Application method for efficiently operating low-temperature-rise energy storage battery cluster in energy storage power supply system

Abstract

The invention relates to the technical field of battery management, and discloses an application method for efficiently operating a low-temperature-rise energy storage battery cluster in an energy storage power supply system, which comprises the following steps of: acquiring current and voltage time sequence data of the energy storage battery cluster, and determining a battery operation attribute corresponding to the battery cluster so as to set a temperature boundary of the battery cluster; calculating a boundary deviation factor corresponding to the temperature boundary to analyze a battery cell state dimension corresponding to the battery cluster, and calculating a boundary deviation factor corresponding to the temperature boundary to analyze a thermal stability state corresponding to the battery cluster; the cell surface distribution temperature and the temperature change track of the cell in the battery cluster are recorded to analyze the thermal aggregation characteristic and the thermal conduction characteristic corresponding to the cell in the battery cluster, and the thermal runaway risk value corresponding to the cell in the battery cluster is calculated; calculating the environment heat exchange intensity corresponding to the battery cluster to determine the heat dissipation collaboration degree of the battery cluster in the energy storage power supply system; and executing low-temperature-rise operation management and control of the battery cluster to obtain an operation management and control result. The operation efficiency of the battery cluster in the energy storage power supply system can be improved.

Description

TECHNICAL FIELD

[0001] The application relates to an application method of high-efficiency operation of a low-temperature-rise energy storage battery cluster in an energy storage power system, and belongs to the technical field of battery management. BACKGROUND

[0002] With large-scale access of renewable energy and surging demand for power system peak shaving, an energy storage power system has become a core facility for guaranteeing stability of a power grid, and a battery cluster as a key carrier for energy storage is directly related to system safety and service life in terms of operation temperature rise control. Current low-temperature-rise operation technology mainly relies on artificial experience regulation or a fixed threshold temperature control scheme, and has significant defects.

[0003] At present, traditional battery cluster operation control methods mainly rely on artificial experience and fixed threshold setting, but operation and maintenance personnel need to monitor a large amount of battery parameters (such as voltage, current and temperature) in real time, and it is difficult to accurately judge temperature boundary deviation risks in complex working conditions, and local thermal runaway may be caused due to response delay. The fixed threshold setting cannot adapt to real-time changes of battery cluster state dimensions (such as aging degree and internal resistance difference) in a dynamic charging and discharging process. For example, when high-rate discharging, spatial non-uniformity (thermal aggregation feature) of cell surface distribution temperature and time nonlinearity (thermal conduction feature) of temperature change trajectory are superimposed, and the temperature in a local area may break through a safety threshold in a few seconds, thereby reducing the operation efficiency of the battery cluster in the energy storage power system. SUMMARY

[0004] The application provides an application method of high-efficiency operation of a low-temperature-rise energy storage battery cluster in an energy storage power system, and the main purpose is to improve the operation efficiency of the battery cluster in the energy storage power system.

[0005] To achieve the above purpose, the application provides an application method of high-efficiency operation of a low-temperature-rise energy storage battery cluster in an energy storage power 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 based on the current-voltage time sequence data, and setting a temperature boundary of the battery cluster; Obtaining charging and discharging process information of the battery cluster, analyzing cell state dimensions corresponding to the battery cluster based on the cell state dimensions, calculating a boundary deviation factor corresponding to the temperature boundary based on the cell state dimensions, and analyzing a thermal stability state corresponding to the battery cluster; Recording cell surface distribution temperature and temperature change trajectory of a cell in the battery cluster, analyzing thermal aggregation features and thermal conduction features corresponding to the cell in the battery cluster, and calculating a thermal runaway risk value corresponding to the cell in the battery cluster in combination with the thermal aggregation features and the thermal conduction features; corresponding to the battery cluster is calculated to determine heat dissipation coordination of the battery cluster in the energy storage power system; In combination with the thermal stability state, the thermal runaway risk value and the heat dissipation coordination, low-temperature rise operation control of the battery cluster is performed to obtain an operation control result.

[0006] Optionally, the battery operation attribute corresponding to the battery cluster is determined based on the current-voltage time series data, including: The current-voltage time series data is subjected to sliding average processing to obtain smoothed current data and smoothed voltage data; Fluctuation features and trend features in the smoothed current data and the smoothed voltage data are respectively extracted; The fluctuation features and the trend features are subjected to feature fusion processing to obtain a fusion feature set; The battery operation attribute corresponding to the battery cluster is determined based on the fusion feature set.

[0007] Optionally, the fluctuation features and the trend features in the smoothed current data and the smoothed voltage data are respectively extracted, including: The smoothed current data is subjected to amplitude feature analysis to obtain current amplitude features; A voltage change rate corresponding to the smoothed voltage data is calculated to obtain voltage change rate features; A correlation coefficient between the smoothed current data and the smoothed voltage data is calculated, and based on the correlation coefficient, current-voltage correlation features in the smoothed current data and the smoothed voltage data are extracted; The current amplitude features, the voltage change rate features and the current-voltage correlation features are subjected to feature merging to obtain fluctuation features; The smoothed current data and the smoothed voltage data are respectively subjected to piecewise linear fitting to obtain piecewise linear curves; Curve trend features are extracted from the piecewise linear curves to obtain trend features.

[0008] Optionally, the battery operation attribute corresponding to the battery cluster is determined to set a temperature boundary of the battery cluster, further including: Application scenario information of the battery cluster is acquired, and external environment conditions and operation modes corresponding to the application scenario are analyzed; In combination with the battery operation attribute, the external environment conditions and the operation modes, heat generation and heat dissipation characteristics of the battery cluster are analyzed; A battery safety technical specification corresponding to the battery cluster is queried, and based on the battery safety technical specification and the heat generation and heat dissipation characteristics, a basic temperature control interval of the battery cluster is derived; 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, set the temperature boundary of the battery cluster.

[0009] Optionally, the acquisition of the charge-discharge process information of the battery cluster is to analyze the corresponding battery cell state dimension of the battery cluster, further comprising: Analyzing the key change characteristics in the charge-discharge process information, and 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, filter out the candidate state dimension 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, filter out the effective state dimension from the candidate state dimension; Analyze the representation comprehensiveness of the effective state dimension, and based on the representation comprehensiveness, determine the corresponding battery cell state dimension of the battery cluster from the effective state dimension.

[0010] Optionally, the calculation of the boundary offset factor corresponding to the temperature boundary based on the battery cell state dimension comprises: Performing feature quantization processing on the battery 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, calculate the boundary offset factor corresponding to the temperature boundary.

[0011] Optionally, the calculation of the boundary offset factor corresponding to the temperature boundary to analyze the thermal stability state corresponding to the battery cluster further comprises: Performing hierarchical division processing on the boundary offset factor to obtain an offset level identifier; Collecting 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; In combination with the temperature distribution dispersion, the center temperature value and the offset level identifier, a thermal stability index of the battery cluster is calculated; In combination with the thermal stability index and the thermal stability level, a thermal stability state corresponding to the battery cluster is analyzed.

[0012] Optionally, the battery core surface distribution temperature and temperature change trajectory of the battery core in the battery cluster are recorded to analyze the thermal aggregation characteristics and thermal conduction characteristics of the battery core in the battery cluster, and the method further comprises: A temperature spatial gradient of the battery core surface distribution temperature is calculated, and a temperature gradient distribution field of the battery core in the battery cluster is constructed based on the temperature spatial gradient; The temperature gradient distribution field is subjected to regional clustering processing to obtain a thermal aggregation core region; A regional heat flux density spectrum corresponding to the thermal aggregation core region is analyzed, and a thermal conduction time sequence correlation matrix corresponding to the temperature change trajectory is constructed; The thermal conduction time sequence correlation matrix is subjected to path evolution processing to obtain a dominant thermal conduction path; In combination with the regional heat flux density spectrum and the dominant thermal conduction path, the thermal aggregation characteristics and the thermal conduction characteristics of the battery core in the battery cluster are analyzed.

