Application method of high-efficiency operation low-temperature-rise energy storage battery cluster in energy storage power system

Abstract

The application relates to the technical field of battery management, and discloses an application method of efficiently operating a low-temperature-rise energy storage battery cluster in an energy storage power supply system, which comprises the following steps: collecting current-voltage time sequence data of the energy storage battery cluster, determining battery operation attributes corresponding to the battery cluster, setting a temperature boundary of the battery cluster, analyzing a cell state dimension corresponding to the battery cluster, calculating a boundary offset factor corresponding to the temperature boundary, analyzing a thermal stability state corresponding to the battery cluster, recording cell surface distribution temperature and temperature change trajectories of cells in the battery cluster, analyzing thermal aggregation characteristics and thermal conduction characteristics corresponding to the cells in the battery cluster, calculating thermal runaway risk values corresponding to the cells in the battery cluster, calculating an environmental heat exchange intensity corresponding to the battery cluster, determining a heat dissipation coordination degree of the battery cluster in the energy storage power supply system, and performing low-temperature-rise operation control of the battery cluster to obtain an operation control result. The application can improve the operation efficiency of the battery cluster in the energy storage power supply system.

Description

Technical Field

[0001] This invention relates to a method for the application of high-efficiency low-temperature rise energy storage battery clusters in energy storage power systems, belonging to the field of battery management technology. Background Technology

[0002] With the large-scale integration of renewable energy and the surge in peak-shaving demand in the power system, energy storage power systems have become core facilities for ensuring grid stability. As a key carrier of energy storage, the temperature rise control of energy storage battery clusters is directly related to the safety and lifespan of the system. Currently, low-temperature rise operation technology mainly relies on manual experience-based regulation or fixed threshold temperature control schemes, which have significant defects.

[0003] Currently, traditional battery cluster operation and management methods rely heavily on manual experience and fixed threshold settings. However, maintenance personnel need to monitor a large number of battery parameters (such as voltage, current, and temperature) in real time. Under complex operating conditions, it is difficult to accurately judge the risk of temperature boundary deviation, which can easily lead to local thermal runaway due to response delays. Fixed threshold settings cannot adapt to the real-time changes in cell state dimensions (such as aging degree and internal resistance difference) during the dynamic charging and discharging process of the battery cluster. For example, during high-rate discharge, the spatial non-uniformity of temperature distribution on the cell surface (heat accumulation characteristics) and the temporal non-linearity of temperature change trajectory (heat conduction characteristics) may cause the temperature in a local area to exceed the safety threshold within a few seconds, thereby reducing the operating efficiency of the battery cluster in the energy storage power system. Summary of the Invention

[0004] This invention provides a method for efficiently operating low-temperature energy storage battery clusters in energy storage power systems, the main purpose of which is to improve the operating efficiency of battery clusters in energy storage power systems.

[0005] To achieve the above objectives, the present invention provides a method for the efficient operation of low-temperature rise energy storage battery clusters in energy storage power systems, comprising:

[0006] Collect current and voltage time-series data of the energy storage battery cluster, and determine the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data, so as to set the temperature boundary of the battery cluster;

[0007] The charging and discharging process information of the battery cluster is obtained to analyze the cell state dimension corresponding to the battery cluster. Based on the cell state dimension, the boundary offset factor corresponding to the temperature boundary is calculated to analyze the thermal stability state corresponding to the battery cluster.

[0008] Record the surface temperature distribution and temperature change trajectory of the cells in the battery cluster to analyze the heat accumulation and heat conduction characteristics of the cells in the battery cluster. Combine the heat accumulation and heat conduction characteristics to calculate the thermal runaway risk value of the cells in the battery cluster.

[0009] Calculate the environmental heat transfer intensity corresponding to the battery cluster to determine the heat dissipation synergy of the battery cluster in the energy storage power system;

[0010] By combining the thermal stability state, the thermal runaway risk value, and the heat dissipation synergy, the low-temperature rise operation control of the battery cluster is performed to obtain the operation control results.

[0011] Optionally, determining the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data includes:

[0012] The current and voltage time series data are processed by moving average to obtain smoothed current data and smoothed voltage data;

[0013] The fluctuation and trend characteristics of the smoothed current and smoothed voltage data are extracted respectively.

[0014] The fluctuation and trend features are fused to obtain a fused feature set.

[0015] Based on the fused feature set, the battery operating attributes corresponding to the battery cluster are determined.

[0016] Optionally, the step of extracting the fluctuation characteristics and trend characteristics from the smoothed current data and smoothed voltage data respectively includes:

[0017] The smoothed current data is subjected to amplitude characteristic analysis to obtain the current amplitude characteristics;

[0018] Calculate the voltage change rate corresponding to the smoothed voltage data to obtain the voltage change rate characteristics;

[0019] Calculate the correlation coefficient between the smoothed current data and the smoothed voltage data, and extract the current-voltage correlation features in the smoothed current data and the smoothed voltage data based on the correlation coefficient;

[0020] The current amplitude feature, the voltage change rate feature, and the current-voltage correlation feature are combined to obtain the fluctuation feature;

[0021] Piecewise linear fitting is performed on the smoothed current data and smoothed voltage data respectively to obtain piecewise linear curves;

[0022] Curve trend extraction is performed on the piecewise linear curve to obtain trend features.

[0023] Optionally, determining the battery operating attributes corresponding to the battery cluster to set the temperature boundary of the battery cluster further includes:

[0024] Obtain the application scenario information of the battery cluster, and analyze the external environmental conditions and operating modes corresponding to the application scenarios;

[0025] The heat generation and dissipation characteristics of the battery cluster are analyzed by combining the battery operating attributes, the external environmental conditions, and the operating mode.

[0026] Query the battery safety technical specifications corresponding to the battery cluster, and deduce the basic temperature control range of the battery cluster based on the battery safety technical specifications and the heat generation and dissipation characteristics;

[0027] A lifetime-temperature balance analysis was performed on the basic temperature control range to obtain the optimized temperature control range;

[0028] Based on the optimized temperature control range, the temperature boundary of the battery cluster is set.

[0029] Optionally, obtaining the charging and discharging process information of the battery cluster to analyze the cell state dimension corresponding to the battery cluster further includes:

[0030] Analyze the key change features in the charging and discharging process information, and establish a state correlation mapping relationship between the key change features and the potential state of the battery cluster cells;

[0031] Based on the state association mapping relationship, candidate state dimensions are selected from the set of cell state descriptions;

[0032] Analyze the matching coefficients between the candidate state dimensions and the actual operating performance of the battery cluster;

[0033] Based on the matching coefficient, effective state dimensions are selected from the candidate state dimensions;

[0034] The comprehensiveness of the representation of the effective state dimensions is analyzed, and based on the comprehensiveness of the representation, the cell state dimension corresponding to the battery cluster is determined from the effective state dimensions.

[0035] Optionally, calculating the boundary offset factor corresponding to the temperature boundary based on the cell state dimension includes:

[0036] The state dimension of the battery cell is subjected to feature quantization processing to obtain state feature values;

[0037] Obtain the reference operating status of the battery cluster under standard test conditions;

[0038] The difference between the state feature value and the feature value corresponding to the reference operating state is calculated to obtain the state offset.

[0039] Query the maximum state offset corresponding to the cell chemical system of the battery cluster;

[0040] By combining the maximum state offset with the state offset, the state offset coefficient corresponding to the battery cluster is calculated;

[0041] Based on the state offset coefficient, calculate the boundary offset factor corresponding to the temperature boundary.

[0042] Optionally, calculating the boundary offset factor corresponding to the temperature boundary to analyze the thermal stability state of the battery cluster further includes:

[0043] The boundary offset factor is classified into different levels to obtain the offset level identifier;

[0044] Collect temperature distribution data of the battery cluster during the current operating cycle;

[0045] Based on the temperature distribution data, the temperature distribution dispersion and the center temperature value of the battery cluster are calculated;

[0046] Query the required thermal stability level of the battery cluster in the current application scenario;

[0047] The thermal stability index of the battery cluster is calculated by combining the temperature distribution dispersion, the center temperature value, and the offset level identifier.

[0048] By combining the thermal stability index and the thermal stability level, the thermal stability state of the battery cluster is analyzed.

