Energy storage device charging and discharging early warning method and system

Abstract

The invention provides an energy storage device charging and discharging early warning method and system, relates to the technical field of energy storage, and provides a data basis for comprehensive state perception by obtaining real-time synchronous multi-source operation parameters and environmental parameters of an energy storage system. Health state evaluation is carried out by fusing the coupling relation of the multi-source parameters, and a comprehensive dynamic system health degree index is generated. The dynamic system health degree index and the charging and discharging switching instruction are adopted for real-time safety permission verification, a verification conclusion is generated, and dynamic association between the system health state and high-risk operation is achieved. And based on the verification conclusion, performing early warning decision through a preset decision mapping table, and generating and executing a graded early warning signal and an equipment control instruction. Through cooperative operation of four steps of state perception, comprehensive evaluation, dynamic verification and accurate intervention, the problem of systematic risk cognition lag caused by lack of multi-source information deep fusion and dynamic correlation analysis in the prior art is solved, and conversion from passive protection to active prevention and control is realized.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a method and system for early warning of charging and discharging of energy storage devices. Background Technology

[0002] The safety and reliability of energy storage devices are crucial for ensuring the stable grid connection and efficient dispatch of large-scale new energy power plants. Especially in complex application scenarios such as integrated agriculture, photovoltaics, and energy storage, energy storage devices need to frequently switch between high-power charging and discharging. Their internal battery clusters, high-voltage control boxes, and other electrical equipment are constantly under harsh conditions of high voltage and high current. If potential fault risks such as insulation degradation, contactor aging, and line overheating are not detected in time, they can easily escalate into serious accidents such as short circuits and fires, posing a significant threat to the safe operation of the entire power plant.

[0003] Currently, safety warnings for energy storage devices largely rely on threshold alarms for basic parameters such as voltage and temperature provided by the battery management system, or simple overcurrent and overvoltage protection mechanisms within the high-voltage box. However, these methods have significant limitations. First, they are usually based on static thresholds of a single parameter, failing to detect gradual changes in system status and potential risks. For example, the insulation performance of the busbars in the high-voltage box gradually decreases with ambient humidity and operating time, and contactor contacts gradually oxidize due to frequent operation, leading to increased contact resistance. The system cannot provide any warnings for these progressive faults until they reach a fixed alarm threshold. Second, existing methods lack comprehensive analysis of the coupling relationships between multiple parameters. For example, when signal lines generate abnormal data due to electromagnetic interference, the system struggles to distinguish it from substantial faults such as busbar overheating or poor circuit contact, potentially leading to false alarms or missed alarms. Furthermore, traditional charge / discharge interlock logic only focuses on the on / off state of the electrical circuit itself, failing to deeply integrate with environmental safety devices such as fire suppression systems and combustible gas detectors. This prevents early intervention in the charging / discharging process when extreme environmental conditions first appear, creating a safety blind spot. Summary of the Invention

[0004] In view of this, the present invention proposes a charging and discharging early warning method and system for energy storage devices, which solves the technical problem that existing early warning methods for energy storage devices lack in-depth integration and dynamic correlation analysis of multi-source information, resulting in lagging systemic risk cognition and insufficient proactive prevention and control capabilities.

[0005] The present invention provides a charging and discharging early warning method for an energy storage device, comprising the following steps:

[0006] Acquire real-time synchronized multi-source operating parameters and environmental parameters of the energy storage system;

[0007] A health status assessment is performed by integrating the multi-source operating parameters and the environmental parameters to determine the multi-parameter coupling relationship, thereby generating a dynamic system health index.

[0008] The dynamic system health index and the received charge / discharge switching command are used to perform real-time security permission verification and generate a verification conclusion.

[0009] Based on the verification results, data is analyzed, and early warning decisions are made through a preset decision mapping table, generating and executing corresponding graded early warning signals and equipment control commands.

[0010] Optionally, the step of using the multi-source operating parameters and the environmental parameters to perform a health status assessment that integrates multi-parameter coupling relationships and generates a dynamic system health index includes:

[0011] Extract the current ripple characteristics and contact temperature transient temperature rise response of the multi-source operating parameters during the charging and discharging transient process;

[0012] Calculate the temperature rise rate between the ripple coefficient increment of the current ripple characteristic quantity and the transient temperature rise response quantity of the contact temperature;

[0013] Using the ripple coefficient increment and the temperature rise rate, a time-series correlation model is constructed and the correlation coefficient of the model is calculated.

[0014] The contactor aging index is calculated using the correlation coefficient, the ripple coefficient increment, and the amplitude of the temperature rise rate.

[0015] The main circuit insulation health assessment is performed using the time-series variation data of the insulation resistance in the energy storage system and the corresponding environmental humidity data, and the main circuit insulation health index is generated.

[0016] The main circuit insulation health index and the contactor aging index are combined to generate a dynamic system health index.

[0017] Optionally, the step of constructing a time-series correlation model and calculating the correlation coefficient of the model using the ripple coefficient increment and the temperature rise rate includes:

[0018] Within the charge / discharge switching time window, multiple data points of the ripple coefficient increment and the temperature rise rate change over time are collected to generate original time-series data pairs.

[0019] The dynamic time warping algorithm is used to eliminate the time lag caused by thermal inertia in the original time series data pairs, and generate synchronized time series data pairs.

[0020] Based on the synchronized time-series data pairs, a linear regression model is constructed with the ripple coefficient increment as the independent variable and the temperature rise rate as the dependent variable to generate a time-series correlation model.

[0021] The synchronized time-series data is input into the time-series correlation model to calculate the Pearson product-moment correlation coefficient, thereby generating the correlation coefficient of the time-series correlation model.

[0022] Optionally, the step of using the time-series variation data of the insulation resistance in the energy storage system and the corresponding environmental humidity data to perform a main circuit insulation health assessment and generate a main circuit insulation health index includes:

[0023] The expected value of the reference insulation resistance at the current moment is calculated by using the time-series variation data of the insulation resistance in the energy storage system and the corresponding environmental humidity data.

[0024] Calculate the relative attenuation rate between the current measured value of the insulation resistance and the expected value of the reference insulation resistance;

[0025] Substitute the relative decay rate into a preset mapping function to calculate the normalized health score.

[0026] The health score is compared with multiple preset risk interval thresholds to determine the risk level corresponding to the health score;

[0027] Based on the aforementioned risk level, a main circuit insulation health index is constructed.

[0028] Optionally, the step of fusing the main circuit insulation health index with the contactor aging index to generate a dynamic system health index includes:

[0029] Real-time acquisition of the first risk level corresponding to the main circuit insulation health index and the second risk level corresponding to the contactor aging index;

[0030] Based on the real-time comparison results of the first risk level and the second risk level, a weight allocation scheme is dynamically configured; wherein, the weight allocation rule is: indicators with higher risk levels are assigned higher weights, and when the risk levels are the same, the main circuit insulation health indicator is assigned a higher weight.

