Energy storage battery fault detection method, device and equipment and storage medium

Abstract

The invention discloses an energy storage battery fault detection method, device and equipment and a storage medium, and relates to the technical field of energy storage, and the method comprises the steps: obtaining the charging current data and cell voltage data of an energy storage power station corresponding to an energy storage battery at each time point, dividing the cell voltage data based on the cell characteristics of the energy storage battery, and obtaining the divided cell voltage data; each fragment is obtained; based on the charging current data and the cell voltage data, determining a target coefficient between the accumulated charging electric quantity and the voltage in each segment, and determining a corresponding coefficient curve by using the target coefficient; determining a contrast curve corresponding to the first normal charging of the energy storage battery, determining a deviation value of the coefficient curve and the contrast curve, and judging whether the deviation value exceeds a target deviation threshold value or not; and if the deviation value exceeds the target deviation threshold, determining that the battery cell of the energy storage battery is abnormal. And the real-time detection on the fault of the battery by depending on a single parameter is prevented.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and in particular to a method, device, equipment and storage medium for detecting energy storage battery faults. Background Art

[0002] With the rapid development of energy storage technology, energy storage batteries have been widely used in fields such as renewable energy, transportation, and power dispatch. The health of energy storage batteries has a crucial impact on their performance and lifespan. Currently, common battery fault detection methods rely primarily on monitoring parameters such as temperature and charge and discharge currents. However, these methods often have limitations, especially in complex battery operating environments or when the battery itself is only slightly damaged, which can lead to inaccurate or delayed fault detection.

[0003] As can be seen from the above, how to prevent reliance on a single parameter for real-time detection of battery failures is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the present invention aims to provide a method, apparatus, device, and storage medium for energy storage battery fault detection, which can prevent reliance on a single parameter for real-time battery fault detection. The specific solution is as follows:

[0005] In a first aspect, the present application provides a method for detecting a fault in an energy storage battery, comprising:

[0006] Obtaining charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point, and dividing the cell voltage data based on cell characteristics of the energy storage battery to obtain segments;

[0007] Determining a target coefficient between the cumulative charged quantity and the voltage in each of the segments based on the charging current data and the battery cell voltage data, and determining a corresponding coefficient curve using the target coefficient;

[0008] Determining a control curve corresponding to the first normal charging of the energy storage battery, determining a deviation value between the coefficient curve and the control curve, and judging whether the deviation value exceeds a target deviation threshold;

[0009] If the deviation value exceeds the target deviation threshold, it is determined that an abnormality exists in the battery cell of the energy storage battery.

[0010] Optionally, dividing the cell voltage data based on cell characteristics of the energy storage battery to obtain segments includes:

[0011] A charging plateau range corresponding to the energy storage battery is obtained, and the cell voltage data is divided based on the charging plateau range to obtain segments.

[0012] Optionally, dividing the cell voltage data based on the charging plateau range to obtain segments includes:

[0013] If the battery cell of the energy storage battery is within the charging plateau period, dividing the battery cell voltage data based on a first division frequency to obtain segments;

[0014] If the battery cell of the energy storage battery is not within the charging plateau period, the battery cell voltage data is divided based on a second division frequency to obtain segments.

[0015] Optionally, determining a target coefficient between the cumulative charged quantity and the voltage in each of the segments based on the charging current data and the battery cell voltage data includes:

[0016] Determining a charge per unit time based on the charging current data and time intervals corresponding to adjacent time points, and determining a cumulative charge per unit time in each of the segments based on the charge per unit time;

[0017] Determining a first relative rate of change of voltage between every two consecutive data points in each of the segments using the cell voltage data;

[0018] determining a second relative change rate of the accumulated charged electricity between every two consecutive data points in each of the segments based on the accumulated charged electricity;

[0019] The first relative change rate and the second relative change rate are used to determine a target coefficient between the accumulated charged quantity and the voltage in each of the segments.

[0020] Optionally, the determining a target coefficient between the accumulated charged power and the voltage in each of the segments by using the first relative change rate and the second relative change rate includes:

[0021] An elasticity index is determined using the first relative change rate and the second relative change rate, and a target coefficient between the cumulative charged quantity and the voltage in each of the segments is determined based on a standard deviation and an average value corresponding to the elasticity index.

