowable value for the change in thermal properties. It represents the ratio of the current change in thermal properties to the allowable range of change, quantifying the relative degree of change in thermal properties; This indicates that the ratio of changes in thermal properties is exponentially calculated to amplify the effect of larger changes; This indicates the negative impact of changes in thermal properties on SOH. This means subtracting the effects of changes in thermal properties from the state of equilibrium (SOH) to obtain the final thermodynamic model health state. For example, when... When the thermal properties remain unchanged, the body is in optimal health. When the temperature reaches a critical value, it indicates that the thermal properties have changed beyond the critical value, and the health status is at its worst.

[0137] This indicates the current core-shell thermal resistance, which reflects the thermal conduction characteristics between the core and the shell. The greater the thermal resistance, the more difficult it is to transfer heat. This indicates the current core heat capacity, which reflects the core's ability to store heat; the higher the heat capacity, the stronger the heat storage capacity. The impact of changes in the thermal properties of supercapacitors on their health status can be assessed by... and This allows for a deeper understanding of the thermal resistance and thermal capacity characteristics of supercapacitors, providing a basis for thermal management strategies.

[0138] The capacity-type health state, the internal resistance-type health state, and the thermodynamic model health state are weighted and fused. Through weighted fusion, a comprehensive health state index can be obtained to assess the overall health status of a single unit. Specifically, firstly, it is necessary to define the weight coefficients for each health state index, for example... The weights representing the volumetric health status. The weight representing the internal resistance type of health status. The weights representing the health state of the thermodynamic model should satisfy the following constraints:

[0139]

[0140] The selection of weighting coefficients should be adjusted according to the specific application scenario and the characteristics of the supercapacitor, for example, in

[0141] In high-temperature applications, the efficiency can be appropriately increased. The value can be adjusted to more sensitively reflect the impact of changes in thermal characteristics on health status; in high-power applications, the value can be appropriately increased. The value of is used to more accurately assess the impact of internal resistance growth on performance.

[0142] Preferably, in the weighted allocation fusion, the capacity-type health state has the highest weight; capacity decay is one of the main factors affecting supercapacitor performance, therefore it is given the highest weight, for example... The value is 0.4. The value is 0.3. The value is 0.3.

[0143] Based on the aforementioned weighting coefficients, the formula for calculating overall health status is as follows:

[0144]

[0145] in, This indicates the impact of volume-based health status on overall health status. This indicates the impact of internally resisted health status on overall health status. This method represents the impact of the thermodynamic model's health status on the overall health status. Through weighted allocation and fusion, health status indicators under different physical degradation mechanisms can be comprehensively evaluated, resulting in a more comprehensive and accurate assessment of the health status of individual units. This method not only considers changes in capacity, internal resistance, and thermal characteristics, but also ensures the reliability and effectiveness of the assessment results through reasonable weight allocation, providing an important basis for the intelligent management and maintenance of supercapacitor energy storage systems.

[0146] In an exemplary embodiment, the step of fusing the individual health status into a module health status using the dynamic weights includes:

[0147] Calculate the weighted average of the individual unit health status assessment. The weighted average is calculated by weighting each data point according to its importance (weight). It comprehensively considers the health status of different individual units and their impact on the overall module health status. Unlike a simple arithmetic mean, the weighted average reflects the different contributions of each individual unit to the module's health status by assigning different weights to each unit. The formula is as follows:

[0148]

[0149] in, No. The health status of an individual unit can be assessed based on factors such as capacity decay, internal resistance increase, and changes in thermal properties. This represents the dynamic weighting coefficient for each individual entity, which can be calculated using the formula provided in the above embodiments, or... It can also be calculated using the following formula:

[0150]

[0151] in, This represents the steepness factor, with a default value typically of 5.0. It controls the concentration of the weight distribution. The larger the value, the more concentrated the weight distribution, and the greater the influence of individual units with large differences on the weight. Indicates the first The capacity variation coefficient of each individual unit is used to reflect the consistency between individual units; This represents the internal resistance difference coefficient, with a typical value of 0.2 / mΩ, used to reflect the impact of internal resistance difference on the weight. Indicates the first The difference between the internal resistance of an individual unit and the average internal resistance of the module; This represents the temperature difference coefficient, with a typical value of 0.1 / ℃, used to reflect the impact of temperature differences on the weights. Indicates the first The difference between the temperature of an individual unit and the average temperature of the module; This represents the average internal resistance of the module; This represents the average temperature of the module. The formula uses an exponential function to highlight the impact of differences between individual units on the weighting. The greater the difference in capacity variation, internal resistance, and temperature of an individual unit, the smaller its weight.