[0049] Optionally, the step of recording the surface temperature distribution and temperature change trajectory of the battery cells within the battery cluster to analyze the heat accumulation and heat conduction characteristics of the corresponding cells within the battery cluster further includes:

[0050] Calculate the temperature spatial gradient of the surface temperature distribution of the battery cell, and construct the temperature gradient distribution field of the battery cells within the battery cluster based on the temperature spatial gradient;

[0051] The temperature gradient distribution field is subjected to regional clustering to obtain the thermal agglomeration core region;

[0052] Analyze the regional heat flux density spectrum corresponding to the heat accumulation core region, and construct the heat conduction time series correlation matrix corresponding to the temperature change trajectory;

[0053] The dominant heat conduction path is obtained by performing path evolution processing on the heat conduction time-series correlation matrix.

[0054] By combining the regional heat flux density spectrum and the dominant heat conduction path, the heat accumulation characteristics and heat conduction characteristics of the cells within the battery cluster are analyzed.

[0055] Optionally, the step of combining the heat accumulation characteristics and the heat conduction characteristics to calculate the thermal runaway risk value corresponding to the cells within the battery cluster includes:

[0056] Calculate the maximum heat flux density and the area of ​​the heat accumulation region corresponding to the heat accumulation characteristic;

[0057] Calculate the thermal diffusion rate and thermal conduction hindrance coefficient corresponding to the aforementioned thermal conduction characteristics;

[0058] The thermal distribution data of the battery cluster and the current ambient temperature are detected, and a thermal distribution map corresponding to the battery cluster is constructed based on the thermal distribution data.

[0059] Based on the thermal distribution map, the highest temperature value and average temperature rise rate corresponding to the battery cluster are determined;

[0060] Combining the current ambient temperature, the maximum heat flux density, the area of ​​the heat accumulation region, the heat diffusion rate, the heat conduction resistance coefficient, the highest temperature value, and the average temperature rise rate, the thermal runaway risk value corresponding to the cells within the battery cluster is calculated using the following formula:

[0061]

[0062] Where A represents the thermal runaway risk value corresponding to the cell within the battery cluster. Indicates the maximum heat flux density. Indicates the area of ​​the heat accumulation zone. This indicates the highest temperature value. Indicates the current ambient temperature. Indicates the thermal diffusion rate, Indicates the rated cooling rate. Indicates the thermal conduction resistance coefficient. This indicates the average rate of temperature rise.

[0063] Optionally, calculating the environmental heat transfer intensity corresponding to the battery cluster includes:

[0064] Temperature data of each region on the surface of the battery cluster is collected to obtain regional surface temperature data;

[0065] Measure the flow rate and temperature of the cooling medium on the surface of the battery cluster.

[0066] Obtain the effective heat exchange area of ​​the battery cluster in contact with the cooling medium;

[0067] By combining the surface temperature data of the area with the temperature of the cooling medium, the average temperature difference of the battery cluster area is calculated.

[0068] Combining the average temperature difference of the region, the flow rate of the cooling medium, and the effective heat exchange area, the environmental heat transfer intensity corresponding to the battery cluster is calculated using the following formula:

[0069]

[0070] Where E represents the environmental heat transfer intensity corresponding to the battery cluster. This represents the basic heat transfer coefficient corresponding to the a-th region on the surface of the battery cluster. This represents the effective heat transfer area corresponding to the a-th region on the surface of the battery cluster. This represents the average temperature difference of the a-th region on the surface of the battery cluster. This represents the flow rate of the cooling medium corresponding to the a-th region on the surface of the battery cluster. This represents the reference flow rate of the medium corresponding to the a-th region on the surface of the battery cluster, where a represents the sequence number of the surface region of the battery cluster, and r represents the number of surface regions of the battery cluster.

[0071] To address the aforementioned problems, this invention also provides an application system for the efficient operation of low-temperature rise energy storage battery clusters in an energy storage power system, the system comprising:

[0072] The temperature boundary setting module is used to collect current and voltage time-series data of the energy storage battery cluster, and determine the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data, so as to set the temperature boundary of the battery cluster.

[0073] The thermal stability state analysis module is used to acquire the charging and discharging process information of the battery cluster, so as to analyze the cell state dimension corresponding to the battery cluster, and calculate the boundary offset factor corresponding to the temperature boundary based on the cell state dimension, so as to analyze the thermal stability state corresponding to the battery cluster.

[0074] The thermal runaway risk value calculation module is used to record the surface temperature distribution and temperature change trajectory of the cells in the battery cluster, so as to analyze the heat accumulation characteristics and heat conduction characteristics of the cells in the battery cluster, and calculate the thermal runaway risk value of the cells in the battery cluster by combining the heat accumulation characteristics and the heat conduction characteristics.

[0075] The heat dissipation synergy analysis module is used to calculate the environmental heat transfer intensity corresponding to the battery cluster, so as to determine the heat dissipation synergy of the battery cluster in the energy storage power system.

[0076] The operation and control module is used to combine the thermal stability state, the thermal runaway risk value and the heat dissipation synergy to perform low temperature rise operation and control of the battery cluster and obtain the operation and control results.

[0077] Compared to the problems described in the background art, this invention, by determining the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data, can establish a link between the actual current and voltage changes and the battery state, providing a basis for setting temperature boundaries, thereby improving the safety and stability of battery cluster operation. Furthermore, by acquiring the charging and discharging process information of the battery cluster and analyzing the cell state dimensions corresponding to the battery cluster, this invention can quantify the internal state of the battery from multiple perspectives, thereby assessing the adaptability to temperature boundaries and ultimately judging the thermal stability of the battery. This invention, by recording the surface temperature distribution and temperature change trajectory of the cells within the battery cluster, analyzes the heat accumulation and heat conduction characteristics of the cells within the battery cluster, and can identify the heat within the battery cluster. The identification of areas prone to heat accumulation and the main pathways of heat transfer provides data support for subsequent low-temperature rise operation control and ensuring efficient and stable battery operation. Furthermore, by calculating the environmental heat exchange intensity corresponding to the battery cluster, this invention can quantitatively assess the heat exchange capacity of the environment in which the battery cluster is located, thereby clarifying its coordination effect with heat dissipation components in the entire energy storage power system. This provides data support for subsequent analysis and processing of heat dissipation synergy. Furthermore, by combining the thermal stability state, the thermal runaway risk value, and the heat dissipation synergy, this invention executes low-temperature rise operation control of the battery cluster, obtaining operation control results. By coordinating the constraint effects of these three factors on battery operation, the invention guides the battery cluster to maintain a stable low-heat operation state, thereby improving the operating efficiency of the battery cluster in the energy storage power system. Therefore, the application method of the high-efficiency low-temperature rise energy storage battery cluster in the energy storage power system provided by this invention can improve the operating efficiency of the battery cluster in the energy storage power system. Attached Figure Description

[0078] Figure 1 This is a flowchart illustrating the application method of a high-efficiency low-temperature rise energy storage battery cluster in an energy storage power system according to an embodiment of the present invention.

[0079] Figure 2 A schematic diagram of the low-temperature rise operation control process in the application method of the high-efficiency operation low-temperature rise energy storage battery cluster in the energy storage power system provided by the present invention.

[0080] Figure 3 This is a schematic diagram of a module for implementing the application of the high-efficiency low-temperature energy storage battery cluster in an energy storage power system according to an embodiment of the present invention.

[0081] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0082] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0083] This application provides a method for efficiently operating a cryogenic energy storage battery cluster in an energy storage power system. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for efficiently operating a cryogenic energy storage battery cluster in an energy storage power system can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0084] Reference Figure 1 The diagram shown is a flowchart illustrating a method for applying a high-efficiency, low-temperature-rise energy storage battery cluster to an energy storage power system according to an embodiment of the present invention. In this embodiment, the method for applying the high-efficiency, low-temperature-rise energy storage battery cluster to an energy storage power system includes:

[0085] S1. Collect current and voltage time-series data of the energy storage battery cluster, and determine the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data, so as to set the temperature boundary of the battery cluster.

[0086] This invention determines the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data. It can establish a link between the actual current and voltage changes and the battery state, providing a basis for setting temperature boundaries, thereby improving the safety and stability of battery cluster operation. The current and voltage time-series data refers to the sequence of current and voltage values ​​of each battery cell in the battery cluster, which is obtained in real time through the battery management system (BMS) or data acquisition equipment and recorded in chronological order. The battery operating attributes refer to the basic properties reflecting the battery's operating state extracted through the processing and analysis of the current and voltage time-series data, such as charge / discharge rate, internal resistance change trend, and consistency deviation.

[0087] As an embodiment of the present invention, determining the battery operating attributes corresponding to the battery cluster based on the current-voltage timing data includes:

[0088] The current and voltage time series data are processed by moving average to obtain smoothed current data and smoothed voltage data;

[0089] The fluctuation and trend characteristics of the smoothed current and smoothed voltage data are extracted respectively.