[0031] The main circuit insulation health index and the contactor aging index are weighted and calculated according to the weighting scheme to generate a dynamic system health index.

[0032] Optionally, the step of performing real-time security license verification using the dynamic system health index and the received charge / discharge switching command to generate a verification conclusion includes:

[0033] Upon receiving a charge / discharge switching command, a safety verification window is activated and command execution is paused.

[0034] During the security verification window period, monitor the time-series changes in the health indicators of the dynamic system;

[0035] The slope of the dynamic system health index within a preset time interval is calculated using the time-series change data;

[0036] A risk assessment is performed based on the slope of change and the current value of the dynamic system health index, and a risk assessment result is generated.

[0037] The verification conclusion is constructed using the risk assessment results and the charge / discharge switching command.

[0038] Optionally, the step of performing a risk assessment based on the change slope and the current value of the dynamic system health index, and generating a risk assessment result, includes:

[0039] When the slope of the change is negative and the absolute value is greater than a preset deterioration threshold, a high-risk assessment result is generated;

[0040] When the slope of the change is negative but the absolute value is less than or equal to the preset deterioration threshold, and the current value of the dynamic system health index is less than the preset safety threshold, a medium-risk assessment result is generated.

[0041] When the slope of the change is non-negative and the current value of the dynamic system health index is less than or equal to the preset safety threshold, a medium-risk assessment result is generated.

[0042] When the slope of the change is non-negative and the current value of the dynamic system health index is greater than the preset safety threshold, a low-risk assessment result is generated.

[0043] Optionally, the step of parsing the data based on the verification conclusion, making an early warning decision through a preset decision mapping table, and generating and executing corresponding graded early warning signals and equipment control commands includes:

[0044] Risk level data is extracted from the verification conclusion, and instruction type data is extracted from the charge / discharge switching instruction;

[0045] Combine the risk level data with the instruction type data into a joint query condition;

[0046] Filter the warning level code and power control parameters corresponding to the joint query conditions from the preset decision mapping table;

[0047] Based on the aforementioned warning level code, the corresponding graded warning signal generation logic is triggered to generate graded warning signals;

[0048] Based on the power control parameters, the corresponding device control command generation logic is triggered to generate device control commands.

[0049] The issuance of the graded early warning signal and the device control command are executed in parallel.

[0050] Optionally, the step of generating device control commands by triggering the corresponding device control command generation logic based on the power control parameters includes:

[0051] The power difference is calculated using the power control parameters and the current operating power data of the energy storage converter to generate a power change difference.

[0052] The power slope change time is calculated using the power change difference and the preset power change rate limit data;

[0053] The power control parameters, the power ramp change time, and the current time data provided by the system clock are combined into instructions to generate a four-element power control instruction.

[0054] The four power control commands are encapsulated according to a preset communication protocol to generate device control commands.

[0055] A second aspect of the present invention provides a charge / discharge early warning system for an energy storage device, comprising:

[0056] The data acquisition module is used to acquire multi-source operating parameters and environmental parameters of the energy storage system in real time.

[0057] The data evaluation module is used to perform a health status assessment by integrating the multi-source operating parameters and the environmental parameters to achieve a multi-parameter coupling relationship, and to generate a dynamic system health index.

[0058] The verification module is used to perform real-time security license verification using the dynamic system health index and the received charge / discharge switching command, and generate a verification conclusion.

[0059] The early warning analysis module is used to parse data based on the verification conclusion, make early warning decisions through a preset decision mapping table, and generate and execute corresponding graded early warning signals and equipment control commands.

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] This invention provides a comprehensive data foundation for subsequent multi-parameter collaborative analysis by acquiring real-time synchronized multi-source operating parameters and environmental parameters of the energy storage system, overcoming the limitations of traditional methods that rely on single parameters. Based on this, a health status assessment is performed by integrating the multi-source operating parameters and environmental parameters to generate a dynamic system health index. This enables a dynamic, continuous, and comprehensive quantitative assessment of the overall system safety status, allowing for the perception of gradual changes in system status and the identification of potential faults before risks reach a fixed threshold, effectively solving the cognitive lag problem caused by static threshold alarms.

[0062] Furthermore, by performing real-time safety clearance verification between dynamic system health indicators and received charge / discharge switching commands, and generating verification conclusions, the system's real-time health status and high-risk charge / discharge operations are dynamically correlated and collaboratively judged. This allows charge / discharge control to no longer rely solely on loop on / off states, but to be deeply integrated with the system's overall health status, completing a fundamental shift from passive protection to proactive prevention. Finally, based on the verification conclusions and through a preset decision mapping table, early warning decisions are made, generating and executing corresponding tiered early warning signals and equipment control commands. This achieves closed-loop linkage between early warning and control, thereby precisely triggering differentiated responses based on risk levels, effectively avoiding false alarms and missed alarms, and eliminating safety blind spots in traditional systems. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating the steps of a charge / discharge early warning method for an energy storage device according to Embodiment 1 of the present invention.

[0065] Figure 2 This is a flowchart illustrating the steps of a charge / discharge early warning method for an energy storage device according to Embodiment 2 of the present invention.

[0066] Figure 3 This is a structural block diagram of a charge and discharge early warning system for an energy storage device provided in Embodiment 3 of the present invention. Detailed Implementation

[0067] This invention provides a charging and discharging early warning method and system for energy storage devices, which addresses the technical problem that existing early warning methods for energy storage devices lack in-depth fusion and dynamic correlation analysis of multi-source information, resulting in lagging systemic risk awareness and insufficient proactive prevention and control capabilities.

[0068] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0069] Example 1

[0070] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a charging and discharging early warning method for an energy storage device provided in Embodiment 1 of the present invention.

[0071] The present invention provides a charging and discharging early warning method for an energy storage device, comprising:

[0072] Step 101: Obtain the multi-source operating parameters and environmental parameters of the energy storage system in real time.

[0073] It should be noted that the energy storage device refers to the main equipment that completes the storage and release of electrical energy, including core components such as battery clusters, battery management systems, and converters. It is the direct physical object protected and controlled by this method. The energy storage system refers to a complete system that includes the energy storage device, as well as a data acquisition module, environmental monitoring equipment, an early warning controller, and a processor that executes this method. It is the operating platform and data source for this method to be implemented. The energy storage system applied in this invention can be based on a high-voltage energy storage system in an integrated "agricultural-solar-storage" scenario. This system is configured with, for example, six 5MWh energy storage battery compartments. The high-voltage control box of the battery cluster serves as a key intermediate unit connecting the battery pack and the energy storage converter. Its internal components (such as the B220602C0065-0088 type high-voltage box) integrate circuit breakers, contactors, fuses, current sensors, battery cluster management units, and other equipment. In actual engineering, such high-voltage boxes have been upgraded with hardware based on a parallel contactor scheme, realizing the physical separation and interlocking of the charging and discharging circuits. This provides a stable and well-defined hardware environment for this method to monitor the transient charging and discharging process.