[0152] This represents the weighted health status of each individual unit. It indicates the overall health status of the module and reflects the overall health condition of the module.

[0153] Furthermore, a bottleneck compensation coefficient, which is positively correlated with the health status dispersion, is introduced. To account for the differences in health status among individual units, a bottleneck compensation coefficient is needed to reflect the impact of the unit with the worst health status in the module on the overall health status. The bottleneck compensation coefficient can be calculated based on the health status dispersion, and the specific calculation formula is as follows:

[0154]

[0155] in, Indicates the short-board compensation coefficient; It represents the standard deviation of the module's health status, reflecting the degree of dispersion of the health status of individual units within the module; This represents the average health status of the module; when the health status of individual units within the module varies significantly, the short-board compensation coefficient is used. Smaller values ​​have a greater negative impact on the module's health status; when the health status consistency of individual units within the module is good, A larger coefficient has a smaller negative impact on the module's health status. Specifically, this coefficient is positively correlated with the health status dispersion, meaning that the greater the difference in health status between individual units, the smaller the shortcoming compensation coefficient, and the greater the negative impact on the module's health status.

[0156] Finally, the module health status is determined by combining the weighted average and the worst individual evaluation value. The module health status can be calculated using the following formula:

[0157]

[0158] in, This represents the weighted average health status, reflecting the overall health status of the module. This represents a bottleneck compensation item, used to reduce the module's health status, bringing it closer to the health status of the worst-performing individual unit. This represents the health status of the worst-performing individual unit in the module. The formula, through a combination of weighted average health status and a bottleneck compensation term, considers both the overall health status of the module and highlights the impact of the worst-performing individual unit on the overall module health status. The bottleneck compensation coefficient... The introduction of this feature makes the module health status closer to the health status of the worst individual unit, thus more accurately reflecting the differences in health status among the individual units in the module.

[0159] The above method can be used to merge the health status assessment results of individual units to obtain more accurate module health status assessment results. This method not only considers the individual differences of individual units, but also highlights the impact of the individual unit with the worst health status in the module on the overall health status through dynamic weights and the short-board compensation coefficient, providing an important guarantee for the reliable operation of the supercapacitor energy storage system.

[0160] In an exemplary embodiment, the decision rule is implemented through a system comprising the following layers:

[0161] The input layer receives a multidimensional feature vector representing the state dispersion. The multidimensional feature vector received by the input layer contains input variables that characterize the current state of the energy storage system and its dispersion. These input variables may include: SOC dispersion, capacity decay rate, equalization current ratio, temperature gradient, and inter-cluster voltage deviation, etc. These variables together constitute a multidimensional feature space for evaluating the health status of the supercapacitor module and providing a basis for subsequent decision-making.

[0162] For example, the SOC dispersion is the standard deviation of the SOC of each individual unit within the module, reflecting the consistency of the state of charge among individual units, and its universe of discourse is... The voltage, current, and temperature data of each unit can be collected in real time through the BMS. Then, based on the collected data, the SOC of each unit is calculated using the open-circuit voltage method, ampere-hour integration method, or Kalman filter algorithm. Finally, the standard deviation of the SOC data of all units is calculated to obtain the SOC dispersion. The larger the SOC dispersion, the more serious the inconsistency between units, which may lead to overcharging or over-discharging of some units and affect the overall performance of the module.