[0090] The fluctuation and trend features are fused to obtain a fused feature set.

[0091] Based on the fused feature set, the battery operating attributes corresponding to the battery cluster are determined.

[0092] The smoothed current data and smoothed voltage data are current and voltage data after removing short-term fluctuations; the fluctuation characteristics and the trend characteristics are key information in the data that reflects short-term changes and long-term trends, respectively; the fused feature set is a feature combination formed by integrating the fluctuation characteristics and trend characteristics.

[0093] Furthermore, current and voltage time series data can be smoothed using a moving average algorithm with a window length of 10 seconds; fluctuation features can be extracted using wavelet transform, and trend features can be extracted using polynomial fitting; fluctuation features and trend features can be fused using feature splicing and standardization methods.

[0094] Furthermore, as an optional embodiment of the present invention, the extraction of fluctuation characteristics and trend characteristics from the smoothed current data and smoothed voltage data respectively includes:

[0095] The smoothed current data is subjected to amplitude characteristic analysis to obtain the current amplitude characteristics;

[0096] Calculate the voltage change rate corresponding to the smoothed voltage data to obtain the voltage change rate characteristics;

[0097] Calculate the correlation coefficient between the smoothed current data and the smoothed voltage data, and extract the current-voltage correlation features in the smoothed current data and the smoothed voltage data based on the correlation coefficient;

[0098] The current amplitude feature, the voltage change rate feature, and the current-voltage correlation feature are combined to obtain the fluctuation feature;

[0099] Piecewise linear fitting is performed on the smoothed current data and smoothed voltage data respectively to obtain piecewise linear curves;

[0100] Curve trend extraction is performed on the piecewise linear curve to obtain trend features.

[0101] The current amplitude feature refers to the set of features (such as maximum, minimum, and average current amplitude) obtained after amplitude feature analysis of smoothed current data, reflecting the magnitude, fluctuation range, and peak distribution of current in different time periods. The voltage change rate feature refers to the quantitative feature characterizing the speed of voltage change over time (such as the voltage rise / fall amplitude per unit time) obtained by calculating the voltage change rate of smoothed voltage data. The current-voltage correlation feature refers to the feature extracted based on the correlation coefficient between smoothed current data and smoothed voltage data, reflecting the degree of linear correlation between the two during the change process. The fluctuation feature refers to the composite feature obtained by combining the current amplitude feature, voltage change rate feature, and current-voltage correlation feature, comprehensively reflecting the overall fluctuation state of current and voltage. The piecewise linear curve refers to the curve obtained by piecewise linear fitting of smoothed current data and smoothed voltage data, which is composed of multiple linear segments connected together, with each segment corresponding to a local linear change relationship. The trend feature refers to the feature obtained by extracting the curve trend of the piecewise linear curve, which can reflect the overall change direction and law of current and voltage data (such as overall rising, falling, or stable trend).

[0102] Furthermore, amplitude characteristics can be obtained by calculating the maximum, minimum, range, and root mean square value of smoothed current data within a set time period; voltage change rate can be obtained by calculating the ratio of the voltage difference between adjacent moments to the corresponding time interval, and then the average or maximum change rate within the time period can be taken as the voltage change rate characteristic; the correlation coefficient between smoothed current data and smoothed voltage data can be calculated using the Pearson correlation coefficient formula, and when the absolute value of the correlation coefficient is greater than a preset threshold (such as 0.6), it is used as the core indicator of current-voltage correlation characteristics; and current can be analyzed using feature splicing methods. The amplitude characteristics, voltage change rate characteristics, and current-voltage correlation characteristics are merged, and the characteristics are combined into a one-dimensional vector in a preset order to obtain the fluctuation characteristics. The data segments can be divided using the sliding window method (e.g., the window duration is set to 5 minutes). The data in each window is linearly fitted using the least squares method to obtain the corresponding data segment fitting line. All the data segment fitting lines are connected to form a piecewise linear curve. The trend characteristics can be obtained by extracting the slope (positive slope indicates an upward trend, negative slope indicates a downward trend), intercept, and duration of each segment fitting line in the piecewise linear curve, combined with the overall distribution pattern of the slope of each segment.

[0103] This invention, by setting the temperature boundary of the battery cluster, can clearly define the upper and lower limits of the allowable temperature of the battery cluster during charging and discharging, preventing overheating or overcooling from causing performance degradation or safety accidents. The temperature boundary is a temperature control range determined comprehensively based on battery operating properties, material characteristics, application requirements, and safety standards.

[0104] As an embodiment of the present invention, the step of determining the battery operating attributes corresponding to the battery cluster in order to set the temperature boundary of the battery cluster further includes:

[0105] Obtain the application scenario information of the battery cluster, and analyze the external environmental conditions and operating modes corresponding to the application scenarios;

[0106] The heat generation and dissipation characteristics of the battery cluster are analyzed by combining the battery operating attributes, the external environmental conditions, and the operating mode.

[0107] Query the battery safety technical specifications corresponding to the battery cluster, and deduce the basic temperature control range of the battery cluster based on the battery safety technical specifications and the heat generation and dissipation characteristics;

[0108] A lifetime-temperature balance analysis was performed on the basic temperature control range to obtain the optimized temperature control range;

[0109] Based on the optimized temperature control range, the temperature boundary of the battery cluster is set.

[0110] The application scenario information refers to the actual application field and deployment scenario of the battery cluster; the external environmental conditions are the external environmental factors affecting battery operation in the application scenario; the operating mode is the working method of the battery cluster in the application scenario; the heat generation and dissipation characteristics are the heat generation rate and heat dissipation capability of the battery cluster during operation; the battery safety technical specifications are the battery operating temperature safety standards formulated by the state or industry; the basic temperature control range is the initial temperature control range determined based on the safety technical specifications and heat generation and dissipation characteristics; the life-temperature balance analysis analyzes the impact of different temperature ranges on battery cycle life and finds a balance between life decay and temperature control; the optimized temperature control range is the temperature range that balances life and operating efficiency after life-temperature balance analysis; and the temperature boundary is the upper and lower limits of the safe operating temperature of the battery cluster finally determined based on the optimized temperature control range.

[0111] Furthermore, application scenario information can be obtained by reviewing project planning documents and equipment procurement contracts for battery clusters, or by conducting on-site investigations. External environmental data can be collected by deploying environmental monitoring equipment such as temperature and humidity sensors and anemometers, and combined with the operation logs recorded by the battery management system (BMS), the external environmental conditions and operating modes can be analyzed. Thermal imagers can be used to monitor the battery surface temperature distribution in real time, and the heat generation power can be calculated by combining the charging and discharging current and voltage data recorded by the BMS. Heat dissipation capacity can be evaluated through methods such as heat dissipation duct wind speed testing and heat dissipation area calculation, and heat generation and dissipation characteristics can be analyzed. The full text of national standards can also be accessed through [the system / platform / etc.]. Search battery safety technical specifications on official websites of systems and industry associations to extract temperature-related limitations. Combine the upper temperature limit in the safety technical specifications with heat generation and dissipation characteristics, deduct safety redundancy to determine a basic upper limit, and refer to low-temperature performance data to set a basic lower limit, thus deriving a basic temperature control range. Conduct cycle life tests at multiple temperature points, record the capacity retention rate after cycles, and obtain the relationship curve between temperature and lifespan decay through data fitting. Select the range where the capacity retention rate meets the requirements as the optimized temperature control range. Perform extreme operating condition verification on the optimized temperature control range to confirm that the battery temperature is stable and performance is normal within the range, and finally set the temperature boundary.

[0112] S2. Obtain the charging and discharging process information of the battery cluster to analyze the cell state dimension corresponding to the battery cluster. Based on the cell state dimension, calculate the boundary offset factor corresponding to the temperature boundary to analyze the thermal stability state corresponding to the battery cluster.

[0113] This invention acquires the charging and discharging process information of the battery cluster and analyzes the cell state dimensions corresponding to the battery cluster. This allows for the quantification of the battery's internal state from multiple perspectives, thereby assessing its adaptability to temperature boundaries and ultimately determining the battery's thermal stability. The charging and discharging process information refers to the raw data recorded by the battery management system, reflecting the dynamic changes in current, voltage, time, and temperature during the charging and discharging process. The cell state dimensions refer to various indicators extracted from the processing of the charging and discharging process information to characterize the battery's internal health and stability, such as capacity decay, internal resistance growth trend, and cell consistency. Furthermore, the charging and discharging process information of the battery cluster can be obtained through the battery management system.

