Battery thermal runaway detection method and system

Abstract

The application provides a battery thermal runaway detection method and system, comprising: calculating the self-discharge rates of a plurality of battery cells under a plurality of charge-discharge cycles to obtain a plurality of first data sets; determining a first correlation coefficient according to the plurality of first data sets; determining a plurality of self-discharge rates corresponding to the battery cells under the latest plurality of charge-discharge cycles; determining a self-discharge rate correction value of the battery cells according to the plurality of self-discharge rates and the first correlation coefficient; calculating a thermal runaway score of the battery cells according to the voltage change rate, real-time current, temperature change rate of the battery cells and heat dissipation amount of the liquid cooling module within a preset time period; correcting the thermal runaway score according to the self-discharge rate correction value to obtain a target thermal runaway score of the battery cells, and further determining a thermal runaway detection result of the battery. The application can improve the accuracy and reliability of battery thermal runaway detection in the liquid cooling heat dissipation energy storage scene.

Description

Technical Field

[0001] This application relates to the field of battery fault detection technology, and in particular to a battery thermal runaway detection method and system. Background Technology

[0002] Early safety warnings for batteries are a core technical challenge in battery management for energy storage systems. Current mainstream battery management systems primarily rely on monitoring external operating parameters such as voltage, current, and temperature of individual battery cells to achieve condition assessment and fault diagnosis. However, in energy storage systems equipped with liquid cooling, the apparent temperature and voltage changes of the cells are easily affected by heat dissipation conditions and equalization control, making it difficult to accurately reflect the internal heat generation and degradation state of the cells. This results in insufficient sensitivity and delayed warnings from traditional detection methods.

[0003] Existing battery thermal runaway detection methods mostly rely on single or limited real-time electrical parameters for threshold judgment, making it difficult to fully utilize the performance degradation characteristics of batteries during long-term cycling. This results in insufficient sensitivity for identifying early internal anomalies and delayed warnings. While some existing technologies introduce more characteristic parameters, they are used only as independent monitoring quantities, failing to achieve effective fusion and mutual correction between multiple parameters. This leads to poor adaptability under multi-cell collaborative operation and complex heat dissipation conditions, resulting in low accuracy and reliability for battery thermal runaway warnings, and failing to meet the high safety requirements of energy storage systems. Summary of the Invention

[0004] This application provides a battery thermal runaway detection method and system. By combining the cell's cycle self-discharge rate with real-time voltage, current, temperature, and liquid cooling heat dissipation, liquid cooling heat dissipation compensation is applied to the temperature change rate, which can eliminate external heat dissipation interference and restore the true heat generation of the cell. Furthermore, by using the self-discharge rate to correct the thermal runaway score, the long-term performance changes and real-time operating status of the cell can be more comprehensively reflected, thereby improving the accuracy and reliability of battery thermal runaway detection.

[0005] In a first aspect, this application provides a battery thermal runaway detection method applied to a server in an energy storage system. The energy storage system further includes an energy storage module and a liquid cooling module connected to the server. The energy storage module is connected to the liquid cooling module and includes multiple battery cells. The method includes: Calculate the self-discharge rate of the multiple cells under multiple charge-discharge cycles to obtain multiple first datasets. Each first dataset includes the self-discharge rate and average self-discharge rate of a single cell at different charge-discharge cycle numbers. A first correlation coefficient is determined based on the plurality of first datasets. The first correlation coefficient characterizes the average correlation similarity between the self-discharge rate and the average self-discharge rate of the plurality of cells under multiple charge-discharge cycles. Based on the first dataset, determine multiple self-discharge rates corresponding to the multiple cells under the latest multiple charge-discharge cycles; and based on the multiple self-discharge rates and the first correlation coefficient, determine multiple self-discharge rate correction values ​​corresponding to the multiple cells. Calculate multiple thermal runaway fractions corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period. The multiple thermal runaway scores are corrected according to the multiple self-discharge rate correction values ​​to obtain multiple target thermal runaway scores corresponding to the multiple cells; The thermal runaway detection result of the battery is determined based on the multiple target thermal runaway scores.

[0006] Secondly, embodiments of this application provide an energy storage system, the system including a server, an energy storage module and a liquid cooling module connected to the server, the energy storage module being connected to the liquid cooling module, the energy storage module including multiple battery cells, wherein the server is used to execute the steps of implementing the method described in the first aspect above.

[0007] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps in the first aspect of embodiments of this application.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program / instructions stored thereon, which is executed by a processor to implement the steps of the method described in the first aspect above.

[0009] As can be seen, in this embodiment, the server calculates the self-discharge rate of multiple cells under multiple charge-discharge cycles to obtain multiple first datasets. Each first dataset includes the self-discharge rate and average self-discharge rate of a single cell at different charge-discharge cycle numbers. A first correlation coefficient is determined based on the multiple first datasets. The first correlation coefficient characterizes the average correlation similarity between the self-discharge rate and the average self-discharge rate of multiple cells under multiple charge-discharge cycles. Multiple self-discharge rates corresponding to the multiple cells in the latest multiple charge-discharge cycles are determined based on the first datasets. Multiple self-discharge rate correction values ​​are determined based on the multiple self-discharge rates and the first correlation coefficient. Multiple thermal runaway scores corresponding to the multiple cells are calculated based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period. The multiple thermal runaway scores are corrected based on the multiple self-discharge rate correction values ​​to obtain multiple target thermal runaway scores corresponding to the multiple cells. The thermal runaway detection result of the battery is determined based on the multiple target thermal runaway scores. Thus, compared with existing thermal runaway detection methods that rely on a single real-time parameter, do not integrate long-term battery cycle characteristics, have ineffective coupling of multiple parameters, and have poor adaptability to complex heat dissipation conditions, this application combines the long-term charge-discharge cycle characteristics of the battery cell with real-time operating parameters to achieve coordinated correction of multiple parameters, thereby improving the accuracy and reliability of thermal runaway detection in energy storage systems and adapting to actual energy storage scenarios with multiple battery cells and liquid cooling. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a system architecture diagram of an energy storage system provided in an embodiment of this application; Figure 2 This is a flowchart of the steps of a battery thermal runaway detection method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the contents of a first dataset of a single battery cell provided in an embodiment of this application; Figure 4 This is a schematic flowchart illustrating a method for determining the thermal runaway fraction of a battery cell, provided in an embodiment of this application. Figure 5 This is an overall flowchart of a battery thermal runaway detection method provided in an embodiment of this application; Figure 6 This is a flowchart of another battery thermal runaway detection method provided in the embodiments of this application; Figure 7 This is a functional unit block diagram of an energy storage system provided in an embodiment of this application; Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0013] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0014] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0015] In the embodiments of this application, "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone; A and B exist simultaneously; B exists alone. Among them, A and B can be singular or plural.

[0016] In this embodiment, the symbol " / " can indicate that the preceding and following objects are in an "or" relationship. Alternatively, the symbol " / " can also represent a division sign, i.e., performing a division operation. For example, A / B can mean A divided by B.

[0017] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0018] In the embodiments of this application, "equal to" can be used with "greater than" and is applicable to technical solutions used when "greater than" is used; it can also be used with "less than" and is applicable to technical solutions used when "less than" is used. When "equal to" is used with "greater than", it is not used with "less than"; when "equal to" is used with "less than", it is not used with "greater than".

[0019] Existing battery thermal runaway detection methods mostly rely on single or limited real-time electrical parameters for threshold judgment, making it difficult to fully utilize the performance degradation characteristics of batteries during long-term cycling. This results in insufficient sensitivity for identifying early internal anomalies and delayed warnings. While some existing technologies introduce more characteristic parameters, they are used only as independent monitoring quantities, failing to achieve effective fusion and mutual correction between multiple parameters. This leads to poor adaptability under multi-cell collaborative operation and complex heat dissipation conditions, resulting in low accuracy and reliability for battery thermal runaway warnings, and failing to meet the high safety requirements of energy storage systems.