[0022] Optionally, determining a corresponding coefficient curve using the target coefficient includes:

[0023] Determining the voltage in each of the segments as the abscissa of the coefficient curve, and determining the target coefficient in each of the segments as the ordinate of the coefficient curve;

[0024] The coefficient curve is determined based on the abscissa and the ordinate.

[0025] Optionally, determining a control curve corresponding to the first normal charging of the energy storage battery, determining a deviation value between the coefficient curve and the control curve, and judging whether the deviation value exceeds a target deviation threshold value includes:

[0026] Determine a control curve corresponding to the first normal charging of the energy storage battery, and determine the sum of the deviations between the coefficient curve and the control curve as a deviation value;

[0027] A target deviation threshold is determined based on the battery type of the energy storage battery and historical fault battery data, and it is determined whether the deviation value exceeds the target deviation threshold.

[0028] In a second aspect, the present application provides a device for detecting a fault in an energy storage battery, comprising:

[0029] A voltage data segmentation module is used to obtain charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point, and to segment the cell voltage data based on the cell characteristics of the energy storage battery to obtain segments;

[0030] a target coefficient determination module, configured to determine a target coefficient between the cumulative charged quantity and the voltage in each of the segments based on the charging current data and the cell voltage data, and determine a corresponding coefficient curve using the target coefficient;

[0031] a deviation value determination module, configured to determine a control curve corresponding to the first normal charging of the energy storage battery, determine a deviation value between the coefficient curve and the control curve, and determine whether the deviation value exceeds a target deviation threshold;

[0032] The battery cell abnormality module is used to determine that an abnormality exists in the battery cell of the energy storage battery if the deviation value exceeds the target deviation threshold.

[0033] In a third aspect, the present application provides an electronic device, comprising:

[0034] Memory, used to store computer programs;

[0035] The processor is used to execute the computer program to implement the aforementioned energy storage battery fault detection method.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program implements the aforementioned energy storage battery fault detection method when executed by a processor.

[0037] The present application obtains charging current data and cell voltage data of an energy storage power station corresponding to an energy storage battery at various time points, and divides the cell voltage data based on the cell characteristics of the energy storage battery to obtain segments; determines a target coefficient between the cumulative charged quantity and the voltage in each segment based on the charging current data and the cell voltage data, and determines a corresponding coefficient curve using the target coefficient; determines a control curve corresponding to the first normal charging of the energy storage battery, determines a deviation between the coefficient curve and the control curve, and determines whether the deviation exceeds a target deviation threshold; and if the deviation exceeds the target deviation threshold, determines that an abnormality exists in the cell of the energy storage battery.

[0038] As can be seen from the above, the present application can fully and meticulously grasp the changes in the electrical characteristics of the battery during the charging process by obtaining the charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point. The cell voltage data is divided based on the cell characteristics of the energy storage battery. This division method makes the analysis of the battery charging process more accurate; then, based on the charging current data and cell voltage data, the target coefficient between the cumulative charged power and voltage in each segment is determined, and the coefficient curve is determined using these target coefficients. By quantifying the relationship between the power and voltage during the battery charging process, the changes in the internal state of the battery can be intuitively seen. Then, the control curve corresponding to the first normal charging of the energy storage battery is determined, and the deviation value of the coefficient curve of the current charging process from the control curve is determined based on the curve. In this way, when the deviation value exceeds the target deviation threshold, it is determined that there is an abnormality in the cell of the energy storage battery, and it can detect in real time and accurately whether the battery has failed in a short time, realize early warning of battery failure, take maintenance or replacement measures in advance, avoid further deterioration of the battery failure, and thus ensure the safe and stable operation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0040] Figure 1 This is a flow chart of a method for detecting energy storage battery faults disclosed in this application;

[0041] Figure 2 A coefficient curve diagram provided for this application;

[0042] Figure 3 This is a structural diagram of an energy storage battery fault detection device disclosed in this application;

[0043] Figure 4 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] At present, common battery fault detection methods mainly rely on the monitoring of parameters such as temperature and charge and discharge current, but these methods often have certain limitations, especially when the battery working environment is complex or the battery itself is slightly damaged, which may lead to inaccurate or delayed fault detection. To this end, the present application provides a method for detecting faults in energy storage batteries. When the deviation value exceeds the target deviation threshold, it is determined that there is an abnormality in the battery cell of the energy storage battery. It can detect in real time and accurately whether the battery has failed in a short period of time, realize early warning of battery failure, take maintenance or replacement measures in advance, avoid further deterioration of battery failure, and thus ensure the safe and stable operation of the energy storage system.