[0013] Optionally, in combination with the thermal aggregation characteristics and the thermal conduction characteristics, a thermal runaway risk value corresponding to the battery core in the battery cluster is calculated, and the method comprises: A maximum heat flux density and a thermal aggregation region area corresponding to the thermal aggregation characteristics are calculated; A thermal diffusion rate and a thermal conduction resistance coefficient corresponding to the thermal conduction characteristics are calculated; Thermal distribution data of the battery cluster and a current environment temperature are detected, and a thermal distribution map corresponding to the battery cluster is constructed based on the thermal distribution data; Based on the thermal distribution map, a highest temperature value and an average temperature rise rate corresponding to the battery cluster are determined; In combination with the current environment temperature, the maximum heat flux density, the thermal aggregation region area, the thermal diffusion rate, the thermal conduction resistance coefficient, the highest temperature value and the average temperature rise rate, a thermal runaway risk value corresponding to the battery core in the battery cluster is calculated by using the following formula: Wherein, A represents a thermal runaway risk value corresponding to the battery core in the battery cluster, represents the maximum heat flux density, represents the thermal aggregation region area, represents the highest temperature value, represents the current environment temperature, represents the thermal diffusion rate, represents a rated cooling rate, represents a thermal conduction resistance coefficient, represents an average temperature rise rate.

[0014] Optionally, 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 using the following formula to calculate the environmental heat exchange intensity corresponding to the battery cluster: 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 regional 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.

[0015] To solve the above problems, the application further provides an application system of a high-efficiency operation low-temperature-rise energy storage battery cluster in an energy storage power supply system, which comprises: a temperature boundary setting module, configured to collect current-voltage time sequence data of the energy storage battery cluster, determine battery operation attributes corresponding to the battery cluster based on the current-voltage time sequence data, and set a temperature boundary of the battery cluster; a thermal stability state analysis module, configured to obtain charge-discharge process information of the battery cluster, analyze a battery cell state dimension corresponding to the battery cluster, calculate a boundary offset factor corresponding to the temperature boundary based on the battery cell state dimension, and analyze a thermal stability state corresponding to the battery cluster; a thermal runaway risk value calculation module, configured to record the surface distribution temperature and temperature change trajectory of the battery cells in the battery cluster, analyze the thermal aggregation characteristics and thermal conduction characteristics of the battery cells in the battery cluster, and calculate the thermal runaway risk value of the battery cells in the battery cluster in combination with the thermal aggregation characteristics and the thermal conduction characteristics; a heat dissipation coordination degree analysis module, configured to calculate the environmental heat exchange intensity corresponding to the battery cluster, and determine the heat dissipation coordination degree of the battery cluster in the energy storage power supply system; a running control module, configured to perform low-temperature-rise running control of the battery cluster in combination with the thermal stability state, the thermal runaway risk value and the heat dissipation coordination degree, and obtain a running control result.

[0016] Compared with the problems in the background art, the application can establish a connection between the current-voltage change in actual operation and the battery state based on the current-voltage time series data, provide a basis for setting a temperature boundary, and thus improve the safety and stability of the battery cluster operation. Furthermore, the application can quantify the battery internal state from multiple angles by obtaining the charging and discharging process information of the battery cluster and analyzing the battery cell state dimension corresponding to the battery cluster, and thus evaluate the adaptability of the temperature boundary, and finally realize the judgment of the thermal stability of the battery. The application can identify the region where heat is easily aggregated and the main path of heat transfer in the battery cluster by recording the surface distribution temperature and temperature change trajectory of the battery cells in the battery cluster, analyzing the thermal aggregation characteristics and thermal conduction characteristics of the battery cells in the battery cluster, and providing data support for subsequent implementation of low-temperature-rise running control and guarantee of efficient and stable operation of the battery. Furthermore, the application can quantify and evaluate the heat exchange capacity of the environment where the battery cluster is located by calculating the environmental heat exchange intensity corresponding to the battery cluster, and thus determine the cooperation effect of the battery cluster with the heat dissipation components in the entire energy storage power supply system, and provide data support for subsequent analysis and processing of the heat dissipation coordination degree. Furthermore, the application can perform low-temperature-rise running control of the battery cluster in combination with the thermal stability state, the thermal runaway risk value and the heat dissipation coordination degree, obtain a running control result, and guide the battery cluster to maintain a low-heat stable running state by coordinating the constraint effects of the three on the battery operation, and thus improve the running efficiency of the battery cluster in the energy storage power supply system. Therefore, the application embodiment provides an application method of efficiently running a low-temperature-rise energy storage battery cluster in an energy storage power supply system, which can improve the running efficiency of the battery cluster in the energy storage power supply system. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of the application method of efficiently running a low-temperature-rise energy storage battery cluster in an energy storage power supply system provided by an embodiment of the application is shown in the figure. Figure 2A low-temperature-rise operation control flow diagram in an application method of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system is provided. Figure 3 A module diagram for implementing the application method of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system is provided.

[0018] The object implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are merely intended to explain the present application and not to limit the present application.

[0020] Embodiments of the present application provide an application method of a high-efficiency low-temperature-rise energy storage battery cluster in an energy storage power supply system. The execution subject of the application method of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system includes but is not limited to at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the application method of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system can be executed by software or hardware installed in 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, etc.

[0021] Reference Figure 1 A flow diagram of the application method of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system is provided. In the embodiment, the application method of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system includes: S1, collecting current-voltage time series data of the energy storage battery cluster, determining battery operation attributes corresponding to the battery cluster based on the current-voltage time series data, and setting a temperature boundary of the battery cluster.

[0022] The present application can establish a connection between current-voltage changes in actual operation and battery state by determining battery operation attributes corresponding to the battery cluster based on the current-voltage time series data, and can provide a basis for setting the temperature boundary, thereby improving the safety and stability of the battery cluster operation. The current-voltage time series data refers to the current and voltage value sequence of each battery unit in the battery cluster recorded in time sequence by a battery management system (BMS) or a data acquisition device in real time. The battery operation attributes refer to the basic properties reflecting the working state of the battery extracted by processing and analyzing the current-voltage time series data, such as charge-discharge rate, internal resistance change trend, consistency deviation, etc.

[0023] As an embodiment of the present application, the battery operation attribute corresponding to the battery cluster is determined based on the current-voltage time series data, including: The current-voltage time series data is subjected to a sliding average processing to obtain smoothed current data and smoothed voltage data; The fluctuation features and trend features in the smoothed current data and smoothed voltage data are extracted respectively; The fluctuation features and trend features are subjected to a feature fusion processing to obtain a fusion feature set; The battery operation attribute corresponding to the battery cluster is determined based on the fusion feature set.

[0024] The smoothed current data and smoothed voltage data are current-voltage data after short-term fluctuations are removed; the fluctuation features and trend features are key information reflecting short-term changes and long-term trends in the data; and the fusion feature set is a feature combination formed after the fluctuation features and trend features are integrated.

[0025] Further, the current-voltage time series data can be subjected to a smoothing processing by a sliding average algorithm with a window length of 10 seconds; the fluctuation features can be extracted by a wavelet transform, and the trend features can be extracted by a polynomial fitting; and the fluctuation features and trend features can be subjected to a fusion processing by a feature splicing and standardization method.