[0020] To address the aforementioned issues, this application provides a battery thermal runaway detection method and system. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0021] Please see Figure 1 , Figure 1 This is a system architecture diagram of an energy storage system provided in an embodiment of this application, such as... Figure 1 As shown, the energy storage system 100 includes a server 110, an energy storage module 120, and a liquid cooling module 130. The energy storage module 120 includes a battery pack 121, which includes multiple battery cells 1211. The liquid cooling module 130 includes a liquid cooling circulation pump 131. The energy storage module 120 is connected to the liquid cooling module 130, and the server 110 is connected to both the energy storage module 120 and the liquid cooling module 130.

[0022] The energy storage module 120 is the core component of the energy storage system 100, responsible for storing and releasing electrical energy. Its internal battery pack 121 is typically composed of several battery cells 1211 connected in series, parallel, or a combination of series and parallel connections. The energy storage module 120 possesses electrical characteristics such as charge storage, charge / discharge conversion, and voltage / current output, serving as the energy carrier for the entire system. In this application, the main functions of the energy storage module 120 are to supply power to the load or receive charging, while simultaneously providing the server 110 with raw data for thermal runaway detection, including cell voltage, current, and temperature, and working in conjunction with the liquid cooling module 130 to maintain the cells within a safe temperature range.

[0023] The liquid cooling module 130 is the core component for thermal management of the energy storage system 100, employing liquid heat exchange to forcibly dissipate heat from the energy storage module 120. The liquid cooling module 130 relies on coolant circulating within its channels to remove heat generated by the battery cell 1211 through convective heat transfer, thereby stabilizing the operating temperature of the battery cell 1211 and preventing thermal runaway caused by excessively high or uneven temperatures. In this application, the function of the liquid cooling module 130 is to cool the battery cell 1211 and simultaneously provide the server 110 with operating parameters such as pump speed, supply liquid temperature, and return liquid temperature. These parameters are used to calculate heat dissipation and correct for the rate of temperature change, eliminating the problem of the apparent temperature of the battery cell 1211 being masked by heat dissipation, restoring the true heat generation state of the battery cell 1211, and improving detection accuracy.

[0024] The liquid-cooled circulation pump 131 is the power drive component of the liquid-cooling module 130, used to provide circulation power for the coolant. The pump speed of the liquid-cooled circulation pump 131 is positively correlated with the coolant flow rate; the higher the pump speed, the greater the coolant circulation flow rate and the stronger the heat dissipation capacity. In this application, the function of the liquid-cooled circulation pump 131 is to drive the coolant to circulate between the energy storage module 120 and the liquid-cooling module 130. Its pump speed signal can be collected by the server 110 and used to calculate the heat dissipation by combining the supply temperature and return temperature.

[0025] The server 110 is the core of the entire energy storage system 100 for computation and control. It is the main control unit of the host computer or battery management system (BMS) and possesses data processing, algorithm calculation, and decision output capabilities. Specifically, the server 110 is an electronic device with computation, storage, and communication functions. It can receive electrical signals, temperature signals, and operating condition signals from the acquisition unit and perform digital processing. In this application, the core functions of the server 110 are: real-time acquisition of cell operation data from the energy storage module 120, receiving heat dissipation information from the liquid cooling module 130, and executing the thermal runaway detection method described in this application, including self-discharge rate calculation, correlation coefficient determination, thermal runaway score calculation, correction value calculation, and thermal runaway determination. Furthermore, the server 110 can not only realize thermal runaway early warning but also further integrate functions such as equalization control, fault recording, data uploading, and multi-level early warning linkage, providing full life-cycle safety management and status monitoring for the energy storage system 100.

[0026] As can be seen, in this embodiment, the energy storage module 120 serves as the energy core, providing operating data for the battery cell 1211; the liquid cooling module 130 achieves forced heat dissipation through the liquid cooling circulation pump 131, and outputs parameters such as pump speed and supply / return liquid temperature; the server 110 serves as the main control unit, collecting and processing various types of data, executing thermal runaway detection algorithms, and realizing early safety warnings and overall control of the battery.

[0027] The following is combined Figure 2 The battery thermal runaway detection method provided in the embodiments of this application will be further explained.

[0028] Please see Figure 2 , Figure 2 This is a flowchart illustrating the steps of a battery thermal runaway detection method provided in an embodiment of this application, as follows: Figure 2 As shown, the method includes the following steps: Step S210: Calculate the self-discharge rate of the multiple cells under multiple charge-discharge cycles to obtain multiple first datasets. Each first dataset includes the self-discharge rate and average self-discharge rate of a single cell at different charge-discharge cycle numbers.

[0029] Among them, the self-discharge rate of the battery cell refers to the proportion of its own capacity that naturally decreases after each charge-discharge cycle under a set SOC (such as 0% or 100%) and left to stand in the open circuit for a preset period of time. It is used to reflect the degree of power loss and health status of the battery cell.

[0030] In this embodiment, during multiple charge-discharge cycles, after each cycle, the cell is adjusted to a set SOC and left open-circuit for a preset time. The self-discharge rate corresponding to this cycle is calculated by the ratio of the capacity difference before and after the resting period to the capacity before the resting period. The self-discharge rate of each cell is calculated sequentially for multiple cycles in the above manner, thereby obtaining the self-discharge rate data of each cell at different cycle numbers. This application does not limit the specific method for calculating the cell self-discharge rate, including but not limited to calculation based on capacity change, calculation based on open-circuit voltage change, estimation based on internal resistance change, or other calculation methods that can reflect the cell self-discharge characteristics.

[0031] The first dataset is constructed separately for each battery cell, including the self-discharge rate of the battery cell under multiple charge-discharge cycles and the self-discharge rate corresponding to different number of cycles, as well as the average self-discharge rate obtained based on the self-discharge rate statistics of multiple cycles, which is used to reflect the self-discharge characteristics and overall degradation level of the battery cell during the cycle aging process.

[0032] Specifically, please refer to Figure 3 , Figure 3 This is a schematic diagram of the contents of the first dataset of a single battery cell provided in an embodiment of this application, as shown below. Figure 3 As shown, the dataset sequentially displays the self-discharge rate of a single cell after the 1st to 5th charge-discharge cycles, as well as the cumulative average self-discharge rate for the corresponding number of cycles, clearly presenting the dynamic change and overall decay trend of the cell's self-discharge rate with the number of cycles.

[0033] It should be noted that the data in the first dataset can be presented in various ways. For example, the self-discharge rate sequence and the cumulative average self-discharge rate sequence can be stored in two separate tables, or the trend of self-discharge rate changes can be displayed intuitively using visualization forms such as line charts and bar charts. It can also be stored and transmitted in a structured data format. This application does not limit the specific presentation and storage format of the first dataset.

[0034] Step S220: Determine a first correlation coefficient based on the plurality of first datasets. The first correlation coefficient characterizes the average correlation similarity between the self-discharge rate and the average self-discharge rate of the plurality of cells under multiple charge-discharge cycles.

[0035] The first correlation coefficient is a statistical measure used to quantify the average correlation similarity between the self-discharge rate sequence and the average self-discharge rate of multiple battery cells under multiple charge-discharge cycles. It reflects the consistency and deviation of the self-discharge characteristics of different battery cells during cycle aging, and can be used to identify battery cells with abnormal self-discharge behavior, providing a basis for subsequent correction of thermal runaway detection.

