Energy storage power station battery aging perception adaptive charging and discharging control method and system

Abstract

The application discloses a battery aging perception adaptive charging and discharging control method and system for energy storage power stations, and relates to the technical field of electric energy storage.The battery aging perception adaptive charging and discharging control method and system for energy storage power stations comprises the following steps: S1, collecting battery working condition physical data, and performing pretreatment on the battery working condition physical data; S2, obtaining a health attenuation factor, evaluating the health state of the battery, determining the health grade of the battery based on the evaluation result, and generating a health grade label; S3, evaluating the charging and discharging regulation ability of each battery, calculating a target charging and discharging current, generating a current instruction set, and implementing a current regulation task; and S4, evaluating the matching degree of the current regulation strategy and the battery state after each round of current regulation, and dynamically adjusting the target current.The problems that the traditional energy storage power station lacks real-time identification and differentiated control mechanism for the aging state of the battery, and is prone to cause system capacity attenuation and thermal safety hazards are solved.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, specifically to an adaptive charge and discharge control method and system for battery aging sensing in energy storage power stations. Background Technology

[0002] With the widespread application of energy storage systems in power regulation, new energy grid connection, grid peak shaving, and backup power, the charging and discharging control strategy of batteries in energy storage power stations has become a key factor affecting system safety, efficiency, and lifespan. In existing energy storage systems, battery charging and discharging control technology is constantly evolving, and various circuit structures and equipment solutions have been proposed to improve efficiency, safety, and system stability.

[0003] For example, the invention disclosed in CN103269099A relates to a battery charging and discharging circuit, which includes a power supply, a charging branch for controlling the charging operation and the magnitude of the charging current, a discharging branch for controlling the discharging operation and the magnitude of the discharging current, and a protection branch for protecting the battery charging and discharging circuit during normal charging and discharging operations. The battery charging and discharging circuit of this invention avoids the use of energy-consuming components, improves the overall efficiency of the battery charging and discharging circuit, and is less prone to heat dissipation problems. It allows multiple charging and discharging circuits to be connected in parallel to simultaneously charge and discharge large-capacity batteries. Furthermore, a short circuit or damage to any single component will not cause the entire charging and discharging circuit to fail; the entire battery charging and discharging circuit is characterized by high efficiency, safety, parallel capability, and good heat dissipation.

[0004] For example, the invention disclosed in CN110224468A provides a battery charging and discharging device, relating to the field of battery manufacturing technology. The battery charging and discharging device provided by this invention is used for mutual charging and discharging of a first battery module and a second battery module. It includes a unidirectional DC / DC converter and a switch for switching the voltage conversion direction of the unidirectional DC / DC converter. Both the first and second battery modules are connected to the unidirectional DC / DC converter. The device also includes a power replenishment device for supplementing power to either the first or second battery module. This battery charging and discharging device is used for charging and discharging between battery modules, maximizing the utilization of the discharged electricity, thus achieving high energy efficiency. The switch for switching the voltage conversion direction of the unidirectional DC / DC converter results in a simple structure and low cost.

[0005] Although the above-mentioned technical solutions have achieved certain results in terms of structural efficiency or energy utilization, they generally have the following shortcomings: First, the current solutions mainly focus on circuit topology optimization and the realization of energy transfer between modules, lacking a real-time sensing mechanism for the actual health status of individual battery cells, and cannot perform personalized control based on aging differences; Second, the system usually adopts a uniform current distribution strategy, ignoring the differences between different batteries in terms of capacity retention, internal resistance degradation, thermal stability and cycle life, which can easily lead to overcharging, over-discharging or overheating of some batteries, accelerating local degradation and increasing the risk of thermal runaway.

[0006] Therefore, in order to address the above issues, there is an urgent need for adaptive charge and discharge control methods and systems for battery aging sensing in energy storage power stations. Summary of the Invention

[0007] Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides an adaptive charge and discharge control method and system for battery aging perception in energy storage power stations. This solves the problem that traditional energy storage power stations lack real-time identification and differentiated control mechanisms for battery aging, which can easily lead to system capacity decay and thermal safety hazards.

[0009] Technical solution

[0010] To achieve the above objectives, this invention provides the following technical solution: an adaptive charge-discharge control method for battery aging perception in energy storage power stations, comprising: S1, collecting battery operating condition physical data of each battery in the energy storage power station, and performing time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization processing on the battery operating condition physical data; S2, obtaining a health degradation factor based on the preprocessed battery operating condition physical data, assessing the battery's health status, determining the battery health level based on the assessment results, and generating a health level label; S3, extracting battery health assessment values, evaluating the charge-discharge regulation capability of each battery, normalizing it into current allocation weights, calculating the target charge-discharge current based on the total power demand, generating a current instruction set, and implementing the current regulation task; S4, assigning health level values ​​to the batteries based on the health level labels, assessing the matching degree between the current regulation strategy and the battery status after each round of current regulation, dynamically adjusting the target current; synchronously recording regulation data to construct a strategy database, driving the update and optimization of the correction parameter table.

[0011] Further, the specific steps for collecting battery operating condition physical data of each battery in the energy storage power station and performing time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization on the battery operating condition physical data are as follows: Collect battery operating condition physical data of each battery in the energy storage power station. This data includes real-time current, real-time battery capacity, real-time battery internal resistance, initial battery capacity, initial battery internal resistance, battery temperature, battery cycle count, protection trigger count, maximum instantaneous current, and maximum operating temperature. Verify the validity of the battery operating condition physical data using an anomaly identification algorithm, removing missing values, incomplete records, and data that do not meet physical boundary conditions. Suppress noise in the battery operating condition physical data using a multi-level filtering algorithm, employing a combined filtering mechanism to reduce short-cycle fluctuations caused by sensor drift, electromagnetic interference, and sampling jitter. Standardize the scale of the battery operating condition physical data using a distribution standardization algorithm to eliminate dimensional differences between data from different sources and dimensions. Map the battery operating condition physical data to a unified interval using a linear normalization algorithm.

[0012] Furthermore, based on the preprocessed battery operating condition physical data, the specific steps for assessing the battery's health status are as follows: Based on the preprocessed battery operating condition physical data of each cell, an exponentially weighted sliding window algorithm is used to construct time-series evolution curves for capacity, internal resistance, temperature, and current data. The slope at the end of each time-series evolution curve is extracted and linearly weighted to obtain the health degradation factor. Further, the maximum allowable operating current and thermal runaway critical temperature are combined to construct a battery health input dataset. Based on the battery health input dataset, the battery health status is assessed. The battery's health status is assessed by: dividing the real-time battery capacity by the initial battery capacity to obtain the capacity retention ratio; dividing the initial battery internal resistance by the real-time battery internal resistance to obtain the internal resistance degradation ratio; summing the battery cycle count, the ratio of maximum instantaneous current to maximum allowable operating current, the ratio of maximum operating temperature to thermal runaway critical temperature, and the number of protection triggers, multiplying this sum by the health degradation factor, and using this as an exponential term in the negative exponent calculation of the natural exponential function to obtain the degradation function term; and then multiplying the capacity retention ratio, the internal resistance degradation ratio, and the degradation function term sequentially, taking the fourth root of the result to obtain the battery health assessment value.

