Energy storage battery pack short circuit fault diagnosis method and system

Abstract

The invention relates to the technical field of battery fault diagnosis, in particular to an energy storage battery pack short circuit fault diagnosis method and system. According to the method, firstly, for each single battery, the descending trend and the fluctuation amplitude of voltage data are analyzed, and the voltage abnormal coefficient of the single battery is determined; analyzing the deviation degree of the temperature rise rate of the surface temperature data relative to the normal working condition, and determining the temperature rise deviation coefficient of the single battery; through the voltage anomaly coefficient and the temperature rise deviation coefficient, analyzing the overall synchronization state of the electrical anomaly and the thermal anomaly, and determining the overall anomaly coefficient of the single battery; on the basis of the overall abnormal coefficients of all the single batteries, possible fault batteries are identified; comparing the difference between the overall abnormal coefficients of the possible fault battery and the adjacent normal battery, and determining a short-circuit fault characteristic value; and judging whether the battery pack has a short-circuit fault according to the short-circuit fault characteristic value. The accuracy of the diagnosis result of the internal short-circuit fault of the battery is improved.

Description

Technical Field

[0001] This invention relates to the field of battery fault diagnosis technology, and specifically to a method and system for diagnosing short-circuit faults in energy storage battery packs. Background Technology

[0002] As a crucial component of modern energy management systems, the safety of energy storage batteries has always been a focus of industry attention. Internal short-circuit faults are a serious problem that can occur during the use of energy storage batteries. If not detected and addressed in a timely manner, they can lead to equipment damage or even fires and other safety accidents. Therefore, how to efficiently and accurately diagnose internal short-circuit faults in energy storage batteries has become an urgent problem to be solved.

[0003] During normal operation and overcharging, due to the complex and variable operating environment, lithium batteries are highly susceptible to short-circuit faults caused by overcharging, over-discharging, mechanical compression, or external impacts. Data-driven methods are commonly used for fault detection, identifying anomalies through feature analysis of operating data such as voltage, current, and temperature. However, the initial amplitude of short-circuit faults is small, and the fault signal is easily masked by measurement noise, leading to misdiagnosis. Furthermore, in practical applications, battery performance dynamically changes under different operating conditions, further complicating fault diagnosis. Therefore, short-circuit fault diagnosis suffers from insufficient real-time performance and accuracy.

[0004] Currently, a common method for diagnosing battery short-circuit faults involves combining a battery short-circuit equivalent model with a battery fuzzy observer. The internal short-circuit fault is determined by the residual between the actual battery state of charge (SOC) measured by the equivalent model and the estimated SOC by the fuzzy observer. However, this method remains susceptible to noise and operating conditions, leading to inaccurate fault diagnosis results. Summary of the Invention

[0005] To address the technical problem of low accuracy in diagnosing internal short-circuit faults in batteries, the present invention aims to provide a method and system for diagnosing short-circuit faults in energy storage battery packs. The specific technical solution adopted is as follows: In a first aspect, embodiments of the present invention provide a method for diagnosing short-circuit faults in energy storage battery packs, the method comprising: Collect voltage and surface temperature data of each individual cell in the battery pack; For each individual cell, analyze the decreasing trend and fluctuation range of the voltage data to determine the voltage anomaly coefficient of the individual cell; analyze the deviation of the surface temperature rise rate from normal operating conditions to determine the temperature rise deviation coefficient of the individual cell. By analyzing the overall synchronization state of electrical and thermal anomalies using voltage anomaly coefficient and temperature rise deviation coefficient, the overall anomaly coefficient of a single cell can be determined. Based on the overall anomaly coefficient of all individual cells, potentially faulty cells are identified; the difference between the overall anomaly coefficient of the potentially faulty cells and the adjacent normal cells is compared to determine the short-circuit fault characteristic value. Based on the short-circuit fault characteristic values, it is determined whether the battery pack has a short-circuit fault.