[0036] In one possible embodiment, determining the first correlation coefficient based on the plurality of first datasets includes: determining, based on the plurality of first datasets, a plurality of self-discharge rates and a plurality of average self-discharge rates corresponding to different charge-discharge cycle counts for the plurality of battery cells; determining, based on the plurality of self-discharge rates and the plurality of average self-discharge rates corresponding to different charge-discharge cycle counts for the plurality of battery cells, a plurality of first curves and a plurality of second curves, wherein a single first curve represents the correspondence between the charge-discharge cycle count and the self-discharge rate of a single battery cell, and a single second curve represents the correspondence between the charge-discharge cycle count and the average self-discharge rate of a single battery cell; calculating an initial correlation coefficient between the first curve and the second curve corresponding to each of the plurality of battery cells to obtain a plurality of initial correlation coefficients; and obtaining the first correlation coefficient based on the average of the plurality of initial correlation coefficients.

[0037] The first curve is plotted with the number of charge-discharge cycles on the horizontal axis and the corresponding self-discharge rate on the vertical axis, which is used to visually represent the trend of the self-discharge rate of a single cell with the number of cycles.

[0038] The second curve is plotted with the number of charge-discharge cycles on the horizontal axis and the corresponding cumulative average self-discharge rate on the vertical axis, which is used to reflect the overall average level of self-discharge rate of a single cell.

[0039] In this embodiment, the calculation of the initial correlation coefficient between the first curve and the second curve using the Pearson correlation coefficient method is included, but is not limited to, the latter. The Pearson correlation coefficient method is a statistical method for measuring the degree of linear correlation between two sets of data. Its value range is [-1, 1], where 1 represents a perfect positive correlation, -1 represents a perfect negative correlation, and 0 represents no linear correlation. This method quantifies the strength of the linear association between the data by calculating the ratio of the covariance to the standard deviation of each set of data. For a single battery cell, the self-discharge rate sequence corresponding to its first curve and the average self-discharge rate sequence corresponding to its second curve are taken as two sets of data. Substituting these into the Pearson correlation coefficient formula, the calculated correlation coefficient is the initial correlation coefficient for that battery cell. This coefficient reflects the degree of linear correlation between the trend of the battery cell's self-discharge rate changing with cycles and its overall average level.

[0040] It should be noted that, in addition to the Pearson correlation coefficient method, the Spearman rank correlation coefficient method, the Kendall rank correlation coefficient method, the cosine similarity method, the mutual information method, or the dynamic time warping (DTW) similarity method can also be used to calculate the initial correlation coefficient. This application does not restrict the specific correlation coefficient calculation method.

[0041] For example, taking 5 battery cells as an example, each battery cell undergoes 5 charge-discharge cycles to obtain its own first dataset. Based on this, 5 first curves and 5 second curves are plotted. The initial correlation coefficients for each battery cell are calculated using the Pearson correlation coefficient method, which are 0.92, 0.88, 0.95, 0.90, and 0.85, respectively. The average value of the above 5 initial correlation coefficients is 0.90, which is the first correlation coefficient obtained in this calculation.

[0042] As can be seen, in this embodiment, by extracting the self-discharge rate and cumulative average self-discharge rate during the cell cycling process to construct a characteristic curve, and calculating the average correlation coefficient of multiple cells, the consistency and deviation of the cell self-discharge characteristics can be quantified as a whole, effectively identifying cells with abnormal self-discharge, providing a stable, reliable and robust quantitative basis for subsequent thermal runaway detection, and improving the accuracy of battery status judgment and fault warning.

[0043] Step S230: Determine multiple self-discharge rates corresponding to the multiple cells under the latest multiple charge-discharge cycles based on the first dataset; and determine multiple self-discharge rate correction values ​​corresponding to the multiple cells based on the multiple self-discharge rates and the first correlation coefficient.

[0044] Among them, the latest self-discharge rates corresponding to multiple charge-discharge cycles are a series of self-discharge rate values ​​collected or calculated within the latest and most recent cycle interval for each cell, taking the last charge-discharge cycle completed at the current moment as the time endpoint and selecting a fixed and preset number of consecutive charge-discharge cycles backward. These values ​​are used to reflect the recent self-discharge state of the cell.

[0045] In one possible embodiment, determining the multiple self-discharge rate correction values ​​corresponding to the multiple battery cells based on the multiple self-discharge rates and the first correlation coefficient includes: determining multiple average self-discharge rates corresponding to each of the multiple battery cells under the latest multiple charge-discharge cycles based on the multiple self-discharge rates; determining a normalization interval based on the first correlation coefficient, wherein the lower limit of the normalization interval is the first correlation coefficient and the upper limit of the normalization interval is the difference between a first preset value and the first correlation coefficient; and scaling the multiple average self-discharge rates to the normalization interval using a minimum-maximum normalization formula to obtain the multiple self-discharge rate correction values ​​corresponding to the multiple battery cells.

[0046] The normalization interval is dynamically determined by the first correlation coefficient. The lower limit is directly taken from the first correlation coefficient, and the upper limit is taken from the difference between the first preset value and the first correlation coefficient. This interval will be adaptively adjusted according to the consistency of the self-discharge characteristics of the cell group, providing a reasonable and unified target range for subsequent data normalization.

[0047] The average self-discharge rate under the latest multiple cycles is scaled to this normalized range. The purpose is to eliminate the influence of the original data dimensions and numerical range while preserving the individual differences of the cells, so that the self-discharge levels of different cells have a comparable scale. Furthermore, outlier interference is suppressed through overall correlation constraints, making the correction results more stable and more suitable for subsequent thermal runaway detection and anomaly judgment.

[0048] For example, assuming the first preset value is 1 and the calculated first correlation coefficient is 0.90, the determined normalization interval is [0.90, 0.10]. Five cells are selected, and their average self-discharge rates in the latest three charge-discharge cycles are 1.20%, 1.50%, 1.35%, 1.60%, and 1.25%, respectively, with a minimum value of 1.20% and a maximum value of 1.60%. According to the minimum-maximum normalization formula: Correction value = Lower normalization limit + (Individual average value - Minimum value) × (Upper normalization limit - Lower normalization limit) / (Maximum value - Minimum value), substituting the values, we can obtain that the self-discharge rate correction values ​​for the five cells are 0.90, 0.30, 0.60, 0.10, and 0.80, respectively, thus completing the effective correction of the self-discharge rate of multiple cells.

[0049] As can be seen, in this embodiment, the overall consistency of the cell group (first correlation coefficient) is used as a constraint to intelligently normalize the average self-discharge rate of individuals in the latest cycle. First, for a single cell, the average self-discharge rate of its latest multiple cycles is calculated to reflect the cell's current true health status. Second, based on the first correlation coefficient representing overall consistency, a unique normalization interval [first correlation coefficient, first preset value - first correlation coefficient] is dynamically set. The width and position of this interval are determined by the similarity between cells. The higher the correlation coefficient (the more consistent the group), the more concentrated the interval, thereby avoiding interference from the overall judgment by extreme values ​​of abnormal cells. Finally, through the minimum-maximum normalization formula, the individual average value is mapped to this exclusive interval. The resulting corrected self-discharge rate value retains the inherent differences between cells while eliminating the influence of dimensions and extreme value fluctuations, making the corrected data have a uniform and comparable scale, providing stable and reliable feature input for subsequent accurate thermal runaway early warning.

[0050] Step S240: Calculate multiple thermal runaway fractions corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period.

[0051] Among them, the voltage change rate refers to how fast the voltage at each cell terminal changes over time within a preset time period. It reflects the stability of the electrochemical state inside the cell and is an important electrical characteristic for judging whether the cell has abnormal decay, increased polarization, or internal short circuit.