[0074] Multi-source operating parameters refer to the set of physical quantities collected from various levels of the energy storage system that reflect its electrical and operational status. These specifically include the total current signal of the battery cluster, the transient response signal of the contactor contact temperature, the timing data of the main circuit insulation resistance, and the battery pack voltage data. Environmental parameters refer to the set of physical quantities describing the local physical environment in which the energy storage device is located. These specifically include ambient temperature data, ambient humidity data, and combustible gas concentration data within the battery compartment.

[0075] In this embodiment of the invention, a current sensor deployed in the high-voltage control box of the battery cluster acquires the total current signal of the battery cluster, characterizing the charging and discharging state. A temperature sensor installed at the contactor contacts in the charging and discharging circuit acquires the transient temperature response signal reflecting the health status of the contacts, aiming to directly monitor potential current-carrying faults such as increased contact resistance and abnormal heating caused by contactor aging and contact oxidation. An insulation monitoring circuit acquires insulation resistance timing data to assess the insulation performance of the main circuit, aiming to identify slow insulation degradation caused by aging of the busbar insulation protective layer or environmental erosion. Simultaneously, environmental temperature and humidity data affecting insulation performance are collected by the in-cabin environmental control equipment, and gas concentration data required for safety warnings is collected by the combustible gas detection system. All sensors are connected to the system host via a communication bus, and the host provides a unified clock synchronization signal to timestamp all data, achieving real-time synchronous acquisition.

[0076] Step 102: Use multi-source operating parameters and environmental parameters to perform a health status assessment that integrates multi-parameter coupling relationships, and generate a dynamic system health index.

[0077] In this embodiment of the invention, the energy storage system extracts the current ripple characteristic quantity of the charging and discharging transient process from the current signal in the multi-source operating parameters, and extracts the transient temperature rise response quantity of the contact point from the temperature signal in the environmental parameters. This step aims to directly capture early electrical and thermal behavioral anomalies caused by contactor mechanical stress and contact oxidation. The ripple coefficient increment and temperature rise rate are calculated, and a time-series correlation model of the two is constructed to calculate the contactor aging index. This process is specifically used to diagnose the contactor performance degradation caused by the combined effects of mechanical stress and electrical wear, and its key lies in overcoming the physical mismatch between the instantaneity of current changes and the hysteresis of temperature response. The energy storage system uses time-series variation data of insulation resistance and environmental humidity data for coupled analysis, and calculates the insulation health index of the main circuit through an insulation attenuation model. This model can effectively isolate the influence of environmental humidity fluctuations on the measured values, thereby accurately quantifying the aging rate of the insulation material itself, and solving the problem of high false alarm and false negative rates in traditional insulation monitoring due to environmental interference. The energy storage system dynamically assigns weights based on the real-time risk levels of two core sub-indicators: the main circuit insulation health index and the contactor aging index. This weighted fusion generates a unified dynamic system health index. This fusion strategy is not a simple weighted average, but a dynamic risk decision-making process that ensures the system focuses on the most pressing safety threats at any given time, thus achieving an accurate profile of the complex system's health status.

[0078] Step 103: Perform real-time security clearance verification using dynamic system health indicators and received charge / discharge switching commands, and generate verification conclusions.

[0079] In this embodiment of the invention, the energy storage system immediately initiates a safety verification window of a preset duration and suspends subsequent command execution. By adding a layer of "software insurance" based on the real-time health status of the system on top of hardware interlocking, an upgrade from passive on / off protection to proactive prevention and intervention is achieved. During this window period, the energy storage system continuously monitors the time-series changes of dynamic system health indicators and calculates the slope of these indicators within a preset time interval to quantitatively capture the evolution trend of the system's health status. Subsequently, the system performs a risk assessment by combining the slope of these changes with the current value of the health indicators. The system logically binds the generated risk assessment result with the received charge / discharge switching command itself to construct a structured final verification conclusion.

[0080] Step 104: Based on the verification results, perform data analysis and make early warning decisions through a preset decision mapping table, generating and executing corresponding graded early warning signals and equipment control commands.

[0081] The preset decision mapping table is a structured data table that defines the mapping relationship between risk level, instruction type and specific response action.

[0082] In this embodiment of the invention, the energy storage system analyzes and verifies the risk level data obtained from the verification results. This data is then combined with the instruction type data parsed from the charge / discharge switching instruction to form a joint query condition input to a preset decision mapping table. The system outputs a warning level code and power control parameters. The energy storage system then generates warning signals of different levels based on the code and generates equipment control instructions containing elements such as target power and rate of change based on the parameters. This decision mapping table-based response mechanism ensures the accuracy and consistency from risk identification to control actions, avoiding delays and uncertainties inherent in human judgment. Finally, the warning signals and equipment control instructions are issued in parallel, achieving synchronous linkage between information warning and equipment control. This forms a closed-loop management system from state perception and intelligent decision-making to proactive intervention, completely eliminating the breakpoints between warning and control in traditional systems.

[0083] Example 2

[0084] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a charging and discharging early warning method for an energy storage device provided in Embodiment 2 of the present invention.

[0085] The present invention provides a charging and discharging early warning method for an energy storage device, comprising:

[0086] Step 201: Obtain the multi-source operating parameters and environmental parameters of the energy storage system in real time.

[0087] In this embodiment of the invention, the specific implementation process of step 201 is similar to that of step 101, and will not be repeated here.

[0088] Step 202: Extract the current ripple characteristic quantity and the contact temperature transient temperature rise response quantity of the multi-source operating parameters during the charging and discharging transient process.

[0089] In this embodiment of the invention, a hybrid algorithm based on wavelet packet transform and empirical mode decomposition is used to perform time-frequency domain joint analysis on the total battery current signal. A parallel contactor scheme is used to extract the effective value of the ripple current and its energy distribution characteristics at the switching frequency during the charge-discharge switching instant, generating current ripple characteristic quantities. Simultaneously, exponential function fitting and first-order differential operations are applied to process the contactor contact temperature signal, calculating the temperature change rate and temperature rise time constant in the initial stage after contact closure, generating transient temperature rise response quantities for the contact. The current ripple characteristic quantities include core parameters characterizing the current quality during the charge-discharge transient process, such as the effective value of the ripple current, ripple frequency distribution characteristics, and energy proportion in a specific frequency band. The transient temperature rise response quantities for the contact include key indicators reflecting the thermal dynamic characteristics of the contactor contacts, such as the initial temperature rise rate, the time required to reach steady-state temperature, and the temperature rise curve fitting parameters.