[0046] See also Figure 1 As shown, an embodiment of the present invention discloses a method for detecting a fault in an energy storage battery, comprising:

[0047] Step S11: acquiring charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point, and dividing the cell voltage data based on cell characteristics of the energy storage battery to obtain segments.

[0048] In this embodiment, charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point are obtained. Since the energy storage power station primarily uses lithium iron phosphate batteries, lithium iron phosphate batteries are used as an example here. Lithium iron phosphate batteries have a plateau period, so it is necessary to first obtain the charging plateau period range corresponding to the lithium iron phosphate battery and then divide the cell voltage data based on the charging plateau period range to obtain various segments. Specifically, dividing the cell voltage data based on the cell characteristics of the energy storage battery to obtain various segments includes: obtaining the charging plateau period range corresponding to the energy storage battery and dividing the cell voltage data based on the charging plateau period range to obtain various segments.

[0049] In a specific embodiment, if the charging plateau range is 3.3V-3.4V, in the non-charging plateau stage, every 5mv is divided into a segment, and in the charging plateau stage, every 10mv is divided into a segment. If the lithium iron phosphate battery is charged from 3200mv to 3500mv, it should be divided into 50 segments, 20 segments from 3200mv to 3300mv, 10 segments from 3300mv to 3400mv, and 20 segments from 3400mv to 3500mv. Specifically, the cell voltage data is divided based on the charging plateau range to obtain each segment, including: if the cell of the energy storage battery is within the charging plateau range, the cell voltage data is divided based on a first division frequency to obtain each segment; if the cell of the energy storage battery is not within the charging plateau range, the cell voltage data is divided based on a second division frequency to obtain each segment.

[0050] Step S12: determining a target coefficient between the accumulated charged quantity and the voltage in each of the segments based on the charging current data and the cell voltage data, and determining a corresponding coefficient curve using the target coefficient.

[0051] In this embodiment, after obtaining each segment, the amount of charge charged per unit time is determined based on the charging current data and the time intervals corresponding to adjacent time points, that is, the amount of charge charged per unit time = current * the two upper and lower time intervals; the cumulative charge charged within each segment is determined based on the amount of charge charged per unit time. For every two consecutive data points in each segment, the cell voltage data is used to determine the first relative rate of change of the voltage between each two consecutive data points, and the second relative rate of change of the cumulative charge charged between each two consecutive data points is determined based on the cumulative charge, so as to determine the elasticity index based on the first relative rate of change and the second relative rate of change. The formula corresponding to the elasticity index is as follows:

[0052] ;

[0053] in, For the kth fragment The cell voltage data corresponding to each data point; For the kth fragment The cumulative charge corresponding to each data point; represents the first relative rate of change; represents the second relative rate of change.

[0054] It is understandable that after obtaining the elasticity index, the target coefficient between the cumulative charged electricity and the voltage in each of the segments is determined using the standard deviation and the average value corresponding to the elasticity index. Specifically, determining the target coefficient between the cumulative charged electricity and the voltage in each of the segments using the first relative change rate and the second relative change rate includes: determining the elasticity index using the first relative change rate and the second relative change rate, and determining the target coefficient between the cumulative charged electricity and the voltage in each of the segments based on the standard deviation and the average value corresponding to the elasticity index. The formula corresponding to the target coefficient is as follows:

[0055] ;

[0056] in, is the target coefficient, is the standard deviation of the elasticity index corresponding to segment k; is the average value of the elasticity index corresponding to segment k. Specifically, the method of determining the target coefficient between the cumulative charged capacity and the voltage in each of the segments based on the charging current data and the cell voltage data includes: determining the charged capacity per unit time based on the charging current data and the time intervals corresponding to adjacent time points, and determining the cumulative charged capacity in each of the segments based on the charged capacity per unit time; determining the first relative change rate of the voltage between every two consecutive data points in each of the segments using the cell voltage data; determining the second relative change rate of the cumulative charged capacity between every two consecutive data points in each of the segments based on the cumulative charged capacity; and determining the target coefficient between the cumulative charged capacity and the voltage in each of the segments using the first relative change rate and the second relative change rate. It is worth mentioning that the target coefficient can be replaced by the Pearson correlation coefficient, the Spearman rank correlation coefficient, the Kendall rank correlation coefficient, etc.