[0026] Further, as an optional embodiment of the present application, the fluctuation features and trend features in the smoothed current data and smoothed voltage data are extracted respectively, including: The smoothed current data is subjected to an amplitude feature analysis to obtain current amplitude features; A voltage change rate corresponding to the smoothed voltage data is calculated to obtain voltage change rate features; A correlation coefficient between the smoothed current data and smoothed voltage data is calculated, and based on the correlation coefficient, current-voltage correlation features in the smoothed current data and smoothed voltage data are extracted; The current amplitude features, voltage change rate features, and current-voltage correlation features are subjected to a feature merging to obtain fluctuation features; The smoothed current data and smoothed voltage data are subjected to a piecewise linear fitting respectively to obtain piecewise linear curves; The piecewise linear curves are subjected to a curve trend extraction to obtain trend features.

[0027] The current amplitude feature refers to a feature set reflecting the amplitude size, fluctuation range and peak value distribution of the current in different time periods obtained by performing amplitude feature analysis on the smoothed current data (such as current maximum value, minimum value, average amplitude value, etc.); the voltage change rate feature refers to a quantitative feature representing the speed of voltage change with time obtained by calculating the voltage change rate of the smoothed voltage data (such as the voltage rise / fall amplitude per unit time); the current-voltage correlation feature refers to a feature extracted based on the correlation coefficient of the smoothed current data and the smoothed voltage data, which represents the linear correlation degree of the two in the change process; the fluctuation feature refers to a composite feature obtained by merging the current amplitude feature, the voltage change rate feature and the current-voltage correlation feature, which comprehensively reflects the overall fluctuation state of the current and voltage; the segmented linear curve refers to a curve obtained by performing segmented linear fitting on the smoothed current data and the smoothed voltage data respectively, which is connected by multiple linear segments, and each segment corresponds to a local linear change relationship; and the trend feature refers to a feature obtained by extracting the curve trend of the segmented linear curve, which can reflect the overall change direction and law of the current and voltage data (such as overall upward, downward or stable trend).

[0028] Further, the current amplitude feature can be obtained by calculating the maximum value, minimum value, range and root mean square value of the smoothed current data in a set time period; the voltage change rate feature can be obtained by calculating the voltage difference between adjacent time points and the ratio of the corresponding time interval, and then taking the average change rate or the maximum change rate in the time period as the voltage change rate feature; the current-voltage correlation feature can be obtained by calculating the correlation coefficient between the smoothed current data and the smoothed voltage data 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 taken as the core index of the current-voltage correlation feature; the fluctuation feature can be obtained by merging the current amplitude feature, the voltage change rate feature and the current-voltage correlation feature through feature splicing, and combining each feature into a one-dimensional vector in a predetermined order; the segmented linear curve can be obtained by dividing the data segments using the sliding window method (such as setting the window length to 5 minutes), performing linear fitting on the data in each window using the least squares method to obtain the fitting straight line of the corresponding data segment, and connecting all the data segment fitting straight lines to form the segmented linear curve; and the trend feature can be obtained by extracting the slope (positive slope indicating an upward trend, negative slope indicating a downward trend), intercept and duration of each fitting straight line in the segmented linear curve, and combining the overall distribution law of the slopes.

[0029] The present application can determine the upper and lower limits of the temperature of the battery cluster during charging and discharging by setting the temperature boundary of the battery cluster, thereby preventing overheating or overcooling from causing performance degradation or safety accidents, wherein the temperature boundary is a temperature control range determined comprehensively according to the battery operation attribute, material characteristics, application requirements and safety standards.

[0030] As an embodiment of the present application, the determining the battery operation attribute corresponding to the battery cluster to set the temperature boundary of the battery cluster 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 based on the battery safety technical specification and the heat generation and heat dissipation characteristics, deriving the basic temperature control interval of the battery cluster; 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, setting the temperature boundary of the battery cluster.

[0031] The application scenario information refers to the actual application field and deployment scenario of the battery cluster; the external environmental conditions are external environmental factors that affect battery operation in the application scenario; the operation mode is the working way of the battery cluster in the application scenario; the heat generation and heat dissipation characteristics are the heat generation rate and heat dissipation capacity of the battery cluster during operation; the battery safety technical specification is the national or industry standard for battery operation temperature safety; the basic temperature control interval is the initial temperature control range determined based on the safety technical specification and the heat generation and heat dissipation characteristics; the life-temperature balance analysis is an analysis of the influence of different temperature intervals on the cycle life of the battery, to find the balance relationship between life decay and temperature control; the optimized temperature control interval is the temperature range that takes into account the life and operation efficiency after life-temperature balance analysis; the temperature boundary is the upper and lower limits of the safe operation temperature of the battery cluster finally determined based on the optimized temperature control interval.

[0032] Further, the application scenario information can be obtained by consulting the project planning document of the battery cluster, the equipment procurement contract, or visiting the application site for investigation; the external environment data can be collected by deploying environmental monitoring equipment such as temperature and humidity sensors and anemometers, and the external environment conditions and operation modes can be analyzed in combination with the operation logs recorded by the battery management system (BMS); the battery surface temperature distribution can be monitored in real time by using a thermal imager, the heat generation power can be calculated in combination with the charging and discharging current and voltage data recorded by the BMS, the heat dissipation capacity can be evaluated by means of testing the air speed of the heat dissipation air duct and accounting the heat dissipation area, and the heat generation and heat dissipation characteristics can be analyzed; the temperature limitation requirements in the battery safety technical specifications can be extracted by searching the battery safety technical specifications through the national standard full-text public system and the official website of the industry association; the upper limit of the temperature in the safety technical specifications can be combined with the heat generation and heat dissipation characteristics, the safety redundancy can be deducted to determine the lower limit, the lower limit can be set by referring to the low-temperature performance data, and the basic temperature control interval can be derived; the cycle life test can be carried out at multiple temperature points, the capacity retention rate after cycling can be recorded, the relationship curve between temperature and life attenuation can be obtained by data fitting, and the interval with the required capacity retention rate can be selected as the optimized temperature control interval; the optimized temperature control interval can be verified under extreme conditions, it is confirmed that the battery temperature in the interval is stable and the performance is normal, and finally the temperature boundary is set.

[0033] S2, obtain the charging and discharging process information of the battery cluster to analyze the corresponding battery cell state dimension of the battery cluster, and calculate the boundary offset factor corresponding to the temperature boundary based on the battery cell state dimension to analyze the thermal stability state corresponding to the battery cluster.

[0034] The application can quantify the internal state of the battery from multiple angles by obtaining the charging and discharging process information of the battery cluster, analyzing the corresponding battery cell state dimension of the battery cluster, evaluating the adaptability of the temperature boundary, and finally judging the thermal stability of the battery, wherein the charging and discharging process information refers to the original data such as current, voltage, time and temperature recorded by the battery management system, reflecting the dynamic changes of the battery in the charging and discharging process; the battery cell state dimension refers to the multiple indexes extracted by processing the charging and discharging process information to represent the internal health and stability of the battery, such as capacity attenuation degree, internal resistance growth trend, and cell consistency, and further, the charging and discharging process information of the battery cluster can be obtained by the battery management system.

[0035] As an embodiment of the application, the obtaining of the charging and discharging process information of the battery cluster to analyze the corresponding battery cell state dimension further comprises: analyzing the key change characteristics in the charging and discharging process information, and establishing a state association mapping relationship between the key change characteristics and the potential state of the battery cluster battery cell; screening a candidate state dimension from the set of cell state descriptions based on the state association mapping relationship; analyzing a matching coefficient of the candidate state dimension and actual operation performance of the battery cluster; screening an effective state dimension from the candidate state dimension based on the matching coefficient; analyzing representation comprehensiveness of the effective state dimension, and determining the cell state dimension corresponding to the battery cluster from the effective state dimension based on the representation comprehensiveness.