[0013] Furthermore, the specific steps for determining the battery health level and generating a health level label based on the assessment results are as follows: Real-time comparison of the battery health assessment value and multi-level health thresholds to determine the battery health level: When the battery health assessment value is less than or equal to the Level 1 health threshold, the battery is determined to be in a severely aged state; when the battery health assessment value is greater than the Level 1 health threshold but less than the Level 2 health threshold, the battery is determined to be in a moderately aged state; when the battery health assessment value is greater than the Level 2 health threshold but less than the Level 3 health threshold, the battery is determined to be in a slightly aged state; when the battery health assessment value is greater than or equal to the Level 3 health threshold, the battery is determined to be in a healthy state; based on the health level determination result, the label encoding rules are invoked to generate a corresponding health level label for the current battery and represent it in a structured identifier format.

[0014] Further, the specific steps for extracting battery health assessment values ​​and evaluating the charge / discharge regulation capability of each battery are as follows: Extract the battery health assessment values ​​of all batteries and evaluate the charge / discharge regulation capability of each battery: Divide the health assessment value of the j-th battery by the sum of the battery health assessment values ​​of all batteries to obtain the health status weighting factor; Subtract the ratio of the battery temperature of the j-th battery to the thermal runaway critical temperature from 1 to obtain the temperature safety correction term; Subtract the ratio of the real-time battery internal resistance of the j-th battery to the initial battery internal resistance from 1 to obtain the internal resistance degradation correction term; Subtract the ratio of the number of protection triggers of the j-th battery to the maximum protection trigger tolerance number from 1 to obtain the protection stability correction term; Multiply the health status weighting factor, temperature safety correction term, internal resistance degradation correction term, and protection stability correction term sequentially to obtain the battery's charge / discharge regulation assessment value.

[0015] Furthermore, the current allocation weights are normalized, and the target charging and discharging currents are calculated based on the total power demand to generate a current command set. The specific steps for implementing the current regulation task are as follows: The charging and discharging regulation evaluation values ​​of all batteries are normalized to obtain the allocation weight of each battery; the controller calculates the target charging current and target discharging current that each battery should bear based on the allocation weight of each battery and the total charging and discharging power demand of the current energy storage station, and constructs a current command set; the current command set is sent to the DC-DC converter circuit connected to the corresponding battery through the high-speed communication bus to perform boost and buck regulation tasks; in charging mode, the boosted current is output to the power conversion circuit, the DC-to-AC conversion is completed by the converter circuit, and the current is introduced into the energy storage battery; in discharging mode, the bucked current is converted by the converter circuit and output to the grid.

[0016] Furthermore, based on health level labels, assign health level values ​​to batteries, and evaluate the matching degree between the current regulation strategy and the battery state after each round of current regulation as follows: Extract the health level labels of each battery and assign a fixed health level value to each battery: health status corresponds to health level value 4, mild aging corresponds to health level value 3, moderate aging corresponds to health level value 2, and severe aging corresponds to health level value 1; after each current regulation task is executed, measure the matching degree between the current regulation strategy and the actual operating state and health status of the battery; divide the difference between the target current and the real-time current by the target current, take the absolute value and subtract one to obtain the current execution error suppression term; divide the battery temperature by the thermal runaway critical temperature and subtract one to obtain the temperature safety control term; divide the current internal resistance of the battery by the initial internal resistance and subtract one to obtain the internal resistance degradation impact term; divide the battery health assessment value by the health level value to obtain the health level adaptation correction term; multiply the current execution error suppression term, temperature safety control term, internal resistance degradation impact term, and health level adaptation correction term in sequence to obtain the regulation execution adaptation assessment value.

[0017] Furthermore, the specific steps for dynamically adjusting the target current are as follows: Based on the control execution adaptation evaluation value of each battery and the corresponding health level value, the target current is dynamically adjusted; when the control execution adaptation evaluation value is less than the adaptation threshold, it is determined that the current current regulation strategy is not effectively matched with the battery state, triggering the target current reduction adjustment mechanism: based on the battery health level value, the corresponding current correction parameter table is called, the current correction factor matching the current level is retrieved, the current target current is corrected according to the current correction factor, a new current instruction set is constructed, and the second round of current regulation task is triggered; when the control execution adaptation evaluation value is greater than or equal to the adaptation threshold, it is determined that the current current regulation strategy matches the battery state, the existing target current remains unchanged, and the execution process of the current current instruction set continues.

[0018] Furthermore, the specific steps for synchronously recording adjustment data to construct a strategy database and driving the update and optimization of the correction parameter table are as follows: After each round of current adjustment task, record the adjustment execution adaptation evaluation value, target current, real-time current, battery health evaluation value, health level value, and voltage adjustment response time for each battery in the current round to construct a battery operation strategy database; based on the battery operation strategy database, calculate the average adjustment execution adaptation evaluation value and target current deviation rate corresponding to each health level; when the target current deviation rate of any health level exceeds the current deviation threshold, it is determined that the current current correction parameter table does not match the actual battery state, triggering the correction parameter table optimization mechanism: the current correction factor is optimized using the weighted historical mean and range analysis method, and the updated current correction parameter table is generated and applied.

[0019] The second aspect of this invention provides an adaptive charge-discharge control system for battery aging perception in energy storage power stations, comprising: a battery operating condition data acquisition and preprocessing module, a battery health assessment and level calibration module, a battery adaptive current distribution control module, and a regulation strategy adaptation and parameter optimization module. The battery operating condition data acquisition and preprocessing module is used to acquire battery operating condition physical data of each battery in the energy storage power station and perform time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization processing on the battery operating condition physical data. The battery health assessment and level calibration module is used to obtain a health degradation factor based on the preprocessed battery operating condition physical data and evaluate the battery. The system assesses the battery's health status, determines its health level based on the evaluation results, and generates a health level label. The battery adaptive current distribution control module extracts battery health assessment values, evaluates the charge / discharge regulation capabilities of each cell, normalizes them into current distribution weights, calculates the target charge / discharge current based on the total power demand, generates a current instruction set, and implements the current regulation task. The regulation strategy adaptation and parameter optimization module assigns health level values ​​to the battery based on the health level label, evaluates the matching degree between the current regulation strategy and the battery state after each round of current regulation, and dynamically adjusts the target current. It also synchronously records regulation data to build a strategy database, driving the update and optimization of the correction parameter table.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) This adaptive charge-discharge control method and system for battery aging perception in energy storage power stations constructs a battery health degradation model, extracts health degradation factors and calculates battery health assessment values, and combines multi-level health thresholds to determine the battery aging level in real time. Each battery generates a structured health level label, enabling the power station management system to implement refined and differentiated control strategies based on the actual aging degree of the battery, avoiding a one-size-fits-all uniform control, significantly extending the battery service life and reducing maintenance costs.