[0057] In this embodiment, after obtaining the target coefficient, the abscissa of the coefficient curve is determined based on the voltage in each of the segments, and the ordinate of the coefficient curve is determined using the target coefficient in each of the segments, so as to determine the coefficient curve based on the abscissa and the ordinate. Specifically, determining the corresponding coefficient curve using the target coefficient includes: determining the voltage in each of the segments as the abscissa of the coefficient curve, and determining the target coefficient in each of the segments as the ordinate of the coefficient curve; and determining the coefficient curve based on the abscissa and the ordinate. In a specific embodiment, Figure 2 A coefficient curve schematic diagram is provided for this embodiment. If the lithium iron phosphate battery is charged from 3200mv to 3500mv, it should be divided into 50 segments. The horizontal axis of the coefficient curve in the figure is the voltage corresponding to the 50 segments, and the vertical axis is the target coefficient corresponding to each voltage of the 50 segments. The unit is dimensionless, thereby obtaining the corresponding coefficient curve.

[0058] Step S13: determining a control curve corresponding to the first normal charging of the energy storage battery, determining a deviation value between the coefficient curve and the control curve, and judging whether the deviation value exceeds a target deviation threshold.

[0059] In this embodiment, after obtaining the coefficient curve, a reference curve corresponding to the first normal charge of the energy storage battery is determined based on the determination method of the coefficient curve, and the deviation value between the coefficient curve and the reference curve is determined, and it is judged whether the deviation value exceeds the target deviation threshold. In a specific embodiment, if the reference curve is , the coefficient curve is , then the formula for the deviation value is as follows:

[0060] ;

[0061] in, is the deviation value; represents the sum of the absolute deviations at all considered points x; is the vertical distance between the control curve and the coefficient curve at each point x. A deviation value is determined by summing the absolute deviations between the control curve and the coefficient curve. A target deviation threshold is determined based on the battery type and historical fault battery data of the energy storage battery to determine whether the deviation value exceeds the target deviation threshold.

[0062] Specifically, determining a control curve corresponding to the first normal charge of the energy storage battery, determining a deviation between the coefficient curve and the control curve, and determining whether the deviation exceeds a target deviation threshold comprises: determining a control curve corresponding to the first normal charge of the energy storage battery, and determining the sum of the deviations between the coefficient curve and the control curve as the deviation value; determining a target deviation threshold based on the battery type and historical fault battery data of the energy storage battery, and determining whether the deviation exceeds the target deviation threshold. It is worth noting that the target deviation threshold can be adjusted based on actual conditions and is not specifically limited herein.

[0063] Step S14: If the deviation value exceeds the target deviation threshold, it is determined that there is an abnormality in the battery cell of the energy storage battery.

[0064] In this embodiment, a determination is made as to whether the deviation value exceeds a target deviation threshold. If so, a determination is made that an abnormality exists in the energy storage battery cell (e.g., a lithium iron phosphate battery), and a corresponding maintenance plan is required. It is worth noting that while this embodiment primarily uses lithium iron phosphate batteries as an example, its core method is highly versatile and can be applied to other types of energy storage batteries.

[0065] As can be seen from the above, the present application can fully and meticulously grasp the changes in the electrical characteristics of the battery during the charging process by obtaining the charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point. The cell voltage data is divided based on the cell characteristics of the energy storage battery. This division method makes the analysis of the battery charging process more accurate; then, based on the charging current data and cell voltage data, the target coefficient between the cumulative charged power and voltage in each segment is determined, and the coefficient curve is determined using these target coefficients. By quantifying the relationship between the power and voltage during the battery charging process, the changes in the internal state of the battery can be intuitively seen. Then, the control curve corresponding to the first normal charging of the energy storage battery is determined, and the deviation value of the coefficient curve of the current charging process from the control curve is determined based on the curve. In this way, when the deviation value exceeds the target deviation threshold, it is determined that there is an abnormality in the cell of the energy storage battery, and it can detect in real time and accurately whether the battery has failed in a short time, realize early warning of battery failure, take maintenance or replacement measures in advance, avoid further deterioration of the battery failure, and thus ensure the safe and stable operation of the energy storage system.