[0052] Among them, the real-time current is the real-time value of the charging and discharging current flowing through each cell within a preset time period, which reflects the current working load intensity of the cell. The current magnitude directly affects the heat generation rate and heat accumulation degree of the cell.

[0053] Among them, the rate of temperature change refers to the magnitude of the change in surface or internal temperature of each cell over a preset time period. It is used to characterize the heat generation and temperature rise trend of the cell itself and is the core thermal characteristic for early warning of thermal runaway.

[0054] Among them, the heat dissipation of the liquid cooling module characterizes the actual heat dissipation capacity of the battery pack cooling system to the cell area during the corresponding time period, reflects the system's effect on suppressing cell temperature rise, and embodies the dynamic balance between heat dissipation and heat generation.

[0055] Step S250: Correct the multiple thermal runaway scores according to the multiple self-discharge rate correction values ​​to obtain multiple target thermal runaway scores corresponding to the multiple cells.

[0056] Understandably, the self-discharge rate correction value is used as a coefficient to assess the consistency and health status of individual cells, adaptively correcting the thermal runaway score calculated based on electrical, thermal, and heat dissipation characteristics. The self-discharge rate correction value is determined by the overall correlation of the cell group and the recent self-discharge level, reflecting the degree of abnormality of the cell relative to other cells: the more abnormal the self-discharge and the worse the consistency of the cell, the lower its self-discharge rate correction value, and the stronger the suppression of the original thermal runaway score; the better the consistency and the more stable the state of the cell, the closer the correction value is to the reasonable range, and the more accurately the thermal runaway score reflects the risk.

[0057] In one possible embodiment, the step of correcting the plurality of thermal runaway scores according to the plurality of self-discharge rate correction values ​​to obtain the plurality of target thermal runaway scores corresponding to the plurality of cells includes: obtaining the plurality of target thermal runaway scores corresponding to the plurality of cells by multiplying the self-discharge rate correction value and the thermal runaway score corresponding to each of the plurality of cells.

[0058] In addition to direct multiplication, corrections can also be made using methods such as weighted summation, weighted average, exponential weighting, additive offset, and nonlinear mapping. For example, the self-discharge rate correction value can be set as a weighting coefficient and weighted summation can be performed with the thermal runaway score; or the thermal runaway score can be segmented and adjusted according to the interval where the self-discharge rate correction value is located; or adaptive fusion correction can be achieved through neural networks, fuzzy logic, etc. This application does not limit the specific correction operation method.

[0059] As can be seen, in this embodiment, by introducing a self-discharge rate correction value to correct the original thermal runaway score, the long-term self-discharge consistency of the cell and the real-time thermal safety status can be fully integrated, effectively suppressing the problem of misjudgment of thermal runaway score caused by individual cell differences, making the final target thermal runaway score more accurate and stable, and more realistically reflecting the actual thermal runaway risk level of the cell, thereby improving the reliability and accuracy of thermal runaway early warning.

[0060] Step S260: Determine the thermal runaway detection result of the battery based on the plurality of target thermal runaway scores.

[0061] Among them, the thermal runaway detection result is the final determination of whether the battery is currently at risk of thermal runaway. It is used to directly output the battery's safety status and provide a basis for early warning and protection control.

[0062] In one possible embodiment, determining the thermal runaway detection result of the battery based on the plurality of target thermal runaway scores includes: determining the magnitude relationship between the plurality of target thermal runaway scores and a preset thermal runaway score threshold; if it is determined that at least one target thermal runaway score is greater than the preset thermal runaway score threshold, then the thermal runaway detection result is determined to be that the battery has experienced thermal runaway; otherwise, the thermal runaway detection result is determined to be that the battery has not experienced thermal runaway.

[0063] It should be noted that, in addition to the threshold comparison methods mentioned above, the following judgment methods are also included, but are not limited to: statistical analysis of multiple target thermal runaway scores, and comprehensive judgment based on the average, variance, maximum, or weighted comprehensive score; trend judgment based on the changing trend, duration, and rate of increase of the target thermal runaway scores; using a multi-threshold grading strategy to classify the detection results into multiple levels such as safe, warning, dangerous, and fault; or combining machine learning models, fuzzy reasoning, expert rules, etc., for intelligent comprehensive judgment. This application does not limit the specific method for determining the thermal runaway detection results.

[0064] As can be seen, in this embodiment, by combining the cell's cycle self-discharge rate with real-time voltage, current, temperature, and liquid cooling heat dissipation, liquid cooling heat dissipation compensation is applied to the temperature change rate, which can eliminate external heat dissipation interference and restore the cell's true heat generation situation; and by using the self-discharge rate to correct the thermal runaway score, the cell's long-term performance changes and real-time operating status can be more comprehensively reflected, improving the accuracy and reliability of battery thermal runaway detection.

[0065] Please see Figure 4 , Figure 4 This is a schematic flowchart illustrating a method for determining the thermal runaway fraction of a battery cell, as provided in an embodiment of this application. Figure 4As shown, in step S240, multiple thermal runaway fractions corresponding to the multiple battery cells are calculated based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period. This specifically includes the following steps: S41. The first thermal runaway parameters corresponding to the multiple battery cells are obtained by multiplying the voltage change rate of each battery cell with the real-time current.

[0066] Among them, the first thermal runaway parameter is a primary characteristic parameter used to characterize the potential heat generation intensity caused by abnormal electrical characteristics of the battery cell under real-time load, providing an early and sensitive basis for subsequent thermal runaway scores.

[0067] Understandably, the rate of voltage change reflects the voltage fluctuation amplitude of the battery cell per unit time, and can sensitively reflect early electrochemical anomalies such as increased internal polarization, abnormally increased impedance, and micro-short circuits; real-time current reflects the current workload intensity of the battery cell, directly determining the scale of heat generation under abnormal conditions. Multiplying the two essentially quantifies the abnormal losses and potential heat generation intensity of the battery cell from an electrical characteristic perspective: the faster the voltage change and the larger the operating current, the higher the internal power loss of the battery cell, the more severe the temperature rise, and the more significant the risk of thermal runaway. This product can detect early anomalies at the electrical characteristic level before the temperature rises significantly, achieving early identification of thermal risks.

[0068] S42. Based on the heat dissipation of the liquid cooling module, the multiple temperature change rates of the multiple battery cells are corrected to obtain multiple second thermal runaway parameters corresponding to the multiple battery cells.

[0069] Among them, the second thermal runaway parameter is a characteristic parameter that reflects the actual heat generation rate and thermal runaway development trend inside the cell after eliminating the influence of heat dissipation. It is purer and closer to the internal thermal state of the cell, and can significantly improve the accuracy of thermal risk assessment.

[0070] It's understandable that the rate of temperature change directly reflects how quickly the battery cell generates heat. However, using only the initial temperature rise ignores the heat removed by the liquid cooling module, leading to an overestimation or underestimation of the actual thermal risk: with strong liquid cooling capacity, even if the battery cell generates a lot of heat, the surface temperature rise may not be significant; with weak cooling capacity, even slight heat generation can cause a rapid temperature increase. Therefore, correcting the rate of temperature change using heat dissipation can offset external interference from the cooling system and restore the true intensity of heat generation and heat accumulation trend inside the battery cell.