[0090] It is worth mentioning that the hybrid algorithm based on wavelet packet transform and empirical mode decomposition can effectively separate the high-frequency ripple component and fundamental current generated by the switching action of power devices during the charging and discharging transient process, accurately capturing the ripple characteristics reflecting the circuit state. By processing the temperature signal through exponential function fitting and differential operation, the interference of ambient temperature fluctuations on the measured value can be eliminated, and the transient heating characteristics caused by contact resistance at the moment of contact closure can be accurately quantified, so that the obtained transient temperature rise response of the contact truly reflects the electrical connection state of the contact.

[0091] Step 203: Calculate the temperature rise rate between the ripple coefficient increment of the current ripple characteristic quantity and the transient temperature rise response quantity of the contact temperature.

[0092] In this embodiment of the invention, the ripple coefficient of the current charging and discharging transient process is calculated using a sliding time window algorithm, and compared with the reference ripple coefficient within a preset reference period to obtain the ripple coefficient increment. Simultaneously, a first-order differential operation is performed on the transient temperature rise response of the contact point, extracting the temperature change slope within a specific time window after contact closure as the temperature rise rate. By calculating the ripple coefficient increment, the impedance characteristic changes caused by contactor contact aging, loose circuit connections, etc., can be effectively quantified, making the characterization of electrical characteristics more sensitive and accurate. Using a first-order differential operation to extract the temperature rise rate eliminates the influence of ambient temperature baseline drift, accurately capturing the instantaneous heating characteristics generated by contact resistance at the moment of contact closure.

[0093] Step 204: Using the ripple coefficient increment and temperature rise rate, construct a time-series correlation model and calculate the correlation coefficient of the model.

[0094] Furthermore, step 204 may include the following sub-steps:

[0095] S11. Within the charging / discharging switching time window, collect multiple data points showing the change of ripple coefficient increment and temperature rise rate over time to generate raw time series data pairs.

[0096] S12. The dynamic time warping algorithm is used to eliminate the time lag caused by thermal inertia in the original time series data pairs and generate synchronized time series data pairs.

[0097] S13. Based on synchronized time series data pairs, construct a linear regression model with ripple coefficient increment as independent variable and temperature rise rate as dependent variable to generate a time series correlation model.

[0098] S14. Calculate the Pearson product-moment correlation coefficient of the synchronized time series data to the input time series correlation model, and generate the correlation coefficient of the time series correlation model.

[0099] It should be noted that the charge / discharge switching time window refers to a pre-defined specific time interval surrounding the execution time of the charge / discharge switching command. Dynamic time warping is an advanced algorithm for handling two time-series signals that exhibit nonlinear distortion or velocity differences on the time axis. The Pearson product-moment correlation coefficient is a statistic between -1 and 1, used to quantify the strength and direction of the linear correlation between two variables.

[0100] In this embodiment of the invention, during the charging / discharging switching time window after the start of the charging / discharging switch, the ripple coefficient increment and temperature rise rate are synchronously collected at a fixed sampling frequency to form an original time-series data pair containing multiple data points. Subsequently, a dynamic time warping algorithm is applied to process this original time-series data pair, finding the optimal bending path to compensate for the response delay of the temperature signal relative to the current signal, generating a synchronized time-series data pair with precisely aligned time axes. Based on the synchronized time-series data pair, the least squares method is used to fit the linear relationship between the ripple coefficient increment and the temperature rise rate, constructing a linear regression model with clear slope and intercept parameters as the time-series correlation model. Finally, the synchronized time-series data pair is input into this time-series correlation model, and the Pearson product-moment correlation coefficient is calculated to generate a correlation coefficient used to quantify the degree of linear correlation between the two variables. The dynamic time warping algorithm eliminates the time lag caused by thermal inertia in the original time-series data pair, effectively solving the physical characteristic mismatch between the instantaneity of current changes and the temperature response delay. Constructing a linear regression model can transform complex multi-physics coupling relationships into explicit mathematical expressions, providing a quantifiable theoretical basis for contactor condition assessment. Calculating the Pearson product-moment correlation coefficient can objectively characterize the correlation strength between the ripple coefficient increment and the temperature rise rate. When the contactor performance deteriorates, its contact resistance increases, leading to a stronger correlation between ripple and temperature rise. The changing trend of this correlation coefficient can serve as an important characteristic indicator of contactor aging.

[0101] Step 205: Calculate the contactor aging index using the correlation coefficient, the ripple coefficient increment, and the amplitude of the temperature rise rate.

[0102] In this embodiment of the invention, the correlation coefficient, ripple coefficient increment amplitude, and temperature rise rate amplitude are normalized to eliminate the influence of dimensions. Then, a linear weighted fusion algorithm is used to comprehensively calculate the three normalized features according to preset weight coefficients, generating a contactor aging index. The weight coefficients are determined based on statistical analysis of a large amount of historical data; the correlation coefficient is weighted at 0.5, and the ripple coefficient increment amplitude and temperature rise rate amplitude are weighted at 0.3 and 0.2 respectively, to highlight the dominant role of the current-temperature correlation characteristics in aging assessment. The contactor aging index is a quantitative parameter that comprehensively characterizes the degree of degradation of the contactor's electrical performance. Its calculation elements include the correlation coefficient reflecting the strength of the current-temperature correlation, the ripple coefficient increment amplitude characterizing the current stress level, and the temperature rise rate amplitude reflecting the thermal response characteristics.

[0103] It is worth mentioning that, due to the limitations of single-parameter evaluation, three complementary features were integrated: the correlation coefficient characterizes the close relationship between current stress and thermal response, and its increase directly leads to an increase in aging indicators; the ripple coefficient increment reflects the changing trend of circuit impedance characteristics; and the temperature rise rate directly reflects the heating of contact resistance. This multi-dimensional integrated evaluation enables the contactor aging indicators to both sensitively capture early performance degradation and stably judge severe aging in later stages, significantly improving the robustness and accuracy of the evaluation.

[0104] Step 206: Use the time-series variation data of insulation resistance in the energy storage system and the corresponding environmental humidity data to conduct a main circuit insulation health assessment and generate main circuit insulation health indicators.

[0105] Furthermore, step 206 may include the following sub-steps:

[0106] S21 uses the time-series variation data of insulation resistance in the energy storage system and the corresponding environmental humidity data to calculate the expected value of the reference insulation resistance at the current moment.