[0036] The key change feature is a significant data feature (such as voltage fluctuation at the end of charging and discharging, and change of power retention time) in the charging and discharging process information that can reflect the change of the cell state; the potential state of the cell refers to the state that the cell may exist according to the charging and discharging law; the state association mapping relationship is the corresponding relationship between the key change feature and the potential state of the cell; the set of cell state descriptions is a set containing various dimension descriptions that can represent the state of the cell; the candidate state dimension is a dimension closely associated with the key change feature screened from the set of cell state descriptions; the matching coefficient is the degree of fit of the candidate state dimension and the actual operation performance of the battery; the effective state dimension refers to the candidate state dimension with a higher matching coefficient with the actual operation performance; and the representation comprehensiveness refers to the coverage of the effective state dimension on various aspects of the state of the cell.

[0037] Further, the key change feature in the charging and discharging process information can be analyzed by a time series data analysis method, and the state association mapping relationship can be established by using an association rule mining technology; based on the state association mapping relationship, a candidate state dimension can be screened from the set of cell state descriptions by a feature matching algorithm, such as a cosine similarity algorithm (calculating the spatial similarity of the key change feature and the state description vector, and retaining the dimensions with a similarity greater than or equal to 0.8); the matching coefficient can be calculated by comparing the description of the candidate state dimension with the actual operation performance of the battery cluster, such as fault records, performance test results, and the like, such as the weighted sum of the overlap degree (the proportion of common features) and the fitting degree (trend consistency) (overlap degree x 0.6 + fitting degree x 0.4, value range 0-1); based on the matching coefficient, the effective state dimension exceeding the threshold value can be screened; and the representation comprehensiveness of the effective state dimension can be analyzed by checking whether it covers the key aspects such as capacity, internal resistance, and efficiency, and finally the dimension that can comprehensively reflect the state of the cell is determined.

[0038] By calculating the boundary offset factor corresponding to the temperature boundary based on the cell state dimension, the present application can dynamically capture the adaptive deviation of the temperature boundary and the actual state of the cell, and provide a quantitative benchmark for thermal stability state analysis, wherein the boundary offset factor is a quantitative reflection of the 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.

[0039] As an embodiment of the present application, the boundary offset factor corresponding to the temperature boundary is calculated based on the state dimension of the battery cell, comprising: characterizing the state dimension of the battery cell to obtain a state characteristic value; obtaining a reference operating state of the battery cluster under standard test conditions; calculating the difference between the state characteristic value and the corresponding characteristic value of the reference operating state to obtain a state offset; querying the maximum state offset corresponding to the chemical system of the battery cluster battery cell; combining 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.

[0040] The state characteristic value is obtained by characterizing the state dimension of the battery cell, which can specifically reflect the quantitative indicators of the state of the battery cell (such as capacity retention rate, internal resistance measured value, charge and discharge efficiency, etc.); The reference operating state is the reference operating state of the battery cluster under standard test conditions (such as 25℃ ambient temperature, 0.5C charge and discharge rate), and its corresponding characteristic value is the typical value of the initial health state of the battery cell; The state offset is the difference between the state characteristic value and the corresponding characteristic value of the reference operating state, which is used to represent the deviation between the actual state and the reference state (such as the capacity retention rate is 5% lower than the reference value, then the state offset is-5%); The maximum state offset is the maximum state offset value (such as the capacity retention rate is not less than 80%, then the maximum state offset is-20%) allowed by the chemical system (such as lithium iron phosphate, ternary lithium) of the battery cluster battery cell within the safe operating range; The state offset coefficient is a normalization coefficient calculated by combining the maximum state offset and the state offset, which reflects the proportion of the actual offset to the maximum allowed offset (such as the state offset is-5% and the maximum is-20%, then the coefficient is 0.25); The boundary offset factor is calculated based on the state offset coefficient, which reflects the quantitative value of the offset of the temperature boundary caused by the change of the state of the battery cell (such as the coefficient 0.25 corresponds to the boundary offset ±2℃).

[0041] Further, the state dimensions of the battery cell can be quantified by statistical analysis methods such as range standardization and Z-score standardization, and the capacity and internal resistance dimensions can be converted into state characteristic values that can be directly calculated; the reference operating state can be obtained from the factory test report of the battery cell and the standard operating test of the new battery, and the corresponding characteristic values (such as the initial capacity of 100 Ah and the internal resistance of 20 mΩ) can be recorded; the state offset can be obtained by absolute value calculation or a relative difference formula ((actual value-reference value) / reference value*100%); the maximum state offset can be determined by referring to the industry standard (such as GB / T31484-2015) or the technical manual of the chemical system of the battery cell; 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, the upper and lower limit ranges of the temperature boundary (such as 20-45℃) can be combined to calculate the boundary offset factor (such as a coefficient of 0.2 and a range of 25℃, an offset factor of 5℃) by a linear mapping formula (offset factor=coefficient*boundary range / 2).

[0042] The present application can comprehensively evaluate the thermal safety risk of the battery under the current state by analyzing the thermal stability state corresponding to the battery cluster, and provide a direct basis for real-time regulation of the thermal management system, wherein the thermal stability state is analyzed according to the boundary offset factor corresponding to the operation stability description of the battery cluster.

[0043] As an embodiment of the present application, the calculation of the boundary offset factor corresponding to the temperature boundary to analyze the thermal stability state corresponding to the battery cluster further comprises: grading the boundary offset factor to obtain an offset grade identifier; collecting temperature distribution data of the battery cluster in the current operating cycle; based on the temperature distribution data, calculating the temperature distribution dispersion and the center temperature value of the battery cluster; querying the thermal stability grade required by the battery cluster in the current application scenario; combining the temperature distribution dispersion, the center temperature value and the offset grade identifier to calculate the thermal stability index of the battery cluster; combining the thermal stability index and the thermal stability grade to analyze the thermal stability state corresponding to the battery cluster.

[0044] The offset level identifier is a level label (such as "0 level" representing no offset, "1 level" representing slight offset, "2 level" representing moderate offset, and "3 level" representing significant offset) divided by a preset threshold range of the boundary offset factor. The temperature distribution data is a real-time temperature collection value of each battery cell in the current operation cycle of the battery cluster. The temperature distribution dispersion is a quantitative index (such as temperature standard deviation, range, etc.) reflecting the uniformity of the internal temperature of the battery cluster. The central temperature value is a representative value (such as average temperature, median temperature, etc.) of the overall temperature of the battery cluster. The thermal stability level is a requirement standard (such as "A level" for precision energy storage scenarios and "B level" for conventional industrial scenarios) of the thermal stability state of the battery cluster in the current application scenario. The thermal stability index is a comprehensive quantitative value (range 0-10, the higher the value, the better the thermal stability) reflecting the thermal stability state, which is calculated by combining the temperature distribution dispersion, the central temperature value, and the offset level identifier. The thermal stability state is determined by combining the thermal stability index and the thermal stability level, and is the stable operation state (such as "stable", "basically stable", "need intervention", etc.) of the battery cluster under the influence of temperature.