[0071] In one possible embodiment, the step of correcting the temperature change rate of the plurality of battery cells according to the heat dissipation of the liquid cooling module to obtain a plurality of second thermal runaway parameters corresponding to the plurality of battery cells includes: calculating a plurality of temperature changes of the plurality of battery cells offset by liquid cooling within a preset time period based on the heat dissipation of the liquid cooling module; compensating the plurality of temperature changes into the plurality of temperature change rates of the plurality of battery cells respectively to obtain a plurality of second thermal runaway parameters corresponding to the plurality of battery cells, wherein the plurality of second thermal runaway parameters are used to reflect the actual heat generation of the plurality of battery cells without cooling by the liquid cooling module.

[0072] Specifically, the calculation of multiple temperature changes offset by liquid cooling within a preset time period for the multiple battery cells is based on the heat dissipation of the liquid cooling module. This includes: calculating the total heat dissipation energy removed from the battery system by the liquid cooling system during this period based on the heat dissipation of the liquid cooling module and the preset time period; and converting the total heat dissipation energy into a corresponding temperature change based on the mass and specific heat capacity of the battery cells. This temperature change is the temperature rise offset by liquid cooling. Wherein, total heat dissipation energy = heat dissipation × preset time period; temperature change = total heat dissipation energy / (cell mass × cell specific heat capacity).

[0073] Specifically, compensating for the temperature change offset by liquid cooling to match the corresponding cell's temperature change rate involves adding the temperature rise suppressed by the cooling system back to the original temperature rise rate, thus restoring the cell's true heat generation rate under uncooled conditions. Since the temperature change is the total temperature rise offset within a preset time period, dividing this temperature change by the preset time period yields the offset temperature change rate. Adding this to the original temperature change rate gives the compensated second thermal runaway parameter. Therefore, the second thermal runaway parameter = original temperature change rate + (offset temperature change ÷ preset time period).

[0074] Furthermore, the preset duration is a continuous time window synchronized with the real-time operating status of the battery cell. It can be selected from the corresponding synchronization period in the latest multiple charge-discharge cycles, any real-time sampling period during the charge-discharge process, or any subsequent period during charge-discharge intervals or in a static state. It is only used to limit the statistical and calculation range of parameters such as voltage change rate, real-time current, temperature change rate, and liquid cooling heat dissipation, so as to realize real-time, dynamic, and online detection of the risk of thermal runaway of the battery cell, rather than relying on offline analysis of historical cycle data.

[0075] For example, taking a single battery cell as an example, the total heat dissipation energy is first calculated based on the heat dissipation capacity of the liquid cooling module and the preset duration. Assuming the heat dissipation capacity of the liquid cooling module is 50W and the preset duration is 10 seconds, the total heat dissipation energy equals the heat dissipation capacity multiplied by the preset duration, which is 50W × 10s = 500J. Then, based on the cell mass and specific heat capacity, the total heat dissipation energy is converted into the temperature change offset by the liquid cooling. Assuming the cell mass is 0.8kg and the cell specific heat capacity is 1000J / (kg· ... If the temperature change is less than 1000 J / (kg·℃), then the total heat dissipation energy is equal to the product of the cell mass and the cell specific heat capacity, which is 500 J ÷ (0.8 kg × 1000 J / (kg·℃)). The temperature change is 0.625℃, which is the portion of the cell temperature rise offset by liquid cooling within the preset time. This temperature change is then compensated for to the original temperature change rate of the corresponding cell. Assuming the original temperature change rate of the cell is 0.2℃ / s, the offset temperature change is first divided by the preset time to obtain the offset temperature change rate, which is 0.625℃ ÷ 10s = 0.0625℃ / s. This is then added to the original temperature change rate to obtain the second thermal runaway parameter, i.e., 0.2℃ / s + 0.0625℃ / s = 0.2625℃ / s. This second thermal runaway parameter reflects the actual heat generation rate of the cell under conditions without liquid cooling, eliminating the temperature rise interference from the cooling system and more accurately characterizing the degree of heat accumulation and the development trend of thermal runaway within the cell.

[0076] It should be understood that the above calculation method is only an illustrative example and this application is not limited thereto; in other embodiments, the equivalent compensation temperature rise can be directly calculated based on parameters such as heat dissipation power, heat exchange efficiency, flow channel distribution, and cell temperature difference weight, or the temperature change rate can be corrected by means of table lookup, fitting formula, empirical coefficient, fuzzy calculation, etc. As long as the purpose of eliminating the influence of liquid cooling heat dissipation and restoring the true heat generation rate of the cell can be achieved, it falls within the protection scope of this application.

[0077] In one possible embodiment, the liquid cooling module includes a liquid cooling circulation pump for driving coolant to circulate and exchange heat between the energy storage module and the liquid cooling module; the heat dissipation of the liquid cooling module is calculated using the pump speed of the liquid cooling circulation pump, the supply temperature of the coolant, and the return temperature of the coolant.

[0078] Among these factors, pump speed determines the coolant circulation flow rate, and the difference between the supply and return coolant temperatures reflects the actual heat absorbed by the coolant. Combining these three factors allows for accurate and real-time calculation of the actual heat dissipation capacity of the liquid-cooled module. Furthermore, the heat dissipation calculation can be further optimized and calibrated by incorporating parameters such as the coolant's specific heat capacity, coolant density, cell heat dissipation area, flow channel heat transfer coefficient, ambient temperature, airflow velocity within the battery pack, and cell surface temperature distribution. This allows for adaptation to the heat dissipation calculation needs under different structures and operating conditions, further improving the accuracy and applicability of the heat dissipation calculation.

[0079] As can be seen, in this embodiment, by compensating and correcting the rate of temperature change based on the liquid cooling heat dissipation, and by accurately calculating heat dissipation in combination with hardware parameters such as pump speed and supply and return liquid temperatures that can be collected in real time, the interference of the cooling system on the temperature rise is effectively eliminated, the true heat generation rate of the battery cell is restored, and the second thermal runaway parameter is more stable and closer to the internal thermal state, which greatly improves the accuracy of thermal runaway detection.

[0080] S43. The plurality of first thermal runaway parameters are scored to obtain a plurality of first thermal runaway scores; and the plurality of second thermal runaway parameters are scored to obtain a plurality of second thermal runaway scores.

[0081] The scoring methods for the first thermal runaway parameter and the second thermal runaway parameter include, but are not limited to, threshold segmentation scoring, linear normalization scoring, nonlinear mapping scoring, interval fitting scoring, expert experience scoring, fuzzy logic scoring, etc. The parameters can be quantitatively assigned according to their magnitude, trend of change or degree of anomaly. This application does not limit the specific scoring method.

[0082] S44. The first thermal runaway score and the second thermal runaway score corresponding to each of the plurality of cells are weighted and summed according to the first preset weight and the second preset weight to obtain the plurality of thermal runaway scores.

[0083] The calculation of the thermal runaway score includes, but is not limited to, using methods such as weighted summation, weighted average, exponential weighted fusion, nonlinear weighted combination, and neural network adaptive weight calculation to combine the first thermal runaway score with the second thermal runaway score. This application does not limit the specific fusion algorithm.

[0084] As can be seen, in this embodiment, by extracting the first and second thermal runaway parameters from the two dimensions of abnormal electrical characteristics and actual internal heat generation, and obtaining the thermal runaway score through scoring and weighted fusion, the potential thermal risks of the battery cell can be accurately identified in the early stage. At the same time, the detection interference caused by liquid cooling is eliminated, so that the thermal runaway score has real-time performance, accuracy and robustness, providing a stable and reliable quantitative basis for subsequent battery thermal runaway determination.