[0163] The capacity decay rate is the average rate of capacity change over the most recent preset number of charge-discharge cycles, such as the last 10 cycles. It reflects the rate of capacity decay of a single cell, and its domain is [not specified]. It can be measured by linear regression of ampere-hour integral data. For example, record the capacity data of each charge-discharge cycle, select the capacity data of the most recent 10 cycles, and perform linear regression analysis on the capacity data and the number of cycles to obtain the capacity decay slope. The larger the capacity decay slope, the faster the capacity decay, which may lead to insufficient capacity of the energy storage system and affect its endurance.

[0164] The balancing current ratio refers to the ratio of the balancing current to the operating current, reflecting the strength of the current balancing strategy. Its domain is [not specified]. The proportion of balancing current can be estimated by monitoring the duty cycle of the balancing MOSFET. For example, by monitoring the duty cycle of the MOSFET in the balancing circuit, the magnitude of the balancing current can be estimated based on the duty cycle and the parameters of the balancing circuit. The balancing current can be compared with the operating current to obtain the proportion of balancing current. The larger the proportion of balancing current, the stronger the balancing strategy, which helps to reduce the SOC dispersion.

[0165] The temperature gradient is the difference between the highest and lowest temperatures within a module, reflecting the temperature differences between individual units. Its domain is [blank]. The temperature gradient can be calculated by using the temperature sensors within the module. For example, by collecting temperature data at various points using temperature sensors distributed within the module, the difference between the highest and lowest temperatures can be calculated to obtain the temperature gradient. The larger the temperature gradient, the more uneven the temperature distribution, which may lead to overheating of some individual units, affecting their performance and lifespan.

[0166] Inter-cluster voltage deviation is the difference between the current cluster voltage and the system average voltage, reflecting the consistency of voltage between different clusters. Its universe of discourse is... The voltage of each cluster bus can be collected by the main controller, and then the difference between the voltage of each cluster and the average voltage of the system can be calculated to obtain the voltage deviation between clusters. Excessive voltage deviation may lead to energy loss or equipment damage, affecting the efficiency and reliability of the energy storage system.

[0167] The decision layer applies a preset rule base for state correction reasoning. The decision layer is responsible for reasoning and analysis based on the data provided by the input layer and the preset rule base, generating correction factors and equilibrium strategy parameters for the overall health state. The decision layer employs fuzzy logic reasoning as the primary reasoning mechanism to handle the fuzziness and uncertainty in multi-dimensional feature vectors and generates reasonable output results based on the preset rule base. The detailed design of the fuzzy reasoning system mainly includes the definition of fuzzy sets of input / output variables, the core fuzzy rule base, and the defuzzification method.

[0168] Input variables are used to characterize the state and discreteness of the supercapacitor module. Each input variable is divided into multiple fuzzy sets to reflect different state levels. The following are some example fuzzy set definitions for input variables:

[0169] SOC discreteness fuzzy set:

[0170] low: Trigonometric membership function.

[0171] medium: Trapezoidal membership function.

[0172] high: Trapezoidal membership function.

[0173] Fuzzy set of capacity decay slope:

[0174] fast (rapid decay): Gaussian membership function.

[0175] slow (slow decay): Gaussian membership function.

[0176] stable (stable decay): Gaussian membership function.

[0177] Temperature gradient fuzzy set:

[0178] small (small temperature difference): Trapezoidal membership function.

[0179] moderate (moderate temperature difference): Trigonometric membership function.

[0180] large (large temperature difference): Trapezoidal membership function.

[0181] The output variables mainly include the SOH correction factor and the equalization weight coefficient. The SOH correction factor is a parameter used to adjust the health status assessment value of the supercapacitor module, while the equalization strategy parameter guides the equalization circuit in its equalization operation. Each output variable is divided into multiple fuzzy sets to reflect different adjustment directions and intensities. The following are the fuzzy set definitions for the example output variables:

[0182] SOH correction factor fuzzy set:

[0183] reduce (lower): Trapezoidal membership function.

[0184] hold (keep): Trigonometric membership function.

[0185] increase (raise): Trapezoidal membership function.

[0186] Fuzzy set of equilibrium weight coefficients:

[0187] low (reduces the priority of current balancing): Trigonometric membership function.