[0023] (2) This adaptive charge-discharge control method and system for battery aging perception in energy storage power stations integrates battery health assessment values ​​with factors such as temperature, internal resistance, and protection status into a comprehensive model, quantifying them into charge-discharge regulation capability indicators. Furthermore, a normalization process is used to generate current allocation weights, enabling each battery cell to dynamically undertake reasonable charge-discharge tasks based on its own state. This mechanism avoids applying large currents to aged or degraded batteries, improving the energy utilization rate of healthy batteries and preventing weak cells from failing or experiencing thermal runaway due to overload, thereby achieving more efficient and safer charge-discharge management.

[0024] (3) This adaptive charge-discharge control method and system for battery aging perception in energy storage power stations introduces the concept of regulation execution adaptation evaluation value to measure the degree of matching between the target current command and the actual operating state of the battery. If insufficient adaptation between the strategy and the battery state is detected, the target current is dynamically adjusted, and the control strategy is quickly corrected by calling the correction parameter table corresponding to the health level, thus realizing a closed-loop feedback control mechanism. This effectively prevents unreasonable current regulation strategies from continuously acting on deteriorated batteries, reducing the probability of safety hazards such as local overheating of the cell, sudden capacity drop, and thermal runaway.

[0025] (4) This adaptive charge-discharge control method and system for battery aging perception in energy storage power stations records and aggregates the control data after each round of control execution into a strategy database, accumulates operational experience over a long period, and periodically evaluates the current deviation rate under each health level. When the deviation exceeds the tolerance threshold, the system automatically triggers a parameter optimization mechanism to iteratively update the current correction factor based on historical data. This self-learning and evolution mechanism enables the system to continuously optimize by becoming more accurate and stable with use, significantly improving its intelligence level and field adaptability. Attached Figure Description

[0026] Figure 1 Flowchart of an adaptive charge-discharge control method for battery aging sensing in energy storage power stations;

[0027] Figure 2 A structural diagram of an adaptive charge-discharge control system for battery aging sensing in energy storage power stations.

[0028] Figure 3 A weighted diagram showing the current distribution of each individual cell within the battery pack;

[0029] Figure 4 This is a schematic diagram of the dynamic current distribution structure of an energy storage system. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Please see Figures 1-4This invention provides a technical solution: an adaptive charge-discharge control method for battery aging perception in energy storage power stations, comprising: S1, collecting battery operating condition physical data of each battery in the energy storage power station, and performing time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization processing on the battery operating condition physical data; S2, obtaining a health degradation factor based on the preprocessed battery operating condition physical data, assessing the health status of the battery, determining the battery health level based on the assessment results, and generating a health level label; S3, extracting battery health assessment values, assessing the charge-discharge regulation capability of each battery, normalizing it into current allocation weights, calculating the target charge-discharge current based on the total power demand, generating a current instruction set, and implementing the current regulation task; S4, assigning health level values ​​to the batteries based on the health level labels, assessing the matching degree between the current regulation strategy and the battery status after each round of current regulation, dynamically adjusting the target current; synchronously recording regulation data to construct a strategy database, driving the update and optimization of the correction parameter table.

[0032] Specifically, the steps for collecting battery operating condition physical data of each battery cell in the energy storage power station and performing time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization on the battery operating condition physical data are as follows: Periodically collect battery operating condition physical data of each battery cell in the energy storage power station. This data includes real-time current, real-time battery capacity, real-time battery internal resistance, initial battery capacity, initial battery internal resistance, battery temperature, battery cycle count, protection trigger count, maximum instantaneous current, and maximum operating temperature. Verify the validity of the battery operating condition physical data using an anomaly identification algorithm, removing missing values, incomplete records, and data that does not meet physical boundary conditions. Missing values ​​refer to data items that were not collected within the sampling period; incomplete records refer to records with missing fields; and data that does not meet physical boundary conditions includes real-time current exceeding the cell's limit. Records are kept for maximum rated current, negative battery internal resistance, and battery temperature below the critical operating environment value. Noise suppression is achieved through a multi-level filtering algorithm on the battery operating condition physical data. A combined filtering mechanism is employed to reduce short-period fluctuations caused by sensor drift, electromagnetic interference, and sampling jitter. This combined filtering mechanism includes a cascaded implementation of weighted moving average filtering, median filtering, and wavelet threshold filtering. A distribution standardization algorithm is used to unify the scale of the battery operating condition physical data, eliminating dimensional differences between data from different sources and dimensions. Variables with inconsistent dimensions, such as real-time current, real-time battery capacity, and real-time battery internal resistance, are processed using mean-standard-deviation standardization. A linear normalization algorithm is used to map the battery operating condition physical data to a unified range, enabling weighted combination processing of different physical characteristics in subsequent calculations.

[0033] In this implementation scheme, by performing time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization on the battery operating condition physical data, the data quality and stability are significantly improved. This ensures that the subsequent health assessment, charge and discharge regulation, and current command generation processes are consistent, highly accurate, and robust. In turn, it improves the accuracy of battery state identification and the real-time matching of regulation strategies in the energy storage system, effectively supporting adaptive charge and discharge control under complex operating conditions.

[0034] Specifically, the steps for obtaining a health degradation factor based on preprocessed battery operating condition physical data and assessing the battery's health status are as follows: Based on the preprocessed battery operating condition physical data of each cell, specifically including real-time battery capacity, real-time battery internal resistance, battery temperature, and battery current, an exponentially weighted sliding window algorithm is used to construct time series evolution curves for capacity, internal resistance, temperature, and current data respectively. A sliding window weight decreasing strategy is used to weight the historical time series data to improve the time sensitivity of feature extraction, and the slope of the last interval of each time series evolution curve is extracted. A weighted average method is used for linear weighting to obtain a health degradation factor reflecting the battery performance evolution trend. Furthermore, the maximum allowable operating current and thermal runaway critical temperature are introduced as thermoelectric safety boundary indicators, and together with the real-time battery operating condition physical data and the health degradation factor, a battery health input dataset is constructed. Based on the battery health input dataset, a rule-based mapping algorithm is used. The battery health status is assessed by dividing the real-time battery capacity by the initial battery capacity to calculate the capacity retention ratio, which characterizes the degree of degradation in battery capacity. The internal resistance degradation ratio is calculated by dividing the initial internal resistance by the real-time internal resistance, which characterizes the trend of internal impedance changes. Then, the battery cycle count, the ratio of maximum instantaneous current to maximum allowable operating current, the ratio of maximum operating temperature to thermal runaway critical temperature, and the number of protection triggers are weighted and summed to comprehensively characterize the load intensity and thermal risk level of the battery during operation. The result is multiplied by a health degradation factor as an exponential term, input into a natural exponential function with a negative exponent, and a degradation function term is constructed to enhance the sensitivity of abnormal battery degradation to health status. Finally, the capacity retention ratio, internal resistance degradation ratio, and degradation function term are multiplied sequentially to obtain a comprehensive health degradation value. The fourth root of the comprehensive health degradation value is then calculated to obtain the battery health assessment value, which supports subsequent current regulation weight allocation and state classification determination.