[0107] S22. Calculate the relative attenuation rate between the current measured value of the insulation resistance and the expected value of the reference insulation resistance;

[0108] S23. Substitute the relative decay rate into the preset mapping function to calculate the normalized health score;

[0109] S24. Compare the health score with multiple preset risk interval thresholds to determine the risk level corresponding to the health score;

[0110] S25. Based on the risk level, construct the main circuit insulation health index.

[0111] It should be noted that time-series variation data refers to a set of data collected at a unified timestamp that reflects the continuous change process and trend of insulation resistance. This enables the system to analyze the gradual changes in insulation performance, rather than relying solely on a single, isolated, instantaneous measurement. Environmental humidity data refers to physical quantity data describing the water vapor content in the local ambient air where the energy storage device is located, collected strictly synchronously with the insulation resistance time-series data. This provides a data foundation for compensating for and eliminating the interference of environmental humidity on insulation measurements.

[0112] The preset risk range thresholds are a set of critical scores, determined in advance through experiments and engineering verification, used to classify the health status of insulation. Specifically: 80-100 points represent the safe range, indicating excellent main circuit insulation performance and a healthy operating state. The system does not trigger warnings, and the charging and discharging process is unrestricted. 60-80 points represent the attention range, indicating slight insulation degradation requiring attention. The system triggers a low-level warning and strengthens monitoring, but the charging and discharging power remains normal. 40-60 points represent the warning range, indicating a significant decline in insulation performance and a clear risk. The system triggers a medium-level warning and generates a maintenance work order, while simultaneously imposing power limits on the charging and discharging process. 0-40 points represent the danger range, indicating severe insulation degradation and an extremely high safety risk. The system immediately triggers the highest alarm and blocks charging and discharging commands, forcibly disconnecting the circuit to ensure safety.

[0113] In this embodiment of the invention, ambient humidity data is input into a preset insulation attenuation model. This model is based on the physical law that the resistivity of insulating materials decreases exponentially after absorbing moisture, and its core function is: This is used to calculate the expected value of the baseline insulation resistance under the current humidity conditions, after eliminating environmental influences. This is the expected value of the reference insulation resistance under the current ambient humidity, that is, the theoretical resistance value under ideal healthy conditions; To reference ambient humidity The initial reference insulation resistance value measured below; The humidity-insulation attenuation coefficient is a constant greater than 0, determined by the properties of the insulating material, and quantifies the degree of negative impact of humidity on resistance. This refers to the currently collected ambient humidity data; For reference ambient humidity.

[0114] Next, the relative attenuation rate of the measured insulation resistance value relative to this reference value is calculated. It is a dimensionless health indicator that directly quantifies the degree to which insulation performance deviates from the ideal healthy state, reflecting the true aging condition better than absolute resistance. Its calculation formula is:

[0115] ;

[0116] in, This refers to the relative attenuation rate. This is the current measured value of the insulation resistance; This is the expected value of the reference insulation resistance.

[0117] Subsequently, the relative decay rate is substituted into a preset S-shaped mapping function (i.e., the preset mapping function) for standardization. This function converts the decay rate into a health score of 0-100. Its core function is:

[0118] ;

[0119] The calculated health score ranges from 0 to 100. The relative attenuation rate represents the degree of attenuation of the measured insulation resistance relative to the baseline expected value. The first shape control parameter is used to adjust the steepness of the S-curve; This is the second shape control parameter, used to adjust the offset of the S-curve.

[0120] The system compares the health score with multiple preset risk range thresholds in real time, automatically determines the risk level, and finally generates a standardized main circuit insulation health index through level numerical conversion. This establishes a scientific mapping relationship from monitoring data to status classification, and the final main circuit insulation health index can comprehensively and accurately reflect the insulation health status of the energy storage system's main circuit.

[0121] Step 207: Integrate the main circuit insulation health index with the contactor aging index to generate a dynamic system health index.

[0122] Furthermore, step 207 may include the following sub-steps:

[0123] S31. Real-time acquisition of the first risk level corresponding to the main circuit insulation health index and the second risk level corresponding to the contactor aging index.

[0124] S32. Based on the real-time comparison results between the first risk level and the second risk level, dynamically configure the weight allocation scheme;

[0125] S33. The main circuit insulation health index and contactor aging index are weighted according to the weight allocation scheme to generate a dynamic system health index.

[0126] In this embodiment of the invention, based on the same set of risk interval thresholds defined in step 206, the values ​​of the main circuit insulation health indicators are analyzed to determine their corresponding first risk level. Simultaneously, the values ​​of the contactor aging indicators are analyzed to determine their corresponding second risk level. The subsequent weight allocation scheme is dynamically configured based on the real-time comparison results of these two risk levels: when the risk level of either indicator is higher, it is considered the primary concern and given a higher weight, such as 70%, while the weight of the other indicator is correspondingly reduced, such as 30%; when the risk levels are the same, a default weight allocation is used, for example, the main circuit insulation health indicator accounts for 60%, and the contactor aging indicator accounts for 40%. Finally, the values ​​of the two indicators are weighted according to this dynamically determined weight to generate a dynamic system health index ranging from 0 to 100. By referencing a unified risk level standard and dynamically merging the weights of the two key indicators, the consistency of the system evaluation scale is ensured. Furthermore, the energy storage system has the ability to identify primary concerns; when the risk of a certain subsystem increases significantly, the system automatically assigns it a higher weight, enabling the comprehensive health index to more sensitively reflect the most pressing safety threats and achieving adaptive adjustment of the evaluation focus.

[0127] Step 208: Perform real-time security clearance verification using dynamic system health indicators and received charge / discharge switching commands, and generate verification conclusions.

[0128] Furthermore, step 208 may include the following sub-steps:

[0129] S41. Upon receiving a charge / discharge switching command, start the safety verification window and pause command execution;

[0130] S42. During the security verification window, monitor the time-series changes in dynamic system health indicators;

[0131] S43. Calculate the slope of the dynamic system health index within a preset time interval using time-series change data;

[0132] S44. Conduct a risk assessment based on the current values ​​of the slope of change and the dynamic system health index, and generate the risk assessment results.

[0133] S45. Using the risk assessment results and charge / discharge switching instructions, construct the verification conclusion.

[0134] In this embodiment of the invention, when the energy storage system receives a charge / discharge switching command, it immediately initiates a two-second safety verification window and suspends the command execution process. During this window, dynamic system health indicators are collected ten times per second, forming time-series change data containing twenty data points. Based on this time-series data, the slope of change within the most recent 0.5-second time interval is calculated using the least squares method. A risk assessment result is generated based on the combination characteristics of the slope and the current value. Finally, this risk assessment result is combined with the command type corresponding to the charge / discharge switching command to construct a verification conclusion that includes options such as allowing execution, prohibiting execution, or reducing power execution.