[0045] Further, the interval threshold (such as |factor|≤0.1 for 0 level and 0.1<|factor|≤0.3 for 1 level) of the boundary offset factor can be set to divide it into levels to obtain the offset level identifier. The temperature distribution data can be generated by real-time collection of the temperature of each battery cell by a distributed temperature sensor array combined with the record of the battery management system. Based on the temperature distribution data, the temperature distribution dispersion can be calculated by using the standard deviation formula (dispersion=√[(Σ(temperature value-average temperature)²) / n]), and the central temperature value can be calculated by using the arithmetic average method. The required thermal stability level can be determined by referring to the application scenario technical specification (such as industrial and commercial energy storage requiring A level thermal stability) of the battery cluster. The thermal stability index can be calculated by using the weighted summation formula (such as dispersion 0.5, deviation 0.2, and level 1 level corresponding index=0.5×0.3+0.2×0.4+0.8×0.3=0.53) combining the temperature distribution dispersion (weight 0.3), the deviation of the central temperature value and the temperature boundary (weight 0.4), and the offset level identifier (weight 0.3). The thermal stability index and the threshold range of the thermal stability level (such as A level requiring index≥8 and B level requiring 6-8) are compared to determine the thermal stability state (such as index 8.2 corresponding to "A level-stable").

[0046] S3, record the surface temperature distribution and temperature change trajectory of the battery cell in the battery cluster to analyze the corresponding thermal aggregation characteristics and thermal conduction characteristics of the battery cell in the battery cluster, and calculate the corresponding thermal runaway risk value of the battery cell in the battery cluster based on the thermal aggregation characteristics and the thermal conduction characteristics.

[0047] The application records the surface temperature distribution and temperature change trajectory of the battery cluster to analyze the corresponding heat accumulation characteristics and heat conduction characteristics of the battery cluster, so as to identify the region where heat is easily accumulated and the main path of heat transfer in the battery cluster, and provide data support for subsequent implementation of low temperature rise operation control and guarantee of efficient and stable operation of the battery. The temperature change trajectory is a process record of the evolution of the surface temperature of a single or multiple battery cells over time within a certain time sequence, which is used to reflect the accumulation and dissipation trend of heat. The heat accumulation characteristics are characteristic indexes obtained based on the analysis of the surface temperature distribution of the battery cell, which are used to describe the concentration degree of heat in the local region of the battery cluster (such as the highest temperature point, regional temperature difference, heat accumulation area ratio, etc.). The heat conduction characteristics are characteristic indexes obtained based on the analysis of the temperature change trajectory, which are used to describe the transmission efficiency and direction of heat within the battery cell and between the battery cells (such as temperature rise rate, temperature equalization rate, heat conduction directionality, etc.). Further, the surface temperature distribution and temperature change trajectory of the battery cluster can be recorded by the linkage of an infrared thermal imager, a distributed thermocouple array and a battery management system (BMS).

[0048] As an embodiment of the application, the recording of the surface temperature distribution and temperature change trajectory of the battery cluster further comprises: calculating the temperature spatial gradient of the surface temperature distribution of the battery cell, and constructing the temperature gradient distribution field of the battery cell in the battery cluster based on the temperature spatial gradient; performing regional clustering processing on the temperature gradient distribution field to obtain a heat accumulation core region; analyzing the regional heat flux density spectrum corresponding to the heat accumulation core region, and constructing a heat conduction time sequence correlation matrix corresponding to the temperature change trajectory; performing path evolution processing on the heat conduction time sequence correlation matrix to obtain a dominant heat conduction path; combining the regional heat flux density spectrum and the dominant heat conduction path to analyze the corresponding heat accumulation characteristics and heat conduction characteristics of the battery cluster.

[0049] The temperature spatial gradient is a physical quantity reflecting the rate of change of the surface temperature of the battery cell in the spatial position (such as the temperature difference between adjacent temperature measurement points divided by the distance); the temperature gradient distribution field is composed of the temperature spatial gradient of each point, and represents the spatial distribution form of the temperature change intensity in the battery cluster (such as the gradient high and low distribution presented in the form of a heat map); the regional clustering processing is an analysis method of classifying regions with similar features in the temperature gradient distribution field (such as dividing regions with a gradient value difference of ≤5% into the same cluster); the heat aggregation core region is obtained by clustering in the temperature gradient distribution field, and is the region with the most intense temperature change and heat concentration (such as the cluster region with the highest gradient value); the regional heat flux density spectrum is the distribution characteristics of the heat transfer intensity per unit area in the heat aggregation core region (such as the heat flux density values and proportions of different sub-regions); the heat conduction time sequence correlation matrix is constructed based on the temperature change trajectory, and reflects the correlation degree of temperature change of different battery cells in the time dimension (such as the higher the matrix element value, the stronger the temperature change synchronization); the path evolution processing is a process of dynamically tracking and filtering the correlation relationship in the heat conduction time sequence correlation matrix to identify the main heat transfer path; the dominant heat conduction path is obtained after the path evolution processing, and is the main channel of heat transfer in the battery cluster (such as the connection path between the battery cells with the highest correlation degree); the heat aggregation feature is obtained by combining the temperature gradient distribution field and the regional heat flux density spectrum, and reflects the heat concentration state (such as the core area, average heat flux density, etc.); the heat conduction feature is determined based on the heat conduction time sequence correlation matrix and the dominant heat conduction path, and reflects the heat transfer state (such as the path transfer efficiency, the number of correlated battery cells, etc.).

[0050] Further, the temperature spatial gradient can be calculated by dividing the temperature difference between adjacent temperature measurement points by the distance between the two points (such as a temperature difference of 3°C and a distance of 2 cm, then the gradient is 1.5°C / cm), and the temperature gradient distribution field can be constructed by a spatial interpolation method; based on the temperature gradient distribution field, a density clustering algorithm (such as setting a clustering radius of 0.5 cm) is used for regional clustering processing, and the cluster region with the largest gradient value is selected as the heat aggregation core region; the heat flux data of each sub-region in the heat aggregation core region is collected by a heat flow sensor array, the area proportion of different heat flux density intervals is counted, and the regional heat flux density spectrum is formed; the temperature change amount ratio of any two battery cells in the same window is calculated with a time window of 10s, and the heat conduction time sequence correlation matrix is constructed; the element values in the heat conduction time sequence correlation matrix are sorted, the top 20% of high correlation relationships are selected for path tracking, and the dominant heat conduction path is obtained by path evolution processing; finally, the area proportion of the heat aggregation core region (weight 0.6) and the peak value of the regional heat flux density spectrum (weight 0.4) are integrated into the heat aggregation feature, and the average correlation value of the dominant heat conduction path (weight 0.5) and the number of battery cells covered by the path (weight 0.5) are integrated into the heat conduction feature.

[0051] The present application calculates the thermal runaway risk value of the battery cluster by combining the heat aggregation feature and the heat conduction feature, which can quantify the degree of the battery cluster developing from the current thermal state to thermal runaway, wherein the thermal runaway risk value is a quantitative value of the possibility of the battery cluster developing to thermal runaway.

[0052] As an embodiment of the present application, the combination of the heat aggregation feature and the heat conduction feature to calculate the thermal runaway risk value of the battery cluster includes: calculating the maximum heat flux density and the heat aggregation area corresponding to the heat aggregation feature; calculating the heat diffusion rate and the heat conduction resistance coefficient corresponding to the heat conduction feature; detecting the thermal distribution data of the battery cluster and the current environment temperature, and constructing the thermal distribution map corresponding to the battery cluster based on the thermal distribution data; determining the maximum temperature value and the average temperature rise rate corresponding to the battery cluster based on the thermal distribution map; combining the current environment temperature, the maximum heat flux density, the heat aggregation area, the heat diffusion rate, the heat conduction resistance coefficient, the maximum temperature value and the average temperature rise rate, and calculating the thermal runaway risk value of the battery cluster by using the following formula.