[0085] Please see Figure 5 , Figure 5 This is an overall flowchart of a battery thermal runaway detection method provided in an embodiment of this application, as shown below. Figure 5 As shown, the method includes the following steps: 50. Start; 510. Perform multiple charge-discharge cycles on the battery cell; 511. Calculate the self-discharge rate and average self-discharge rate of the battery cell under each charge-discharge cycle to obtain a first dataset; 512. Calculate the initial correlation coefficient based on the first dataset; 513. Calculate the average self-discharge rate of the battery cell under the latest multiple charge-discharge cycles; 514. Obtain the first correlation coefficient based on the average of multiple initial correlation coefficients; 515. Determine the normalization interval based on the first correlation coefficient; then, combining steps 513 and 515, the average self-discharge rate is scaled to the normalization interval to achieve step 516; 516. Obtain the self-discharge rate correction value of the battery cell; 517. Obtain the voltage, current, and temperature data of the battery cell within a preset time period, and the liquid... 518. Calculate the voltage change rate and temperature change rate of the cell within a preset time period; then, combining steps 517 and 518, correct the voltage change rate according to the current and correct the temperature change rate according to the heat dissipation to achieve step 519; Step 519. Obtain the first thermal runaway parameter and the second thermal runaway parameter; then, after scoring the first thermal runaway parameter and the second thermal runaway parameter, a weighted sum can be obtained to obtain the thermal runaway score of the cell; 520. Obtain the thermal runaway score of the cell; further, by using the self-discharge rate correction value to correct the thermal runaway score, step 521 can be achieved; 521. Obtain the target thermal runaway score of the cell; 522. Determine the thermal runaway detection result of the battery according to the target thermal runaway score.

[0086] As can be seen, in this embodiment, by combining the cell's cycle self-discharge rate with real-time voltage, current, temperature, and liquid cooling heat dissipation, liquid cooling heat dissipation compensation is applied to the temperature change rate, which can eliminate external heat dissipation interference and restore the cell's true heat generation situation; and by using the self-discharge rate to correct the thermal runaway score, the cell's long-term performance changes and real-time operating status can be more comprehensively reflected, improving the accuracy and reliability of battery thermal runaway detection.

[0087] It is understandable that charge / discharge capacity and self-discharge rate are both core parameters that can characterize the long-term performance consistency of a battery cell and reflect its internal health status. Both exhibit regular changes with the number of charge / discharge cycles, and both can be used to construct datasets, calculate correlation coefficients and correction values ​​through multiple charge / discharge cycle data, possessing the same computational logic compatibility. Therefore, a calculation approach completely consistent with the self-discharge rate can be adopted, replacing the self-discharge rate with charge / discharge capacity, calculating correction values ​​through charge / discharge capacity-related parameters, and correcting the thermal runaway score. This can also achieve the quantification of individual battery cell performance differences, thereby accurately reflecting the true thermal runaway risk of the battery cell, without changing the original calculation framework.

[0088] Please see Figure 6 , Figure 6 This is a flowchart of another battery thermal runaway detection method provided in an embodiment of this application, as follows: Figure 6 As shown, the method includes the following steps: Step S610: Calculate the charge and discharge capacity of the multiple cells within the rated voltage range under multiple charge and discharge cycles to obtain multiple second datasets. Each second dataset includes the charge and discharge capacity and average charge and discharge capacity of a single cell within the rated voltage range corresponding to different charge and discharge cycles.

[0089] The charge / discharge capacity refers to the amount of charge actually charged or discharged by a battery cell within its rated voltage range during a single charge / discharge cycle, typically measured in ampere-hours (Ah) or milliampere-hours (mAh). It is calculated as follows: within the rated voltage range, the charge / discharge current of the battery cell is integrated over time, i.e., Q = ∫∣I(t)∣dt, where I(t) is the real-time current and t is the charge / discharge duration. Alternatively, it can be simplified to the product of the average current and the charge / discharge duration, thus obtaining the charge / discharge capacity for a single cycle. The average charge / discharge capacity is then obtained by averaging the capacities from multiple cycles.

[0090] Step S620: Determine a second correlation coefficient based on the plurality of second datasets. The second correlation coefficient characterizes the average correlation similarity between the charge-discharge capacity and the average charge-discharge capacity of the plurality of cells within the rated voltage range under multiple charge-discharge cycles.

[0091] In a specific embodiment, the second correlation coefficient adopts the same calculation approach as the first correlation coefficient in step S220, except that the self-discharge rate is replaced with the charge-discharge capacity and the average self-discharge rate is replaced with the average charge-discharge capacity. Specifically, it includes: determining multiple charge-discharge capacities and multiple average charge-discharge capacities corresponding to different charge-discharge cycles for the multiple cells based on the multiple second datasets; determining multiple third curves and multiple fourth curves based on the multiple charge-discharge capacities and multiple average charge-discharge capacities corresponding to different charge-discharge cycles for the multiple cells, where a single third curve represents the correspondence between the charge-discharge cycle number and charge-discharge capacity of a single cell, and a single fourth curve represents the correspondence between the charge-discharge cycle number and average charge-discharge capacity of a single cell; calculating the intermediate correlation coefficient between the third curve and the fourth curve for each of the multiple cells to obtain multiple intermediate correlation coefficients; and obtaining the second correlation coefficient based on the average of the multiple intermediate correlation coefficients.

[0092] Step S630: Determine multiple charge / discharge capacities of the multiple cells within the rated voltage range corresponding to the latest multiple charge / discharge cycles based on the second dataset; and determine multiple charge / discharge capacity correction values ​​corresponding to the multiple cells based on the multiple charge / discharge capacities and the second correlation coefficient.

[0093] In a specific embodiment, the charge / discharge capacity correction value adopts the same calculation approach as the self-discharge rate correction value in step S230, except that the self-discharge rate is replaced with the charge / discharge capacity, the average self-discharge rate is replaced with the average charge / discharge capacity, the first correlation coefficient is replaced with the second correlation coefficient, and the self-discharge rate correction value is replaced with the charge / discharge capacity correction value. This will not be elaborated further here.

[0094] Step S640: Calculate multiple thermal runaway fractions corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period.

[0095] Step S650: Correct the multiple thermal runaway scores according to the multiple charge / discharge capacity correction values ​​to obtain multiple target thermal runaway scores corresponding to the multiple cells.

[0096] In a specific embodiment, the approach is consistent with that in step S250, where the thermal runaway score is corrected based on the self-discharge rate correction value. The only difference is that the self-discharge rate correction value is replaced with the charge / discharge capacity correction value. This includes, but is not limited to, multiplying the charge / discharge capacity correction values ​​corresponding to each of the multiple cells by the thermal runaway score of the corresponding cell to obtain multiple target thermal runaway scores.

[0097] Step S660: Determine the thermal runaway detection result of the battery based on the plurality of target thermal runaway scores.

[0098] In a specific embodiment, the judgment approach is exactly the same as that used in step S260 to determine the battery thermal runaway detection result based on the thermal runaway score. The only difference is that the target thermal runaway score after self-discharge rate correction is replaced with the target thermal runaway score after charge-discharge capacity correction. This will not be elaborated further here.

[0099] As can be seen, this embodiment provides another battery thermal runaway detection scheme. By using charge and discharge capacity as the core characterization parameter and adopting the same calculation framework as the self-discharge rate, it quantifies the long-term performance consistency difference of the battery cell, accurately corrects the thermal runaway score, effectively integrates the long-term health status of the battery cell and real-time thermal risk characteristics, improves the accuracy and adaptability of thermal runaway detection, and maintains the consistency and feasibility of the method.