[0035] The specific formula for calculating the battery health assessment value is as follows:

[0036] ;

[0037] In the formula, S represents the battery health assessment value. Indicates real-time battery capacity. This indicates the real-time internal resistance of the battery. Indicates the initial battery capacity. This indicates the initial internal resistance of the battery. Indicates the number of battery cycles. Indicates the maximum instantaneous current. Indicates the maximum permissible operating current. Indicates the maximum operating temperature. denoted by , where P represents the critical temperature for thermal runaway, P represents the number of protection triggers, and k represents the health decay factor.

[0038] In this implementation scheme, an exponentially weighted sliding window algorithm is introduced to construct time-series evolution curves of capacity, internal resistance, temperature, and current data, and the terminal slope is extracted to construct a health degradation factor, enhancing the sensitivity to battery performance evolution trends. By fusing the maximum allowable operating current and the critical temperature for thermal runaway to construct a battery health input dataset, the responsiveness of health status assessment to electrical and thermal safety boundaries is improved. Through joint modeling of the capacity retention ratio, internal resistance degradation correction term, and degradation function term in the form of a natural exponential function, a multi-factor coupled expression of capacity degradation trend, impedance degradation trend, and operational risk level is achieved. Furthermore, the fourth root of the comprehensive health degradation value is calculated to ensure that the health assessment results have good numerical stability and distinguishability under nonlinear mapping, providing a scientific, quantitative, and executable assessment basis for battery graded management, refined charge and discharge regulation, and fault early warning.

[0039] Specifically, the steps for determining the battery health level and generating a health level label based on the assessment results are as follows: Real-time comparison of the battery health assessment value with multi-level health thresholds to determine the battery health level. The multi-level health thresholds are a segmented indicator system constructed based on large-scale historical operating samples, including a Level 1 health threshold, a Level 2 health threshold, and a Level 3 health threshold. These three thresholds correspond to the grading boundaries of battery health status from deterioration to excellence, respectively. When the battery health assessment value is less than or equal to the Level 1 health threshold, the battery is determined to be in a severely aged state, indicating severe capacity decay, significant internal resistance degradation, and potential thermal safety hazards. When the battery health assessment value is greater than the Level 1 health threshold but less than the Level 2 health threshold, the battery is determined to be in a moderately aged state, indicating a significant decline in operating performance. The battery is considered to be in a slightly aged state when its health assessment value is greater than the level 2 health threshold but less than the level 3 health threshold, indicating early signs of degradation but still strong controllability. When the battery health assessment value is greater than or equal to the level 3 health threshold, the battery is considered to be in a healthy state, indicating high capacity retention, low internal resistance degradation, and good operational stability. Based on the above health level determination results, a predefined tag encoding rule is invoked to generate a corresponding health level tag for the current battery. The health level tag is recorded in a structured identifier format, including the battery number, health level code, determination timestamp, and corresponding health assessment value, for subsequent control allocation, historical traceability, and intelligent operation and maintenance tasks.

[0040] In this implementation plan, a graded judgment mechanism for battery health level is constructed by comparing the battery health assessment value with multi-level health thresholds in real time. Combined with predefined label coding rules, a structured health level label with information such as battery number, health level code, judgment timestamp and health assessment value is generated. This enables accurate identification and calibration of batteries with different aging levels, effectively improving the accuracy and timeliness of battery health status management, and providing stable basic data support for subsequent differentiated regulation, current command set generation and intelligent charge and discharge control.

[0041] Specifically, the steps for extracting battery health assessment values ​​and evaluating the charge / discharge regulation capabilities of each battery are as follows: Extract battery health assessment value data for all batteries in the energy storage power station and establish corresponding mapping relationships according to battery numbers; evaluate the charge / discharge regulation capabilities of each battery separately: First, divide the battery health assessment value of the j-th battery by the arithmetic sum of the battery health assessment values ​​of all batteries to calculate the health status weight factor of the j-th battery, which measures the relative proportion of the battery in the overall health status; Second, obtain the ratio of the battery temperature of the j-th battery to the thermal runaway critical temperature, and subtract one from the ratio to calculate the temperature safety correction term, which reflects the current battery thermal stability regulation space; Third, calculate the... The ratio of the real-time internal resistance of the battery to the initial internal resistance is used to obtain an internal resistance degradation correction term, which reflects the impact of the degree of battery conductivity degradation on the control capability. Further, the cumulative number of protection triggers during the operation of the j-th battery is obtained, and the ratio of the number of protection triggers to the maximum protection trigger tolerance number is calculated. This ratio is then subtracted to obtain a protection stability correction term, which quantifies the degree of constraint of abnormal trigger frequency on the control task. Finally, the health status weighting factor, temperature safety correction term, internal resistance degradation correction term, and protection stability correction term are multiplied sequentially to output the charge / discharge control evaluation value of the j-th battery, serving as an important basis for subsequent current distribution and power regulation.

[0042] The specific calculation formula for the charge / discharge regulation evaluation value is as follows:

[0043] ;

[0044] In the formula, X represents the charge / discharge regulation evaluation value. This represents the battery health assessment value of the j-th battery. This represents the temperature of the j-th battery. This indicates the critical temperature for thermal runaway. This represents the real-time internal resistance of the j-th battery. This indicates the initial internal resistance of the battery. This indicates the number of times the protection is triggered for the j-th battery. This indicates the maximum number of times protection can be triggered, and N represents the total number of batteries.