[0135] By setting a safety verification window and pausing command execution, critical safety assessment time is provided, effectively preventing blind switching operations when the health status is unclear. Monitoring the time-series changes of dynamic system health indicators and calculating the slope of change can keenly capture deteriorating trends in system health, achieving a leap from static assessment to dynamic early warning. Risk assessment results are generated based on a dual judgment criterion of slope and current value, considering both the system's immediate state and changing trends, making the assessment more comprehensive and accurate. Finally, verification conclusions are constructed by combining the type of charge / discharge switching command, ensuring a close integration of safety decisions with specific operational scenarios and providing reliable safety assurance for the charging and discharging process of the energy storage system.

[0136] Further, step S44 may include the following sub-steps:

[0137] S441. When the slope of the change is negative and the absolute value is greater than the preset deterioration threshold, a high-risk assessment result is generated.

[0138] S442. When the slope of the change is negative but the absolute value is less than or equal to the preset deterioration threshold, and the current value of the dynamic system health index is less than the preset safety threshold, a medium-risk assessment result is generated.

[0139] S443. When the slope of change is non-negative and the current value of the dynamic system health index is less than or equal to the preset safety threshold, a medium-risk assessment result is generated.

[0140] S444. When the slope of change is non-negative and the current value of the dynamic system health index is greater than the preset safety threshold, a low-risk assessment result is generated.

[0141] In this embodiment of the invention, the system risk is accurately assessed by analyzing the changing trends and real-time status of dynamic system health indicators. The assessment process uses a preset deterioration threshold and a preset safety threshold as core judgment benchmarks. The preset deterioration threshold is the critical slope for determining whether the system's health status is in an accelerated deterioration phase, and its value is set at 0.1 per minute. The preset safety threshold is the minimum health score for defining whether the system is in a safe operating state, and its value is set at 80 points. This standard is consistent with the health assessment benchmarks for other parts of the system.

[0142] The specific assessment logic is as follows: When the slope of the system health indicator is negative and its absolute value exceeds a preset deterioration threshold, it indicates that the system health is rapidly deteriorating, and a high-risk assessment result is generated. If the slope is negative but the absolute value does not exceed the deterioration threshold, and the current health indicator is below 80, a medium-risk assessment result is generated. If the slope is non-negative, but the current health indicator is still not higher than 80, a medium-risk assessment result is also generated. Only when the slope is non-negative and the current health indicator is higher than 80, indicating that the system is both in a safe state and showing a stable or improving trend, is it confirmed as low-risk, and a low-risk assessment result is generated. This overcomes the limitations of traditional single-threshold judgment, enabling timely capture of dynamic risk changes in the system while avoiding misjudgments caused by short-term fluctuations, thus improving the accuracy and reliability of risk assessment.

[0143] Step 209: Based on the verification results, perform data analysis and make early warning decisions through a preset decision mapping table, generating and executing corresponding graded early warning signals and equipment control commands.

[0144] Furthermore, step 209 may include the following sub-steps:

[0145] S51. Extract the risk level data from the verification conclusion and extract the instruction type data from the charge / discharge switching instruction;

[0146] S52. Combine risk level data with instruction type data into joint query conditions;

[0147] S53. Filter out the warning level code and power control parameters corresponding to the joint query conditions from the preset decision mapping table;

[0148] S54. Based on the warning level code, trigger the corresponding graded warning signal generation logic to generate graded warning signals;

[0149] S55. Based on the power control parameters, trigger the corresponding device control command generation logic to generate device control commands;

[0150] S56. Parallel execution of issuing graded early warning signals and equipment control commands.

[0151] In this embodiment of the invention, risk level data is extracted from the verification results. This risk level data includes three levels: high risk, medium risk, and low risk. Simultaneously, instruction type data is parsed from the charge / discharge switching instructions. This instruction type data includes two types: charging to discharging and discharging to charging. The risk level data and instruction type data are combined to form a joint query condition. By combining risk level and instruction type to form a joint query condition, differentiated early warning and control strategies can be formulated for different operating scenarios, improving the accuracy of the system response.

[0152] The combined query conditions are then input into a preset decision mapping table, which stores the warning level codes and power control parameters corresponding to various risk levels and instruction type combinations. The use of the preset decision mapping table enables rapid mapping from assessment conclusions to execution strategies, significantly shortening system response time. Based on the output warning level code, the corresponding signal generation logic is triggered: Level 1 warning codes generate audible and visual alarms and SMS notifications; Level 2 warning codes generate monitoring center pop-up warnings; and Level 3 warning codes generate operation log records. Simultaneously, based on the output power control parameters, the equipment control instruction generation logic is triggered, generating control instructions containing target power values ​​and rates of change. The hierarchical warning signal generation logic triggered by the warning level code ensures hierarchical and categorized management of warning information, avoiding interference from excessive warnings while guaranteeing timely delivery of important warnings. Finally, the system executes the issuance of hierarchical warning signals and equipment control instructions in parallel, ensuring that warning information transmission and equipment control are synchronized. This synchronized information warning and equipment control effectively shortens the system's risk response cycle and provides dual protection for the safe and stable operation of the energy storage system.

[0153] Furthermore, step S55 may include the following sub-steps:

[0154] S551. Calculate the power difference using power control parameters and the current operating power data of the energy storage converter to generate the power change difference.

[0155] S552. Calculate the ramp time using the power change difference and preset power change rate limit data to generate the power ramp change time.

[0156] S553: ​​Combine the power control parameters, power ramp change time, and current time data provided by the system clock into instructions to generate a four-element power control instruction;

[0157] S554. Encapsulate the four-element power control commands according to the preset communication protocol to generate device control commands.

[0158] A pre-defined communication protocol is a complete set of standardized data formats and interaction rules that are pre-set to enable reliable data exchange between different devices.

[0159] In this embodiment of the invention, the target power value in the power control parameters is calculated by differentiating it from the current operating power value collected in real time by the energy storage converter, resulting in a signed power change difference that characterizes the magnitude and direction of power adjustment. Then, the absolute value of this power change difference is divided by a preset power change rate limit, which is set to 10% of the system's rated power per second, to calculate the precise power ramp change time. Next, the target power value, the preset power change rate limit, the calculated power ramp change time, and the precise timestamp provided by the system clock are combined to generate a power control command containing four key elements. Finally, this four-element power control command is encapsulated according to a standard data frame format of a preset communication protocol (such as the Modbus-RTU communication protocol) to generate a device control command that can be directly sent to the energy storage converter.