[0114] As an embodiment of the present invention, the step of obtaining the charging and discharging process information of the battery cluster to analyze the cell state dimension corresponding to the battery cluster further includes:

[0115] Analyze the key change features in the charging and discharging process information, and establish a state correlation mapping relationship between the key change features and the potential state of the battery cluster cells;

[0116] Based on the state association mapping relationship, candidate state dimensions are selected from the set of cell state descriptions;

[0117] Analyze the matching coefficients between the candidate state dimensions and the actual operating performance of the battery cluster;

[0118] Based on the matching coefficient, effective state dimensions are selected from the candidate state dimensions;

[0119] The comprehensiveness of the representation of the effective state dimensions is analyzed, and based on the comprehensiveness of the representation, the cell state dimension corresponding to the battery cluster is determined from the effective state dimensions.

[0120] The key change features are significant data features in the charging and discharging process information that reflect changes in the cell state (such as voltage fluctuations at the end of charging and discharging, changes in charge retention time, etc.); the potential cell state refers to the possible state of the cell inferred from the charging and discharging rules; the state correlation mapping relationship is the correspondence between key change features and potential cell states; the cell state description set is a set containing various dimensions that may characterize the cell state; the candidate state dimension is a dimension closely related to the key change features selected from the cell state description set; the matching coefficient is the degree of fit between the candidate state dimension and the actual operating performance of the battery; the effective state dimension refers to the candidate state dimension with a high matching coefficient with the actual operating performance; and the comprehensiveness of representation refers to the degree of coverage of various aspects of the cell state by the effective state dimension.

[0121] Furthermore, key change features in the charging and discharging process information can be analyzed using time-series data analysis methods, and state association mapping relationships can be established using association rule mining techniques. Based on the state association mapping relationships, candidate state dimensions can be screened from the set of cell state descriptions using feature matching algorithms, such as the cosine similarity algorithm (calculating the spatial similarity between key change features and state description vectors, retaining dimensions with similarity ≥ 0.8). By comparing the descriptions of candidate state dimensions with the actual operating performance of the battery cluster, such as fault records and performance test results, matching coefficients can be calculated, such as the weighted sum of overlap (proportion of common features) and consistency (trend consistency) (overlap × 0.6 + consistency × 0.4, value range 0-1). Based on the matching coefficients, thresholds can be set to screen out valid state dimensions that exceed the threshold. By checking whether the valid state dimensions cover key aspects such as capacity, internal resistance, and efficiency, the comprehensiveness of their representation can be analyzed, and finally, dimensions that can comprehensively reflect the cell state can be determined.

[0122] This invention calculates the boundary offset factor corresponding to the temperature boundary based on the cell state dimension, which can dynamically capture the adaptation deviation between the temperature boundary and the actual state of the cell, providing a quantitative benchmark for thermal stability analysis. The boundary offset factor is a quantitative reflection of the degree of deviation of the actual temperature from the upper and lower limits of the boundary after integrating the cell state dimension features corresponding to the temperature boundary.

[0123] As an embodiment of the present invention, the step of calculating the boundary offset factor corresponding to the temperature boundary based on the cell state dimension includes:

[0124] The state dimension of the battery cell is subjected to feature quantization processing to obtain state feature values;

[0125] Obtain the reference operating status of the battery cluster under standard test conditions;

[0126] The difference between the state feature value and the feature value corresponding to the reference operating state is calculated to obtain the state offset.

[0127] Query the maximum state offset corresponding to the cell chemical system of the battery cluster;

[0128] By combining the maximum state offset with the state offset, the state offset coefficient corresponding to the battery cluster is calculated;

[0129] Based on the state offset coefficient, calculate the boundary offset factor corresponding to the temperature boundary.

[0130] The state characteristic values ​​are obtained by quantifying the cell state dimensions and specifically reflect the quantitative indicators of the cell state (such as capacity retention rate, measured internal resistance, charge / discharge efficiency, etc.). The reference operating state is the baseline operating state of the battery cluster under standard test conditions (such as ambient temperature of 25℃ and charge / discharge rate of 0.5C), and its corresponding characteristic values ​​are typical values ​​of the initial health state of the cell. The state offset is the difference between the state characteristic values ​​and the corresponding characteristic values ​​of the reference operating state, used to characterize the degree of deviation between the actual state and the baseline state (e.g., if the capacity retention rate is 5% lower than the reference value, the state offset is -5%). The maximum state offset... The displacement is the maximum permissible state deviation of the battery cluster cell chemical system (such as lithium iron phosphate and ternary lithium) within the safe operating range (e.g., if the capacity retention rate is not less than 80%, then the maximum state deviation is -20%). The state deviation coefficient is a normalized coefficient calculated by combining the maximum state deviation and the state deviation, reflecting the proportion of the actual deviation to the maximum permissible deviation (e.g., when the state deviation is -5% and the maximum is -20%, the coefficient is 0.25). The boundary deviation factor is calculated based on the state deviation coefficient and reflects the quantitative value of the degree of deviation of the temperature boundary caused by changes in cell state (e.g., a coefficient of 0.25 corresponds to a boundary deviation of ±2℃).

[0131] Furthermore, statistical analysis methods (such as range standardization and Z-score standardization) can be used to quantify the cell state dimensions, converting dimensions such as capacity and internal resistance into directly calculable state characteristic values. Reference operating states can be obtained from cell factory test reports and new battery standard operating condition tests, recording their corresponding characteristic quantities (e.g., initial capacity 100Ah, internal resistance 20mΩ). The state offset can be obtained through absolute value calculation or the relative difference formula ((actual value - reference value) / reference value × 100%). The maximum state offset can be determined by consulting industry standards for cell chemical systems (e.g., GB / T31484-2015) or manufacturer technical manuals. The state offset coefficient (absolute value, range 0-1) can be calculated by the ratio of the state offset to the maximum state offset. Based on the state offset coefficient and the upper and lower limits of the temperature boundary (e.g., 20-45℃), the boundary offset factor can be calculated using the linear mapping formula (offset factor = coefficient × boundary range / 2) (e.g., when the coefficient is 0.2 and the range is 25℃, the offset factor = 5℃).

[0132] This invention analyzes the thermal stability state of the battery cluster to comprehensively assess the thermal safety risks of the battery in its current state, providing a direct basis for the real-time control of the thermal management system. The thermal stability state is an operational stability description of the battery cluster analyzed based on the boundary offset factor.

[0133] As an embodiment of the present invention, the step of calculating the boundary offset factor corresponding to the temperature boundary to analyze the thermal stability state of the battery cluster further includes:

[0134] The boundary offset factor is classified into different levels to obtain the offset level identifier;

[0135] Collect temperature distribution data of the battery cluster during the current operating cycle;

[0136] Based on the temperature distribution data, the temperature distribution dispersion and the center temperature value of the battery cluster are calculated;

[0137] Query the required thermal stability level of the battery cluster in the current application scenario;

[0138] The thermal stability index of the battery cluster is calculated by combining the temperature distribution dispersion, the center temperature value, and the offset level identifier.

[0139] By combining the thermal stability index and the thermal stability level, the thermal stability state of the battery cluster is analyzed.

[0140] The offset level identifier is a level label for the boundary offset factor divided according to a preset threshold range (e.g., "Level 0" indicates no offset, "Level 1" indicates slight offset, "Level 2" indicates moderate offset, and "Level 3" indicates significant offset); the temperature distribution data is the real-time temperature acquisition value of each cell in the battery cluster during the current operating cycle; the temperature distribution dispersion is a quantitative indicator reflecting the temperature uniformity within the battery cluster (e.g., temperature standard deviation, range, etc.); the center temperature value is a representative value of the overall temperature of the battery cluster (e.g., average temperature, median temperature, etc.); the thermal stability level is the standard requirement for the thermal stability state of the battery cluster in the current application scenario (e.g., "Level A" is suitable for precision energy storage scenarios, and "Level B" is suitable for conventional industrial scenarios); the thermal stability index is a comprehensive quantitative value reflecting the thermal stability state (range 0-10, with higher values ​​indicating better thermal stability) calculated by combining the temperature distribution dispersion, center temperature value, and offset level identifier; the thermal stability state is the stable operating state of the battery cluster under temperature influence, determined by combining the thermal stability index and thermal stability level (e.g., "stable," "basically stable," "requires intervention," etc.).