[0045] In this embodiment, Table 1 is a data table of charge and discharge regulation evaluation values, which lists the battery health evaluation values, battery temperature, thermal runaway critical temperature, real-time battery internal resistance, initial battery internal resistance, number of protection triggers, number of protection tolerances, total number of batteries, and charge and discharge regulation evaluation values ​​of the five batteries in the same battery pack during the current control cycle. The specific data is explained as follows: For battery 1, the health assessment value is 0.95, the temperature is 30°C, the real-time internal resistance is 1.80Ω, the protection was triggered 2 times, and the calculated regulation assessment value is 0.296; For battery 2, the health assessment value is 0.92, the temperature is 33°C, the real-time internal resistance is 1.85Ω, the protection was triggered 3 times, and the calculated regulation assessment value is 0.169; For battery 3, the health assessment value is 0.90, the temperature is 31°C, the real-time internal resistance is 1.90Ω, the protection was triggered 1 time, and the calculated regulation assessment value is 0.152; For battery 4, the health assessment value is 0.93, the temperature is 32°C, the real-time internal resistance is 1.82Ω, the protection was triggered 2 times, and the calculated regulation assessment value is 0.243; For battery 5, the health assessment value is 0.91, the temperature is 34°C, the real-time internal resistance is 1.87Ω, the protection was triggered 3 times, and the calculated regulation assessment value is 0.140.

[0046] Table 1. Data Table of Charge and Discharge Regulation Evaluation Values

[0047]

[0048] like Figure 3 The figure shows the current allocation weights calculated based on health assessment results for a battery pack consisting of five cells. The current allocation weight for each cell is obtained by normalizing its corresponding charge / discharge regulation assessment value. The sum of the weights for all cells is 1, which guides the target charge / discharge current allocation strategy for the current cycle. As can be seen in the figure, cell 1 has the highest allocation weight at 29.6%, indicating a better health condition and stronger current carrying capacity; cell 5 has the lowest allocation weight at 14.0%, indicating relatively lower regulation capability. Figure 3 This intuitively reflects the responsiveness of the differentiated current distribution strategy in this invention to battery status, which helps to achieve coordinated control of aging sensing and energy efficiency optimization.

[0049] This implementation scheme constructs a multi-factor evaluation system based on battery health assessment values, battery temperature, real-time battery internal resistance, initial battery internal resistance, protection trigger counts, and maximum protection trigger tolerance counts. This system accurately reflects the charge-discharge regulation capability of each battery cell within a specific operating cycle. A health status weighting factor measures the relative health level of the battery, a temperature safety correction term quantifies the thermal stability margin, an internal resistance degradation correction term measures changes in conductivity, and a protection stability correction term assesses control stability, thereby achieving a quantitative characterization of regulation capability. This method improves the scientific basis of current allocation and the precision of differentiated regulation, providing technical support for achieving dual optimization of safety and performance under multi-cell collaborative operation.

[0050] Specifically, normalization is used to assign current allocation weights. Based on the total power demand, the target charging and discharging current is calculated, and a current command set is generated. The specific steps for implementing the current regulation task are as follows: First, the charging and discharging regulation evaluation values ​​of all batteries are normalized. A standard normalization algorithm is used to calculate the allocation weight of each battery, ensuring that the sum of the weights of each battery is strictly 1.0 to guarantee the rationality and consistency of current allocation. The controller calls the allocation weights of each battery and performs coupled calculations with the total charging and discharging power demand of the current energy storage station. Based on the target total power value, allocation is performed to obtain the target charging current and target discharging current that each battery should bear, forming a structured current command set. The current command set includes the battery number, current value, current direction, and current effective duration. The controller is used to receive the allocation weights and total power demand. The controller receives input parameters such as power demand and regulation assessment values, performs power allocation calculations, and generates control commands containing target current values ​​and adjustment directions. The controller then sends the current command set to the corresponding DC-DC converter circuit connected to the battery via a configured high-speed communication bus protocol, achieving efficient transmission of control commands. In charging mode, the DC-DC converter circuit performs a boost operation, boosting the input current before outputting it to the power conversion circuit. The power conversion circuit utilizes a bidirectional converter module to efficiently convert DC to AC, ultimately guiding the AC current into the energy storage battery for precise energy injection. In discharging mode, the DC-DC converter circuit performs a buck operation, stepping down the battery output current before it undergoes a stable DC-to-AC conversion via the bidirectional converter module in the power conversion circuit, ultimately outputting the current to the grid to complete the energy release process.

[0051] In this implementation scheme, the charge and discharge regulation assessment values ​​of each battery are normalized. Combined with the current total charge and discharge power demand of the energy storage power station, the target charging current and target discharging current that each battery should bear are calculated, forming a current command set to ensure the rationality and accuracy of current allocation. The controller receives the allocation weight, power demand, and regulation assessment values ​​as input, and outputs control commands containing the target current value and adjustment direction, ensuring that each battery participates in the power regulation process according to its operating capacity. By sending the current command set to the DC-DC converter circuit to complete the boost and buck tasks, the dynamic adjustment efficiency and control accuracy in charging and discharging modes are further improved. This strategy achieves differentiated current allocation, enhances the adaptability to battery operating states, and effectively improves the response speed of current regulation tasks, the safety level of battery operation, and the overall power regulation capability of the energy storage system.

[0052] Specifically, the steps for assigning health level values ​​to batteries based on health level labels and evaluating the matching degree between the current regulation strategy and the battery state after each round of current regulation are as follows: First, extract the health level labels of all batteries in the energy storage power station, and assign a fixed health level value to each battery according to the label information. The health level value corresponding to a healthy battery state is recorded as 4, the health level value corresponding to a slightly aged battery state is recorded as 3, the health level value corresponding to a moderately aged battery state is recorded as 2, and the health level value corresponding to a severely aged battery state is recorded as 1. Then, after each current regulation task is completed, the degree of adaptation of the current current regulation strategy to the actual battery operating state and battery health state is measured in real time. First, calculate the current execution error suppression term: take the absolute value of the difference between the target current value and the real-time current value of each battery, divide it by the target current value, and subtract the resulting ratio to reflect the degree of consistency between the target current and the actual executed current. Second, calculate the temperature safety control term: divide the current battery temperature of each battery by the thermal runaway critical temperature, and then subtract the ratio to characterize the impact of the current temperature state on the control safety. Next, the internal resistance degradation impact term is calculated: the current real-time internal resistance of each cell is divided by the initial internal resistance, and then the ratio is subtracted to assess the degree of impact of changes in internal resistance on current regulation. Then, the health level adaptation correction term is calculated: the health assessment value of each cell is divided by the corresponding health level value, reflecting the adaptation relationship between the health assessment value and the health level. Finally, the current execution error suppression term, temperature safety control term, internal resistance degradation impact term, and health level adaptation correction term are multiplied sequentially to comprehensively reflect the adaptability of the regulation strategy to the battery operating state and health state in the current cycle, obtaining the regulation execution adaptation assessment value for the current cycle.

[0053] The specific formula for calculating the adjustment implementation adaptation assessment value is as follows:

[0054] ;

[0055] In the formula, Q represents the assessment value of the adjustment implementation. Indicates the target current. Indicates real-time current. Indicates battery temperature. This indicates the critical temperature for thermal runaway. This indicates the real-time internal resistance of the battery. S represents the initial battery internal resistance, S represents the battery health assessment value, and L represents the health level value.