[0188] medium (maintains the default equilibrium strategy): Trigonometric membership function.

[0189] high (increases current balancing priority): Trapezoidal membership function.

[0190] The core fuzzy rule base is used to perform inference based on the multidimensional feature vectors of the input layer and to generate correction factors and equilibrium strategy parameters for the overall health status. The following is an example of the core fuzzy rule base:

[0191]

[0192] Defuzzification is the process of converting the fuzzy set results obtained from fuzzy inference into precise numerical values. Fuzzy inference systems process input variables through fuzzy logic to obtain the fuzzy set membership degree of output variables. However, in practical applications, precise numerical results are required. For example, the SOH correction factor requires a specific percentage adjustment value. Therefore, the fuzzy set results must be converted into precise numerical values. This process is called defuzzification.

[0193] Defuzzification can be achieved using the Center of Gravity (CoG) method, which calculates the centroid of the membership function of the fuzzy set as the result of defuzzification. This method is suitable for continuous membership functions. The specific calculation formula for the Center of Gravity method is as follows:

[0194]

[0195] in, This represents the precise value after defuzzification; Indicates the first Membership degree of a fuzzy set; Indicates the first The output universe values ​​corresponding to each fuzzy set; This represents the number of fuzzy sets. In practical applications, the defuzzification method can be optimized by assigning greater weights to certain specific fuzzy set output values. For example, a 1.2-fold weight can be applied to the "reduce" and "increase" outputs, thereby enhancing the sensitivity of SOH correction and making the system react more quickly to adverse factors. Additionally, the SOH correction amount can be forced to not exceed ±10% to avoid over-correction and prevent the system from making overly aggressive decisions in extreme cases. In practical applications, a suitable defuzzification method can be selected based on specific needs and system characteristics, and corresponding engineering optimizations can be performed.

[0196] The output layer generates the cluster health status correction factor and equilibrium strategy parameters. The output layer is the decision output part of the entire evaluation system. Its main function is to generate the cluster health status correction factor and corresponding equilibrium strategy parameters based on the reasoning results of the decision layer.

[0197] In an exemplary embodiment, the method for assessing the overall health status of a supercapacitor energy storage system further includes:

[0198] Dynamic compensation for the health status of individual cells is performed based on ambient temperature and operating current. The performance of supercapacitors is affected by various factors, with ambient temperature and operating current being two key factors. Ambient temperature generally affects the internal chemical reaction rate, internal resistance, and capacitance of the supercapacitor; operating current generally affects the polarization effect, heat generation, and energy efficiency. Traditional health status assessment methods are usually based on constant reference conditions, ignoring these dynamic changes, leading to inaccurate assessment results. Therefore, introducing a dynamic compensation mechanism can more realistically reflect the health status of supercapacitors under different operating conditions.

[0199] Specifically, the core idea of ​​the dynamic compensation method is to dynamically adjust the original SOH assessment value based on changes in ambient temperature and operating current; its basic formula is as follows:

[0200]

[0201] in, Indicates the health status after compensation; This represents the original health status assessment value; Indicates the temperature compensation coefficient; Indicates the current compensation coefficient; Indicates the current ambient temperature; Indicates reference temperature; Indicates the current operating current; Indicates the reference current; This represents the temperature compensation term, reflecting the impact of temperature changes on the performance of the supercapacitor. This represents the current compensation term, reflecting the impact of current changes on the performance of the supercapacitor.

[0202] Temperature compensation coefficient This reflects the sensitivity of supercapacitor performance to temperature, and is essentially the activation energy. The macroscopic manifestation, The specific calculation formula is as follows:

[0203]

[0204] in, The activation energy of an electrochemical reaction is expressed in eV, for example, in NMC (nickel-cobalt-manganese oxide) materials. LFP (lithium iron phosphate) materials ; Represents Boltzmann's constant. Reference temperature.