[0006] Secondly, a short-circuit fault diagnosis system for energy storage battery packs is provided, the system comprising the following modules: The data acquisition module is used to collect voltage and surface temperature data of each individual cell in the battery pack. The first feature analysis module is used to analyze the decreasing trend and fluctuation range of voltage data for each individual cell to determine the voltage anomaly coefficient of the individual cell; and to analyze the deviation of the surface temperature data temperature rise rate from normal operating conditions to determine the temperature rise deviation coefficient of the individual cell. The second feature analysis module analyzes the overall synchronization state of electrical and thermal anomalies through voltage anomaly coefficient and temperature rise deviation coefficient, and determines the overall anomaly coefficient of a single cell. The fault identification module is used to identify potentially faulty batteries based on the overall anomaly coefficient of all individual batteries; and to determine the short-circuit fault characteristic value by comparing the difference between the overall anomaly coefficient of the potentially faulty battery and the adjacent normal battery. The diagnostic decision module is used to determine whether the battery pack has a short circuit fault based on the short circuit fault characteristic values.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the various possible implementations of the first aspect.

[0008] Fourthly, embodiments of the present invention provide a computer program product comprising: computer program code, which, when executed on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0009] Fifthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the various possible implementations of the first aspect.

[0010] The embodiments of the present invention have at least the following beneficial effects: This invention, through in-depth analysis of the abnormal electrical and thermal characteristics of each individual cell in a battery pack, determines the voltage anomaly coefficient and temperature rise deviation from water absorption for each cell. Its advantage lies in reducing interference from measurement noise and complex operating conditions during monitoring. By comprehensively considering the overall state and synchronous changes of both, it provides a more comprehensive assessment of the individual cell's operating status, determines the overall anomaly coefficient, and identifies potentially faulty cells based on this coefficient. Finally, considering the characteristic differences between potentially faulty cells and adjacent normal cells, it determines whether the battery pack has an internal short-circuit fault, thus accurately analyzing the probability of an internal short-circuit fault in the battery pack. This helps to compensate for the lack of real-time performance in short-circuit fault diagnosis and improves the accuracy of internal short-circuit fault diagnosis results. Attached Figure Description

[0011] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a method for diagnosing short-circuit faults in an energy storage battery pack according to an embodiment of the present invention; Figure 2 This is a system block diagram of a short-circuit fault diagnosis system for an energy storage battery pack, provided as an embodiment of the present invention. Detailed Implementation

[0013] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a short-circuit fault diagnosis method and system for energy storage battery packs proposed according to the present invention.

[0014] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0015] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0016] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0018] The embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a short-circuit fault diagnosis method and system for energy storage battery packs provided by the present invention.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a short-circuit fault diagnosis method for an energy storage battery pack according to an embodiment of the present invention. The method includes the following steps: Step S100: Collect voltage data and surface temperature data of each individual cell in the battery pack.

[0021] When an internal short circuit fault occurs in a battery pack, the operating parameters of some individual cells within the pack, such as current, voltage, and internal resistance, will typically exhibit abnormal changes. By identifying and extracting the abnormal characteristics of these operating parameters, the battery short circuit fault can be diagnosed.

[0022] This invention uses a battery management system to collect the voltage data at both ends of each individual cell in the battery pack in real time.

[0023] If a battery pack experiences a prolonged short-circuit fault, it can trigger thermal runaway, causing a rapid increase in battery temperature. Therefore, temperature sensors are placed on the surface of each individual cell within the battery pack to collect and record the surface temperature data in real time. Temperature monitoring is crucial for detecting the risk of thermal runaway.

[0024] The acquisition frequency for both voltage and surface temperature data was set to 10 Hz, and the voltage and surface temperature data were normalized using the minimax method to achieve uniformity of data units.