[0141] Furthermore, the battery can be classified into different levels by setting interval thresholds for the boundary offset factor (e.g., |factor|≤0.1 for level 0, 0.1<|factor|≤0.3 for level 1), thus obtaining the offset level identifier. The temperature of each cell can be collected in real time using a distributed temperature sensor array, and combined with the battery management system records to generate temperature distribution data. Based on the temperature distribution data, the temperature distribution dispersion is calculated using the standard deviation formula (dispersion = √[(Σ(temperature value - average temperature)²) / n]), and the center temperature value is calculated using the arithmetic mean method. The application scenario technical specifications of the battery cluster can be consulted (e.g., industrial and commercial energy storage must meet certain requirements). To determine the required thermal stability level, we calculate the thermal stability index using a weighted summation formula, considering factors such as temperature distribution dispersion (weight 0.3), deviation between the center temperature value and the temperature boundary (weight 0.4), and offset level indicator (weight 0.3). For example, with dispersion of 0.5 and deviation of 0.2, the index for Level 1 is calculated as: 0.5 × 0.3 + 0.2 × 0.4 + 0.8 × 0.3 = 0.53. The thermal stability index is then compared with the threshold range for each thermal stability level (e.g., Level A requires an index ≥ 8, Level B requires 6-8) to determine the thermal stability state (e.g., an index of 8.2 corresponds to "Level A - Stable").

[0142] S3. Record the surface temperature distribution and temperature change trajectory of the cells in the battery cluster to analyze the heat accumulation and heat conduction characteristics of the cells in the battery cluster. Combine the heat accumulation and heat conduction characteristics to calculate the thermal runaway risk value of the cells in the battery cluster.

[0143] This invention analyzes the heat accumulation and heat conduction characteristics of the cells within a battery cluster by recording the surface temperature distribution and temperature change trajectory of the cells. This allows for the identification of areas prone to heat accumulation and the main heat transfer paths within the battery cluster, providing data support for subsequent low-temperature rise operation control and ensuring efficient and stable battery operation. Specifically, recording the surface temperature distribution and temperature change trajectory of the cells within the battery cluster is used to analyze the heat accumulation and heat conduction characteristics of the cells. The temperature change trajectory is a record of the evolution of the surface temperature of one or more cells over a time series, reflecting... The heat accumulation and dissipation trend includes the heat accumulation characteristics, which are characteristic indicators (such as the highest temperature point, regional temperature difference, and heat accumulation area ratio) obtained based on the temperature distribution analysis of the cell surface, used to describe the degree of heat concentration in local areas within the battery cluster; and the heat conduction characteristics, which are characteristic indicators (such as temperature rise rate, temperature equalization rate, and heat conduction directionality) obtained based on temperature change trajectory analysis, used to describe the efficiency and direction of heat transfer within and between cells. Furthermore, the cell surface temperature distribution and temperature change trajectory within the battery cluster can be recorded in conjunction with an infrared thermal imager, a distributed thermocouple array, and a battery management system (BMS).

[0144] As an embodiment of the present invention, the step of recording the surface temperature distribution and temperature change trajectory of the battery cells within the battery cluster to analyze the heat accumulation and heat conduction characteristics of the corresponding cells within the battery cluster further includes:

[0145] Calculate the temperature spatial gradient of the surface temperature distribution of the battery cell, and construct the temperature gradient distribution field of the battery cells within the battery cluster based on the temperature spatial gradient;

[0146] The temperature gradient distribution field is subjected to regional clustering to obtain the thermal agglomeration core region;

[0147] Analyze the regional heat flux density spectrum corresponding to the heat accumulation core region, and construct the heat conduction time series correlation matrix corresponding to the temperature change trajectory;

[0148] The dominant heat conduction path is obtained by performing path evolution processing on the heat conduction time-series correlation matrix.

[0149] By combining the regional heat flux density spectrum and the dominant heat conduction path, the heat accumulation characteristics and heat conduction characteristics of the cells within the battery cluster are analyzed.

[0150] The temperature spatial gradient is a physical quantity reflecting the rate of change of the cell surface temperature in space (e.g., the ratio of temperature difference to distance between adjacent temperature measurement points); the temperature gradient distribution field is composed of the temperature spatial gradients at each point, characterizing the spatial distribution of the degree of temperature change within the battery cluster (e.g., the gradient distribution presented in the form of a heat map); the region clustering process is an analytical method that groups regions with similar characteristics in the temperature gradient distribution field into one category (e.g., regions with gradient value differences ≤5% are classified into the same cluster); the heat accumulation core region is the region with the most drastic temperature change and concentrated heat obtained from clustering in the temperature gradient distribution field (e.g., the cluster region with the highest gradient value); the regional heat flux density spectrum is the distribution characteristic of heat transfer intensity per unit area within the heat accumulation core region (e.g., the heat flux density values ​​and proportions of different sub-regions); the heat conduction time sequence... The correlation matrix is ​​constructed based on temperature change trajectories, reflecting the degree of correlation of temperature changes among different cells over time (e.g., higher element values ​​indicate stronger synchronicity of temperature changes). The path evolution process is the dynamic tracking and filtering of correlation relationships in the thermal conduction time-series correlation matrix to identify the main heat transfer paths. The dominant thermal conduction path is obtained after path evolution processing and represents the main channels for heat transfer within the battery cluster (e.g., the connection path between cells with the highest correlation). The heat accumulation characteristic is obtained by combining the temperature gradient distribution field and the regional heat flux density spectrum, reflecting the characteristics of the heat concentration state (e.g., core area, average heat flux density, etc.). The thermal conduction characteristic is determined based on the thermal conduction time-series correlation matrix and the dominant thermal conduction path, reflecting the characteristics of the heat transfer state (e.g., path transfer efficiency, number of associated cells, etc.).

[0151] Furthermore, the spatial temperature gradient can be calculated by dividing the temperature difference between adjacent temperature measurement points by the distance between the two points (e.g., a gradient of 1.5℃ / cm is obtained when the temperature difference is 3℃ and the distance is 2cm). Then, a temperature gradient distribution field is constructed using spatial interpolation. Based on the temperature gradient distribution field, a density clustering algorithm (e.g., setting the cluster radius to 0.5cm) is used to perform regional clustering, and the clustering region with the largest gradient value is selected as the core region of heat accumulation. Heat flow data of each sub-region within the core region of heat accumulation are collected by a heat flow sensor array, and the area ratio of different heat flow density intervals is statistically analyzed to form a regional heat flow density spectrum. Using a 10s time window, the ratio of temperature changes of any two cells within the same window is calculated to construct a thermal conduction time-series correlation matrix. The element values ​​in the thermal conduction time-series correlation matrix are sorted, and the top 20% of high correlation relationships are selected for path tracing. The dominant thermal conduction path is obtained through path evolution processing. Finally, the area ratio of the heat accumulation core region (weight 0.6) and the peak value of the regional heat flux density spectrum (weight 0.4) are integrated into the heat accumulation feature, and the average correlation value of the dominant thermal conduction path (weight 0.5) and the number of cells covered by the path (weight 0.5) are integrated into the thermal conduction feature.

[0152] This invention calculates the thermal runaway risk value of the cells within the battery cluster by combining the heat accumulation characteristics and the heat conduction characteristics. This can quantify the degree to which the cells within the battery cluster develop from the current thermal state to thermal runaway. The thermal runaway risk value is a quantitative value representing the probability of thermal runaway occurring for the cells within the battery cluster.

[0153] As an embodiment of the present invention, the step of calculating the thermal runaway risk value corresponding to the cells within the battery cluster by combining the thermal accumulation characteristics and the thermal conduction characteristics includes:

[0154] Calculate the maximum heat flux density and the area of ​​the heat accumulation region corresponding to the heat accumulation characteristic;

[0155] Calculate the thermal diffusion rate and thermal conduction hindrance coefficient corresponding to the aforementioned thermal conduction characteristics;

[0156] The thermal distribution data of the battery cluster and the current ambient temperature are detected, and a thermal distribution map corresponding to the battery cluster is constructed based on the thermal distribution data.

[0157] Based on the thermal distribution map, the highest temperature value and average temperature rise rate corresponding to the battery cluster are determined;

[0158] Combining the current ambient temperature, the maximum heat flux density, the area of ​​the heat accumulation region, the heat diffusion rate, the heat conduction resistance coefficient, the highest temperature value, and the average temperature rise rate, the thermal runaway risk value corresponding to the cells in the battery cluster is calculated using the following formula.