[0160] Example 3

[0161] Please see Figure 3 , Figure 3 This is a structural block diagram of a charge and discharge early warning system for an energy storage device provided in Embodiment 3 of the present invention.

[0162] The present invention provides a charge and discharge early warning system for an energy storage device, comprising:

[0163] Data acquisition module 301 is used to acquire multi-source operating parameters and environmental parameters of the energy storage system in real time.

[0164] Data evaluation module 302 is used to evaluate the health status by integrating multi-source operating parameters and environmental parameters to form a multi-parameter coupling relationship, and to generate dynamic system health indicators.

[0165] Verification module 303 is used to perform real-time security license verification using dynamic system health indicators and received charge / discharge switching commands, and generate verification conclusions.

[0166] The early warning analysis module 304 is used to parse data based on the verification conclusion, make early warning decisions through a preset decision mapping table, and generate and execute corresponding graded early warning signals and equipment control commands.

[0167] Furthermore, the data evaluation module 302 can perform the following steps:

[0168] Extract the current ripple characteristics and contact temperature transient temperature rise response of multi-source operating parameters during the charging and discharging transient process;

[0169] Calculate the temperature rise rate between the ripple coefficient increment of the current ripple characteristic quantity and the transient temperature rise response of the contact temperature.

[0170] A time-series correlation model was constructed using the ripple coefficient increment and temperature rise rate, and the correlation coefficient of the model was calculated.

[0171] The aging index of the contactor is calculated using the correlation coefficient, the increment of the ripple coefficient, and the amplitude of the temperature rise rate.

[0172] The main circuit insulation health assessment is performed using time-series variation data of insulation resistance in the energy storage system and corresponding environmental humidity data, and the main circuit insulation health index is generated.

[0173] The main circuit insulation health index is integrated with the contactor aging index to generate a dynamic system health index.

[0174] Furthermore, the data evaluation module 302 can also perform the following steps:

[0175] Within the charge / discharge switching time window, multiple data points showing the change of ripple coefficient increment and temperature rise rate over time are collected to generate raw time-series data pairs.

[0176] The dynamic time warping algorithm is used to eliminate the time lag caused by thermal inertia in the original time series data pairs and generate synchronized time series data pairs.

[0177] Based on synchronized time-series data pairs, a linear regression model is constructed with the ripple coefficient increment as the independent variable and the temperature rise rate as the dependent variable to generate a time-series correlation model.

[0178] The Pearson product-moment correlation coefficient is calculated by synchronizing the time series data and inputting the time series correlation model to generate the correlation coefficient of the time series correlation model.

[0179] Furthermore, the data evaluation module 302 can also perform the following steps:

[0180] The expected value of the reference insulation resistance at the current moment is calculated by using the time-series variation data of the insulation resistance in the energy storage system and the corresponding environmental humidity data.

[0181] Calculate the relative attenuation rate between the current measured value of the insulation resistance and the expected value of the reference insulation resistance;

[0182] Substitute the relative decay rate into the preset mapping function to calculate the normalized health score;

[0183] The health score is compared with multiple preset risk range thresholds to determine the risk level corresponding to the health score;

[0184] Based on the risk level, an insulation health index for the main circuit is constructed.

[0185] Furthermore, the data evaluation module 302 can also perform the following steps:

[0186] Real-time acquisition of the first risk level corresponding to the main circuit insulation health index and the second risk level corresponding to the contactor aging index;

[0187] Based on the real-time comparison results between the first risk level and the second risk level, a weight allocation scheme is dynamically configured. The weight allocation rule is as follows: indicators with higher risk levels are assigned higher weights, and when the risk levels are the same, the main circuit insulation health indicators are assigned higher weights.

[0188] The main circuit insulation health index and contactor aging index are weighted according to the weight allocation scheme to generate a dynamic system health index.

[0189] Furthermore, the verification module 303 can perform the following steps:

[0190] Upon receiving a charge / discharge switching command, a safety verification window is activated and command execution is paused.

[0191] During the security verification window, monitor the time-series changes in dynamic system health indicators;

[0192] The slope of the dynamic system health index within a preset time interval is calculated using time-series change data.

[0193] A risk assessment is conducted based on the change slope and the current values ​​of dynamic system health indicators, and a risk assessment result is generated.

[0194] The verification conclusions are constructed using the risk assessment results and charge / discharge switching instructions.

[0195] Furthermore, the verification module 303 can also perform the following steps:

[0196] When the slope of the change is negative and the absolute value is greater than the preset deterioration threshold, a high-risk assessment result is generated;

[0197] When the slope of the change is negative but the absolute value is less than or equal to the preset deterioration threshold, and the current value of the dynamic system health index is less than the preset safety threshold, a medium-risk assessment result is generated.

[0198] When the slope of change is non-negative and the current value of the dynamic system health index is less than or equal to the preset safety threshold, a medium-risk assessment result is generated.

[0199] When the slope of the change is non-negative and the current value of the dynamic system health index is greater than the preset safety threshold, a low-risk assessment result is generated.

[0200] Furthermore, the early warning analysis module 304 can perform the following steps:

[0201] Risk level data is extracted from the verification results, and instruction type data is extracted from the charge / discharge switching instructions.

[0202] Combine risk level data with instruction type data into joint query conditions;

[0203] Filter the warning level code and power control parameters corresponding to the joint query conditions from the preset decision mapping table;

[0204] Based on the warning level code, the corresponding hierarchical warning signal generation logic is triggered to generate hierarchical warning signals;

[0205] Based on the power control parameters, the corresponding device control command generation logic is triggered to generate device control commands.

[0206] The issuance of graded early warning signals and equipment control commands is carried out in parallel.

[0207] Furthermore, the early warning analysis module 304 can also perform the following steps:

[0208] The power difference is calculated using power control parameters and the current operating power data of the energy storage converter to generate the power change difference.

[0209] The power ramp change time is calculated by using the power change difference and the preset power change rate limit data;

[0210] The power control parameters, power ramp change time, and current time data provided by the system clock are combined into instructions to generate a four-element power control instruction.

[0211] The four-element power control commands are encapsulated according to a preset communication protocol to generate device control commands.

[0212] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for early warning of charge and discharge of an energy storage device, characterized in that, Includes the following steps: Acquire real-time synchronized multi-source operating parameters and environmental parameters of the energy storage system; A health status assessment is performed by integrating the multi-source operating parameters and the environmental parameters to determine the multi-parameter coupling relationship, thereby generating a dynamic system health index. The dynamic system health index and the received charge / discharge switching command are used to perform real-time security permission verification and generate a verification conclusion. Based on the verification results, data is analyzed, and early warning decisions are made through a preset decision mapping table, generating and executing corresponding graded early warning signals and equipment control commands.