[0053] The maximum heat flux density refers to the maximum heat per unit area per unit time in the heat aggregation core area, which is in units of W / m² and reflects the intensity of heat aggregation. The heat aggregation area refers to the total area of the region in the heat aggregation feature whose temperature exceeds the set threshold, which is in units of m² and represents the range of heat concentration. The heat diffusion rate refers to the distance of heat diffusion per unit time in the battery cluster, which is in units of m / s and reflects the speed of heat transfer. The heat conduction resistance coefficient is a dimensionless parameter (value 0-1, 0 represents no resistance, and 1 represents complete resistance) representing the degree of resistance to heat transfer between cells, which reflects the difficulty of heat conduction. The maximum temperature value is the highest temperature reading of the cell in the thermal distribution map, which is in units of ℃ and represents the local extreme temperature level. The average temperature rise rate refers to the average increase of the overall temperature of the battery cluster per unit time, which is in units of ℃ / min and reflects the overall temperature rise trend. The current environment temperature is the temperature of the external environment where the battery cluster is located, which is in units of ℃ and serves as a reference for temperature calculation.

[0054] Further, the heat flow density of each point in the heat accumulation area can be measured by a heat flow sensor array, and the maximum heat flow density is taken as the maximum heat flow density; a temperature threshold is set in the heat distribution map, and the total area of the region exceeding the threshold is calculated by using an image segmentation algorithm to obtain the heat accumulation area; the average distance of the boundary movement per unit time is calculated by continuously recording the boundary coordinates of the high-temperature region to obtain the heat diffusion rate; the thermal conduction resistance coefficient is calculated based on the temperature difference and distance ratio of adjacent battery cells, combined with the thermal conductivity coefficient of the battery shell material; the highest temperature value is extracted from the heat distribution data as the highest temperature value; the average temperature change per unit time is calculated by selecting temperature data within a certain time to obtain the average temperature rise rate; and the current environment temperature is directly read by the temperature sensor placed around the battery cluster.

[0055] Further, as another embodiment of the application, the current environment temperature, the maximum heat flow density, the heat accumulation area, the heat diffusion rate, the thermal conduction resistance coefficient, the highest temperature value and the average temperature rise rate are combined to calculate the thermal runaway risk value of the battery cells in the battery cluster by using the following formula, which includes: Wherein, A represents the thermal runaway risk value of the battery cells in the battery cluster, represents the maximum heat flow density, represents the heat accumulation area, represents the highest temperature value, represents the current environment temperature, represents the heat diffusion rate, represents the rated cooling rate, represents the thermal conduction resistance coefficient, represents the average temperature rise rate.

[0056] The rated cooling rate is the temperature value that the battery thermal management system can reduce per unit time under standard working conditions (such as ambient temperature 25℃, full load operation), which is obtained by standard cooling efficiency test before the system is shipped.

[0057] It should be noted that the thermal runaway risk calculation formula regards the thermal runaway possibility of the battery cluster as a mapping of the heat accumulation potential energy and the cooling counterbalance ability, and the maximum heat flow density (representing heat generation intensity), the heat accumulation area (representing heat spread range), the temperature difference between the battery cell and the environment (representing heat driving potential energy), and the rate difference between cooling and heat generation are combined. (representing the heat control ability), the ratio of the two is established; the core is that the parameters such as heat flow, temperature and rate are relatively stable in the calculation period, and the quantification of the battery cluster thermal runaway risk is realized through linear proportional operation.

[0058] In specific applications, for example, in the new energy vehicle power battery cluster scene, the formula can separate the contribution of the heat production end characteristics (maximum heat flow density, heat accumulation area) and the heat control end characteristics (cooling-heat production rate difference, temperature difference asynchrony) of the battery cluster complex heat interaction process; for the working condition of continuous high-power discharge (high , strong heat production) and liquid cooling system full power heat dissipation (high , strong heat control), or covering-20℃~45℃ ambient temperature and idling, rapid acceleration, fast charging and other operating conditions, the thermal runaway risk prediction error can be reduced.

[0059] S4, calculate 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.

[0060] The application can quantitatively evaluate the heat exchange ability of the environment where the battery cluster is located by calculating the environmental heat exchange intensity corresponding to the battery cluster, and then determine the cooperation effect of the battery cluster with the heat dissipation component in the entire energy storage power supply system, thereby providing data support for subsequent analysis and processing of heat dissipation coordination degree, wherein the environmental heat exchange intensity is the heat exchanged between the energy storage battery cluster and the surrounding environment per unit time, representing the heat exchange ability of the cluster and the environment (the larger the value, the more intense the heat exchange).

[0061] As an embodiment of the application, the calculation of the environmental heat exchange intensity corresponding to the battery cluster comprises: acquire temperature data of each region on the surface of the battery cluster to obtain regional surface temperature data; measure the cooling medium flow rate and cooling medium temperature corresponding to the cooling medium on the surface of the battery cluster; obtain the effective heat exchange area of the battery cluster in contact with the cooling medium; combine the regional surface temperature data and the cooling medium temperature to calculate the regional average temperature difference of the battery cluster; combine the regional average temperature difference, the cooling medium flow rate and the effective heat exchange area, and calculate the environmental heat exchange intensity corresponding to the battery cluster by using the following formula.

[0062] The regional surface temperature data refers to real-time temperature records of several sub-regions divided on the surface of the battery cluster (such as temperature values of the front, side and top regions), reflecting the heat dissipation state of different parts; the cooling medium flow rate refers to the speed of the cooling medium (such as air blown by a heat dissipation fan or cooling liquid of a liquid cooling system) flowing 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 of heat exchange; the effective heat exchange area refers to the actual area of the surface of the battery cluster directly contacting the cooling medium and undergoing heat exchange, excluding regions not contacting or being heat-insulated; and the regional average temperature difference refers to the arithmetic average of the temperature difference between the surface temperature of each sub-region and the cooling medium temperature, reflecting the power of overall heat exchange.

[0063] Further, the regional surface temperature data can be obtained by uniformly arranging temperature collection pieces on the surface of the battery cluster and continuously recording the readings of each piece; the cooling medium flow rate can be obtained by using a flowmeter to measure the flow speed of the cooling medium close to different positions on the surface of the cluster and taking the average value as the cooling medium flow rate; the cooling medium temperature can be directly read by placing a temperature sensor in the cooling medium flow path; the effective heat exchange area can be obtained by measuring the size of the region on the surface of the cluster contacting the cooling medium and excluding the area of shielded or sealed parts; and the regional average temperature difference can be obtained by calculating the temperature difference between the surface temperature of each sub-region and the cooling medium temperature and then taking the arithmetic average.

[0064] Further, as another embodiment of the present application, the environmental heat exchange intensity corresponding to the battery cluster is calculated by combining the regional average temperature difference, the cooling medium flow rate and the effective heat exchange area, 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 regional 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, and 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.

[0065] The basic heat exchange coefficient is the heat exchange rate per unit area and per unit temperature difference corresponding to the region on the surface of the battery cluster, which is obtained by material thermal conductivity performance testing and surface roughness analysis; and the medium reference flow rate is the average flow speed of the cooling medium (such as air or cooling liquid) corresponding to the region on the surface of the battery cluster, which is obtained by flowmeter or flow speed probe measurement.

[0066] It is explained that the environmental heat exchange intensity calculation formula decomposes the overall heat exchange effect of the battery cluster surface into the linear superposition of the heat exchange contribution of each sub-region; by introducing the basic heat exchange coefficient , the inherent heat exchange capacity of the surface material and structure of the region is characterized, the effective heat exchange area , the area actually participating in heat exchange of the region is quantified, the regional average temperature difference , the heat exchange dynamics of the region is reflected, and the flow rate correction term is coupled to realize the decomposition calculation of the complex surface heat exchange; the core is to assume that the heat exchange parameters (flow rate, temperature difference, etc.) in each sub-region are uniformly distributed, and the calculation complexity of the overall heat exchange of the cluster is simplified by linear superposition of local characteristics.