[0205] Temperature compensation coefficient The calibration method was derived using a constant current cycling experiment. For example, in... Select at least 5 temperature points within the range, then fix the SOC and current for 100 cycles. Finally, fit the relationship between capacity decay rate and temperature, as shown in the following formula:

[0206]

[0207] Temperature compensation coefficient Typical values ​​for NMC532 material include: LFP material .

[0208] Current compensation coefficient The sensitivity of supercapacitor performance to the influence of current is essentially a macroscopic manifestation of the relationship between polarization voltage and current. Under low current conditions, the Butler-Volmer equation describing the relationship between current and overpotential (polarization voltage) can be approximated as:

[0209]

[0210] Performing a Taylor expansion on the above formula yields a linear approximation formula:

[0211]

[0212] Current compensation coefficient The specific calculation formula is as follows:

[0213]

[0214] in, Represents the gas constant; Indicates reference temperature; This represents the transmission coefficient, which is usually taken as 0.5; Indicates the number of electrons in a reaction, such as in a lithium-ion battery. ; This represents the Faraday constant, 96485 C / mol; Indicates the reference current.

[0215] Current compensation coefficient The calibration method can employ a multi-current step test, for example, in Tests were conducted at the specified temperature, and the following conditions were applied. Current pulse, measuring instantaneous voltage drop , fitting Assuming ,but:

[0216]

[0217] The above dynamic compensation formula can be used to dynamically adjust the health status of supercapacitors according to changes in ambient temperature and operating current. This method is based on the multi-physics coupling effect of electrochemical systems and can more accurately reflect the performance changes of supercapacitors under different operating conditions, providing a reliable basis for intelligent management and optimized control of energy storage systems.

[0218] In an exemplary embodiment, the step of establishing a bidirectional feedback mechanism between health state and state of charge includes:

[0219] Based on the parameters obtained from the health status assessment, the open-circuit voltage mapping relationship and polarization voltage calculation parameters in the state-of-charge estimation are corrected; as the battery ages... The curve may drift and needs to be corrected based on the health status assessment results. The correction formula for the state of charge estimation is as follows:

[0220]

[0221] in, This indicates the corrected SOC value; This represents the state-of-charge estimate based on the extended Kalman filter (EKF). express The slope of the curve reflects the impact of changes in state of charge (SOC) on voltage. As battery usage time increases, battery capacity gradually decreases, and internal resistance increases, leading to greater errors in SOC estimation. By introducing a health state factor, this aging effect can be effectively compensated for, improving the accuracy of SOC estimation. Furthermore, under battery aging conditions, traditional SOC estimation methods may exhibit significant errors. This formula improves the accuracy of SOC estimation by considering the influence of health state.

[0222] Based on the voltage change characteristics and current time-series data obtained during the state of charge (SCC) estimation process, the health status assessment parameters are updated. By utilizing this feedback information to update the SCC parameters, the accuracy and real-time performance of SCC assessment can be improved. The specific formula for SCC update is as follows:

[0223]

[0224] in, It represents the amount of change in the state of charge, and the magnitude of the change in the state of health. This represents the mapping relationship between changes in health status during the state of charge estimation process; It represents voltage time-series data, reflecting the voltage changes of the battery at different points in time; It represents current time-series data, reflecting the changes in battery current at different points in time; This represents the derivative of the charge / discharge terminal voltage with respect to the state of charge (SOC), i.e., the terminal voltage slope, which is associated with the degradation of the active material.

[0225] The above method enables joint estimation and collaborative optimization of state of charge and health status. This method closely integrates the estimation processes of state of charge and health status through a two-way feedback mechanism, thereby improving the accuracy of state of health and state of charge estimation and providing a reliable basis for intelligent management and optimized control of supercapacitor energy storage systems.

[0226] Furthermore, refer to Figure 2 This disclosure also provides a cluster health status assessment system for a supercapacitor energy storage system, comprising:

[0227] The first execution unit 100 is configured to collect electrical and temperature parameters of each individual unit within the energy storage cluster in real time.