[0056] This implementation scheme extracts battery health level labels and assigns corresponding health level values. Combining key parameters such as target current, real-time current, battery temperature, real-time battery internal resistance, initial battery internal resistance, and battery health assessment values, it calculates current execution error suppression, temperature safety control, internal resistance degradation impact, and health level adaptation correction terms. This systematically constructs a control execution adaptation assessment value, achieving a fine-grained characterization of the battery's current operating state and the adaptability of the current regulation strategy. This method can ensure the rationality of battery current regulation while dynamically feeding back battery state deviations, enhancing the health matching capability of the control scheme, improving the response accuracy of the current regulation strategy, and providing quantitative basis for subsequent target current adjustments and parameter table optimization. Ultimately, this improves the operational stability and energy utilization efficiency of the entire energy storage system under varying battery health conditions.

[0057] Specifically, the steps for dynamically adjusting the target current are as follows: Based on the adjustment execution adaptation evaluation value of each battery and the corresponding health level value of each battery, the target current within the current scheduling cycle is dynamically adjusted to ensure that the battery regulation strategy is real-time and targeted; when the adjustment execution adaptation evaluation value of any battery is less than the adaptation threshold, it is determined that the current current regulation strategy has not formed an effective matching relationship with the real-time operating status of the battery and the health status reflected by the health level label, triggering the target current reduction adjustment mechanism to prevent mismatched regulation from causing further degradation of battery performance or operational risks; during the reduction adjustment process, based on the battery's health level value, a pre-built current correction parameter table is called to retrieve the current correction factor that strictly corresponds to the current health level value; the current correction parameter table is the basis for guiding the adaptive correction of the target current, recording the target current adjustment ratio coefficient and current under different health level values. The adjustment boundary and minimum adjustment resolution are determined. The current correction factor is a key value in the current correction parameter table used to quantify the target current adjustment magnitude. Its value is determined based on the operational statistical characteristics of the corresponding health level. It is used to multiplicatively correct the current target current to ensure that the correction result meets the adjustment response speed requirements without causing over-adjustment risk. Based on the current correction factor, the parameters of the current target current are corrected item by item to construct a new current command set, which is then sent to the DC-DC converter circuit connected to the corresponding battery via a high-speed communication bus to trigger the second round of current regulation task. When the battery's regulation execution adaptation evaluation value is greater than or equal to the adaptation threshold, it is determined that the current current regulation strategy is highly matched with the battery's health level value, health level label, real-time operating status, and historical regulation records. The target current remains unchanged in the current cycle, and the existing current command set regulation execution process continues to be used to ensure the coordinated optimization of system stability and energy distribution efficiency.

[0058] This implementation scheme achieves precise adjustment of the battery target current under different health levels by introducing a dynamic matching mechanism between the current correction parameter table and the current correction factor. This mechanism is based on a joint judgment of the control execution adaptation evaluation value and the health level value, combined with the target current adjustment ratio coefficient, current adjustment boundary, and minimum adjustment resolution recorded in the current correction parameter table. This effectively solves the problem of untimely response and insufficient adaptation of current regulation strategies when health status changes. By calling the current correction factor matched to the battery health level value, quantitative correction is applied to the target current, ensuring that the current regulation task is targeted, accurate, and dynamically adaptable. This improves the matching degree between the charge / discharge control strategy and the actual battery operating state, significantly enhancing the stability, safety, and efficiency of the energy storage system.

[0059] Specifically, the steps for synchronously recording adjustment data to construct a strategy database and driving the update and optimization of the correction parameter table are as follows: After each round of current adjustment task is completed, the operating data of each battery in the current round is comprehensively recorded. The operating data includes the control execution adaptation evaluation value, target current, real-time current, battery health evaluation value, health level value, and voltage adjustment response time. A battery operation strategy database is constructed to quantitatively analyze the execution effect of the current adjustment task. Based on the constructed battery operation strategy database, the corresponding average control execution adaptation evaluation value and target current deviation rate are calculated for each health level value. The target current deviation rate is obtained by dividing the difference between the target current and the real-time current by the target current. If the target current deviation rate corresponding to any health level value exceeds the current deviation threshold, it is determined that the currently used current correction parameter table fails to match the battery's operating state, triggering the update and optimization process of the current correction parameter table. During the update and optimization process, a weighted historical mean method is used to aggregate the target current deviation rate data from historical rounds. Specifically, this involves: using the target current deviation rate recorded in each round of current regulation as the base data, and combining it with the frequency of occurrence of the corresponding health level value, assigning higher weights to samples at higher health levels, and then aggregating them using an arithmetic weighted average formula to enhance the stability identification capability of the deviation trend; simultaneously, using range analysis to extract the fluctuation range of the target current deviation rate under the current health level value, specifically calculating the difference between the maximum and minimum deviation rates in all historical data corresponding to the health level to quantify the dispersion of the current regulation effect at this level; finally, based on the weighted mean result to characterize the deviation center trend, and combined with the range to reflect the strategy volatility, the current correction factor under the current health level value is derived and updated. The updated current correction factor is applied to the corresponding health level value, replacing the old parameters in the original current correction parameter table, ensuring that the target current in subsequent current regulation tasks is more consistent with the actual battery carrying capacity.

[0060] In this implementation scheme, a battery operation strategy database is built after each round of current regulation task to achieve fine quantification of the battery operation status and the execution effect of the current regulation strategy. Furthermore, based on the battery operation strategy database, dynamic statistical analysis is performed on the target current deviation rate and regulation execution adaptation evaluation value under different health levels. This enables timely identification of the deviation relationship between the current correction parameter table and the actual operation status, triggering the update and optimization of the current correction factor. This improves the accuracy of target current generation, enhances the adaptability, responsiveness and stability of the current regulation strategy, and ensures the safety of regulation execution and energy conversion efficiency of batteries at each health level.

[0061] like Figure 2As shown, the second aspect of this invention provides an adaptive charge-discharge control system for battery aging perception in energy storage power stations, comprising: a battery operating condition data acquisition and preprocessing module, a battery health assessment and level calibration module, a battery adaptive current distribution control module, and a regulation strategy adaptation and parameter optimization module. The battery operating condition data acquisition and preprocessing module is used to acquire battery operating condition physical data of each battery in the energy storage power station and perform time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization processing on the battery operating condition physical data. The battery health assessment and level calibration module is used to obtain a health degradation factor based on the preprocessed battery operating condition physical data and assess... The battery health status is assessed, and a health level label is generated. The battery adaptive current distribution control module extracts the battery health assessment value, evaluates the charge and discharge regulation capability of each cell, normalizes it into current distribution weights, calculates the target charge and discharge current based on the total power demand, generates a current instruction set, and implements the current regulation task. The regulation strategy adaptation and parameter optimization module assigns health level values ​​to the battery based on the health level label, evaluates the matching degree between the current regulation strategy and the battery status after each round of current regulation, and dynamically adjusts the target current. The regulation data is recorded synchronously to build a strategy database and drive the update and optimization of the correction parameter table.