[0025] Step S200: For each individual cell, analyze the decreasing trend and fluctuation range of the voltage data to determine the voltage anomaly coefficient of the individual cell; analyze the degree of deviation of the surface temperature rise rate from normal operating conditions to determine the temperature rise deviation coefficient of the individual cell.

[0026] Internal short-circuit faults refer to the phenomenon where the positive and negative electrodes inside a battery come into contact, causing discharge due to potential difference and forming a low-impedance path. Internal short-circuit faults that precede thermal runaway typically exist in some individual cells within the battery pack. Lithium-ion battery internal short-circuit faults exhibit evolutionary characteristics; when a battery is in a state of internal micro-short circuitry for an extended period, it can lead to localized temperature increases, eventually evolving into a severe thermal runaway accident. In actual monitoring, interference from electrical parameter measurement noise and complex operating conditions makes it difficult to accurately identify the abnormal characteristics of short-circuit faults. The following analysis addresses this issue.

[0027] Taking a single cell in a battery pack as an example, a short-circuit fault will cause abnormal electrical and thermal characteristics. The abnormal electrical characteristics manifest as an overall fluctuating downward trend in voltage data, with the amplitude of voltage fluctuations increasing as the short-circuit fault worsens. Based on this, feature analysis is performed on data within a local range corresponding to real-time data. For example, a sampling window of size 30×1 is used to analyze the changing trend characteristics of the data within the sampling window to determine whether abnormal electrical and thermal characteristics have occurred. Each time point corresponds to a sampling window; for example, for the current analysis time, the current analysis time is used as the last time point in the sampling window.

[0028] First, the voltage anomaly is analyzed, examining the downward trend and fluctuation range of the voltage data to determine the voltage anomaly coefficient of each individual cell. Specifically: The analysis focuses on the trend of voltage data within a sampling window to determine the characteristic value of this trend. More specifically, the Mankendall detection algorithm is used to calculate a standardized statistic of the voltage data within the sampling window, which serves as the trend statistic. It should be noted that the standardized statistic is the value obtained by standardizing the S-statistic calculated by the Mankendall detection algorithm. The trend statistic reflects the changing characteristics of the voltage data within the currently analyzed sampling window. A smaller trend statistic indicates a more pronounced overall voltage decline trend, and a more pronounced voltage decline trend indicates a higher probability of abnormal electrical characteristics. Therefore, the trend characteristic value is negatively correlated with the voltage anomaly coefficient.

[0029] Furthermore, to reduce measurement noise interference and accurately extract the fluctuation and decrease characteristics of voltage data, curve fitting is performed on the voltage data within the sampling window to determine the fluctuation amplitude of the voltage data. The method for obtaining this fluctuation amplitude is as follows: using the least squares method, curve fitting is performed on the voltage data within the sampling window to obtain a fitted curve; the extreme points on the fitted curve are obtained, and the absolute value of the difference between adjacent extreme points is calculated; taking any extreme point as an example, the absolute value of the difference between the current extreme point and the next extreme point is calculated as the voltage change value of the current extreme point; the sum of the absolute values ​​of the differences between the voltage change values ​​of the first extreme point and all other extreme points is calculated as the fluctuation amplitude of the voltage data of a single cell within the current sampling window.

[0030] The voltage anomaly coefficient is obtained by combining the fluctuation amplitude represented by the extreme value difference after curve fitting of the voltage data, and the characteristic value of the change trend. Among them, the fluctuation amplitude is positively correlated with the voltage anomaly coefficient, while the characteristic value of the change trend is negatively correlated with the voltage anomaly coefficient.

[0031] In some embodiments, the fluctuation amplitude represented by the extreme value difference corresponding to the fitted curve is used as the numerator, the characteristic value of the change trend is used as the denominator, and the corresponding ratio is used as the voltage anomaly coefficient.