[0228] The second execution unit 200 is configured to generate dynamic weight coefficients based on the performance differences between units;

[0229] The third execution unit 300 is configured to perform a single-unit health status assessment based on multiple physical degradation mechanisms;

[0230] The fourth execution unit 400 is configured to merge the individual health status into the module health status based on the dynamic weight coefficient;

[0231] The fifth execution unit 500 is configured to generate cluster health status based on the state dispersion characteristics between modules through decision rules;

[0232] The sixth execution unit 600 is configured to establish a two-way feedback mechanism between the health state and the charged state.

[0233] Any aspects of this invention not described in detail are well-known to those skilled in the art.

[0234] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for assessing the overall health status of a supercapacitor energy storage system, characterized in that, include: Real-time acquisition of electrical and temperature parameters of each individual unit within the energy storage cluster; Dynamic weighting coefficients are generated based on performance differences between individual units; Perform individual health status assessments based on multiple physical degradation mechanisms; Based on the dynamic weighting coefficients, the individual health status is merged into the module health status; Based on the state dispersion characteristics between modules, clustered health states are generated through decision rules. Establish a two-way feedback mechanism between health status and state of charge.

2. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 1, characterized in that, The step of generating dynamic weighting coefficients based on inter-unit performance differences includes: Calculate the first difference index that reflects the characteristics of capacity distribution; Calculate the second difference index that reflects the internal resistance distribution characteristics; Calculate the third difference index that reflects the temperature uniformity characteristics; The first difference index, the second difference index, and the third difference index are combined by weighting to generate the dynamic weight coefficient of each individual unit.

3. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 2, characterized in that, The first difference index is quantified by the ratio of the standard deviation to the mean of the volume data; The second difference index is quantified by the ratio of the standard deviation to the mean of the internal resistance data; The third difference index is quantified by dividing the temperature deviation between the unit temperature and the preset temperature by a dynamic temperature threshold, which is the smaller value between the rated temperature margin and the ambient temperature statistical threshold.

4. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 1, characterized in that, The steps for performing an individual health status assessment based on multiple physical degradation mechanisms include: Assess capacity-related health status associated with capacity decay; Assess internal resistance-related health conditions associated with increased internal resistance. Assess the health status of thermodynamic models related to changes in thermal properties; The capacity-type health state, the internal resistance-type health state, and the thermodynamic model health state are weighted and fused.

5. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 4, characterized in that, In the weighted allocation fusion, the capacity-type health status has the highest weight.

6. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 1, characterized in that, The step of using the dynamic weights to fuse individual health states into module health states includes: Calculate the weighted average of the individual health status assessments; Introduce a bottleneck compensation coefficient that is positively correlated with the dispersion of health status; The module health status is determined by combining the weighted average value and the worst individual evaluation value.

7. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 1, characterized in that, The decision rules are implemented through a system comprising the following layers: The input layer receives a multidimensional feature vector representing the discreteness of the state; The output layer generates cluster health status correction factors and balancing strategy parameters; The decision-making layer applies a preset rule base to perform state correction reasoning.

8. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 1, characterized in that, Also includes: The health status of individual cells is dynamically compensated based on ambient temperature and operating current.

9. The method for assessing the overall health status of a supercapacitor energy storage system according to claim 1, characterized in that, The steps for establishing a two-way feedback mechanism between health state and state of charge include: Based on the parameters obtained from the health status assessment, the open-circuit voltage mapping relationship and polarization voltage calculation parameters in the state of charge estimation are corrected. The health status assessment parameters are updated based on the voltage change characteristics and current time series data obtained during the state of charge estimation process.

10. A collaborative assessment system for the health status of a supercapacitor energy storage system, characterized in that, include: The first execution unit is configured to collect electrical and temperature parameters of each individual unit within the energy storage cluster in real time. The second execution unit is configured to generate dynamic weight coefficients based on the performance differences between individual units; The third execution unit is configured to perform individual health status assessments based on multiple physical degradation mechanisms; The fourth execution unit is configured to merge the individual health status into the module health status based on the dynamic weighting coefficient; The fifth execution unit is configured to generate cluster health status based on the state dispersion characteristics between modules through decision rules; The sixth execution unit is configured to establish a two-way feedback mechanism between the health state and the charged state.

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