[0062] like Figure 4As shown, the energy storage system structure consists of a charging path, an energy storage battery pack, a discharging path, and a battery aging monitoring unit. Multiple functional components are coupled and connected through power transmission paths and signal transmission paths. The charging path includes a renewable energy / grid input source, a transformer, a PSC converter, an adjustable DD / DC module, a current sensor, and a dynamic distribution controller. In charging mode, renewable energy or the grid provides input power to the system. After voltage adjustment by the transformer, energy integration and rectification are completed by the PSC converter, followed by voltage boosting and multi-channel current regulation by the adjustable DD / DC module. The current sensor monitors the output current in real time. The dynamic distribution controller generates differentiated charging instructions based on the battery operating condition physical data, battery health assessment values, and health level labels provided by the battery aging monitoring unit, achieving graded matching charging control for each battery cell. The energy storage battery pack, as the system's energy storage unit, consists of multiple independent batteries. The battery aging monitoring unit is responsible for collecting real-time battery operating condition physical data such as voltage, current, temperature, and resistance of each battery cell and uploading it to the dynamic distribution controller as the basic data support for the current regulation strategy. The discharge and charging paths are symmetrically structured, including an adjustable DD / DC module, a PSC converter, a transformer, a current sensor, and a grid / load output port. In discharge mode, the DC power released by the energy storage battery pack is regulated and stepped down by the DD / DC module, then converted into AC power by the PSC converter and output to the external grid or load. The current sensor records the current data in real time during the discharge process, providing a basis for the controller to evaluate execution errors and adjustment effects. The signal transmission path is used to dynamically allocate high-frequency data communication between the controller, the battery aging monitoring unit, and the current sensing devices, while the power transmission path is used to complete the orderly flow and precise regulation of energy during charging and discharging.

[0063] This implementation scheme achieves differentiated full-process management of each battery in an energy storage power station by constructing a charge-discharge control structure that includes a battery operating condition data acquisition and preprocessing module, a battery health assessment and level calibration module, a battery adaptive current distribution control module, and a regulation strategy adaptation and parameter optimization module. Accurate acquisition and preprocessing of battery operating condition physical data ensures the timeliness, completeness, and comparability of data input. A health status assessment mechanism and health level calibration rules based on health degradation factors significantly improve the accuracy of battery operating status identification. Combined with multi-dimensional quantitative analysis of charge-discharge regulation capabilities and a weight normalization mechanism, dynamic current allocation based on battery health status is achieved. By introducing a target current adjustment mechanism that links the regulation execution adaptation assessment value with the health level value, and constructing an optimization mechanism for a battery operating strategy database and current correction parameter table, the matching between the control strategy and the actual battery operating status is strengthened, effectively improving the operational safety, energy utilization efficiency, and health evolution controllability of the energy storage power station in the context of multi-battery heterogeneity.

[0064] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0065] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A battery aging sensing adaptive charge / discharge control method for energy storage power stations, characterized in that, Includes the following steps: S1 collects the battery operating condition physical data of each battery in the energy storage power station, and performs time alignment, anomaly removal, multi-level filtering, scale standardization and interval normalization processing on the battery operating condition physical data. S2, based on the preprocessed battery operating condition physical data, obtain the health degradation factor, assess the health status of the battery, determine the battery health level based on the assessment results and generate a health level label; The specific steps for obtaining the health degradation factor based on the preprocessed battery operating condition physical data and assessing the battery's health status are as follows: Based on the preprocessed battery operating condition physical data of each cell, the exponentially weighted sliding window algorithm is used to construct time series evolution curves for capacity, internal resistance, temperature, and current data. The slope at the end of each time series evolution curve is extracted and linearly weighted to obtain the health degradation factor. Furthermore, the maximum allowable operating current and the critical temperature for thermal runaway are combined to construct a battery health input dataset. Based on the battery health input dataset, the health status of the battery is evaluated; the capacity retention ratio is obtained by dividing the real-time battery capacity by the initial battery capacity. Divide the initial battery internal resistance by the real-time battery internal resistance to obtain the internal resistance degradation ratio. The sum of the battery cycle count, the ratio of maximum instantaneous current to maximum allowable operating current, the ratio of maximum operating temperature to thermal runaway critical temperature, and the number of protection triggers is multiplied by the health degradation factor and used as an exponential term for the negative exponent calculation of the natural exponential function to obtain the degradation function term. The capacity retention ratio, the internal resistance degradation ratio, and the degradation function term are multiplied in sequence, and the result is taken as the fourth root to obtain the battery health assessment value. S3 extracts battery health assessment values, evaluates the charge and discharge regulation capabilities of each battery cell, normalizes them into current allocation weights, calculates the target charge and discharge current based on the total power demand, generates a current instruction set, and implements the current regulation task. S4 assigns a health level value to the battery based on the health level label, evaluates the matching degree between the current adjustment strategy and the battery state after each round of current adjustment, and dynamically adjusts the target current. Synchronously record adjustment data to build a strategy database, driving the update and optimization of the correction parameter table; The specific steps for assigning health level values ​​to batteries based on health level labels and evaluating the matching degree between the current regulation strategy and the battery state after each round of current regulation are as follows: Extract the health level labels of each battery and assign a fixed health level value to each battery: health status corresponds to health level value 4, mild aging corresponds to health level value 3, moderate aging corresponds to health level value 2, and severe aging corresponds to health level value 1; after each current regulation task is executed, measure the degree to which the current regulation strategy matches the actual operating state and health status of the battery. Divide the difference between the target current and the real-time current by the target current, take the absolute value and subtract one to obtain the current execution error suppression term; divide the battery temperature by the thermal runaway critical temperature and subtract one to obtain the temperature safety control term. Divide the current internal resistance of the battery by the initial internal resistance of the battery, and subtract one to obtain the internal resistance degradation effect term. Divide the battery health assessment value by the health level value to obtain the health level adaptation correction item; The current execution error suppression term, temperature safety control term, internal resistance degradation impact term, and health level adaptation correction term are multiplied sequentially to obtain the control execution adaptation evaluation value.