[0066] Accordingly, see Figure 3 As shown, the present application also provides an energy storage battery fault detection device, comprising:

[0067] A voltage data segmentation module 11 is configured to obtain charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point, and to segment the cell voltage data based on cell characteristics of the energy storage battery to obtain segments;

[0068] a target coefficient determination module 12, configured to determine a target coefficient between the cumulative charged quantity and the voltage in each of the segments based on the charging current data and the cell voltage data, and determine a corresponding coefficient curve using the target coefficient;

[0069] a deviation value determination module 13, configured to determine a control curve corresponding to the first normal charging of the energy storage battery, determine a deviation value between the coefficient curve and the control curve, and determine whether the deviation value exceeds a target deviation threshold;

[0070] The cell abnormality module 14 is configured to determine that an abnormality exists in the cell of the energy storage battery if the deviation value exceeds the target deviation threshold.

[0071] As can be seen from the above, the present application can fully and meticulously grasp the changes in the electrical characteristics of the battery during the charging process by obtaining the charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point. The cell voltage data is divided based on the cell characteristics of the energy storage battery. This division method makes the analysis of the battery charging process more accurate; then, based on the charging current data and cell voltage data, the target coefficient between the cumulative charged power and voltage in each segment is determined, and the coefficient curve is determined using these target coefficients. By quantifying the relationship between the power and voltage during the battery charging process, the changes in the internal state of the battery can be intuitively seen. Then, the control curve corresponding to the first normal charging of the energy storage battery is determined, and the deviation value of the coefficient curve of the current charging process from the control curve is determined based on the curve. In this way, when the deviation value exceeds the target deviation threshold, it is determined that there is an abnormality in the cell of the energy storage battery, and it can detect in real time and accurately whether the battery has failed in a short time, realize early warning of battery failure, take maintenance or replacement measures in advance, avoid further deterioration of the battery failure, and thus ensure the safe and stable operation of the energy storage system.

[0072] In some specific implementations, the voltage data division module 11 may specifically include:

[0073] The plateau range acquiring unit is configured to acquire a charging plateau range corresponding to the energy storage battery, and divide the cell voltage data based on the charging plateau range to obtain segments.

[0074] In some specific implementations, the voltage data division module 11 may specifically include:

[0075] a first data dividing unit, configured to divide the cell voltage data based on a first dividing frequency to obtain segments if the cell of the energy storage battery is within the charging plateau range;

[0076] The first data dividing unit is configured to divide the cell voltage data based on a second dividing frequency to obtain segments if the cell of the energy storage battery is not within the charging platform period.

[0077] In some specific implementations, the target coefficient determination module 12 may specifically include:

[0078] a cumulative charged power determination unit, configured to determine the charged power per unit time based on the charging current data and time intervals corresponding to adjacent time points, and determine the cumulative charged power in each of the segments based on the charged power per unit time;

[0079] a first change rate determining unit, configured to determine a first relative change rate of the voltage between every two consecutive data points in each of the segments using the cell voltage data;

[0080] a second change rate determining unit, configured to determine, based on the accumulated charged power, a second relative change rate of the accumulated charged power between every two consecutive data points in each of the segments;

[0081] The coefficient determination unit is used to determine the target coefficient between the accumulated charged power and the voltage in each of the segments by using the first relative change rate and the second relative change rate.

[0082] In some specific implementations, the target coefficient determination module 12 may specifically include:

[0083] The elasticity index determining unit is configured to determine an elasticity index using the first relative change rate and the second relative change rate, and determine a target coefficient between the accumulated charged power and the voltage in each of the segments based on a standard deviation and an average value corresponding to the elasticity index.

[0084] In some specific implementations, the target coefficient determination module 12 may specifically include:

[0085] a coordinate determining unit, configured to determine the voltage within each of the segments as the abscissa of the coefficient curve, and to determine the target coefficient within each of the segments as the ordinate of the coefficient curve;

[0086] A coefficient curve determining unit is configured to determine the coefficient curve based on the abscissa and the ordinate.

[0087] In some specific implementations, the deviation value determination module 13 may specifically include:

[0088] a control curve determining unit, configured to determine a control curve corresponding to the first normal charging of the energy storage battery, and determine a deviation sum between the coefficient curve and the control curve as a deviation value;

[0089] The deviation value judgment unit is used to determine a target deviation threshold based on the battery type of the energy storage battery and historical fault battery data, and to judge whether the deviation value exceeds the target deviation threshold.

[0090] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 4This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the energy storage battery fault detection method disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0091] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0092] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0093] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of implementing the energy storage battery fault detection method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 may further include a computer program capable of implementing other specific tasks.