[0032] In other embodiments, taking the i-th sampling window as an example, the voltage anomaly coefficient corresponding to the i-th sampling window The calculation formula is: Where N is the number of extreme points on the fitted curve corresponding to the voltage data within the i-th sampling window; The voltage change value at the k-th extreme point on the fitted curve corresponding to the voltage data within the i-th sampling window; is the voltage change value at the first extreme point on the fitted curve corresponding to the voltage data within the i-th sampling window; exp is an exponential function with the natural constant as the base. Let be the statistical measure of the voltage data change trend within the i-th sampling window. The voltage anomaly coefficient reflects the overall trend and fluctuation amplitude of the voltage within the sampling window.

[0033] The trend statistics reflect the trend characteristics of voltage data. Under short-circuit fault conditions, the voltage generally shows a downward trend, and the obtained trend statistics at this time... The smaller and negative, and The larger the absolute value of , the faster the corresponding downward trend. Therefore, to reflect this characteristic, an exponential function is used to... Mapping is performed, which also avoids the denominator being zero. For the numerator in the formula for calculating the voltage anomaly coefficient, after an internal short-circuit fault occurs, the fluctuation amplitude of the monitored voltage data usually increases. Therefore, the change characteristics of the fluctuation amplitude are reflected by calculating the difference between the subsequent fluctuation amplitude and the first fluctuation amplitude.

[0034] Secondly, an analysis of temperature anomalies reveals that, in terms of abnormal thermal characteristics, the heat generated by internal short circuits in individual cells mainly comes from Joule heating of the positive and negative electrodes. Under normal high-power operation conditions, the battery temperature may also rise, but the temperature change is relatively slow. However, after an internal short circuit fault occurs, the heat change is more significant.

[0035] Analyze the deviation of the surface temperature rise rate from normal operating conditions to determine the temperature rise deviation coefficient of a single cell. Specifically: The changes in surface temperature data within the sampling window are analyzed to determine the significant temperature rise value. More specifically, a first-order difference sequence of surface temperature data within the sampling window is obtained, and the average value of all elements in the first-order difference sequence is taken as the significant temperature rise value for the current sampling window. This significant temperature rise value characterizes the degree of change in the surface temperature data within the current sampling window.

[0036] The average significant temperature rise of a normal single cell under various operating conditions over the same time period is used as the temperature rise benchmark. It should be noted that the normal single cells under other operating conditions were manually selected. This temperature rise benchmark characterizes the temperature change features of a normal single cell under various operating conditions.

[0037] The temperature rise deviation coefficient is determined by comparing the significant temperature rise value of the current sampling window with the temperature rise reference value. In one embodiment of the present invention, the absolute value of the difference between the significant temperature rise value of the current sampling window and the temperature rise reference value is calculated, and the normalized absolute value of the difference is used as the temperature rise deviation coefficient.

[0038] The larger the temperature rise deviation coefficient, the more significant the temperature rise characteristics of the battery within the sampling window. By acquiring the temperature change characteristics of the battery during actual operation and using the degree of temperature change of the battery under normal operation under various working conditions as a reference, the temperature rise characteristics caused by internal short circuit faults can be accurately assessed.

[0039] Step S300: Analyze the overall synchronization state of electrical and thermal anomalies using voltage anomaly coefficient and temperature rise deviation coefficient to determine the overall anomaly coefficient of a single cell.

[0040] If an internal short-circuit fault occurs in a single cell, its abnormal electrical and thermal characteristics are highly synchronized in time. Therefore, the internal short-circuit fault state of each single cell can be analyzed by analyzing the coupling relationship between the voltage anomaly coefficient and the temperature rise deviation coefficient.

[0041] A two-dimensional Cartesian coordinate system is constructed, with the standardized voltage anomaly coefficient as the x-coordinate of the feature points and the standardized temperature rise deviation coefficient as the y-coordinate. Each sampling window has its own corresponding voltage anomaly coefficient and temperature rise deviation coefficient, and each sampling window corresponds to one feature point. It should be noted that, in order to eliminate the influence of different variation amplitudes of the voltage anomaly coefficient and temperature rise deviation coefficient on subsequent coupling analysis, the voltage anomaly coefficient and temperature rise deviation coefficient are standardized to have the same numerical scale.