[0067] The application can understand the adaptation degree of the battery cluster heat dissipation demand and the system heat dissipation capacity by determining the heat dissipation coordination degree of the battery cluster in the energy storage power supply system, wherein the heat dissipation coordination degree represents the matching degree of the heat dissipation demand and the system heat dissipation resource of the battery cluster in the energy storage power supply system, further, based on the environmental heat exchange intensity, combined with the rated heat dissipation power of the energy storage power supply system, the heat dissipation coordination degree of the battery cluster in the energy storage power supply system is determined by calculating the proportional relationship between the two, S5, in combination with the thermal stability state, the thermal runaway risk value and the heat dissipation coordination degree, the low temperature rise operation control of the battery cluster is performed to obtain an operation control result.

[0068] The application can guide the battery cluster to maintain a low heat stable operation state by combining the thermal stability state, the thermal runaway risk value and the heat dissipation coordination degree, performing the low temperature rise operation control of the battery cluster, and obtaining the operation control result, and further improving the operation efficiency of the battery cluster in the energy storage power supply system.

[0069] Further, when performing the low temperature rise operation control, it is continuously monitored whether the thermal stability state is continuous (if the temperature fluctuation is intensified and the temperature is continuously rising, the intervention is triggered), the thermal runaway risk value is dynamic (when the risk value rises, the constraint is strengthened), and the heat dissipation coordination degree is matched (when the coordination degree is insufficient, the heat dissipation linkage is optimized); when the thermal stability state is stable, the risk value is lower than the warning value, and the heat dissipation coordination is good, the charging and discharging power is maintained, and the air volume of the heat dissipation equipment is adjusted; if the thermal stability state is broken (such as temperature sudden rise), the risk value approaches the critical value or the coordination degree decreases, the charging and discharging power is immediately reduced, or the standby heat dissipation circuit is started, and the power adjustment, heat dissipation action and temperature response are recorded synchronously, and integrated as an operation control result (such as a certain battery cluster is controlled, and the temperature rise is ≤2℃ within 2 hours, and the charging and discharging power is stable), and the low temperature rise operation control process of the application method for efficiently operating the low temperature rise energy storage battery cluster in the energy storage power supply system can be referred to for further intuitive understanding.Figure 2 As shown, it is the low-temperature-rise operation control process diagram in the application method of efficiently operating the low-temperature-rise energy storage battery cluster in the energy storage power supply system provided by the application. It should be noted that in the present application, Figure 2 The flowchart presented is only for the low-temperature-rise operation control processing of the application method of efficiently operating the low-temperature-rise energy storage battery cluster in the energy storage power supply system, and is not limited to the low-temperature-rise operation control processing of the application method of efficiently operating the low-temperature-rise energy storage battery cluster in the energy storage power supply system in actual different application scenarios.

[0070] Compared with the problems described in the background art, the present application can establish a connection between the current-voltage change in actual operation and the battery state by determining the battery operation attribute corresponding to the battery cluster based on the current-voltage time sequence data, and can provide a basis for setting the temperature boundary, thereby improving the safety and stability of the battery cluster operation. Further, the present application can quantify the internal state of the battery from multiple angles by obtaining the charging and discharging process information of the battery cluster and analyzing the cell state dimension corresponding to the battery cluster, and then evaluate the adaptability of the temperature boundary, and finally realize the judgment of the thermal stability of the battery. The present application can identify the region where heat is easily accumulated and the main path of heat transfer in the battery cluster by recording the cell surface distribution temperature and temperature change trajectory of the cells in the battery cluster to analyze the heat accumulation characteristics and heat conduction characteristics of the cells in the battery cluster, and can provide data support for subsequent implementation of low-temperature-rise operation control and protection of efficient and stable operation of the battery. Further, the present application can quantify and evaluate the heat exchange capacity of the environment in which the battery cluster is located by calculating the environmental heat exchange intensity corresponding to the battery cluster, and then determine the cooperation effect of the battery cluster with the heat dissipation components in the entire energy storage power supply system, thereby providing data support for subsequent analysis and processing of the heat dissipation coordination degree. Further, the present application executes the low-temperature-rise operation control of the battery cluster by combining the thermal stability state, the thermal runaway risk value and the heat dissipation coordination degree, obtains the operation control result, and cooperates the three to constrain the battery operation, thereby guiding the battery cluster to maintain a low-heat stable operation state, and further improving the operation efficiency of the battery cluster in the energy storage power supply system. Therefore, the application method of efficiently operating the low-temperature-rise energy storage battery cluster in the energy storage power supply system provided by the present application can improve the operation efficiency of the battery cluster in the energy storage power supply system.

[0071] As Figure 3 shown, it is an application system function module diagram of a kind of efficiently operating low-temperature-rise energy storage battery cluster in the energy storage power supply system of the present application.

[0072] The application system 300 of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system can be installed in an electronic device. According to the functions implemented, the application system of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system can include a temperature boundary setting module 301, a thermal stable state analysis module 302, a thermal runaway risk value calculation module 303, a heat dissipation coordination degree analysis module 304, and a running control module 305. The modules in the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0073] In the embodiments of the present application, the functions of each module / unit are as follows: The temperature boundary setting module 301 is configured to collect current-voltage time series data of the energy storage battery cluster, determine the battery running attributes corresponding to the battery cluster based on the current-voltage time series data, and set the temperature boundary of the battery cluster. The thermal stable state analysis module 302 is configured to obtain the charge-discharge process information of the battery cluster, analyze the cell state dimension corresponding to the battery cluster, calculate the boundary offset factor corresponding to the temperature boundary based on the cell state dimension, and analyze the thermal stable state corresponding to the battery cluster. The thermal runaway risk value calculation module 303 is configured to record the cell surface distribution temperature and temperature change trajectory of the cells in the battery cluster, analyze the thermal aggregation characteristics and thermal conduction characteristics of the cells in the battery cluster, calculate the thermal runaway risk value of the cells in the battery cluster in combination with the thermal aggregation characteristics and the thermal conduction characteristics, and analyze the thermal stable state corresponding to the battery cluster. The heat dissipation coordination degree analysis module 304 is configured to calculate the environmental heat exchange intensity corresponding to the battery cluster, and determine the heat dissipation coordination degree of the battery cluster in the energy storage power supply system. The running control module 305 is configured to combine the thermal stable state, the thermal runaway risk value, and the heat dissipation coordination degree to perform low-temperature-rise running control of the battery cluster, and obtain a running control result.

[0074] In detail, the modules in the application system 300 of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system in the embodiments of the present application use the same technical means as the application method of the high-efficiency low-temperature-rise energy storage battery cluster in the energy storage power supply system described in the above Figure 1 , and can produce the same technical effects, which will not be described here.

[0075] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0076] Finally, it should be noted that in the above embodiments, each embodiment can be combined with or independent of each other, and the deletion of any one does not affect the technical implementation of the other embodiments. The above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. An application method for efficiently operating a low-temperature rise energy storage battery cluster in an energy storage power supply system, characterized in that: The method comprises: Collecting current and voltage time series data of an energy storage battery cluster, and determining battery operating properties corresponding to the battery cluster based on the current and voltage time series data to set a temperature boundary of the battery cluster; Acquiring charging and discharging process information of the battery cluster to analyze a cell state dimension corresponding to the battery cluster, and calculating a boundary offset factor corresponding to the temperature boundary based on the cell state dimension to analyze a thermal stability state corresponding to the battery cluster; Recording the surface distribution temperature and temperature change trajectory of the cells in the battery cluster to analyze the heat accumulation characteristics and heat conduction characteristics corresponding to the cells in the battery cluster, and calculating the thermal runaway risk value corresponding to the cells in the battery cluster by combining the heat accumulation 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 system; In combination with the thermal stability state, the thermal runaway risk value, and the heat dissipation coordination degree, low temperature rise operation control of the battery cluster is performed to obtain an operation control result.

2. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The determining, based on the current and voltage time series data, battery operation attributes corresponding to the battery cluster includes: Performing sliding average processing on the current and voltage time series data to obtain smoothed current data and smoothed voltage data; extracting fluctuation characteristics and trend characteristics from the smoothed current data and the smoothed voltage data respectively; Performing feature fusion processing on the fluctuation feature and the trend feature to obtain a fused feature set; Based on the fused feature set, battery operation attributes corresponding to the battery cluster are determined.

3. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 2, characterized in that: The extracting of fluctuation characteristics and trend characteristics from the smoothed current data and the smoothed voltage data respectively includes: Performing amplitude characteristic analysis on the smoothed current data to obtain current amplitude characteristics; Calculating a voltage change rate corresponding to the smoothed voltage data to obtain a voltage change rate characteristic; calculating a correlation coefficient between the smoothed current data and the smoothed voltage data, and extracting current-voltage correlation features from the smoothed current data and the smoothed voltage data based on the correlation coefficient; Merging the current amplitude feature, the voltage change rate feature, and the current-voltage correlation feature to obtain a fluctuation feature; Performing piecewise linear fitting on the smoothed current data and the smoothed voltage data to obtain piecewise linear curves; Curve trend extraction is performed on the piecewise linear curve to obtain trend characteristics.

4. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The determining of battery operating attributes corresponding to the battery cluster to set a temperature boundary of the battery cluster further includes: Obtaining application scenario information of the battery cluster, and analyzing external environmental conditions and operating modes corresponding to the application scenario; Analyzing heat generation and heat dissipation characteristics of the battery cluster based on the battery operating properties, the external environmental conditions, and the operating mode; querying the battery safety technical specifications corresponding to the battery cluster, and deriving a basic temperature control range of the battery cluster based on the battery safety technical specifications and the heat generation and heat dissipation characteristics; Performing a life-temperature balance analysis on the basic temperature control range to obtain an optimized temperature control range; Based on the optimized temperature control range, a temperature boundary of the battery cluster is set.

5. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The acquiring of the charge and discharge process information of the battery cluster to analyze the cell status dimension corresponding to the battery cluster further includes: parsing key change features in the charge and discharge process information, and establishing a state association mapping relationship between the key change features and the potential states of the battery cells of the battery cluster; Based on the state association mapping relationship, screening candidate state dimensions from the battery cell state description set; Analyzing the matching coefficient between the candidate state dimensions and the actual operating performance of the battery cluster; Based on the matching coefficient, screening out a valid state dimension from the candidate state dimensions; The representation comprehensiveness of the effective state dimension is analyzed, and based on the representation comprehensiveness, the cell state dimension corresponding to the battery cluster is determined from the effective state dimension.

6. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The calculating, based on the cell state dimension, a boundary offset factor corresponding to the temperature boundary includes: Performing feature quantization processing on the state dimension of the battery cell to obtain a state feature value; Obtaining a reference operating state of the battery cluster under standard test conditions; Calculating the difference between the state characteristic value and the characteristic value corresponding to the reference running state to obtain a state offset; Query the maximum state offset corresponding to the battery cell chemical system of the battery cluster; Calculating a state offset coefficient corresponding to the battery cluster by combining the maximum state offset and the state offset; Based on the state offset coefficient, a boundary offset factor corresponding to the temperature boundary is calculated.

7. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The calculating of the boundary offset factor corresponding to the temperature boundary to analyze the thermal stability state corresponding to the battery cluster further includes: Performing a grade classification process on the boundary offset factor to obtain an offset grade identifier; Collecting temperature distribution data of the battery cluster during a current operation cycle; Calculating the temperature distribution dispersion and the center temperature value of the battery cluster based on the temperature distribution data; Query the thermal stability level required for the battery cluster in the current application scenario; Calculating a thermal stability index of the battery cluster based on the temperature distribution dispersion, the center temperature value, and the offset level identifier; The thermal stability state corresponding to the battery cluster is analyzed in combination with the thermal stability index and the thermal stability grade.

8. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The recording of the surface distribution temperature and temperature change trajectory of the cells in the battery cluster to analyze the heat accumulation characteristics and heat conduction characteristics corresponding to the cells in the battery cluster further includes: Calculating the temperature spatial gradient of the surface distribution temperature of the battery cells, and constructing a temperature gradient distribution field of the battery cells in the battery cluster based on the temperature spatial gradient; Performing regional clustering processing on the temperature gradient distribution field to obtain a heat accumulation core area; Analyze the regional heat flux density spectrum corresponding to the heat accumulation core area, and construct a heat conduction time series correlation matrix corresponding to the temperature change track; performing path evolution processing on the heat conduction time series correlation matrix to obtain a dominant heat conduction path; The heat concentration characteristics and heat conduction characteristics corresponding to the battery cells in the battery cluster are analyzed in combination with the regional heat flux density spectrum and the dominant heat conduction path.

9. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The calculating the thermal runaway risk value corresponding to the battery cells in the battery cluster by combining the heat accumulation characteristics and the heat conduction characteristics includes: Calculating the maximum heat flux density and the heat accumulation area corresponding to the heat accumulation characteristics; Calculating the heat diffusion rate and heat conduction retardation coefficient corresponding to the heat conduction characteristics; 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; The thermal runaway risk value corresponding to the battery cells in the battery cluster is calculated using the following formula based on 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 maximum temperature value, and the average temperature rise rate: Among them, A represents the thermal runaway risk value corresponding to the battery cells in the battery cluster, represents the maximum heat flux density, represents the area of ​​heat accumulation region, Indicates the maximum temperature value, Indicates the current ambient temperature. represents the thermal diffusion rate, Indicates the rated cooling rate, represents the heat conduction resistance coefficient, Indicates the average rate of temperature rise.

10. The method for applying a high-efficiency low-temperature rise energy storage battery cluster to an energy storage power supply system according to claim 1, characterized in that: The calculating of the environmental heat exchange intensity corresponding to the battery cluster includes: Collecting temperature data of various areas on the surface of the battery cluster to obtain regional surface temperature data; measuring a cooling medium flow rate and a cooling medium temperature corresponding to a cooling medium on a surface of the battery cluster; Obtaining an effective heat exchange area between the battery cluster and the cooling medium; Calculating a regional average temperature difference of the battery cluster by combining the regional surface temperature data with the cooling medium temperature; The environmental heat exchange intensity corresponding to the battery cluster is calculated using the following formula based on the regional average temperature difference, the cooling medium flow rate, and the effective heat exchange area: Among them, E represents the environmental heat exchange intensity corresponding to the battery cluster, represents the basic heat transfer coefficient corresponding to the ath area on the surface of the battery cluster, represents the effective heat exchange area corresponding to the ath region on the surface of the battery cluster, represents the average temperature difference of the region corresponding to the ath region on the surface of the battery cluster, represents the cooling medium flow rate corresponding to the ath area on the surface of the battery cluster, It represents the medium reference flow velocity corresponding to the ath area on the surface of the battery cluster, a represents the serial number of the area on the surface of the battery cluster, and r represents the number of areas on the surface of the battery cluster.

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