[0159] The maximum heat flux density refers to the maximum amount of heat passing through a unit area per unit time in the core heat accumulation region, expressed in W / m², reflecting the intensity of heat accumulation. The area of ​​the heat accumulation region refers to the total area of ​​the region where the temperature exceeds a set threshold in the heat accumulation feature, expressed in m², representing the range of heat concentration. The heat diffusion rate refers to the distance heat diffuses within the battery cluster per unit time, expressed in m / s, reflecting the speed of heat transfer. The thermal conduction resistance coefficient is a dimensionless parameter (value 0-1, where 0 represents no resistance and 1 represents complete resistance) characterizing the degree of resistance to heat transfer between cells, reflecting the ease of heat conduction. The highest temperature value is the highest temperature reading of the cell in the thermal distribution map, expressed in °C, representing the local extreme temperature level. The average temperature rise rate refers to the average temperature rise of the entire battery cluster per unit time, expressed in °C / min, reflecting the overall temperature rise trend. The current ambient temperature is the temperature of the external environment where the battery cluster is located, expressed in °C, serving as a reference for temperature calculation.

[0160] Furthermore, the heat flux density at each point within the heat accumulation area can be measured using a heat flux sensor array, and the maximum value can be taken as the maximum heat flux density. A temperature threshold is set in the heat distribution map, and an image segmentation algorithm is used to calculate the total area of ​​the region exceeding the threshold, thus obtaining the area of ​​the heat accumulation region. By continuously recording the boundary coordinates of the high-temperature region, the average distance the boundary moves per unit time is calculated to obtain the heat diffusion rate. Based on the ratio of the temperature difference to the distance between adjacent cells, combined with the thermal conductivity of the cell casing material, the heat conduction resistance coefficient is calculated. The value of the highest temperature point is extracted from the heat distribution data as the highest temperature value. Temperature data over a certain period of time is selected, and the ratio of the change in average temperature to time is calculated to obtain the average temperature rise rate. The current ambient temperature is directly read by temperature sensors placed around the battery cluster.

[0161] Furthermore, as another embodiment of the present invention, the thermal runaway risk value corresponding to the cell within the battery cluster is calculated using the following formula, combining the current ambient temperature, the maximum heat flux density, the area of ​​the heat accumulation region, the heat diffusion rate, the heat conduction resistance coefficient, the highest temperature value, and the average temperature rise rate: (The formula includes...)

[0162]

[0163] Where A represents the thermal runaway risk value corresponding to the cell within the battery cluster. Indicates the maximum heat flux density. Indicates the area of ​​the heat accumulation zone. This indicates the highest temperature value. Indicates the current ambient temperature. Indicates the thermal diffusion rate, Indicates the rated cooling rate. Indicates the thermal conduction resistance coefficient. This indicates the average rate of temperature rise.

[0164] The rated cooling rate is the temperature reduction of the battery cell per unit time under standard operating conditions (such as ambient temperature 25°C and full load operation), which is obtained through standard cooling performance testing before the system leaves the factory.

[0165] It should be noted that the formula for calculating the thermal runaway risk treats the possibility of thermal runaway of the battery cluster as a mapping between accumulated thermal potential energy and cooling counterbalancing capability, by extracting the maximum heat flux density in the heat accumulation region. (Characterizing heat generation intensity), area of ​​heat accumulation region (Characterizing the range of heat spread), combined with the current temperature difference between the battery cell and the environment. (Characterizing thermally driven potential energy), the rate difference between cooling and heat generation. (Characterizing thermal control capability) and constructing the ratio relationship between the two; its core is to assume that parameters such as heat flow, temperature, and rate are relatively stable within the calculation period, and to quantify the risk of thermal runaway of battery clusters through linear proportional calculation.

[0166] In practical applications, such as in the scenario of power battery clusters for new energy vehicles, the formula can decompose the complex thermal interaction process of the battery cluster into the contributions of heat-generating end characteristics (maximum heat flux density, area of ​​heat accumulation region) and heat-controlling end characteristics (difference between cooling and heat generation rates, temperature asynchrony); for continuous high-power discharge (high (High heat generation) and the liquid cooling system dissipates heat at full capacity (high) When operating under conditions of strong thermal control, or when covering ambient temperatures of -20℃ to 45℃ and operating conditions such as idling, rapid acceleration, and fast charging, the error in predicting the risk of thermal runaway can be reduced.

[0167] S4. Calculate the environmental heat transfer intensity corresponding to the battery cluster to determine the heat dissipation synergy of the battery cluster in the energy storage power system.

[0168] This invention, by calculating the environmental heat exchange intensity corresponding to the battery cluster, can quantitatively evaluate the heat exchange capacity of the environment in which the battery cluster is located, thereby clarifying its cooperation effect with heat dissipation components in the entire energy storage power system, and providing data support for subsequent analysis and processing of heat dissipation synergy. The environmental heat exchange intensity is the amount of heat exchanged between the energy storage battery cluster and the surrounding environment per unit time, which characterizes the heat exchange capacity between the cluster and the environment (the larger the value, the more intense the heat exchange).

[0169] As an embodiment of the present invention, the calculation of the environmental heat transfer intensity corresponding to the battery cluster includes:

[0170] Temperature data of each region on the surface of the battery cluster is collected to obtain regional surface temperature data;

[0171] Measure the flow rate and temperature of the cooling medium on the surface of the battery cluster.

[0172] Obtain the effective heat exchange area of ​​the battery cluster in contact with the cooling medium;

[0173] By combining the surface temperature data of the area with the temperature of the cooling medium, the average temperature difference of the battery cluster area is calculated.

[0174] The environmental heat transfer intensity corresponding to the battery cluster is calculated using the following formula, taking into account the average temperature difference in the region, the flow rate of the cooling medium, and the effective heat transfer area.

[0175] The surface temperature data refers to the real-time temperature records of several sub-regions on the surface of the battery cluster (such as the temperature values ​​of the front, side, and top regions), reflecting the heat dissipation status of different parts; the cooling medium flow rate refers to the speed at which the cooling medium (such as the air blown out by the cooling fan or the coolant in the liquid cooling system) flows on the surface of the battery cluster, affecting the efficiency of heat removal; the cooling medium temperature refers to the temperature of the cooling medium itself, serving as the reference temperature for heat exchange; the effective heat exchange area refers to the actual area of ​​the battery cluster surface that is in direct contact with the cooling medium and undergoes heat exchange, excluding areas that are not in contact or are insulated; the average temperature difference of the region refers to the arithmetic mean of the differences between the surface temperature of each sub-region and the temperature of the cooling medium, reflecting the overall dynamics of heat exchange.

[0176] Furthermore, temperature acquisition plates can be evenly distributed on the surface of the battery cluster, and the readings of each plate can be continuously recorded to obtain the surface temperature data of the region; a flow meter can be used to measure the flow velocity of the cooling medium at different positions close to the cluster surface, and the average value can be taken as the cooling medium flow velocity; a temperature sensor can be placed in the cooling medium flow path to directly read the cooling medium temperature; by measuring the size of the area in contact with the cooling medium on the cluster surface and deducting the area of ​​the obstructed or sealed parts, the effective heat exchange area can be obtained; after calculating the difference between the surface temperature of each sub-region and the cooling medium temperature, the arithmetic mean can be obtained to obtain the average temperature difference of the region.

[0177] Furthermore, as another embodiment of the present invention, the environmental heat transfer intensity corresponding to the battery cluster is calculated using the following formula, combining the average temperature difference of the region, the flow rate of the cooling medium, and the effective heat transfer area:

[0178]

[0179] Where E represents the environmental heat transfer intensity corresponding to the battery cluster. This represents the basic heat transfer coefficient corresponding to the a-th region on the surface of the battery cluster. This represents the effective heat transfer area corresponding to the a-th region on the surface of the battery cluster. This represents the average temperature difference of the a-th region on the surface of the battery cluster. This represents the flow rate of the cooling medium corresponding to the a-th region on the surface of the battery cluster. This represents the reference flow rate of the medium corresponding to the a-th region on the surface of the battery cluster, where a represents the sequence number of the surface region of the battery cluster, and r represents the number of surface regions of the battery cluster.

[0180] The basic heat transfer coefficient is the heat exchange rate per unit area and per unit temperature difference corresponding to the surface region of the battery cluster, which is obtained through material thermal conductivity testing and surface roughness analysis; the medium reference flow velocity is the average flow velocity of the cooling medium (such as air or coolant) corresponding to the surface region of the battery cluster, which is obtained through actual measurement by a flow meter or flow velocity probe.