[0042] Taking the i-th sampling window as the current sampling window as an example, feature points within the i-th sampling window and the preceding M consecutive sampling windows are plotted in a Cartesian coordinate system. In this embodiment of the invention, the value range of M is set to [10, 15]. Under normal conditions, the distribution of each feature point is more concentrated, while the distribution of feature points after an internal short-circuit fault is more discrete, and they rise synchronously in the positive directions of the X and Y axes. Therefore, the Euclidean distance between any two feature points is calculated, and the mean of all Euclidean distances is used as the data dispersion of the i-th sampling window. The resulting data dispersion The smaller the value, the more concentrated the distribution of the obtained feature points.

[0043] Then, the least squares method is used to fit straight lines to the feature points corresponding to the continuous sampling windows, and the slope of the fitted line is obtained as the asynchronicity index of the i-th sampling window. The closer this asynchronicity index is to 1, the more pronounced the synchronization characteristics between the voltage anomaly coefficient and the temperature rise deviation coefficient.

[0044] The overall anomaly coefficient of individual data points is determined by combining data dispersion and asynchronicity metrics. In some embodiments, the formula for calculating this overall anomaly coefficient is: Where norm is the normalization function, which can be a maximum-minimum normalization function. When the anomaly index is 1, the overall anomaly coefficient is set to 1.

[0045] Overall anomaly coefficient The larger the value, the more significant the overall state and synchronous change characteristics of electrical and temperature anomalies under the continuous sampling window.

[0046] Data Dispersion and asynchronous indicators These findings reflect the abnormal characteristics of a single battery cell under internal short-circuit faults from different perspectives. Under internal short-circuit faults, there is a clear synchronous change relationship between the corresponding abnormal characteristics. By combining the overall state and synchronous changes of electrical and temperature abnormal characteristics, the operating status of the single battery cell can be more comprehensively evaluated.

[0047] Step S400: Based on the overall abnormality coefficient of all individual cells, identify potentially faulty cells; compare the difference in the overall abnormality coefficient of the potentially faulty cells with that of adjacent normal cells to determine the short-circuit fault characteristic value.

[0048] The overall anomaly coefficient of each individual cell in the battery pack is determined by the methods in steps S100 to S300.

[0049] As battery packs age and manufacturing defects occur, performance inconsistencies between individual cells become increasingly apparent. This leads to frequent overcharging and over-discharging of some individual cells during actual use. Frequent overcharging and over-discharging can cause internal short circuits in the battery. The more individual cells in the battery pack that experience internal short circuits, the greater the likelihood of thermal runaway, potentially resulting in more serious accidents such as equipment damage.

[0050] When there is a significant difference in the overall anomaly coefficient between a faulty battery and a normal battery, especially between adjacent faulty batteries and normal batteries, the difference is even more pronounced; and when the overall anomaly coefficient of a single battery cell shows obvious outliers, it can be considered a potentially faulty battery.

[0051] Based on the above analysis, the SOS anomaly detection algorithm is used to perform outlier analysis on the overall anomaly coefficient of all individual cells. Cells with anomaly probability values ​​greater than a preset anomaly threshold are identified as potentially faulty cells. More specifically, the difference in the overall anomaly coefficient between individual cells is used as the metric distance between them in the SOS detection algorithm. The SOS (Stochastic Outlier Selection) algorithm is used to calculate the anomaly probability value of all individual cells within the sampling window at the current time. Cells with anomaly probability values ​​greater than the preset anomaly threshold are identified as potentially faulty cells. Cells other than those identified as potentially faulty cells are considered normal cells. It should be noted that the preset anomaly threshold is determined through analysis of long-term battery observation data. In this embodiment, the preset anomaly threshold can be set to 0.5; in other embodiments, this value can be adjusted by the implementer according to actual conditions.