2. The adaptive charge / discharge control method for battery aging sensing in energy storage power stations according to claim 1, characterized in that: The specific steps for collecting the battery operating condition physical data of each battery in the energy storage power station and performing time alignment, anomaly removal, multi-level filtering, scale standardization, and interval normalization on the battery operating condition physical data are as follows: Collect battery operating condition physical data for each battery in the energy storage power station. The battery operating condition physical data includes real-time current, real-time battery capacity, real-time battery internal resistance, initial battery capacity, initial battery internal resistance, battery temperature, battery cycle count, protection trigger count, maximum instantaneous current, and maximum operating temperature. The battery operating condition physical data is validated using an anomaly detection algorithm, eliminating missing values, incomplete records, and data that do not meet physical boundary conditions. A multi-level filtering algorithm is used to suppress noise in the battery operating condition physical data, employing a combined filtering mechanism to reduce short-period fluctuations caused by sensor drift, electromagnetic interference, and sampling jitter. A distribution standardization algorithm is used to unify the scale of the battery operating condition physical data, eliminating dimensional differences between data from different sources and dimensions. Finally, a linear normalization algorithm is used to perform interval mapping on the battery operating condition physical data, mapping it to a unified interval range.

3. The adaptive charge-discharge control method for battery aging sensing in energy storage power stations according to claim 1, characterized in that: The specific steps for determining the battery health level and generating a health level label based on the evaluation results are as follows: Real-time comparison of battery health assessment values ​​and multi-level health thresholds determines the battery health level: when the battery health assessment value is less than or equal to the first-level health threshold, the battery is determined to be in a severely aged state. When the battery health assessment value is greater than the first-level health threshold and less than the second-level health threshold, the battery is judged to be in a moderate aging state. When the battery health assessment value is greater than the level 2 health threshold but less than the level 3 health threshold, the battery is judged to be in a state of mild aging. When the battery health assessment value is greater than or equal to the level 3 health threshold, the battery is determined to be in a healthy state. Based on the health level determination result, the label encoding rules are invoked to generate a corresponding health level label for the current battery and represent it in a structured identifier format.

4. The adaptive charge-discharge control method for battery aging sensing in energy storage power stations according to claim 1, characterized in that: The specific steps for extracting battery health assessment values ​​and evaluating the charge / discharge regulation capabilities of each battery cell are as follows: Extract the battery health assessment value of all batteries and evaluate the charge and discharge regulation capability of each battery: divide the health assessment value of the j-th battery by the sum of the battery health assessment values ​​of all batteries to obtain the health status weight factor. Subtracting the ratio of the battery temperature of the j-th battery to the critical temperature of thermal runaway from 1 yields the temperature safety correction term; subtracting the ratio of the real-time battery internal resistance of the j-th battery to the initial battery internal resistance from 1 yields the internal resistance degradation correction term. Subtracting the ratio of the number of protection triggers of the j-th battery to the maximum protection trigger tolerance number yields the protection stability correction term; The battery's charge and discharge regulation evaluation value is obtained by multiplying the health status weighting factor, temperature safety correction term, internal resistance degradation correction term, and protection stability correction term in sequence.

5. The adaptive charge-discharge control method for battery aging sensing in energy storage power stations according to claim 1, characterized in that: The normalization is used as the current allocation weight. The target charging and discharging current is calculated based on the total power demand, and a current instruction set is generated. The specific steps for implementing the current regulation task are as follows: The charging and discharging regulation evaluation values ​​of all batteries are normalized to obtain the allocation weight of each battery. Based on the allocation weight of each battery and the total charging and discharging power demand of the current energy storage power station, the controller calculates the target charging current and target discharging current that each battery should bear and constructs a current command set. The current command set is sent to the DC-DC converter circuit connected to the corresponding battery via a high-speed communication bus to perform boost and buck regulation tasks. In charging mode, the boosted current is output to the power conversion circuit, which uses the converter circuit to complete the DC to AC conversion and then guides the current into the energy storage battery; in discharging mode, the bucked current is converted by the converter circuit and then output to the grid.

6. The adaptive charge-discharge control method for battery aging sensing in energy storage power stations according to claim 1, characterized in that: The specific steps for dynamically adjusting the target current are as follows: Based on the adjustment and adaptation evaluation value of each battery and the corresponding health level value, the target current is dynamically adjusted. When the adjustment and adaptation evaluation value is less than the adaptation threshold, it is determined that the current current adjustment strategy is not effectively matched with the battery state, triggering the target current reduction adjustment mechanism: based on the battery health level value, the corresponding current correction parameter table is called to retrieve the current correction factor that matches the current level, and the current target current is corrected according to the current correction factor to construct a new current instruction set and trigger the second round of current adjustment task. When the adjustment and adaptation evaluation value is greater than or equal to the adaptation threshold, it is determined that the current current adjustment strategy matches the battery state, the existing target current remains unchanged, and the execution process of the current current instruction set continues.

7. The adaptive charge-discharge control method for battery aging sensing in energy storage power stations according to claim 1, characterized in that: The specific steps for constructing the strategy database using synchronized recording adjustment data and driving the update and optimization of the correction parameter table are as follows: After each round of current regulation task is completed, the regulation execution adaptation evaluation value, target current, real-time current, battery health evaluation value, health level value and voltage regulation response time of each battery in the current round are recorded to build a battery operation strategy database. Based on the battery operation strategy database, the average regulation execution adaptation evaluation value and target current deviation rate corresponding to each health level are calculated. When the target current deviation rate of any health level exceeds the current deviation threshold, it is determined that the current current correction parameter table does not match the actual battery state, triggering the correction parameter table optimization mechanism: the current correction factor is optimized by using the weighted historical mean and range analysis method, and the updated current correction parameter table is generated and applied.

8. An adaptive charge-discharge control system for battery aging sensing in energy storage power stations, employing the adaptive charge-discharge control method for battery aging sensing in energy storage power stations as described in any one of claims 1-7, characterized in that: include: The system includes a battery operating condition data acquisition and preprocessing module, a battery health assessment and level calibration module, a battery adaptive current distribution control module, and a regulation strategy adaptation and parameter optimization module, among which: The battery condition data acquisition and preprocessing module is used to collect the battery condition physical data of each battery in the energy storage power station, and to perform time alignment, anomaly removal, multi-level filtering, scale standardization and interval normalization processing on the battery condition physical data. The battery health assessment and level calibration module is used to obtain a health degradation factor based on preprocessed battery operating condition physical data, assess the health status of the battery, determine the battery health level based on the assessment results, and generate a health level label. The battery adaptive current distribution control module is used to extract battery health assessment values, evaluate the charging and discharging regulation capabilities of each battery cell, normalize them into current distribution weights, calculate the target charging and discharging current based on the total power demand, generate a current instruction set, and implement the current regulation task. The adjustment strategy adaptation and parameter optimization module is used to assign health level values ​​to the battery based on the health level label, evaluate the matching degree between the current adjustment strategy and the battery state after each round of current adjustment, and dynamically adjust the target current; it also records adjustment data to build a strategy database and drives the update and optimization of the correction parameter table.

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