2. The method for early warning of charging and discharging of an energy storage device according to claim 1, characterized in that, The step of using the multi-source operating parameters and the environmental parameters to perform a health status assessment that integrates multi-parameter coupling relationships and generates a dynamic system health index includes: Extract the current ripple characteristics and contact temperature transient temperature rise response of the multi-source operating parameters during the charging and discharging transient process; Calculate the temperature rise rate between the ripple coefficient increment of the current ripple characteristic quantity and the transient temperature rise response quantity of the contact temperature; Using the ripple coefficient increment and the temperature rise rate, a time-series correlation model is constructed and the correlation coefficient of the model is calculated. The contactor aging index is calculated using the correlation coefficient, the ripple coefficient increment, and the amplitude of the temperature rise rate. The main circuit insulation health assessment is performed using the time-series variation data of the insulation resistance in the energy storage system and the corresponding environmental humidity data, and the main circuit insulation health index is generated. The main circuit insulation health index and the contactor aging index are combined to generate a dynamic system health index.

3. The method for early warning of charging and discharging of an energy storage device according to claim 2, characterized in that, The step of constructing a time-series correlation model and calculating the correlation coefficient of the model using the ripple coefficient increment and the temperature rise rate includes: Within the charge / discharge switching time window, multiple data points of the ripple coefficient increment and the temperature rise rate change over time are collected to generate original time-series data pairs. The dynamic time warping algorithm is used to eliminate the time lag caused by thermal inertia in the original time series data pairs, and generate synchronized time series data pairs. Based on the synchronized time-series data pairs, a linear regression model is constructed with the ripple coefficient increment as the independent variable and the temperature rise rate as the dependent variable to generate a time-series correlation model. The synchronized time-series data is input into the time-series correlation model to calculate the Pearson product-moment correlation coefficient, thereby generating the correlation coefficient of the time-series correlation model.

4. The method for early warning of charging and discharging of an energy storage device according to claim 2, characterized in that, The step of using time-series variation data of insulation resistance in the energy storage system and corresponding environmental humidity data to perform a main circuit insulation health assessment and generate main circuit insulation health indicators includes: The expected value of the reference insulation resistance at the current moment is calculated by using the time-series variation data of the insulation resistance in the energy storage system and the corresponding environmental humidity data. Calculate the relative attenuation rate between the current measured value of the insulation resistance and the expected value of the reference insulation resistance; Substitute the relative decay rate into a preset mapping function to calculate the normalized health score. The health score is compared with multiple preset risk interval thresholds to determine the risk level corresponding to the health score; Based on the aforementioned risk level, a main circuit insulation health index is constructed.

5. The method for early warning of charging and discharging of an energy storage device according to claim 2, characterized in that, The step of fusing the main circuit insulation health index with the contactor aging index to generate a dynamic system health index includes: Real-time acquisition of the first risk level corresponding to the main circuit insulation health index and the second risk level corresponding to the contactor aging index; Based on the real-time comparison results of the first risk level and the second risk level, a weight allocation scheme is dynamically configured; wherein, the weight allocation rule is: indicators with higher risk levels are assigned higher weights, and when the risk levels are the same, the main circuit insulation health indicator is assigned a higher weight. The main circuit insulation health index and the contactor aging index are weighted and calculated according to the weighting scheme to generate a dynamic system health index.

6. The method for early warning of charging and discharging of an energy storage device according to claim 1, characterized in that, The step of performing real-time security clearance verification using the dynamic system health index and the received charge / discharge switching command to generate a verification conclusion includes: Upon receiving a charge / discharge switching command, a safety verification window is activated and command execution is paused. During the security verification window period, monitor the time-series changes in the health indicators of the dynamic system; The slope of the dynamic system health index within a preset time interval is calculated using the time-series change data; A risk assessment is performed based on the slope of change and the current value of the dynamic system health index, and a risk assessment result is generated. The verification conclusion is constructed using the risk assessment results and the charge / discharge switching command.

7. The method for early warning of charging and discharging of an energy storage device according to claim 6, characterized in that, The step of performing a risk assessment based on the slope of change and the current value of the dynamic system health index, and generating a risk assessment result, includes: When the slope of the change is negative and the absolute value is greater than a preset deterioration threshold, a high-risk assessment result is generated; When the slope of the change is negative but the absolute value is less than or equal to the preset deterioration threshold, and the current value of the dynamic system health index is less than the preset safety threshold, a medium-risk assessment result is generated. When the slope of the change is non-negative and the current value of the dynamic system health index is less than or equal to the preset safety threshold, a medium-risk assessment result is generated. When the slope of the change is non-negative and the current value of the dynamic system health index is greater than a preset safety threshold, a low-risk assessment result is generated.

8. The method for early warning of charging and discharging of an energy storage device according to claim 1, characterized in that, The steps of parsing data based on the verification conclusion, making early warning decisions through a preset decision mapping table, and generating and executing corresponding graded early warning signals and equipment control commands include: Risk level data is extracted from the verification conclusion, and instruction type data is extracted from the charge / discharge switching instruction; Combine the risk level data with the instruction type data into a joint query condition; Filter the warning level code and power control parameters corresponding to the joint query conditions from the preset decision mapping table; Based on the aforementioned warning level code, the corresponding graded warning signal generation logic is triggered to generate graded warning signals; Based on the power control parameters, the corresponding device control command generation logic is triggered to generate device control commands. The issuance of the graded early warning signal and the device control command are executed in parallel.

9. A method for early warning of charging and discharging of an energy storage device according to claim 8, characterized in that, The step of generating device control commands by triggering the corresponding device control command generation logic based on the power control parameters includes: The power difference is calculated using the power control parameters and the current operating power data of the energy storage converter to generate a power change difference. The power slope change time is calculated using the power change difference and the preset power change rate limit data; The power control parameters, the power ramp change time, and the current time data provided by the system clock are combined into instructions to generate a four-element power control instruction. The four power control commands are encapsulated according to a preset communication protocol to generate device control commands.

10. A charging and discharging early warning system for an energy storage device, characterized in that, include: The data acquisition module is used to acquire multi-source operating parameters and environmental parameters of the energy storage system in real time. The data evaluation module is used to perform a health status assessment by integrating the multi-source operating parameters and the environmental parameters to achieve a multi-parameter coupling relationship, and to generate a dynamic system health index. The verification module is used to perform real-time security license verification using the dynamic system health index and the received charge / discharge switching command, and generate a verification conclusion. The early warning analysis module is used to parse data based on the verification conclusion, make early warning decisions through a preset decision mapping table, and generate and execute corresponding graded early warning signals and equipment control commands.

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