[0052] Then, the absolute value of the difference between the overall anomaly coefficient of each potentially faulty battery and its adjacent normal batteries is calculated. The mean of all absolute values ​​is taken as the short-circuit fault characteristic value of the battery pack. This first characteristic value reflects the probability of the battery pack having an internal short-circuit fault characteristic within the sampling window. It should be noted that adjacent normal batteries are normal batteries that are physically adjacent to the potentially faulty battery, such as batteries that are directly adjacent to the potentially faulty battery in the four cardinal directions (up, down, left, and right).

[0053] The short-circuit fault characteristic value is determined by comparing the differences in the overall abnormality coefficients of the potentially faulty battery with those of the adjacent normal batteries. This includes: using the absolute value of the difference between the overall abnormality coefficients of the potentially faulty battery and each adjacent normal battery as the single deviation difference between the potentially faulty battery and each adjacent normal battery; and using the average value of the single deviation differences between the potentially faulty battery and all adjacent normal batteries as the short-circuit fault characteristic value of the potentially faulty battery.

[0054] Step S500: Determine whether the battery pack has a short circuit fault based on the short circuit fault characteristic value.

[0055] This invention provides a more comprehensive assessment of the operational status of individual cells by deeply analyzing their abnormal electrical and thermal characteristics, and further integrating their overall state and synchronous changes. It then analyzes the characteristic differences between potentially faulty cells and their adjacent healthy cells to accurately analyze the possibility of internal short-circuit faults within the battery pack.

[0056] Short-circuit fault diagnosis of battery packs based on short-circuit fault characteristic values.

[0057] The system compares the short-circuit fault characteristic values ​​of potentially faulty batteries with a preset short-circuit fault diagnosis threshold. If a short-circuit fault characteristic value exceeds the preset threshold, the battery pack is considered to have an internal short-circuit fault. Otherwise, the battery pack is considered to be in good operating condition. This helps to compensate for the deficiencies in real-time performance and accuracy of short-circuit fault diagnosis.

[0058] This invention acquires historical voltage and temperature data of the battery pack when a short-circuit fault exists. Following the steps described above, each sampling window in the obtained historical data is analyzed to obtain the corresponding short-circuit fault characteristic value. The historical data records the typical characteristics of the battery pack when an internal short-circuit fault occurs. Based on the short-circuit fault characteristic value calculated from the historical data, fault modes can be directly learned and extracted from actual operating data, thereby establishing a quantitative reference benchmark for fault state determination. Furthermore, the average of all short-circuit fault characteristic values ​​when a short-circuit fault exists is used as a preset short-circuit fault diagnosis threshold, and this preset fault diagnosis threshold is always greater than 0.

[0059] Please see Figure 2 As shown, Figure 2 This invention provides a system block diagram of a short-circuit fault diagnosis system for an energy storage battery pack, comprising: The data acquisition module is used to collect voltage and surface temperature data of each individual cell in the battery pack. The first feature analysis module is used to analyze the decreasing trend and fluctuation range of voltage data for each individual cell to determine the voltage anomaly coefficient of the individual cell; and to analyze the deviation of the surface temperature data temperature rise rate from normal operating conditions to determine the temperature rise deviation coefficient of the individual cell. The second feature analysis module analyzes the overall synchronization state of electrical and thermal anomalies through voltage anomaly coefficient and temperature rise deviation coefficient, and determines the overall anomaly coefficient of a single cell. The fault identification module is used to identify potentially faulty batteries based on the overall anomaly coefficient of all individual batteries; and to determine the short-circuit fault characteristic value by comparing the difference between the overall anomaly coefficient of the potentially faulty battery and the adjacent normal battery. The diagnostic decision module is used to determine whether the battery pack has a short-circuit fault based on the short-circuit fault characteristic values. Optionally, the transmission medium can be a wired link, such as, but not limited to, coaxial cable, optical fiber, and digital subscriber line, or a wireless link, such as, but not limited to, Wireless Fidelity (WIFI), Bluetooth, and mobile device networks.