[0100] Please see Figure 7 , Figure 7 A functional unit block diagram of an energy storage system provided in this application embodiment, such as Figure 7 As shown, the energy storage system 100 includes the following units: The first calculation unit 710 is configured to calculate the self-discharge rate of the plurality of battery cells under multiple charge-discharge cycles, thereby obtaining a plurality of first datasets, each first dataset including the self-discharge rate and average self-discharge rate of a single battery cell at different charge-discharge cycle numbers; determine a first correlation coefficient based on the plurality of first datasets, wherein the first correlation coefficient characterizes the average correlation similarity between the self-discharge rate and the average self-discharge rate of the plurality of battery cells under multiple charge-discharge cycles; determine a plurality of self-discharge rates corresponding to the plurality of battery cells under the latest multiple charge-discharge cycles based on the first datasets; and determine a plurality of self-discharge rate correction values ​​corresponding to the plurality of battery cells based on the plurality of self-discharge rates and the first correlation coefficient. The second calculation unit 720 is used to calculate multiple thermal runaway fractions corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate and heat dissipation of the liquid cooling module within a preset time period. The processing unit 730 is used to correct the plurality of thermal runaway scores according to the plurality of self-discharge rate correction values ​​to obtain a plurality of target thermal runaway scores corresponding to the plurality of cells; and to determine the thermal runaway detection result of the battery according to the plurality of target thermal runaway scores.

[0101] In one embodiment, determining the first correlation coefficient based on the plurality of first datasets includes: determining, based on the plurality of first datasets, a plurality of self-discharge rates and a plurality of average self-discharge rates corresponding to different charge-discharge cycle counts for the plurality of battery cells; determining, based on the plurality of self-discharge rates and the plurality of average self-discharge rates corresponding to different charge-discharge cycle counts for the plurality of battery cells, a plurality of first curves and a plurality of second curves, wherein a single first curve represents the correspondence between the charge-discharge cycle count and the self-discharge rate of a single battery cell, and a single second curve represents the correspondence between the charge-discharge cycle count and the average self-discharge rate of a single battery cell; calculating the initial correlation coefficient between the first curve and the second curve corresponding to each of the plurality of battery cells to obtain a plurality of initial correlation coefficients; and obtaining the first correlation coefficient based on the average of the plurality of initial correlation coefficients.

[0102] In one embodiment, determining the multiple self-discharge rate correction values ​​corresponding to the multiple battery cells based on the multiple self-discharge rates and the first correlation coefficient includes: determining multiple average self-discharge rates corresponding to each of the multiple battery cells under the latest multiple charge-discharge cycles based on the multiple self-discharge rates; determining a normalization interval based on the first correlation coefficient, wherein the lower limit of the normalization interval is the first correlation coefficient and the upper limit of the normalization interval is the difference between a first preset value and the first correlation coefficient; and scaling the multiple average self-discharge rates to the normalization interval using a minimum-maximum normalization formula to obtain the multiple self-discharge rate correction values ​​corresponding to the multiple battery cells.

[0103] In one embodiment, calculating multiple thermal runaway scores corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period includes: obtaining multiple first thermal runaway parameters corresponding to the multiple battery cells based on the product of the voltage change rate and real-time current of each of the multiple battery cells; correcting the multiple temperature change rates of the multiple battery cells according to the heat dissipation of the liquid cooling module to obtain multiple second thermal runaway parameters corresponding to the multiple battery cells; scoring the multiple first thermal runaway parameters to obtain multiple first thermal runaway scores; and scoring the multiple second thermal runaway parameters to obtain multiple second thermal runaway scores; and weighting and summing the first thermal runaway scores and second thermal runaway scores corresponding to each of the multiple battery cells according to a first preset weight and a second preset weight to obtain the multiple thermal runaway scores.

[0104] In one embodiment, the step of correcting the temperature change rate of the plurality of battery cells according to the heat dissipation of the liquid cooling module to obtain a plurality of second thermal runaway parameters corresponding to the plurality of battery cells includes: calculating a plurality of temperature changes of the plurality of battery cells offset by liquid cooling within a preset time period based on the heat dissipation of the liquid cooling module; compensating the plurality of temperature changes to the plurality of temperature change rates of the plurality of battery cells respectively to obtain a plurality of second thermal runaway parameters corresponding to the plurality of battery cells, wherein the plurality of second thermal runaway parameters are used to reflect the actual heat generation of the plurality of battery cells without cooling by the liquid cooling module.

[0105] In one embodiment, the liquid cooling module includes a liquid cooling circulation pump, which drives the coolant to circulate and exchange heat between the energy storage module and the liquid cooling module; the heat dissipation of the liquid cooling module is calculated using the pump speed of the liquid cooling circulation pump, the supply temperature of the coolant, and the return temperature of the coolant.

[0106] In one embodiment, the step of correcting the plurality of thermal runaway scores according to the plurality of self-discharge rate correction values ​​to obtain the plurality of target thermal runaway scores corresponding to the plurality of cells includes: obtaining the plurality of target thermal runaway scores corresponding to the plurality of cells by multiplying the self-discharge rate correction value and the thermal runaway score corresponding to each of the plurality of cells.

[0107] In one embodiment, the method further includes: calculating the charge-discharge capacity of the plurality of cells within a rated voltage range under multiple charge-discharge cycles to obtain a plurality of second datasets, each second dataset including the charge-discharge capacity and average charge-discharge capacity of a single cell within a rated voltage range corresponding to different charge-discharge cycle numbers; determining a second correlation coefficient based on the plurality of second datasets, the second correlation coefficient representing the average correlation similarity between the charge-discharge capacity and average charge-discharge capacity of the plurality of cells within a rated voltage range under multiple charge-discharge cycles; determining a plurality of charge-discharge capacities of the plurality of cells within a rated voltage range corresponding to the latest multiple charge-discharge cycles based on the second datasets; and determining a plurality of charge-discharge capacity correction values ​​corresponding to the plurality of cells based on the plurality of charge-discharge capacities and the second correlation coefficients; calculating a plurality of thermal runaway scores corresponding to the plurality of cells based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period; correcting the plurality of thermal runaway scores according to the plurality of charge-discharge capacity correction values ​​to obtain a plurality of target thermal runaway scores corresponding to the plurality of cells; and determining the thermal runaway detection result of the battery based on the plurality of target thermal runaway scores.

[0108] In one embodiment, determining the thermal runaway detection result of the battery based on the plurality of target thermal runaway scores includes: determining the magnitude relationship between the plurality of target thermal runaway scores and a preset thermal runaway score threshold; if it is determined that at least one target thermal runaway score is greater than the preset thermal runaway score threshold, then the thermal runaway detection result is determined to be that the battery has experienced thermal runaway; otherwise, the thermal runaway detection result is determined to be that the battery has not experienced thermal runaway.

[0109] As can be seen, in this embodiment, by combining the cell's cycle self-discharge rate with real-time voltage, current, temperature, and liquid cooling heat dissipation, liquid cooling heat dissipation compensation is applied to the temperature change rate, which can eliminate external heat dissipation interference and restore the cell's true heat generation situation; and by using the self-discharge rate to correct the thermal runaway score, the cell's long-term performance changes and real-time operating status can be more comprehensively reflected, improving the accuracy and reliability of battery thermal runaway detection.

[0110] Figure 8 This is a structural block diagram of an electronic device provided in an embodiment of this application. For example... Figure 8 As shown, the electronic device 800 may include one or more of the following components: a processor 801 and a memory 802 coupled to the processor 801, wherein the memory 802 may store one or more computer programs, which may be configured to implement the methods described in the examples above when executed by one or more processors 801.

[0111] Processor 801 may include one or more processing cores. Processor 801 connects to various parts within the electronic device 800 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 802, and by calling data stored in memory 802. Optionally, processor 801 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 801 may integrate one or more of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. It is understood that the aforementioned modem may also not be integrated into processor 801, but may be implemented separately through a communication chip.