[0181] It should be noted that the formula for calculating the environmental heat transfer intensity decomposes the overall heat transfer effect on the surface of the battery cluster into a linear superposition of the heat transfer contributions of each sub-region; by introducing a basic heat transfer coefficient... Characterizes the inherent heat transfer capacity of the surface material and structure of the region, and the effective heat transfer area. The area of ​​the quantified region that actually participates in heat exchange, and the average temperature difference of the region. It reflects the regional heat transfer dynamics and is corrected by the flow rate term. The coupling method enables the decomposition calculation of heat transfer on complex surfaces. Its core is to assume that the heat transfer parameters (flow rate, temperature difference, etc.) in each sub-region are uniformly distributed, and to simplify the calculation complexity of the overall heat transfer of the cluster by linear superposition of local characteristics.

[0182] This invention, by determining the heat dissipation synergy of the battery cluster within the energy storage system, can understand the degree of compatibility between the battery cluster's heat dissipation requirements and the system's heat dissipation capacity. The heat dissipation synergy represents the matching degree between the battery cluster's heat dissipation requirements and the system's heat dissipation resources. Furthermore, based on the environmental heat transfer intensity and the rated heat dissipation power of the energy storage system, the heat dissipation synergy of the battery cluster within the energy storage system is determined by calculating the proportional relationship between the two.

[0183] S5. Combining the thermal stability state, the thermal runaway risk value, and the heat dissipation synergy, perform low-temperature rise operation control of the battery cluster to obtain the operation control result.

[0184] This invention combines the thermal stability state, the thermal runaway risk value, and the heat dissipation synergy to perform low-temperature rise operation control of the battery cluster, obtains operation control results, and coordinates the constraint effects of the three factors on battery operation to guide the battery cluster to maintain a stable operation state with low heat generation, thereby improving the operating efficiency of the battery cluster in the energy storage power system.

[0185] Furthermore, during the implementation of low-temperature rise operation control, continuous monitoring is conducted on whether the thermal stability state is sustained (intervention is triggered if temperature fluctuations intensify or temperatures continue to rise), the dynamics of the thermal runaway risk value (constraints are strengthened when the risk value increases), and the matching of heat dissipation coordination (heat dissipation linkage is optimized when coordination is insufficient). When the thermal stability state is stable, the risk value is below the warning value, and heat dissipation coordination is good, the charging and discharging power is maintained, and the airflow of the heat dissipation equipment is finely adjusted. If the thermal stability state is compromised (e.g., a sudden temperature rise), the risk value approaches the critical point, or the coordination decreases, the charging and discharging power is immediately reduced, or the backup heat dissipation circuit is activated. The power adjustment, heat dissipation action, and temperature response are recorded simultaneously and integrated into the operation control results (e.g., after control, the temperature rise of a certain battery cluster is ≤2℃ within 2 hours, and the charging and discharging power is stable). Specifically, for a more intuitive understanding of the low-temperature rise operation control process of the efficient operation of the low-temperature rise energy storage battery cluster in the energy storage power system in this application, please refer to [reference needed]. Figure 2 The diagram shown is a schematic of the low-temperature rise operation control process in the application method of the high-efficiency low-temperature rise energy storage battery cluster in the energy storage power system provided by the present invention. It should be noted that in the present invention, Figure 2 The flowchart presented is only for the low-temperature rise operation management and control of the application method of high-efficiency low-temperature rise energy storage battery clusters in energy storage power systems, and is not limited to the low-temperature rise operation management and control of the application method of high-efficiency low-temperature rise energy storage battery clusters in energy storage power systems in different actual application scenarios.

[0186] Compared to the problems described in the background art, this invention, by determining the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data, can establish a link between the actual current and voltage changes and the battery state, providing a basis for setting temperature boundaries, thereby improving the safety and stability of battery cluster operation. Furthermore, by acquiring the charging and discharging process information of the battery cluster and analyzing the cell state dimensions corresponding to the battery cluster, this invention can quantify the internal state of the battery from multiple perspectives, thereby assessing the adaptability to temperature boundaries and ultimately judging the thermal stability of the battery. This invention, by recording the surface temperature distribution and temperature change trajectory of the cells within the battery cluster, analyzes the heat accumulation and heat conduction characteristics of the cells within the battery cluster, and can identify the heat within the battery cluster. The identification of areas prone to heat accumulation and the main pathways of heat transfer provides data support for subsequent low-temperature rise operation control and ensuring efficient and stable battery operation. Furthermore, by calculating the environmental heat exchange intensity corresponding to the battery cluster, this invention can quantitatively assess the heat exchange capacity of the environment in which the battery cluster is located, thereby clarifying its coordination effect with heat dissipation components in the entire energy storage power system. This provides data support for subsequent analysis and processing of heat dissipation synergy. Furthermore, by combining the thermal stability state, the thermal runaway risk value, and the heat dissipation synergy, this invention executes low-temperature rise operation control of the battery cluster, obtaining operation control results. By coordinating the constraint effects of these three factors on battery operation, the invention guides the battery cluster to maintain a stable low-heat operation state, thereby improving the operating efficiency of the battery cluster in the energy storage power system. Therefore, the application method of the high-efficiency low-temperature rise energy storage battery cluster in the energy storage power system provided by this invention can improve the operating efficiency of the battery cluster in the energy storage power system.

[0187] like Figure 3 The diagram shown is a functional block diagram of an application system for a high-efficiency low-temperature energy storage battery cluster in an energy storage power system according to the present invention.

[0188] The high-efficiency low-temperature rise energy storage battery cluster application system 300 described in this invention can be installed in an electronic device. Depending on the functions implemented, the high-efficiency low-temperature rise energy storage battery cluster application system may include a temperature boundary setting module 301, a thermal stability state analysis module 302, a thermal runaway risk value calculation module 303, a heat dissipation synergy analysis module 304, and an operation control module 305. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0189] In this embodiment of the invention, the functions of each module / unit are as follows:

[0190] The temperature boundary setting module 301 is used to collect current and voltage time-series data of the energy storage battery cluster, and determine the battery operating attributes corresponding to the battery cluster based on the current and voltage time-series data, so as to set the temperature boundary of the battery cluster.

[0191] The thermal stability state analysis module 302 is used to acquire the charging and discharging process information of the battery cluster, so as to analyze the cell state dimension corresponding to the battery cluster, and calculate the boundary offset factor corresponding to the temperature boundary based on the cell state dimension, so as to analyze the thermal stability state corresponding to the battery cluster.

[0192] The thermal runaway risk value calculation module 303 is used to record the surface temperature distribution and temperature change trajectory of the cells in the battery cluster, so as to analyze the heat accumulation characteristics and heat conduction characteristics of the cells in the battery cluster, and calculate the thermal runaway risk value of the cells in the battery cluster by combining the heat accumulation characteristics and the heat conduction characteristics.

[0193] The heat dissipation synergy analysis module 304 is used to calculate the environmental heat transfer intensity corresponding to the battery cluster, so as to determine the heat dissipation synergy of the battery cluster in the energy storage power system.

[0194] The operation control module 305 is used to combine the thermal stability state, the thermal runaway risk value and the heat dissipation synergy to perform low temperature rise operation control of the battery cluster and obtain the operation control result.

[0195] In detail, the modules in the application system 300 of the high-efficiency low-temperature energy storage battery cluster in the energy storage power system described in this embodiment of the invention adopt the same characteristics as described above during use. Figure 1 The high-efficiency operation of the low-temperature energy storage battery cluster described in the article is the same as the application method of the energy storage power system, and can produce the same technical effect, so it will not be repeated here.

[0196] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0197] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An application method of high-efficiency operation of a low-temperature-rise energy storage battery cluster in an energy storage power system, characterized in that, The method comprises: Collecting current-voltage time series data of the energy storage battery cluster, determining the battery operation attribute corresponding to the battery cluster based on the current-voltage time series data, and setting the temperature boundary of the battery cluster; Obtaining the charging and discharging process information of the battery cluster to analyze the cell state dimension corresponding to the battery cluster, calculating the boundary offset factor corresponding to the temperature boundary based on the cell state dimension, and analyzing the thermal stability state corresponding to the battery cluster; Recording the cell surface distribution temperature and temperature change trajectory of the battery cluster, analyzing the thermal aggregation characteristics and heat conduction characteristics corresponding to the battery cluster, and calculating the thermal runaway risk value corresponding to the battery cluster based on the thermal aggregation characteristics and the heat conduction characteristics; Calculating the environmental heat exchange intensity corresponding to the battery cluster to determine the heat dissipation coordination degree of the battery cluster in the energy storage power supply system; Combining the thermal stability state, the thermal runaway risk value and the heat dissipation coordination degree, performing low temperature rise operation control of the battery cluster, and obtaining the operation control result.