[0060] It should be noted that the device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0061] This invention provides a computer device. Exemplarily, the computer device includes: a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the computer device can perform the aforementioned method for diagnosing short-circuit faults in any energy storage battery pack.

[0062] Furthermore, embodiments of the present invention also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform the energy storage battery pack short-circuit fault diagnosis method provided in the embodiments of the present invention.

[0063] In this embodiment of the invention, the device can be divided into functional modules according to the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and is only a logical functional division. In actual implementation, there may be other division methods.

[0064] When each module is divided according to its function, the device may also include a signal uploading module, a determination module, and an adjustment module. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0065] It should be understood that the apparatus provided in this embodiment of the invention is used to perform the above-described method for diagnosing short-circuit faults in energy storage battery packs, and therefore can achieve the same effect as the above-described implementation method.

[0066] When using integrated units, the device may include a processing module and a storage module. When applied to a device, the processing module can be used to control and manage the device's operations. The storage module can be used to support the device in executing program code, etc. The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as described in this disclosure. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of Digital Signal Processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0067] In addition, the device provided in the embodiments of the present invention may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the short-circuit fault diagnosis method for energy storage battery packs provided in the above embodiments.

[0068] This invention also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the energy storage battery pack short-circuit fault diagnosis method provided in the above embodiments.

[0069] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the short-circuit fault diagnosis method for energy storage battery packs provided in the above embodiments.

[0070] In this invention, the apparatus, computer-readable storage medium, computer program product, or chip provided in the embodiments are all used to execute the corresponding methods described above. Therefore, the beneficial effects they achieve can be referred to the beneficial effects in the corresponding methods described above, and will not be repeated here. Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways.

[0071] The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, 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; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0072] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0073] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0075] The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for diagnosing short-circuit faults in energy storage battery packs, characterized in that, The method includes the following steps: Collect voltage and surface temperature data of each individual cell in the battery pack; For each individual cell, analyze the decreasing trend and fluctuation range of the voltage data to determine the voltage anomaly coefficient of the individual cell; analyze the deviation of the surface temperature rise rate from normal operating conditions to determine the temperature rise deviation coefficient of the individual cell. By analyzing the overall synchronization state of electrical and thermal anomalies using voltage anomaly coefficient and temperature rise deviation coefficient, the overall anomaly coefficient of a single cell can be determined. Based on the overall anomaly coefficient of all individual cells, potentially faulty cells are identified; the difference between the overall anomaly coefficient of the potentially faulty cells and the adjacent normal cells is compared to determine the short-circuit fault characteristic value. Based on the short-circuit fault characteristic values, it is determined whether the battery pack has a short-circuit fault.

2. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 1, characterized in that, The analysis of the decreasing trend and fluctuation amplitude of the voltage data determines the voltage anomaly coefficient of a single cell, including: Analyze the changing trend of voltage data within the sampling window and determine the characteristic value of the changing trend within the sampling window; By combining the fluctuation amplitude represented by the extreme value difference after curve fitting of voltage data, and the characteristic value of the changing trend, the voltage anomaly coefficient is obtained.

3. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 2, characterized in that, The analysis of the voltage data change trend within the sampling window, and the determination of the change trend characteristic values ​​within the sampling window, includes: Using the Menkendall detection algorithm, the statistical trend of voltage data change within the sampling window is calculated.

4. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 2, characterized in that, The voltage anomaly coefficient is obtained by combining the fluctuation amplitude represented by the extreme value difference after curve fitting of the voltage data, and the characteristic value of the changing trend, including: The least squares method is used to fit the voltage data within the sampling window to obtain the fitted curve; The fluctuation amplitude represented by the extreme value difference corresponding to the fitted curve is used as the numerator, the characteristic value of the change trend is used as the denominator, and the corresponding ratio is used as the voltage anomaly coefficient.

5. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 1, characterized in that, The analysis of the surface temperature data deviates from the rate of temperature rise relative to normal operating conditions, determining the temperature rise deviation coefficient of a single cell, including: Analyze the changes in surface temperature data within the sampling window to determine the significant temperature rise. The average of the significant temperature rise values ​​of a normal battery under various operating conditions is used as the temperature rise benchmark value; Compare the significant temperature rise value of the current sampling window with the baseline temperature rise value to determine the temperature rise deviation coefficient.

6. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 1, characterized in that, The method involves analyzing the overall synchronization state of electrical and thermal anomalies using voltage anomaly coefficients and temperature rise deviation coefficients to determine the overall anomaly coefficient of a single battery cell, including: The standardized voltage anomaly coefficient is used as the x-axis of the feature point, and the standardized temperature rise deviation coefficient is used as the y-axis of the feature point; each sampling window has its own corresponding voltage anomaly coefficient and temperature rise deviation coefficient, and each sampling window corresponds to one feature point. Obtain the feature points corresponding to the continuous sampling window, and calculate the mean of the Euclidean distance between any two feature points as the data dispersion. The feature points are fitted with straight lines, and the slope of the fitted line is obtained as an indicator of the asynchronicity of the sampling window. By combining the data dispersion and the asynchronicity index, the overall anomaly coefficient of individual data is determined.

7. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 1, characterized in that, The method of identifying potentially faulty batteries based on the overall anomaly coefficient of all individual cells includes: The SOS anomaly detection algorithm is used to analyze outliers in the overall anomaly coefficient of all individual cells. The anomaly probability values ​​are determined and individual cells with anomaly probability values ​​greater than the preset anomaly threshold are selected as potentially faulty cells.

8. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 1, characterized in that, The step of comparing the overall anomaly coefficients of potentially faulty batteries with those of adjacent normal batteries to determine short-circuit fault characteristic values ​​includes: The difference between the overall anomaly coefficient of a potentially faulty battery and each adjacent normal battery is used as the single deviation difference; The average of the single deviation differences between the potentially faulty battery and all adjacent normal batteries is used as the short-circuit fault characteristic value.

9. The method for diagnosing short-circuit faults in energy storage battery packs according to claim 1, characterized in that, The step of determining whether the battery pack has a short-circuit fault based on the short-circuit fault characteristic value includes: The short-circuit fault characteristic value of a potentially faulty battery is compared with a preset short-circuit fault diagnosis threshold. If a short-circuit fault characteristic value is greater than the preset short-circuit fault diagnosis threshold, the battery pack is determined to have a short-circuit fault.

10. A short-circuit fault diagnosis system for energy storage battery packs, characterized in that, The system includes the following modules: The data acquisition module is used to collect voltage and surface temperature data of each individual cell in the battery pack. The first feature analysis module is used to analyze the decreasing trend and fluctuation range of voltage data for each individual cell to determine the voltage anomaly coefficient of the individual cell; and to analyze the deviation of the surface temperature data temperature rise rate from normal operating conditions to determine the temperature rise deviation coefficient of the individual cell. The second feature analysis module analyzes the overall synchronization state of electrical and thermal anomalies through voltage anomaly coefficient and temperature rise deviation coefficient, and determines the overall anomaly coefficient of a single cell. The fault identification module is used to identify potentially faulty batteries based on the overall anomaly coefficient of all individual batteries; and to determine the short-circuit fault characteristic value by comparing the difference between the overall anomaly coefficient of the potentially faulty battery and the adjacent normal battery. The diagnostic decision module is used to determine whether the battery pack has a short circuit fault based on the short circuit fault characteristic values.

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