[0112] The memory 802 may include random access memory (RAM) or read-only memory (ROM). The memory 802 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 802 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method examples described above. The data storage area may also store data created by the electronic device 800 during use.

[0113] It is understood that the electronic device 800 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, WiFi (Wireless Fidelity) module, speaker, Bluetooth module, sensor, etc., without limitation.

[0114] This application also provides a computer storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements some or all of the steps of any of the methods described in the above method embodiments.

[0115] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0116] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0120] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute partial steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, volatile memory, or non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM), etc., which are various media capable of storing program code.

[0121] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A method for detecting battery thermal runaway, characterized in that, A server applied to an energy storage system, the energy storage system further comprising an energy storage module and a liquid cooling module connected to the server, the energy storage module being connected to the liquid cooling module, and the energy storage module comprising multiple battery cells; the method comprising: Calculate the self-discharge rate of the multiple cells under multiple charge-discharge cycles to obtain multiple first datasets. Each first dataset includes the self-discharge rate and average self-discharge rate of a single cell at different charge-discharge cycle numbers. A first correlation coefficient is determined based on the plurality of first datasets. The first correlation coefficient characterizes the average correlation similarity between the self-discharge rate and the average self-discharge rate of the plurality of cells under multiple charge-discharge cycles. Based on the first dataset, determine multiple self-discharge rates corresponding to the multiple cells under the latest multiple charge-discharge cycles; and based on the multiple self-discharge rates and the first correlation coefficient, determine multiple self-discharge rate correction values ​​corresponding to the multiple cells. Calculate multiple thermal runaway fractions corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period. The multiple thermal runaway scores are corrected according to the multiple self-discharge rate correction values ​​to obtain multiple target thermal runaway scores corresponding to the multiple cells; The thermal runaway detection result of the battery is determined based on the multiple target thermal runaway scores.

2. The method according to claim 1, characterized in that, Determining the first correlation coefficient based on the plurality of first datasets includes: Based on the multiple first datasets, determine the multiple self-discharge rates and multiple average self-discharge rates of the multiple battery cells corresponding to different charge-discharge cycles; Multiple first curves and multiple second curves are determined based on multiple self-discharge rates and multiple average self-discharge rates corresponding to different charge-discharge cycle numbers of the multiple cells. A single first curve represents the correspondence between the charge-discharge cycle number and self-discharge rate of a single cell, and a single second curve represents the correspondence between the charge-discharge cycle number and average self-discharge rate of a single cell. Calculate the initial correlation coefficient between the first curve and the second curve corresponding to each of the plurality of battery cells to obtain a plurality of initial correlation coefficients; The first correlation coefficient is obtained by averaging the plurality of initial correlation coefficients.

3. The method according to claim 1, characterized in that, The step of determining multiple self-discharge rate correction values ​​corresponding to the multiple cells based on the multiple self-discharge rates and the first correlation coefficient includes: Based on the multiple self-discharge rates, the multiple average self-discharge rates corresponding to the multiple cells under the latest multiple charge-discharge cycles are determined respectively. A normalization interval is determined based on the first correlation coefficient, wherein the lower limit of the normalization interval is the first correlation coefficient, and the upper limit of the normalization interval is the difference between a first preset value and the first correlation coefficient. The multiple average self-discharge rates are scaled to the normalization interval using the minimum-maximum normalization formula to obtain multiple self-discharge rate correction values ​​corresponding to the multiple cells.

4. The method according to any one of claims 1-3, characterized in that, The calculation of multiple thermal runaway fractions corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate of the multiple battery cells within a preset time period and the heat dissipation of the liquid cooling module includes: The first thermal runaway parameters corresponding to the multiple battery cells are obtained by multiplying the voltage change rate of each battery cell with the real-time current. Based on the heat dissipation of the liquid cooling module, the multiple temperature change rates of the multiple battery cells are corrected to obtain multiple second thermal runaway parameters corresponding to the multiple battery cells. The plurality of first thermal runaway parameters are scored to obtain a plurality of first thermal runaway scores; and the plurality of second thermal runaway parameters are scored to obtain a plurality of second thermal runaway scores; The first thermal runaway score and the second thermal runaway score corresponding to each of the plurality of cells are weighted and summed according to the first preset weight and the second preset weight to obtain the plurality of thermal runaway scores.

5. The method according to claim 4, characterized in that, The temperature change rate of the plurality of battery cells is corrected according to the heat dissipation of the liquid cooling module to obtain a plurality of second thermal runaway parameters corresponding to the plurality of battery cells, including: Calculate the multiple temperature changes of the multiple cells that are offset by liquid cooling within the preset time period based on the heat dissipation of the liquid cooling module. The multiple temperature changes are respectively compensated to the multiple temperature change rates of the corresponding multiple battery cells to obtain multiple second thermal runaway parameters for the multiple battery cells. The multiple second thermal runaway parameters are used to reflect the actual heat generation of the multiple battery cells without cooling by the liquid cooling module.

6. The method according to claim 5, characterized in that, The liquid cooling module includes a liquid cooling circulation pump, which drives the coolant to circulate and exchange heat between the energy storage module and the liquid cooling module; the heat dissipation of the liquid cooling module is calculated by the pump speed of the liquid cooling circulation pump, the supply temperature of the coolant, and the return temperature of the coolant.

7. The method according to claim 1, characterized in that, The step of correcting the multiple thermal runaway scores based on the multiple self-discharge rate correction values ​​to obtain multiple target thermal runaway scores corresponding to the multiple cells includes: The multiple target thermal runaway scores for the multiple battery cells are obtained by multiplying the self-discharge rate correction value for each battery cell and the thermal runaway score.

8. The method according to claim 1, characterized in that, The method further includes: Calculate the charge and discharge capacity of the multiple cells within the rated voltage range under multiple charge and discharge cycles to obtain multiple second datasets. Each second dataset includes the charge and discharge capacity and average charge and discharge capacity of a single cell within the rated voltage range corresponding to different charge and discharge cycles. A second correlation coefficient is determined based on the multiple second datasets. The second correlation coefficient characterizes the average correlation similarity between the charge-discharge capacity and the average charge-discharge capacity of the multiple cells in the rated voltage range under multiple charge-discharge cycles. Based on the second dataset, determine multiple charge-discharge capacities of the multiple cells within the rated voltage range corresponding to the latest multiple charge-discharge cycles; and determine multiple charge-discharge capacity correction values ​​corresponding to the multiple cells based on the multiple charge-discharge capacities and the second correlation coefficient. Calculate multiple thermal runaway fractions corresponding to the multiple battery cells based on the voltage change rate, real-time current, temperature change rate, and heat dissipation of the liquid cooling module within a preset time period. The multiple thermal runaway scores are corrected according to the multiple charge and discharge capacity correction values ​​to obtain multiple target thermal runaway scores corresponding to the multiple cells; The thermal runaway detection result of the battery is determined based on the multiple target thermal runaway scores.

9. The method according to claim 8, characterized in that, The step of determining the thermal runaway detection result of the battery based on the plurality of target thermal runaway scores includes: Determine the magnitude relationship between the multiple target thermal runaway scores and the preset thermal runaway score threshold; If it is determined that at least one target thermal runaway score is greater than the preset thermal runaway score threshold, then the thermal runaway detection result is determined to be that the battery has experienced thermal runaway. Otherwise, the thermal runaway detection result is determined to be that the battery has not experienced thermal runaway.

10. An energy storage system, characterized in that, The system includes a server, an energy storage module and a liquid cooling module connected to the server, the energy storage module being connected to the liquid cooling module, and the energy storage module including multiple battery cells, wherein the server is used to perform the steps in the method as described in any one of claims 1-9.

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