[0094] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned energy storage battery fault detection method. The specific steps of this method can be referred to the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0095] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0096] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0097] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0098] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0099] The above is a detailed introduction to the technical solution provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for detecting a fault in an energy storage battery, characterized in that: include: Obtaining charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point, and dividing the cell voltage data based on cell characteristics of the energy storage battery to obtain segments; Determining a target coefficient between the cumulative charged quantity and the voltage in each of the segments based on the charging current data and the battery cell voltage data, and determining a corresponding coefficient curve using the target coefficient; Determining a control curve corresponding to the first normal charging of the energy storage battery, determining a deviation value between the coefficient curve and the control curve, and judging whether the deviation value exceeds a target deviation threshold; If the deviation value exceeds the target deviation threshold, it is determined that an abnormality exists in the battery cell of the energy storage battery.

2. The energy storage battery fault detection method according to claim 1, characterized in that: The dividing the cell voltage data based on the cell characteristics of the energy storage battery to obtain segments includes: A charging plateau range corresponding to the energy storage battery is obtained, and the cell voltage data is divided based on the charging plateau range to obtain segments.

3. The energy storage battery fault detection method according to claim 2, characterized in that: The dividing the cell voltage data based on the charging plateau range to obtain segments includes: If the battery cell of the energy storage battery is within the charging plateau period, dividing the battery cell voltage data based on a first division frequency to obtain segments; If the battery cell of the energy storage battery is not within the charging plateau period, the battery cell voltage data is divided based on a second division frequency to obtain segments.

4. The energy storage battery fault detection method according to claim 1, characterized in that: The determining, based on the charging current data and the cell voltage data, a target coefficient between the cumulative charged quantity and the voltage in each of the segments includes: Determining a charge per unit time based on the charging current data and time intervals corresponding to adjacent time points, and determining a cumulative charge per unit time in each of the segments based on the charge per unit time; Determining a first relative rate of change of voltage between every two consecutive data points in each of the segments using the cell voltage data; determining a second relative change rate of the accumulated charged electricity between every two consecutive data points in each of the segments based on the accumulated charged electricity; The first relative change rate and the second relative change rate are used to determine a target coefficient between the accumulated charged quantity and the voltage in each of the segments.

5. The energy storage battery fault detection method according to claim 4, characterized in that: The determining of a target coefficient between the accumulated charge amount and the voltage in each of the segments by using the first relative change rate and the second relative change rate includes: An elasticity index is determined using the first relative change rate and the second relative change rate, and a target coefficient between the cumulative charged quantity and the voltage in each of the segments is determined based on a standard deviation and an average value corresponding to the elasticity index.

6. The energy storage battery fault detection method according to claim 1, characterized in that: The determining a corresponding coefficient curve using the target coefficient includes: Determining the voltage in each of the segments as the abscissa of the coefficient curve, and determining the target coefficient in each of the segments as the ordinate of the coefficient curve; The coefficient curve is determined based on the abscissa and the ordinate.

7. The energy storage battery fault detection method according to any one of claims 1 to 6, characterized in that: Determining a control curve corresponding to the first normal charging of the energy storage battery, determining a deviation value between the coefficient curve and the control curve, and judging whether the deviation value exceeds a target deviation threshold value includes: Determine a control curve corresponding to the first normal charging of the energy storage battery, and determine the sum of the deviations between the coefficient curve and the control curve as a deviation value; A target deviation threshold is determined based on the battery type of the energy storage battery and historical fault battery data, and it is determined whether the deviation value exceeds the target deviation threshold.

8. A device for detecting faults in an energy storage battery, characterized in that: include: A voltage data segmentation module is used to obtain charging current data and cell voltage data of the energy storage power station corresponding to the energy storage battery at each time point, and to segment the cell voltage data based on the cell characteristics of the energy storage battery to obtain segments; a target coefficient determination module, configured to determine a target coefficient between the cumulative charged quantity and the voltage in each of the segments based on the charging current data and the cell voltage data, and determine a corresponding coefficient curve using the target coefficient; a deviation value determination module, configured to determine a control curve corresponding to the first normal charging of the energy storage battery, determine a deviation value between the coefficient curve and the control curve, and determine whether the deviation value exceeds a target deviation threshold; The battery cell abnormality module is used to determine that an abnormality exists in the battery cell of the energy storage battery if the deviation value exceeds the target deviation threshold.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the energy storage battery fault detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, the energy storage battery fault detection method according to any one of claims 1 to 7 is implemented.

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