2. The method of claim 1, wherein the high-efficiency, low-temperature-rise, energy storage battery cluster is applied to an energy storage power system, and the energy storage power system comprises a plurality of energy storage battery clusters, a plurality of energy storage battery clusters are connected in parallel, and the energy storage battery cluster is connected to a power grid through a power converter. The method comprises: Performing sliding average processing on the current-voltage time series data to obtain smoothed current data and smoothed voltage data; Respectively extracting fluctuation characteristics and trend characteristics from the smoothed current data and the smoothed voltage data; Performing feature fusion processing on the fluctuation characteristics and the trend characteristics to obtain a fusion feature set; Based on the fusion feature set, the battery operation attribute corresponding to the battery cluster is determined.

3. The application method of high-efficient operation of low-temperature-rise energy storage battery cluster in energy storage power system according to claim 2, characterized in that, The method comprises: Performing amplitude feature analysis on the smoothed current data to obtain current amplitude characteristics; Calculating the voltage change rate corresponding to the smoothed voltage data to obtain voltage change rate characteristics; Calculating the correlation coefficient between the smoothed current data and the smoothed voltage data, and extracting the current-voltage correlation characteristics from the smoothed current data and the smoothed voltage data based on the correlation coefficient; Performing feature merging on the current amplitude characteristics, the voltage change rate characteristics and the current-voltage correlation characteristics to obtain fluctuation characteristics; Respectively performing piecewise linear fitting on the smoothed current data and the smoothed voltage data to obtain piecewise linear curves; Performing curve trend extraction on the piecewise linear curves to obtain trend characteristics.

4. The application method of the high-efficiency operation low-temperature rise energy storage battery cluster in an energy storage power system as described in claim 1, characterized in that, The method further comprises: Obtaining application scenario information of the battery cluster, analyzing external environmental conditions and operation modes corresponding to the application scenario; Combining the battery operation attribute, the external environmental conditions and the operation mode, analyzing the heat generation and heat dissipation characteristics of the battery cluster; Querying the battery safety technical specification corresponding to the battery cluster, and deriving the basic temperature control interval of the battery cluster based on the battery safety technical specification and the heat generation and heat dissipation characteristics; Performing life-temperature balance analysis on the basic temperature control interval to obtain an optimized temperature control interval; Based on the optimized temperature control interval, the temperature boundary of the battery cluster is set.

5. The application method of the high-efficiency operation low-temperature rise energy storage battery cluster in an energy storage power system as described in claim 1, characterized in that, The obtaining the charging and discharging process information of the battery cluster to analyze the corresponding cell state dimension of the battery cluster further includes: Resolving the key change characteristics in the charging and discharging process information, establishing a state association mapping relationship between the key change characteristics and the potential state of the battery cluster cell; Based on the state association mapping relationship, the candidate state dimension is selected from the cell state description set; Analyze the matching coefficient of the candidate state dimension and the actual operation performance of the battery cluster; Based on the matching coefficient, the effective state dimension is selected from the candidate state dimension; Analyze the representation comprehensiveness of the effective state dimension, and determine the corresponding cell state dimension of the battery cluster based on the representation comprehensiveness.

6. The application method of the high-efficiency operation low-temperature rise energy storage battery cluster in an energy storage power system as described in claim 1, characterized in that, The calculation of the boundary offset factor corresponding to the temperature boundary based on the cell state dimension includes: Carrying out feature quantization processing on the cell state dimension to obtain a state feature value; Obtain the reference running state of the battery cluster under standard test conditions; Calculate the difference between the state feature value and the corresponding feature value of the reference running state to obtain a state offset; Query the maximum state offset corresponding to the chemical system of the battery cluster cell; Combine the maximum state offset and the state offset to calculate the state offset coefficient corresponding to the battery cluster; Based on the state offset coefficient, the boundary offset factor corresponding to the temperature boundary is calculated.

7. The application method of the high-efficiency operating low-temperature rise energy storage battery cluster in an energy storage power system as described in claim 1, characterized in that, The calculation of the boundary offset factor corresponding to the temperature boundary to analyze the thermal stability state of the battery cluster further includes: Carrying out grade division processing on the boundary offset factor to obtain an offset grade identifier; Collecting the temperature distribution data of the battery cluster in the current running period; Based on the temperature distribution data, calculate the temperature distribution dispersion and the center temperature value of the battery cluster; Query the thermal stability level required in the current application scenario of the battery cluster; Combine the temperature distribution dispersion, the center temperature value and the offset grade identifier to calculate the thermal stability index of the battery cluster; Combine the thermal stability index and the thermal stability level to analyze the thermal stability state of the battery cluster.

8. The application method of the high-efficiency operation low-temperature rise energy storage battery cluster in an energy storage power system as described in claim 1, characterized in that, The record of the cell surface distribution temperature and the temperature change trajectory of the battery cluster core is used to analyze the thermal aggregation characteristics and the heat conduction characteristics of the battery cluster core, and further includes: Calculate the temperature spatial gradient of the cell surface distribution temperature, and construct the temperature gradient distribution field of the battery cluster core based on the temperature spatial gradient; Carrying out regional clustering processing on the temperature gradient distribution field to obtain a thermal aggregation core area; Analyze the regional heat flux density spectrum corresponding to the thermal aggregation core area, and construct the heat conduction time sequence correlation matrix corresponding to the temperature change trajectory; Carrying out path evolution processing on the heat conduction time sequence correlation matrix to obtain a dominant heat conduction path; Combine the regional heat flux density spectrum and the dominant heat conduction path to analyze the thermal aggregation characteristics and the heat conduction characteristics of the battery cluster core.

9. The method of claim 1, wherein the high efficiency low temperature rise energy storage battery cluster energy storage power system application is characterized by, The combination of the thermal aggregation characteristics and the heat conduction characteristics to calculate the thermal runaway risk value corresponding to the battery cluster core includes: calculating a maximum heat flux density and a heat accumulation area corresponding to the heat accumulation feature; calculating a heat diffusion rate and a heat conduction resistance coefficient corresponding to the heat conduction feature; detecting thermal distribution data of the battery cluster and a current ambient temperature, and constructing a thermal distribution map corresponding to the battery cluster based on the thermal distribution data; determining a maximum temperature value and an average temperature rise rate corresponding to the battery cluster based on the thermal distribution map; combining the current ambient temperature, the maximum heat flux density, the heat accumulation area, the heat diffusion rate, the heat conduction resistance coefficient, the maximum temperature value, and the average temperature rise rate, and calculating a thermal runaway risk value corresponding to the core in the battery cluster by using the following formula: wherein A represents a thermal runaway risk value corresponding to the core within the battery cluster, represents a maximum heat flux density, represents a thermal accumulation area, represents a maximum temperature value, represents a current ambient temperature, represents a heat diffusion rate, represents a rated cooling rate, represents a thermal conduction retardation coefficient, represents an average temperature rise rate.

10. The method of claim 1, wherein the high efficiency low temperature rise energy storage battery cluster energy storage power system is characterized by, The calculation of the environmental heat exchange intensity corresponding to the battery cluster comprises: collecting temperature data of each region on the surface of the battery cluster to obtain regional surface temperature data; measuring the cooling medium flow rate and the cooling medium temperature corresponding to the cooling medium on the surface of the battery cluster; obtaining the effective heat exchange area of the battery cluster in contact with the cooling medium; combining the regional surface temperature data and the cooling medium temperature to calculate the regional average temperature difference of the battery cluster; combining the regional average temperature difference, the cooling medium flow rate, and the effective heat exchange area, and calculating the environmental heat exchange intensity corresponding to the battery cluster by using the following formula: wherein E represents the environmental heat exchange intensity corresponding to the battery cluster, represents the basic heat exchange coefficient corresponding to the a-th region on the surface of the battery cluster, represents the effective heat exchange area corresponding to the a-th region on the surface of the battery cluster, represents the region average temperature difference corresponding to the a-th region on the surface of the battery cluster, represents the cooling medium flow rate corresponding to the a-th region on the surface of the battery cluster, represents the medium reference flow rate corresponding to the a-th region on the surface of the battery cluster, a represents the serial number of the region on the surface of the battery cluster, and r represents the number of regions on the surface of the battery cluster.

Images